Lean Construction 4.0 - Driving A Digital Revolution

LEAN CONSTRUCTION 4.0

This book introduces and develops the novel concept of Lean Construction 4.0. The capability of Lean Construction to efectively adapt the architecture-engineering-construction (AEC) industry to this new era of digital transformation requires a reconceptualization of the triad people-processes-technology as a foundation for the theoretical and practical framework of Lean Construction. Therefore, a shift towards Lean Construction 4.0 is required. Lean Construction 4.0 is a new systems-wide thinking approach where synergies and overlaps between Lean Construction and digital/smart technologies go far beyond BIM to reshape the way we design, manage, and operate capital projects in the modern age of automation. This pioneering new book brings together the views of world experts at the interface of Lean Construction and digital/smart technologies, in order to channel research eforts, to introduce and discuss current research and practice, challenges and drivers, and future perspectives of Lean Construction 4.0. It is not the aim of the book to keep adding digits to the term ‘Lean Construction’ to ‘catch up’ with the industry revolutions as they go on. Instead, after reading this book, it will be undeniable for readers that the triad process-people-technology as proposed by Lean Construction 4.0 is required to achieve an efective, long-lasting digital transformation of the AEC industry. Thus, the aim of Lean Construction 4.0 is better explained by what it evokes: a future vision of construction systems comprising people, processes, and technology using Industry 4.0/5.0 as a basis for technological innovation in the AEC industry coupled with Lean Construction theory and practice as a jettison for improved processes and systems integration. The Lean Construction 4.0 concept coined and developed in this edited book is unique and the chapters provide practitioners and academics with a provocative refection on the theoretical and practical aspects that shape the Lean Construction 4.0 concept. More importantly, Lean Construction 4.0 proposes a rationale for the AEC industry not only to survive, but to thrive!

LEAN CONSTRUCTION 4.0 Driving a Digital Revolution of Production Management in the AEC Industry

Edited by Vicente A. González, Farook Hamzeh, and Luis Fernando Alarcón

Cover image: © Yagi Studio/Getty Images First published 2023 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2023 selection and editorial matter Vicente A. González, FarookHamzeh, and Luis Fernando Alarcón; individual chapters, the contributors The right of Vicente A. González, Farook Hamzeh, and Luis Fernando Alarcón to be identifed as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identifcation and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-367-71420-8 (hbk) ISBN: 978-0-367-71449-9 (pbk) ISBN: 978-1-003-15093-0 (ebk) DOI: 10.1201/9781003150930 Typeset in Bembo by codeMantra

DEDICATION AND ACKNOWLEDGMENTS To my wife, Florencia, and my daughters,Valentina, and Annabella. Over the ups and downs of my academic career, Florencia has been at all times my greatest supporter. So, thanks for that love…I dedicate this book to you! Valentina and Annabella, you are and will always be the engine of my life! This book is also dedicated to the memory of my dad,Vicente, who always believed in me, even when nobody else did. Dad, you will always be myguiding light. Vicente A. González To my wife, Nadine, and my son Adrian. Nadine, you have supported me throughout my career.Your patience and love have kept me going.Adrian, you bring all the joy and excitement to life.You give me inspiration with your ingenuity, creativity and love.To all those who have inspired me and continue to give me inspiration. Farook Hamzeh To my wife, Luchy, my daughters Isabel, Carolina, Daniela, Gabriela, María Teresa, Luchita, Constanza, Catalina and my son Luis Felipe. Thank you for joining me on a fascinating academic and family adventure in the study and learning of the Lean world. Luchy, nothing would have been possible without your love and dedication. Luis Fernando Alarcón Many thanks to the great editorial and formatting support provided by Salam Khalife, PhD Candidate at University of Alberta, and Dr Sajjad Hassanpour, completing his second PhD degree at University of Auckland. Vicente, Farook and Luis Fernando

CONTENTS

Foreword Preface About the editors About the authors

x xii xvi xvii

PART 1

Introduction

1 Lean Construction 4.0: Beyond the New Production ManagementPhilosophy Vicente A. González, Farook Hamzeh, Luis Fernando Alarcón, and Salam Khalife PART 2

Teoretical and Practical Perspectives for Lean Construction 4.0 2 Towards Lean Construction Site 4.0: Integrating Lean and DigitalTechnologies Kevin McHugh, Bhargav Dave, Algan Tezel, Lauri Koskela, and Viranj Patel 3 The Implications of the 4.0 Revolution in the AEC Industry on the Lean Construction Paradigm: Identifying the Status Quo and Drawing the Path Forward Evangelos Pantazis, Eyuphan Koc, and Lucio Soibelman 4 Proposing a House of Lean Construction 4.0 Makram Bou Hatoum and Hala Nassereddine vii

15 17

35 50

Contents

5 A Shared Responsibility: Ethical and Social Dilemmas of Using AI in the AEC Industry Paz Arroyo, Annett Schöttle, and Randi Christensen

6 The Interplay between Construction Supply Chain and BIM throughKitting: A Lean-Based View Zakaria Dakhli and Zoubeir Lafhaj

7 Implementing Lean-BIM Duality: Balance between People, Process,and Technology Daniel Heigermoser and Borja García de Soto

PART 3

Simulation Modeling and Virtual Lean Construction 8 Simulation and Modeling Facets in Lean Construction Mani Poshdar, Mohammed Adel Abdelmegid, Vicente A. González, Michael O’Sullivan, and Luis Fernando Alarcón 9 Modelling Construction Production Environments as ComplexAdaptive Systems Ali Lahouti and Tariq S. Abdelhamid

117 119

137

10 Social Network Analysis to Support Implementation and Understanding of Lean Construction Rodrigo F. Herrera and Luis Fernando Alarcón

157

11 Exploring the Socio-Technical Nature of Lean-Based Production Planning and Control Using Immersive Virtual Reality Canlong Liu, Vicente A. González, Ignacio Pavez, and Roy C. Davies

172

PART 4

Digital Production Planning, Control and Monitoring in Lean Construction 12 MetViz: LPS Metric Visualization, Monitoring, and Analysis Systemfor Project Control Lynn Shehab, Ali Ezzeddine, Gunnar Lucko, and Farook Hamzeh 13 Productivity Function: Mathematical Foundation for Production Management in Construction Ricardo Antunes, Vicente A. González, Michael O’Sullivan, Omar Rojas, and Kenneth Walsh viii

193 195

209

Contents

14 Digital Twins to Enable Flexibility in Of-Site Construction Beda Barkokebas, Fatima Alsakka, Farook Hamzeh, and Mohamed Al-Hussein 15 Use of the Digital Situation Picture to Decrease Waste in the Design and Construction Process Olli Seppänen 16 UAS Applications to Support Lean Construction Implementation Dayana Bastos Costa, Masoud Gheisari, and Luis Fernando Alarcón PART 5

223

240 254

Digital Lean Project Delivery

273

17 Integrating Project Delivery and Information Technology: Challenges and Opportunities Eder Martínez, Ali Ezzeddine, and Borja García de Soto

275

18 Blockchain Governance for Integrated Project Delivery 4.0 Daniel M. Hall, Jens Hunhevicz, and Marcella M. M. Bonanomi 19 Decision Models to Support the Selection and Implementation of Lean Construction Luis Fernando Alarcón, Keith R. Molenaar, Alfonso Bastías, and Harrison A. Mesa PART 6

288

306

Concluding Remarks

323

20 The Future of Lean Construction 4.0 Vicente A. González, Farook Hamzeh, and Luis Fernando Alarcón

325

Index

337

FOREWORD

The Lean Construction movement emerged in 1993 with the formation of the International Group for Lean Construction. In the following 29 years, the movement has grown in number of advocates and organizations dedicated to its advance. To cite one example, currently 24 countries throughout the world have national institutes dedicated to Lean Construction. Where it has been adopted, Lean Construction has delivered on its promise of better satisfed customers, better quality of work life, and better fnancial performance of participating companies. However, sometimes it feels as if Lean Construction is the beta vs VHS in the 1980s’ videotape competition. Despite being the superior alternative, it struggles to dislodge traditional construction thinking and practice. Might it be the case that advocates of Lean Construction have failed to take leadership of industry initiatives such as digitalization? Might it also be the case that digitalization without the Lean philosophy will fail to achieve the desired advances in industry performance? My answer to these questions is an emphatic ‘YES’! This book ofers a strategy that has Lean Construction embodying and leading the digital transformation of the industry. Lean is a fundamental philosophy of managing human organizations dedicated to production, from planning a family reunion to producing skyscrapers. But philosophies, however powerful, need tools and methods in order to do better planning, designing, and making. Read this book to educate yourself about the power and possibilities ofered by digitalization. But keep in mind the Lean principle of continuous improvement. Principles don’t do anything until they inform action. Improvement happens through inventing, adapting, and applying tools and methods that are better ft for purpose in delivering value and eliminating waste. Fundamental change is not simple or easy. This book is an invitation to those individuals and companies who are willing and able to try something new and to invest the time and energy needed to be successful. Accepting this invitation will not only beneft yourselves individually but will contribute to the industry transformation that is so desperately needed. Glenn Ballard Research Associate with University of California Berkeley’s Project Production Systems Laboratory April 2022

Foreword

I want you and everyone else who wants to make our industry the best it can be to read this book. Not just because the authors are the best people I know who can describe what Lean Construction 4.0 will look like. But because you decide for yourself that it’s worth taking the time to make the efort to look beyond the day-to-day problems that consume every current and future practitioner. Now you’re thinking, ‘OK, how do I decide?’ Try the approach I learned when faced with the same challenge. First, make a list of all the chapter titles in the frst column and in another what you make of what the chapters are about. Hopefully, you can get ahold of a summary from the publisher’s webpage for the book. But do this however you can. Then add a column called ‘What we do today’ so you can briefy note what you and/or others do now to accomplish what the authors are describing. This will require refection for some things and be very quick for about half of the titles because there is nothing to say about what we are not doing today. Now add another column for what you think the authors are describing as best you can based on your quick survey. You’re almost home. Finally, add another column to describe how you think the new LC4.0 practices could make design, procurement, pre-fabrication, onsite construction and assembly, and building commissioning better. Pay special attention to worker safety, quality, and time because that’s money. Now you can make your decision and will be half-way to really thinking diferently. If you decide this is too much efort, all I can do is wish you the best of luck in the rest of your career going from one crisis to the next. If you decide to dive in, I want you to commit to considering new ideas, no matter how diferent. And I want you to add another column to the list you made. That’s for describing at least one thing your organization can do if you can persuade others to read the book. That’s why I encourage you recruit a group to read the book together with you leading a conversation about how Lean Construction 4.0 can make their lives a lot better. And expand your list to include everyone in other design and building companies, the asset developer/owner, and the people who will use and maintain the facility. Once you’ve gotten just a few people, to see these possibilities, you’re on your way to changing things by working together to educate decision makers and helping them see how to start moving towards the future of Construction. Yes, this will require commitment and efort. No matter! I can tell you from my own experience that this is the best and most interesting work, especially if you want to feel part of something much bigger than yourself and become a true leader. Dean Reed, Board Member at Center for Innovation in the Design and Construction Industry May 2022

PREFACE

Back in 2019, it was very clear for us that Industry 4.0 (I4.0) and its suit of smart and digital technologies (SDT) were taking several industries, and particularly manufacturing, by storm. These industries were doing things diferently and their pathway to an I4.0-driven digitization was apparent and mature in several cases. Due to the advent of I4.0, smart production systems equipped with sensing and autonomous technologies, self-organizing systems, or fully implemented cyber-physical systems where the physical and digital merge were a common place in these industries. So, organizations adopting the ‘I4.0 ideal’, and the recently coined ‘Industry 5.0 (I5.0) paradigm’, were smarter, more connected, more competitive, and they were making more and more emphasis on a human-centric and sustainable vision for their development. But we also realized that this I4.0-led transformation was taking place within the architecture-engineering-construction (AEC) industry. While we needed to confront the fact that the digital transformation in the AEC sector was slow and not without problems, it was gaining momentum, and more importantly, it was relentless. However, there was a fundamental question that strongly called our attention: Was technology alone able to make an efective and meaningful shift of the AEC industry towards a more productive and competitive industry? By the time we decided to write and edit this book, we did not have yet the right answer for this question, but we knew that the making of this book will take us through an exploration to fnd out this answer. Our point of departure was that the AEC industry is diferent from other industries in various aspects. For instance, the AEC industry lags, from a technological standpoint, other industries successfully adopting I4.0-driven SDT such as manufacturing. Another relevant aspect to consider is that the design and production nature of AEC’s projects is one-of-a-kind (each AEC product, i.e., projects, is a prototype itself that consists of the design and assembly of fxed objects on-site, temporary multi-organizations, and production facilities that adapt to varying site conditions), bringing very production-specifc challenges that are unique to the AEC industry. The AEC business environment is also very dynamic and complex with a very fragmented supply chain that presents a low degree of production traceability across. Soon we realized that the answer to the question posed did involve not only technology but also production management, and as such, people who deliver the projects. We envisioned the goal of the book was to take the reader through a journey of discovery from production management theory to I4.0-driven SDT applications in the AEC industry, having people at the core. This approach was novel as existing xii

Preface

books were treating issues in the AEC industry from either a technology-centric perspective or production-centric perspective, so we were planning to ofer an integrated approach in the book. To do so, we used Lean Construction as the production theoretical basis, which was established back in 1993 and it emerged as a production management theory tailored to the AEC industry. Its main source of inspiration was the Toyota Production System and the Western interpretation of it as Lean Manufacturing and Lean Thinking. Lean Construction has evolved from the mere adoption of Lean Thinking principles in the AEC industry to a production management theory in its own right, thanks to the research eforts carried out by academics and practitioners within the International Group from Lean Construction (IGLC) covering diverse research areas such as production planning and control, human and social aspects of organizations, health and safety, education and synergies with sustainability and information technology (IT). The technology perspective encompassed I4.0-driven processes, principles, and SDT. The people perspective was mainly grounded in some of the people and culture-driven principles from Lean Thinking, and the human-centered ideals from I5.0, which essentially shift the focus from a technology-centric view in I4.0 to a human-centric view with SDT still playing a key role within I5.0. Thus, we coined the term ‘Lean Construction 4.0’ to drive a digital transformation in the way AEC projects are managed and delivered, which is based on a process (production philosophy)-people (culture)-technology (SDT) triad. We hope that Lean Construction 4.0 as a new systems-wide thinking will help to reshape the way we design, manage, and operate capital projects in the modern age of automation. In this edited book, we bring together the view of world experts at the interface of Lean Construction and I4.0-driven SDT. We intend to introduce and discuss current research and practice, challenges and drivers, and future perspectives of Lean Construction 4.0. However, this is better explained by what Lean Construction 4.0 evokes: a future vision of construction systems comprising people, processes, and technology using I4.0 as a basis for technological innovation in the AEC industry, and Lean Construction theory and practice as a jettison for improved processes, systems integration, and respect for the environment. With this book, we aim to provide a provocative refection on the theoretical and practical aspects that shape the Lean Construction 4.0 concept guided by the following questions: • •

• • •

How do Lean Construction principles merge efectively with the functionalities and features associated with SDT? How does the duality of Lean-SDT interact with people and culture in practical terms? Diferent aspects related to implementation, adoption, culture, management processes, human-computer interaction, among others, need to be discussed. What are the main barriers and challenges to implement Lean Construction 4.0 in industry? What are the envisioned gains and overall benefts? How can we educate AEC stakeholders (e.g., students, practitioners, academics) on the challenges and benefts of Lean Construction 4.0? How can we avoid the temptation of placing technology at the core rather than people and culture when we think about the Lean Construction 4.0 concept?

We have tried to address these questions organizing the book into six parts containing 20 chapters. The arrangement of parts and chapters is described here: •

Part 1 - Introduction. It contains Chapter 1, where a general notion of Lean Construction 4.0 and the rationale for developing it are provided, along with a discussion on the value for industry and a vision for development and implementation. xiii

Preface

•

Part 2 – Theoretical and Practical Perspectives for Lean Construction 4.0. The following chapters constitute this part: Chapter 2 addresses the synergy between Lean Construction and Construction Site 4.0. Chapter 3 analyzes in detail the symbiotic relationship between production theory (Lean Construction) and technology (Construction 4.0). Chapter 4 proposes the ‘House of Lean Construction 4.0’, establishing a complementary theoretical perspective for the Lean Construction 4.0 vision. Chapter 5 explores the ethical and social dilemmas that are intrinsic in decision-making and how they will impact the decisions made by AI algorithms, bearing in mind the Lean principle of ‘respect for people’. Chapter 6 discusses the theoretical and practical aspects of implementing advanced supply chain practices such as Kitting, enabled with building information modeling (BIM). Chapter 7 introduces a construction management tool based on BIM that merges Last Planner System with 3D visualization and data analytics, providing the necessary insight for an AEC organization to efectively adopt Lean-BIM. Part 3 – Simulation Modeling and Virtual Lean Construction. The following chapters constitute this part: Chapter 8 discusses how computer simulation modeling can support Lean process objectives and impact an AEC organization. Chapter 9 introduces and discusses the SDT’s role in facilitating the understanding of crew planning and management of project production operations. Chapter 10 reviews the use of social network analysis (SNA) to assess the social characteristics of the work environment within AEC organizations implementing Lean Construction such as collaboration, trust, teamwork, transparent communication, commitment management, decentralization. Chapter 11 explores the relationship between immersive virtual reality (IVR) and Lean Construction involving the process-people-technology triad. Part 4 – Digital Production Planning, Control, and Monitoring in Lean Construction. The following chapters constitute this part: Chapter 12 illustrates the development of an LPS-based Metric Visualization (MetViz) system that is used to monitor and control productivity represented by LPS metrics. Chapter 13 proposes a Lean, mathematically driven production theory for the AEC industry to better understand the production mechanisms of repetitive processes in project-driven systems. Chapter 14 presents a digital twin system to increase production fexibility in of-site construction using real-time data, BIM, and manufacturing expertise. Chapter 15 provides an overview of new digital technologies that enable continuous measuring and decreasing waste during design and construction. Chapter 16 discusses the linkages between the features and functionalities of unmanned aerial systems (UAS), and Lean Construction principles and practices. Part 5 – Digital Lean Project Delivery. The following chapters constitute this part: Chapter 17 provides insights into how IT can support the transition to more integrated approaches of project delivery within the AEC industry. Chapter 18 discusses how blockchain technologies can act as the foundation for Integrated Project Delivery 4.0. Chapter 19 reviews the application of several modeling approaches to frame systematic decision analysis to implement Lean practices across a range of project delivery methods. Part 6 – Concluding Remarks. It contains Chapter 20, which addresses the future of Lean Construction 4.0 proposing an industry-wide strategy for implementation, a people-process-technology functional model to balance appropriately the components of the Lean Construction 4.0’s triad, and how Lean Construction 4.0 can enable a shift in AEC organizations to a more holistic, human-centric perspective for the adoption of SDT. xiv

Preface

We make an open invitation to students, educators, researchers, and practitioners in the AEC industry to review this book and refect on the diferent ways that Lean Construction can support and contribute to the adoption of I4.0-driven SDT within AEC organizations (from a theoretical and practical standpoint), but bearing in mind that people and culture shall ‘always’ be kept at the core. Although not all answers are found in this book, we are sure that we are providing a robust complementary perspective for the advancement of the AEC industry. More importantly, we are proposing the rationale for industry not only to survive, but to thrive, and a foundation for future discussions and developments as this is just the beginning! Vicente A. González, University of Alberta, June 2022 Farook Hamzeh, University of Alberta, June 2022 Luis Fernando Alarcón, Pontifcia Universidad Catolica de Chile, June 2022

ABOUT THE EDITORS

Vicente A. Gonzálezis a Professor within the Civil and Environmental Engineering Department at the University of Alberta, Canada. He holds a Construction Engineering (Hons) degree from the University of Valparaiso, Chile, and ME and PhD degrees from the Pontifcia Universidad Catolica de Chile. Before joining the University of Alberta, Vicente was a faculty member at The University of Auckland (New Zealand) for over 12 years, where he currently holds an Honorary Academic position. His research interests are at the interface of Construction Engineering and Management and Computer Science, pioneering the Lean Construction 4.0 concept. He has secured USD 45+ million in research and teaching grants, and the largest corporate sponsorship in the history of the University of Auckland. Vicente is a prolifc author, and member of editorial boards and international construction engineering and management organizations. He has supervised to completion over 100 undergraduate research projects, and Master and PhD theses over his career. Farook Hamzeh, PhD, UC Berkeley, is a Lean Construction expert. His theoretical and applied research in the US, Canada, and the MENA region aim at improving the design and construction of projects. Farook is an Associate Professor in the Civil and Environmental Engineering Department at the University of Alberta. He was full time faculty at Colorado State University and at the American University of Beirut. He is an active member of the International Group of Lean Construction (IGLC) and has published heavily on Lean Construction and related topics. Farook has worked for more than seven years in the construction industry on several mega projects: the $1.7 billion Cathedral Hill Hospital in San Francisco, the 333 m high Rose Rotana Hotel in Dubai, Losail motor-bike racetrack in Qatar, Olympic Tower in Qatar, Al-Amal Oncology Hospital in Qatar, Serail 1374 Building in downtown Beirut, and Sibline Cement factory second production line in Lebanon. Luis Fernando Alarcónis a Professor of Civil Engineering and Director of the Production Management Center (GEPUC) at the Catholic University of Chile (PUC). He obtained his Engineering degree from PUC and a MEng, MS and PhD from UC Berkeley. He has been actively involved in research on Lean Construction, risk modeling, and IT in construction. He is a founding member of the International Group for Lean Construction, a member of the Chilean Institute of Engineers, the Pan American Academy of Engineering, and the National Academy of Construction in the US. xvi

ABOUT THE AUTHORS

Tariq S. Abdelhamid,PhD, PMP, CM-LEAN, is a Chief Lean Enterprise Ofcer with the Student Life and Engagement Division at Michigan State University (MSU) since 2013. He is also an Associate Professor of Lean Construction at MSU since 2000, and Lean production and projects teams coach since 1995. Trained at Ford Motor Company in Lean Production, and by Greg Howell & Glenn Ballard (Lean Construction Institute (LCI) co-founders), he is the co-founder and co-editor of the Lean Construction Journal, a certifed AGC LCEP and Improved LCI instructor, and a current LCI Research Fellow. Mohammed Adel Abdelmegid is a Professional Teaching Fellow at the University of Auckland, leading the Master of Engineering Project Management. He fnished a PhD in Construction Management in July 2020 at the University of Auckland. Before the PhD studies, he worked for seven years as a structural engineer on several major projects in the Middle East such as the Hilton King’s Ranch Hotel in Egypt and the Holy Mosque of Mecca in Saudi Arabia. He is an active member of the Project Management Institute (PMI), and a certifed Project Management and Risk Management Professional. Mohamed Al-Husseinis a Professor at the University of Alberta and holder of the NSERC Industrial Research Chair in the Industrialization of Building Construction. Dr Al-Hussein’s current research initiatives include prototyping of automated and semi-automated machinery for fabrication of steel and wood-framed construction components, application of lean and ergonomics principles to improve the safety and productivity of industrialization construction operations, and development of plant layout and process improvement measures for panelized and modular construction. Dr Al-Hussein’s research has been published in over 380 peer-reviewed journal and conference papers. Fatima Alsakka is a Graduate Research Assistant and PhD student in Construction Engineering and Management at the University of Alberta. She holds a Bachelor’s degree in Construction Engineering and a Master’s degree in Civil Engineering from the American University of Beirut. Fatima’s research in areas ranging from 3D concrete printing, to Lean Construction, construction management, and manufacturing has garnered a research grant and several academic awards. Fatima’s current interests include deploying digital xvii

About the authors

technologies (e.g., digital twins, artifcial intelligence, computer vision) to solve problems in of-site construction. Ricardo Antunesis an Independent Researcher, Senior Project Manager, and Senior Automation Engineer. His work focuses on mathematically represent, predict, control, and automate the behavior of project-driven systems. Paz Arroyo is a Quality Leader at DPR Construction; she leads the development and escalation of trainings to promote proactive quality conversations. She holds a PhD in Civil and Environmental Engineering from the University of California, Berkeley. She has construction industry experience internationally, with a strong background in lean and decision-making. She co-founded Collabdecisions.com in 2018 and has published over 30 peer-reviewed papers in lean, sustainability, decision-making, and quality. Dr Arroyo also worked as a Professor in the School of Engineering at the Catholic University of Chile. Beda Barkokebas is currently a PhD candidate in Construction Engineering and Management at the University of Alberta, and also has extensive experience working with a number of diferent ofsite construction enterprises. Beda’s research interests encompass the development of integrated solutions for process improvement in ofsite construction manufacturing by applying building information modeling, simulation, and real-time data. His current research involves the development of digital twins to monitor production and reduce bottlenecks while collecting valuable insights that can inform future process improvement initiatives. Dayana Bastos Costa is an Associate Professor in the Structural and Construction Engineering Department of the School of Engineering at the Federal University of Bahia, Brazil. She has a BSc in Civil Engineering (Federal University of Bahia) and MSc and Ph.D. in Civil Engineering – Construction Management (Federal of Rio Grande do Sul-Brazil). Dr. Costa’s research includes construction management and technology to improve industry performance, involving aspects related to production, quality, safety, and sustainability, integrating with digital technologies such as unmanned aerial systems and building information modeling. Alfonso Bastías is a Visiting Professor in the Department of Civil, Environmental, and Architectural Engineering at the University of Colorado Boulder, and had been involved in research projects with the National Regulatory Research Institute, National Cooperative Highway Research Program, U.S. General Services Administration, Federal Highway Administration, California and Colorado Department of Transportation, Construction Industry Institute. Before joining CU Boulder, he was the Director of Civil Engineering School at Universidad del Desarrollo and Universidad Diego Portales in Chile, and Chief Innovation Ofcer at Ingendesa among other important roles throughout his professional career. Marcella M. M. Bonanomi is a Research Scholar at PoliS-Lombardia, Lombardy regional government institute for research, and Project Manager of SPOTTED-Satellite Open Data for Smart City Services Development, a project funded by the European Commission. Previously, Marcella was a research associate and lecturer at the Chair of Innovative and Industrial Construction at ETH Zurich. She received her PhD in Architecture, Built Environment, and Construction Engineering from Politecnico di Milano. Her work combines xviii

About the authors

organization, technology and management to study new governance strategies, management practices, and organizational models for the built environment innovation. Makram Bou Hatoum is a Civil Engineer with a Master’s in Construction Management from the American University of Beirut, and a PhD in Construction Engineering and Project Management from the University of Kentucky (UKY). His research areas include Construction 4.0, Lean Construction, construction workforce, and safety management. He was selected as a plenary speaker at the 30th Annual Conference of the International Group for Lean Construction, where he presented his ‘Construction 4.0 Process Reengineering (CPR4.0)’ framework. He was also named by UKY as one of ten Southeast Conference (SEC) Emerging Scholars in 2021. Randi Christensen is an internationally acknowledged lean expert and sustainability director in COWI. Randi was chairman for Lean Construction DK for six years, and later the Lean and Innovation Manager on a +£5bn infrastructure project in London. She is leading COWI’s sustainability activities within Renewable Energy and Major Infrastructure Projects across Scandinavia, UK, and North America. As co-founder of Collabdecisions.com she believes change starts with collaboration and transparent decision-making. She holds a PhD (2008) and an MSc in Engineering Management (2003). Zakaria Dakhli, PhD, is a Research Associate in digital services and business models for of-site construction supporting the Institute for Manufacturing and the Centre for Digital Built Britain. Before joining the University of Cambridge, Zakaria was a research engineer at the Industrial Research chair ‘Construction 4.0’ in France launched in partnership with Bouygues Construction. Zakaria also worked as a consultant for several French construction companies for which he outlined the strategy map for a Construction 4.0 transition. Currently, Zakaria investigates how the construction industry could transition towards digital service-based business models. Bhargav Dave is a co-founder and CEO of VisiLean Ltd, a Finnish startup specializing in cloud-based construction management platform. Bhargav has worked between the interface of digital technologies and construction for his entire career. He has a Bachelor’s degree in Construction Technology and Master’s and PhD in developing Digital Solutions for Construction Management. His work cuts across the feld of computer integrated construction, knowledge management, supply chain management, and virtual environments. He has authored over 45 scholarly articles and industry reports. Bhargav has led innovation projects in digitization of construction processes, including BIMforLean, Virtual Big Room, intelligent products (integrating Internet of Things, Lean and BIM for the construction lifecycle), and Otaniemi3D (digitizing operations monitoring through IoT, BIM, and Big Data). Roy C. Davies,PhD, is a pioneer in Mixed and Virtual Reality from the late 1990s, specifcally in the usability and applications for solving real-world problems. His early career saw the creation of the Flexible Reality Research Centre at the University of Lund, Sweden, then the largest VR lab in Scandinavia. Subsequently, Roy has founded several companies based around emerging technologies, and presently works in an academic and technical capacity at two tertiary institutions, mentors startup businesses, and is pursuing new business and academic opportunities of his own. xix

About the authors

Ali Ezzeddineholds a Master’s degree in civil engineering from the American University of Beirut. Ali has worked as a PMO Engineer at MAN Enterprise, where he assisted in the deployment of a digital construction control room. He also worked as a research assistant at the University of Alberta on topics related to project control within the Last Planner System. Recently, he has been working as a research assistant at New York University Abu Dhabi on topics related to industrialized construction and game engine technology. Borja García de Soto is an Assistant Professor of Civil and Urban Engineering at New York University Abu Dhabi (NYUAD) and the Director of the S.M.A.R.T. Construction Research Group at NYUAD. He does research in the areas of construction automation, cybersecurity in the AEC industry, artifcial intelligence, industrialized and Lean Construction, and BIM. Borja received his PhD from ETH Zurich and MSc in Civil Engineering from UC Berkeley. He has extensive experience in the construction industry and holds Professional Engineer (PE) licenses in California and Florida. Masoud Gheisariis an Associate Professor in the Rinker School of Construction Management at the University of Florida. He is leading the Human-Centered Technology in Construction (HCTC) research group. His research focuses on the theoretical and experimental investigation of human-computer/robot systems in construction. To date, he has authored more than 100 peer-reviewed papers in the felds of Virtual and Augmented Reality (VR/ AR) and Human-Drone Interaction in construction and his research has been supported by several funding agencies, including NIOSH/CPWR, National Science Foundation, U.S. Department of Labor, and ELECTRI International. Daniel M. Hall is an Assistant Professor in the Department of Management in the Built Environment at TU Delft. The theme of his research is to enhance governance, industrialization, circularity, and innovation in the construction industry through a transformation from fragmented project delivery methods to new organizational and informational models that integrate the complex supply chain. Previously, he was Assistant Professor of Innovative and Industrial Construction at ETH Zurich. He holds a Doctor of Philosophy (2017) in Civil and Environmental Engineering (CEE) from Stanford University. Daniel Heigermoseris a technology and strategy professional passionate about transforming the construction industry and digital twin technology for buildings and cities. He is currently working as Digital Business & Technology Strategist at Siemens, Germany. Prior, Daniel advised clients in the AEC industry while working for PwC’s Capital Project and Infrastructure practice and was responsible for managing major building and infrastructure projects for a leading international contractor. He holds a Master’s degree in Civil Engineering from ETH Zurich and business management from Imperial College Business School. Rodrigo F. Herrera is an Assistant Professor in the School of Civil Engineering at the Pontifcia Universidad Católica de Valparaíso (Chile). He holds a civil engineering degree from the Pontifcia Universidad Católica de Valparaíso (Chile), Master’s in Higher Education Management from the Universidad de Alcalá (Spain), PhD in Engineering Science from the Pontifcia Universidad Católica de Chile (Chile), and PhD in Transportation Infrastructures and Territory from the Universitat Politècnica de València (Spain) degrees. His main research interests are related to Lean Project Management, Lean Design, team collaboration, Virtual Design and Construction, and BIM implementation. xx

About the authors

Jens Hunhevicz is a doctoral candidate in the Department of Civil, Environmental and Geomatics Engineering at ETH Zurich. His research investigates the applications and implementation of blockchain in construction for supply chain integration and innovative forms of governance. Prior to his doctoral studies, he received an MSc in Civil Engineering from ETH Zurich with areas of specialization in construction management and geotechnical engineering. Salam Khalifeis a PhD. candidate in Construction Engineering and Management program at the University of Alberta. Salam works with Dr Farook Hamzeh in the feld of lean design management. Specifcally, she works on improving value delivery on construction projects through studying communication patterns and collaborative approaches. Salam is also interested in the social impacts of construction projects. She strives to bring project stakeholders to a mutual understanding of project success and bring projects to the required level of efectiveness on social, environmental, and economic levels. Eyuphan Koc is an Assistant Professor of Civil Engineering at Bogazici University. His research focuses on convergent and data-driven approaches for the design, operation, and maintenance of civil systems with a special focus on sustainability and resilience. Dr Koc holds MS degrees in Systems Architecting and Engineering, and Spatial Data Science from USC where he also earned his PhD in 2021. At USC, he was supported by the prestigious Viterbi and Gammel PhD fellowships. His research has been disseminated internationally in peer-reviewed journals and conferences, and has been funded by the California Department of Transportation and U.S. National Science Foundation. Lauri Koskelajoined the University of Huddersfeld in October 2014 when he was nominated Professor of Construction/Project Management. Previously, he worked at the University of Salford as Professor of Lean, Theory Based Project and Production Management. Prior that he was involved in applied research at VTT Technical Research Centre of Finland. Since 1991, Lauri has been involved in research on Lean Construction, especially focusing on underlying theories of production management and project management. He is a founding and continuously active member of the International Group for Lean Construction. Zoubeir Laf haj is a Professor at Centrale Lille, France, Researcher at the Lam3 research Center in Civil Engineering and holder of the ‘Construction 4.0 chair’, an industrial research chair that deals with the challenges of modernizing the construction industry in France and Europe. He is the Founder and President of the French Institute of Lean Construction. He has written fve books, more than 150 scientifc papers, supervised 32 PhD theses, and 17 postdoctoral researchers. He is a scientifc leader, playing key role with multi-stakeholders at international levels. Ali Lahouti, PhD, PMP®, CM-Lean, is a Lean Manager with Barton Malow Enterprises where he serves as a member in cross-functional teams – whether on a construction project jobsite, or in the corporate administrative ofce – to implement concepts, principles, and practices of Lean Thinking. As Graduate Research Assistant, Ali worked at Construction Industry Research & Education Center, School of Planning, Design, & Construction, Michigan State University. He is also a frequent contributor, and a reviewer for the Lean Construction Journal. xxi

About the authors

Canlong Liu is a PhD candidate in Construction Management within the Faculty of Engineering at the University of Auckland. Before starting his PhD program in 2019, Canlong obtained an MSc degree from the University of Wolverhampton majoring in Construction Management. Canlong’s doctoral research focuses on social dynamics of Last Planner System (LPS) implementation in projects. He is particularly interested in exploring the diference in people’s communication patterns and emotional states when working in Lean and non-Lean environments, using virtual simulated environment in which emergent digital techniques (e.g., Mixed Reality) are adopted as experimental tools. Gunnar Lucko, PhD, is a Professor of Civil Engineering at the Catholic University of America and Director of its Construction Engineering and Management Program. His scholarship has been recognized with the 2013 Daniel W. Halpin Award for Scholarship in Construction of ASCE and several university awards. His research interests are quantifying performance and reducing delays in infrastructure and building projects and mathematical modeling, analysis, and optimization of schedules. For this, he uses singularity functions to express space, cost, and resource constraints in linear and repetitive schedules towards integrated project planning and control. Kevin McHugh brings experience from 25 years of construction management with his academic research. Kevin manages the digital process to integrate the production control process with the project management KPI’s using visual management and identifying highrisk activities, of site and on-site production activities to monitor progress. Kevin is involved in deploying an ‘Integrated project delivery model’ on large-scale projects. Kevin is focused on providing a lean project delivery system that identifes opportunities for continuous improvement. Kevin has collaborated on conference paper and book chapters to support his research. Eder Martínez is a Professor of Virtual Design and Construction at the University of Applied Sciences and Arts Northwestern (FHNW). He has several years of experience in diferent areas of the construction industry, including the execution of infrastructure, process improvement, digitalization, and new product development. Eder complemented these practical experiences with PhD studies at UC Berkeley, aiming to work and research on contemporary industry topics supporting productivity improvement in the construction sector. Harrison A. Mesa is an Assistant Professor of Project Management within the School of Civil Construction, Faculty of Engineering, at Pontifcia Universidad Católica de Chile. Harrison has a PhD (double degree) in Civil Engineering (2016) from the Pontifcia Universidad Católica de Chile and the University of Colorado Boulder. His research and teaching expertise includes lean project delivery, integrated project delivery, risk management, and project management. He is a Principal Researcher at the National Excellence Center for the Timber Industry (CENAMAD)(Code FB210015) in the area of industrialization and construction management. Keith R. Molenaar is the Dean of the College of Engineering and Applied Science and K. Stanton Lewis Professor of Construction Engineering and Management at the University of Colorado Boulder. He holds a BS degree in Architectural Engineering, and MS and PhD degrees in Civil Engineering from the University of Colorado Boulder. Molenaar has xxii

About the authors

published more than 250 journal articles, technical reports, and conference proceedings. He has been elected as a Fellow of the Design-Build Institute of America (2019), the National Academy of Construction (2017), and the Pan-American Academy of Engineering (2012). Hala Nassereddineis an Assistant Professor of Construction Engineering and Project Management in the Civil Engineering Department at the University of Kentucky. She also holds an appointment as a Research Engineer for the Construction Engineering and Project Management Group at the Kentucky Transportation Center. Her research work includes identifying potential disruptors and roadblocks that must be surmounted to transform the construction industry, developing frameworks that promote innovation, proposing methodologies for integrating Construction 4.0 technologies into existing processes, and investigating strategies to leverage Lean Construction in the era of Construction 4.0. Michael O’Sullivanis a Senior Lecturer in the Department of Engineering Science at the University of Auckland. He holds a BSc and an MPhil from the University of Auckland and an MS and a PhD from Stanford University. His research into applying operations research to complex systems spans over 20 years and over 100 publications. He is President of the Operations Research Society of New Zealand (ORSNZ), Deputy Director of Te Pūnaha Matatini (one of ten Centres of Research Excellence in NZ), and member of the Global Partnership for Artificial Intelligence (GPAI). Ignacio Pavez,PhD, is currently an Assistant Professor in the School of Business and Economics at Universidad del Desarrollo, where he teaches graduate and undergraduate courses in organization development and change, team development, leadership, appreciative inquiry, and corporate sustainability. His research focuses on the foundations and practices of positive organizational change, the study of teams in organizations, the effect of appreciation in human organizing and change, and how organizations can integrate sustainability into their business strategy to become agents of world benefit. He holds a PhD in Organizational Behavior from Case Western Reserve University. Evangelos Pantazis is the computational design lead for IBI Group and co-founder of Topotheque Design Research Studio. His research work focuses on the integration of generative design techniques with environmental analysis and digital construction methods using multi-agent design systems. He holds a PhD in Civil and Environmental Engineering from the University of Southern California, an MS in the field of Computer Aided Architectural Design from the ETH in Zurich, and a Diploma in Architectural Engineering from the Aristotle’s University of Thessaloniki. His work has been published in international peer- reviewed journals and conferences and has been exhibited at the Venice Biennale of Architecture and the Modern Art Museum of Lausanne (ELAC). Viranj Patelis a Lean and Digitalization enthusiast who has progressed towards excelling in Lean Project delivery for half a decade. Under the guidance of Prof. Lauri Koskela and Dr. Bhargav Dave, he started his research journey during his postgraduation in Advanced Project Management at the University of Huddersfield. Presently at VisiLean, he plays the role of Product Owner and Manager, steering the research and development toward enabling digital-Lean practices for construction projects. xxiii

About the authors

Mani Poshdar has experienced a combination of academic and industry roles since his graduation in 1998. His involvement in the Lean Construction community started in 2012 upon commencing his PhD studies. He has served the International Group of Lean Construction as a researcher, reviewer, member of the editorial team, and track champion. His current research focuses on simulation for construction operations management in four main areas: building construction management and project planning; building science and techniques; digital and interaction design; and design innovation. Omar Rojas is a Professor of Decision Sciences at Universidad Panamericana, Guadalajara, México. He obtained his PhD (Mathematics) from La Trobe University. He’s been an invited researcher at the Newton Institute in Cambridge, UK, and Airlangaa University in Indonesia. His areas of interest are related to applied mathematics, in particular simulation and optimization methods. Annett Schöttle is partner at refne Projects AG, a business consultancy for Lean Construction, Integrated Project Delivery, and Building Information Modeling. She is passionate about to transforming the construction industry and improving teams to deliver successful building projects. She is an international expert in the feld of Lean Construction and collaboration, and in decision-making processes, has published a number of conference and journal papers. Annett holds a Dr-Ing from the Karlsruhe Institute of Technology (KIT) and is co-founder of Collabdecisions.com. Olli Seppänen is an Associate Professor in the Department of Civil Engineering at the Aalto University School of Engineering. His feld of expertise as a professor is operations management in construction. His research interests include Lean Construction, real-time production control, location-based management systems, Lean design management, construction logistics, and digitalized construction operations. Lynn Shehab is a PhD student in Construction Engineering and Management at the University of Alberta. Her research interests include Lean Construction, production planning and control, simulation and psychological and physiological factors infuencing labor productivity. Lucio Soibelmanis the Fred Champion Estate Chair in Engineering Professor at the University of Southern California. He obtained his BS and MS degrees from UFRGS, Brazil, and worked as a construction manager for ten years before he obtained his PhD in Civil Engineering Systems from MIT. During the last 30 years, he focused his research on advanced data acquisition, management, visualization, and mining for construction and operations of advanced infrastructure systems. He has published over 200 books, books chapters, journal papers, and conference articles, and performed research with funding from many diferent funding agencies. Algan Tezel is a Lecturer and Programme Director in Construction Management and Engineering in the Department of Civil Engineering at Aston University. He holds a PhD in Construction Management from the School of the Built Environment at the University of Salford. His research interests include construction production management, Lean Construction, and digital construction. He is a civil engineer with fve years of practical industry experience. xxiv

About the authors

Kenneth Walshis a Professor of Civil, Environmental, and Infrastructure Engineering and Vice President for Strategic Initiatives at George Mason University, Virginia’s largest public research university located in Fairfax. He holds the Bachelor of Science in Engineering, Master of Science, and Doctor of Philosophy degrees in Civil Engineering from Arizona State University. Ken has been active in the International Group for Lean Construction for many years, where he has served as Secretary General and Conference Chair, with research interests in process modeling and serious games and simulation.

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PART 1

Introduction

1 LEAN CONSTRUCTION 4.0 Beyond the New Production ManagementPhilosophy Vicente A. González, Farook Hamzeh, Luis Fernando Alarcón, and Salam Khalife Introduction The origins of Lean Construction can be traced back to the production management principles established in Toyota by Taichi Ohno (Womack et al., 1990). At the core of ‘Lean’ Thinking, as we know it in the Western world, there is a simple set of fve principles to design Lean Production systems: Specify value, Identify the value stream, Flow, Pull, and Perfection (Womack & Jones, 2003). At the beginning of the 1990s, a number of researchers paid attention to the appreciable infuence that Lean Thinking had in the manufacturing sector. As a result, a new production philosophy for the architecture-engineering-construction (AEC) industry was proposed serving as the theoretical basis for Lean Construction (Koskela, 1992) and enabling the foundation of the International Group for Lean Construction (IGLC) with its frst conference held back in 1993 (Alarcón, 1997). At the heart of Lean Construction is also the Transformation, Flow, and Value (TFV) theory (Koskela, 1992) that goes beyond the traditional transformation view which focuses on the conversion of inputs into outputs using the work breakdown structure. TFV additionally endorses the fow view, representing continuous fow, pull, and continuous improvement, and the value view, representing the voice of the customer, satisfaction of purposes, and value-adding activities. Based on these views of production, synergies between Lean Construction and SDT technologies are being explored throughout this book. On the one hand, Lean Construction has reshaped the landscape of the AEC industry all over the world, providing the principles, tools, and methods to dramatically improve projects performance (Cassino, 2013). On the other hand, the development and use of smart and digital technologies (SDT) have caused a transformation in diferent industry and business domains over the past decade, particularly in the manufacturing industry (Lasi et al., 2014; Porter & Heppelmann, 2014; Rüßmann et al., 2015). Some argue that this transformation represents ‘Industry 4.0’ (I4.0), or the Fourth Industrial Revolution which encompasses a set of emerging technologies and concepts that allow for connectivity, fexibility, efcient processes, and real-time integration among the value chain participants (Pagliosa et al., 2019). This is bringing benefts to the manufacturing sector in terms of performance, management, economics, and workforce (Rüßmann et al., 2015).

DOI: 10.1201/9781003150930-2

Vicente A. González et al.

I4.0 is also infuencing the AEC industry, which has started to adopt some of its related SDTs (Sawhney et al., 2020) such as: machine learning and predictive models for projects’ decision making (Mansouri et al., 2020); mixed-reality (MR) and robotics (Ahmed, 2018; Zhang et al., 2020); computer simulation and modeling (Abdelmegid et al., 2020; AbouRizk, 2010); and digital twinning (Sacks et al., 2020). Even though there are eforts to leverage some of the I4.0-related SDT within the AEC industry in what is called ‘Construction 4.0’, it still lags behind manufacturing. Some argue that the ‘Construction 4.0’ concept can bridge this gap by enabling planning, design, and delivery of buildings, infrastructure, and other type of projects more efectively via the digital transformation of AEC organizations (Sawhney et al., 2020). However, little attention has been paid on how Lean Thinking can be the steppingstone for an efective and truly signifcant adoption of I4.0-related SDT. It is undeniable that the AEC industry is currently facing a growth in terms of the SDT’s uptake, which is perceived as a more or less fast phenomenon in relation to its historical technology progress (Mansouri et al., 2020). In this regard, Lean Construction could be in the driver seat for an I4.0-focused transformation in the AEC industry, but there are theoretical and practical challenges to be faced. In terms of theory, the ‘Lean Construction 4.0’ concept that we propose invites to go beyond the current production theory and question whether or not new or adapted Lean principles can arise, and new research methodological approaches that see ft are necessary. In terms of practice, we argue that the Lean Construction 4.0 concept will bring challenges related to implementation, empirical validation, human centric systems, and ethical and moral predicaments associated with the SDT use (Hamzeh et al., 2021). In this chapter, we introduce our Lean Construction 4.0 notion that underpins this book. We argue that Lean Construction 4.0 represents a forward vision on how the AEC should adopt I4.0-related SDT, rather than the current state of afairs in this industry. Thus, the goal of this chapter is to identify the need for Lean Construction 4.0 and to explore the role of Lean Construction for an efective digital transformation of the AEC industry.

Lean and Industry 4.0 Industrial revolutions represent technology leaps that reshaped entire industries starting with the steam age and mechanization (First Industrial Revolution), followed by the electricity age and associated mass production (Second Industrial Revolution), and the information age with the incorporation of information communication technologies (ICT), automation, and microelectronics (Third Industrial Revolution) (Xu et al., 2018). In 2011, the I4.0 term was adopted in Germany to represent a strategic efort to revolutionize the manufacturing sector taking advantage from SDT, which essentially acknowledged theinitiation of the Fourth Industrial Revolution or cyber physical systems (CPS) age. The I4.0 notion is often referred to diferent concepts such as autonomously controlled and digitalized Smart Factory, CPS, decentralized self-organization, and individualized product and service developments, among others (Lasi et al., 2014). With the transition from a machine-driven manufacturing to a digitally integrated and smart manufacturing (Oztemel & Gursev, 2020), I4.0 has enabled improved agility, operations performance, and proftability within frms (Rosin et al., 2020). In general, SDT that can be considered under the I4.0 umbrella are as follows: (1) Big data analytics; (2) Autonomous-adaptative robotics; (3) Computer simulation; (4) Systems integration; (5) Internet of Things (IoT) and sensors; (6) Cybersecurity; (7) Cloud computing; (8) Mixed-reality; (9) Additive manufacturing; (10) Artifcial intelligence (AI), and (11) Machine-to-machine (M2M) communication (Rüßmann et al., 2015; Satoglu et al., 2018). 4

Lean Construction 4.0 Table 1.1 The 14 Toyota production system principles (adapted from Liker & Meier, 2006) TPS principle

Explanation

1 Philosophy as the foundation 2 3 4 5 6 7 8 9 10 11 12 13 14

Base managerial decisions on a long-term philosophy, even at the expense of short-term fnancial goals Creation of an ongoing process fow Value-added fow is the main focus, linking processes and people. Use of pull systems Avoid overproduction. Leveling out workload Create a continuous, uninterrupted fow. Culture of stopping to fx problems Get quality right from the beginning. Standardizing tasks and processes Foundation for ongoing improvement and workers empowerment. Use of visual control Sign and label everyplace. Use of only reliable technology Test technology that aligns to the purpose of your people and process. Growing leaders Encourage people to understand the work, live the philosophy, and teach it to others. Development of outstanding people Encourage people and teams to follow company’s and teams philosophy. Respecting the extended network Use and abuse your partners is unacceptable. Respect for of partners/suppliers humanity is fundamental. Going and seeing for yourself Assist to deeply understand the situation. Making decisions gradually By consensus, thoroughly evaluating and considering all choices; implementing decisions quickly. Becoming a learning organization Through persistent refection and ongoing improvement.

On the other hand, Lean Thinking is a production management philosophy evolved from the Toyota Production System (TPS). Table 1.1 describes the 14 TPS principles that underpin Lean Thinking according to Liker and Meier (2006, p.8). Lean Thinking modifed the classical understanding of organization roles in frms, emphasizing collaborative work, multiskilling, satisfaction in the workplace, teamwork, ongoing improvement, and waste elimination (Womack et al., 1990). In turn, the growing attention of the Lean Thinking impacts in manufacturing and the willingness of other industrial domains to adopt its principles infuenced in the late 20th century the emergence of Lean Construction (Forbes≈& Ahmed, 2010). This fnally resulted in the foundation of the Lean Construction principles and the inception of the TFV model, which is the cornerstone of Lean Construction (Koskela, 1992,2000). To understand how Lean Construction 4.0 can take place efectively in the AEC industry, the underlying mechanisms associated with the relationship between Lean and I4.0 should be understood. In this regard, it is necessary to look into the research undertaken in manufacturing to investigate the synergies and integration between Lean Thinking and I4.0. For instance, Mayr et al. (2018) suggested three views for a Lean and I4.0 integration: (1) Lean as an I4.0 enabler; (2) I4.0 accelerates Lean uptake; and (3) there is a positive correlation between them both. Zuehlke (2010) has argued that production systems complexity can be decreased via Lean practices, and a heavy dependence on technology will not always enhance the system performance but make it more complex instead. But it is also true that technological concepts such as automation are not foreign to Lean, e.g., the Lean principle of automation or ‘automation with a human touch’ acknowledges that repeating and adding value activities 5

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are prone to automation, so there might be a natural extension of the Lean principles to Industry 4.0 as such (Satoglu et al., 2018). In this regard, I4.0-related SDT has the potential to eliminate or reduce any of the seven Lean waste identifed originally by Shingo et al. (2005) and Taiichi (1988). For instance, augmented-reality, in which 3D virtual objects are overlaid on the physical world in real time, can enable the visualization of operation-instructions and items/goods specifcations, helping to eliminate excessive motion, transportation, and defective parts. Digital twinning can support waste elimination by using computer simulation and cloud computing to model, simulate, and optimize manufacturing processes and assess alternative system designs, reducing transportation, waiting, overprocessing, and defective parts (Satoglu et al., 2018). Even more, Satoglu et al. (2018) argued that Lean Thinking ultimately engenders a ‘waste hunting’ and ‘adding-value’ ecosystem in manufacturing frms, helping to articulate a robust and efective I4.0 development and implementation. In this regard, Hamzeh et al. (2021, p.211) stated that ‘a sense of purpose (production management theory) and problem-driven view (Lean-based methodologies) can be provided to optimize the design and use of SDT’. We believe that this view can be brought to the AEC industry to drive the development of Lean Construction 4.0.

Why Lean Construction 4.0? SDT are revolutionizing entire industries, modifying the nature of their business structures and competition. The advent of information technology (IT) over the past 50 years has pushed changes in competition and strategy two times: Automation in the 1970s, and Internet in the 1980s. At present, a third IT wave is taking place that includes smart connect products with IT ubiquitous presence being a key facet of this change (Porter & Heppelmann, 2014). A reasonable expectation is being created to unleash large productivity boosts and economic rise as a result of the efciencies, and enhanced competition and innovation that the digital transformation driven by SDT uptake is generating in frms. As a matter of fact, this is coincidental with Porter and Heppelmann’s (2014) views about the SDT impacts on business and the I4.0 benefts for manufacturing (Xu et al., 2018). In addition, Porter and Heppelmann (2017) identifed a gap between the physical world and the digital world enabled by SDT due to the inability of existing business systems and processes to represent real-world information to users, e.g., when 2D drawings are used to illustrate engineering systems details; however, these systems are in reality fully 3D entities. Therefore, the quality of decision-making is jeopardized. From their perspective, the relevance of human’s role in technology utilization has been traditionally neglected as it cannot keep up with the unique motor and cognitive skills of people. As such, they acknowledge that more robust human interfaces are needed to link the physical and the digital worlds, and in turn, interact seamlessly with the human world. This refection matches well with the layers carried within the Lean triad, to say, process (physical world), people (human world), and technology (digital world), making it fundamental for the I4.0-related SDT implementation in organizations. Figure 1.1 shows a comparison between the manufacturing and AEC industries using the metaphor of industrial revolutions in manufacturing, which is based on Xu et al. (2018), Sawhney et al. (2020), and Swenson and Chang (2020). Today, manufacturing is facing the I4.0 transformation, which is still underway! However, the AEC industry’s reluctance for a widespread adoption of SDT has kept away the opportunity to accomplish an ‘I3.0 transformation’, which is a vital prerequisite for an ‘I4.0’ transformation (Farmer, 2016). Problems that negatively afect the performance of the AEC industry, such as low innovation levels, little information integration, poor production traceability, fragmentation of the supply chain, 6

Lean Construction 4.0

Figure 1.1

Comparison of industry revolutions and technological progress between the manufacturing and AEC industries

and myopic and obsolete construction management (Koskela, 2000; Sawhney et al., 2020; Zhou et al., 2016), are likely having a detrimental efect on the AEC industry transition through the I3.0 transformation. The ‘Construction 4.0’ concept proposed by Sawhney et al. (2020) has enabled exciting technical and practical possibilities to assist the AEC industry transition from the I3.0 to the I4.0 states. However, there are three reasons preventing this to occur: (1) Construction 4.0 as it stands has not still provided a solid, well-reasoned, applicable framework that recognizes system’s relationships and autonomy to take decentralized and entirely synchronized decisions in automated environments for supply chain and production management (Sacks et al., 2020); (2) Construction 4.0 ignores the intricacies and linkages necessary between production management theory and SDT to make I4.0 a feasible scenario in the AEC industry horizon (Hamzeh et al., 2021); and (3) Construction 4.0 seems to focus on technological aspects of adoption rather than on a more comprehensive framework that pay attention to people and processes in tandem. Lean Construction provides much more than a set of production theory and methodological principles for AEC practices to be validated and enhanced in an ongoing basis (Koskela, 2000). Lean Construction, in fact, provides a three-layered structure of ‘principles/culture’, ‘practices’, and ‘tools/methods’ as stated by Pekuri et al. (2012). Essentially, Lean Construction represents the foundation to approach the processes-people-technology challenges associated with a I3.0 full transformation (and further I4.0) in the AEC industry. Researchers have not yet reached a consensus in terms of whether Lean as an enabler of I4.0 is more efective than the reverse in the manufacturing industry, as a full understanding of the synergistic forces involving the integration of Lean Thinking and I4.0 is still being investigated. Nevertheless, there is enough evidence supporting the notion that merging Lean Thinking and I4.0 is achievable and signifcantly benefcial for those frms willing to take the risk of a combined implementation strategy (Mayr et al., 2018; Satoglu et al., 2018; Xu et al., 2018). 7

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In that respect, we believe that the same can apply within the AEC industry and the making of this book is a testament of the possibilities and opportunities that Lean Construction 4.0 as an integrated Lean Construction-I4.0 framework brings for the industry. In order to answer the question about ‘Why Lean Construction 4.0?’, we see necessary to understand the linkages and synergies between Lean Construction, and the I4.0-related SDT and principles. We cannot ignore, however, that an emerging body of Lean Construction knowledge developed over the past 30 years has Unraveled certain connections with IT, digital technologies, and I4.0-related technologies. By developing a scoping review of the IGLC conference proceedings from 1996 to 2020, we have found that 236 papers explored jointly with Lean Construction themes the following major areas: (1) Building Information Modeling (BIM), visualization, and virtual construction; (2) computing applications and information systems, and (3) industrialization and prefabrication. Research on I4.0 advanced technologies is still lacking within the Lean Construction community. However, this is laying the foundations for the establishment of Lean Construction 4.0. In short, Lean Construction 4.0 provides the ingredients to not only allow an efective digital transformation to take place in the AEC industry, but also pay attention to those aspects that are fundamental to it, i.e., process (production philosophy)-people (culture)-technology. We also acknowledge that the Lean Construction 4.0 is aspirational in nature, but it makes a strong case for Lean Construction to keep evolving and serving the AEC industry.

Value of Lean Construction 4.0 for Both Academia and Industry With every developing paradigm shift that reshapes an industry, the eforts in related academia and in corresponding practices would go into overdrive to address the subsequent fundamental changes. In the current shift of the AEC sector towards an I4.0-driven industry, there is a call for industry and academia to prioritize Lean principles within this transformation. Amid the Third Industrial Revolution, one of the leading voices in management, Peter Drucker, announced to The Economist (2001, p.12): What has changed manufacturing, and dramatically increased productivity, are the new concepts. Information, Control, Automation and Robotics Technologies are less important than new ideas about manufacturing, which in advance are comparable to the arrival of mass production 80 years ago. By these new ideas, Drucker was referring to Lean Thinking, where the critical role is not for techniques and processes, but rather for the strive to make ‘knowledge productive’ through motivating people to work in a Lean manner. Following suit, we advocate for Lean Construction 4.0 to be concerned with expanding the view from merely the technological advancement embedded in I4.0, to permanently balancing the three core Lean elements or triad, namely process or (management) philosophy, people or culture, and technology. The management philosophy is based on Lean principles (e.g., delivering value, reducing waste, continuous improvement). The culture is based on people motivated by the Lean transformation. All this is supported by the methods and technologies ofered by I4.0. As depicted in Figure 1.2, the current situation of the AEC industry is positioned halfway between the full integration of I4.0-related SDT, which is paving the way for the technological advancement in the AEC sector, and Lean foundations. The balance within the process (philosophy)-people (culture)-technology triad promoted by Lean is 8

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Figure 1.2

The integration of I4.0 with Lean towards lean construction 4.0

often overlooked or underachieved, leaving organizations with mediocre results. After all, the management philosophy is needed to provide the ‘productive knowledge’, the knowledge to know what to do and how to do it, thus achieving Lean processes and operations. The people/culture pillar is also critical to sustain people and teams that are the engine and backbone of any organizational transformation. Accordingly, organizations need to engage their people, train, and develop them individually while promoting teamwork. However, some researchers expressed their concerns about a possible discrepancy where I4.0 support high level of automation and digitization, while Lean Thinking promotes intense human integration and efciency (Pagliosa et al., 2019). Nonetheless, Kolberg and Zühlke (2015) proved that I4.0 ofers innovative technologies, yet favors cost-efcient and lower levels of complexity solutions similar to what Lean Thinking advocates; additionally, those innovative solutions are to support humans that would take the same leading role. Another aspect that needs to embrace these concepts and act as the practical underpinning for Lean Construction 4.0 is the environmental aspect. Protecting the environment and diminishing the environmental efects should be part of the key concerns in the shift to Lean Construction 4.0. In order to ensure the Lean triad balance, and expand on the current progress, academia is expected to play a vital role in supporting the merge between Lean Construction and I4.0-related SDT to meet the vision of Lean Construction 4.0. Such integration would leverage these technologies to ensure better outcomes on all levels in the AEC industry. While the manufacturing sector has been proposing frameworks to merge I4.0 with Lean Thinking approaches to respond to the competitive growing market, examples: (Buer et al., 2018; Kolberg & Zühlke, 2015), a better and mature understanding of I4.0 and its associated SDT is still needed (Tortorella et al., 2021). Likewise, the AEC industry is in need for more systematic implementation approaches that actually endow the industry professionals with means, guidelines, appropriate technologies, and roadmaps for implementation. Academia can ofer this help and contribute in a cost-efective manner to avoid the investment of ‘randomly trying’ I4.0-related SDT, and alternatively, perform in rational manner experimentation, analysis, and evaluation, then reveal lessons learned. By conducting pilots testing, problems of rushing to SDT without proper planning are avoided. 9

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An ample opportunity for research is anticipated in developing frameworks for integration based on empirical evidence, analyzing synergies, and comprehensive conceptual models that link construction operations to relevant I4.0 SDT. These eforts would be in line with governmental plans around the world to expand the proper implementation of I4.0related SDT in the AEC sector (Tortorella & Fettermann, 2018). In addition, research eforts could beneft from the funding that governments and industry are allocating into these areas. Research endeavors shall target the diferent stakeholders in the AEC supply chain and expand on collaborative and experimental works. Collective resources from colleges, universities, and industry frms are expected to fulfll a better transition to Lean Construction 4.0. These collaboration and networking eforts would help academia in gaining further trust from the industry by preparing better future skilled workforce equipped with the needed knowledge and mindset on technologies, culture, and management philosophy based on Lean Construction. In return, the industry would have a direct beneft from the proposed road maps and frameworks for a digital construction environment that would consequently lead to higher productivity and performance in the AEC industry.

A Vision for Development and Implementation While some SDT solutions have been incorporated into the AEC industry for a decade now, investments in technology have intensifed recently. In fact, the urge for change using further technological advancement over the coming years has never been stronger (Bartlett et al., 2020). Several broad attempts to establish the foundation for adoption and execution of I4.0 technologies in AEC projects have been observed. In what follows, we provide our own view on the general strategies that need to be considered when implementing Lean Construction 4.0. This set of guidelines acts as basis for the development of any future frameworks and implementation plans to support the future vision for the digital transformation in the AEC industry, thereby achieving Lean Construction 4.0. According to Kotter (1995), any transformation in an organization should go through several initial steps, starting from establishing the sense of urgency for this transformation, followed by composing the lead team to do the change efort, and then creating and communicating the vision with its corresponding strategies. To this end, having a complete vision to direct the changes is very critical, where Kotter stressed that ‘without a sensible vision, a transformation efort can easily dissolve into a list of confusing and incompatible projects that can take the organization in the wrong direction or nowhere at all’ (Kotter, 1995, p.5). Consequently, the proposed guidelines and criteria represent the point of departure, and provide a general vision for every organization when implementing I4.0 SDT within a Lean framework: 1

Consciousness about human-centered systems: Any future implementation plan shall consider the existing workforce and teams to establish a clear vision on their degree of involvement and empowerment. The basis for projects upgrades to SDT and the transformation toward I4.0 is people’s collaboration and management. With collaboration, this shift is believed to be better applied by discussing and engaging people on all levels, not only during the execution phase but also early on during the planning phase. This step is similar to the concept of involving the Last Planner in the planning process. The Last Planner system (LPS) is a production planning philosophy and set of procedures that facilitate work fow through coordination and commitments among all team members (the last planners) (Ballard, 2000). Similarly, I4.0-SDT implementation 10

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requires intensive collaboration mimicking the social dynamics engendered by LPS. Additionally, this technological shift should be initiated by capturing the organizational culture and stakeholders’ values through involving teams and discussing change to have this gradual cultural and technological shift. The need for new workforce that have the knowledge in advanced SDT is obviously inevitable; thus, managers and any lead group should be the connecting bridge between the new workforce and the existing teams that have the know-how of the work processes. Brainstorming the existing and probable new obstacles need to be coordinated among teams. This would help in removing any concerns about new SDT implementations, training, time investments, and integration among other processes. Digital collaboration is the new trend, yet it should not abort human interaction and coordination. Evaluation of long-term benefts: Another important consideration is the long-term benefts over short-term gains, as per the Lean philosophy. Designing the digital transformation of the processes and moving to smart operators, machines, and automated work, whether in design or construction, requires a known budget target. Similar to target value design (TVD), which is a management technique that uses collaboration to achieve a target construction cost while maintaining value (Zimina et al., 2012), the transformation and the implementation plan of construction companies should start with the end in mind. This means that a wide view on the expected results should be assessed earlier, specifcally assessing the value of such transformation. Here, we need to go beyond the monetary value, to include the achievement of value that is of beneft for the diferent parties such as owners, design team, and construction team (Giménez et al., 2020). Development of human trust in the decision-making process: While currently, the digital world is providing means to aid in the decision-making process, such as the use of AI, ethical dilemmas are arising on this matter, specifcally when decisions are made by algorithms instead of humans (Arroyo et al., 2021). Sound decisions shall be made by people in charge with the help of AI and algorithms shall be transparent to decision makers. The concern here is that at the beginning, algorithms are reliant on human-input, yet after enough historical data is collected, algorithms can be independent of human input based on the machine learning concept. Therefore, between harvesting the benefts of AI in the AEC industry, where algorithms propose solutions that human could never develop on their own, and between keeping power over the decision processes, human-AI trust is needed to improve Lean workfow (Schia et al., 2019). Acknowledgment of technology as means to the ends: Technology is in service of the work (fnal product), teams (internal stakeholders), end-users (external stakeholders), and the industry as a whole. The idea here is to treat SDT as the means not the goal or the end target. Organizations in the AEC industry shall be aware not to follow the trend of I4.0 technologies without sufcient knowledge and evaluation plan to what is applicable within the constraints of the project and the company. When robots were introduced for the frst time, people feared they would take over, as they were envisioned as a desired result. In that respect, Isaac Asimov introduced the famous three rules: robots may not harm humans, must obey rules and orders by humans, and must protect their existence without conficting the frst two rules (Asimov, 1950). Since then, researchers and inventors emphasized the idea of having robots and smart machines to primarily serve the human race (means not ends). Following suit, SDT shall assist humans in multiple construction tasks and operations, specifcally those that go beyond their capability, as part of serving the end goal which is successful projects with lower waste, higher value, and higher customers’ satisfaction. 11

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Integration of the whole rather than fragmented and partial solutions: The problem of fragmentation and partial development of processes on a project or within a construction factory is a prevalent and pressing problem within the AEC industry (Blanco et al., 2018). When it comes about partial solutions, such as improving the efciency of one construction operation, for immediate benefts, this is done at the expense of other processes and results in a big disconnect across the overall project. Lean, otherwise, focuses on what is called global optimization rather than local optimization. Accordingly, with the implementation of I4.0-related SDT, companies need to understand their current practices, their capabilities, and how to adopt these SDT within their culture, vision, and collective processes to achieve an integrative, not siloed implementation of SDT. Respect to sustainability pillars: when we talk about smart cities, this should take place on the basis of social and environmental respect. Any implementation and framework should be human-centered and have an environmentally friendly design. Sustainability goes hand in hand with Lean concepts and approaches, and this shall be the same for Lean Construction 4.0.

The AEC industry has to face the fact that sooner or later a digital transformation is coming, whether in a slow or a fast rhythm. The principles identifed above as part of the overall vision of Lean Construction 4.0 are recommended as a checklist when proposing implementation strategies. Finally, with Lean Construction 4.0 opportunities come some risks and challenges that will be discussed in detail in Chapter 20 of this book.

Conclusion The digital transformation is ongoing and taking over many industries, and the AEC is part of this shift. We believe the adaptation to meet the current changes is not a straightforward approach and needs a transition phase where diferent stakeholders need to collaborate and share concerns. Focusing only on cutting-edge applications and SDT without actually considering the challenges and the realistic impacts is detrimental. Improving productivity, connectivity, and collaboration on projects should be drivers to implementing Lean Construction 4.0. Current practices should be evaluated, and improvements should be implemented where applicable with the needed resources. A set of guidelines were highlighted as part of the vision for implementing Lean Construction 4.0. Lean Construction 4.0 shall focus on providing integrated approaches for AEC organizations by ofering solutions that consider diferent aspects of the processes involved, the existing labor and teams, and their potential in adopting SDT and implementing them in a gradual and consistent manner to sustain the digital transformation of these organizations. We envision that Lean Construction 4.0 would open avenues for research to generate more challenging ideas that tackle the 14 Lean principles and what expansions might be needed in connection with SDT. The vision of Lean Construction 4.0 for the upcoming years is based on a dynamic process that integrates technologies and processes with people within a Lean culture.

References Abdelmegid, M. A., González, V. A., Poshdar, M., O’Sullivan, M., Walker, C. G., & Ying, F. (2020). Barriers to Adopting Simulation Modelling in Construction Industry. Automation Construction, 111, 103046. https://doi.org/10.1016/j.autcon.2019.103046

Lean Construction 4.0 AbouRizk, S. (2010). Role of Simulation in Construction Engineering and Management. Journal Construction Engineering Management, 136(10), 1140–1153. https://doi-org.login.ezproxy.library.ualberta.ca/10.1061/(ASCE)CO.1943-7862.0000220 Ahmed, S. (2018). A Review on Using Opportunities of Augmented Reality and Virtual Reality in Construction Project Management. Organization, Technology & Management Construction: International Journal, 10(1), 1839–1852. Alarcón, L. F. (1997). Lean Construction (A.A.Balkema (ed.)). Rotterdam. Arroyo, P., Schöttle, A., & Christensen, R. (2021). The Ethical and Social Dilemma of AI Uses in the Construction Industry. Proceedings of the 29th Annual Conference of the International Group for Lean Construction, 227–236. https://doi.org/10.24928/2021/0188 Asimov, I. (1950). Runaround. I, Robot (The Isaac Asimov Collection (ed.)). New York City, Doubleday.(First Published 1942). Ballard, H. G. (2000). The Last Planner System of Production Control. University of Birmingham. Bartlett, K., Blanco, J. L., Johnson, J., Fitzgerald, B., Mullin, A., & Ribeirinho, M. J. R. (2020). Rise of the Platform Era: The Next Chapter in Construction Technology. McKinsey & Company, 9. Retrieved from https://www.mckinsey.com/industries/private-equity-and-principal-investors/ our-insights/rise-of-the-platform-era-the-next-chapter-in-construction-technology Blanco, J. L., Mullin, A., Pandya, K., Parsons, M., & Ribeirinho, M. J. (2018). Seizing Opportunity in Today’s Construction Technology Ecosystem. McKinsey & Company, September. Buer, S.-V., Strandhagen, J. O., & Chan, F. T. S. (2018). The Link Between Industry 4.0 and Lean Manufacturing: Mapping Current Research and Establishing a Research Agenda. International Journal Prodcedure Research, 56(8), 2924–2940. https://doi-org.login.ezproxy.library.ualberta.ca/10.108 0/00207543.2018.1442945 Cassino, K. (2013). Lean Construction-Leveraging Collaboration and Advanced Practices to Increase Project Efciency. Intelligence, McGraw Hill Construction. Drucker, P. (2001). The Economist. The Economist, November 3, 12. Farmer, M. (2016). The Farmer Review of the Uk Construction Labour Model. Construction Leadership Council. Forbes, L. H., & Ahmed, S. M. (2010). Modern Construction: Lean Project Delivery and Integrated Practices. CRC Press. Giménez, Z., Mourgues, C., Alarcón, L. F., Mesa, H., & Pellicer, E. (2020). Value Analysis Model to Support the Building Design Process. Sustainability, 12(10), 4224. https://doi.org/10.3390/su12104224 Hamzeh, F., González, V. A., Alarcon, L. F., & Khalife, S. (2021). Lean Construction 4.0: Exploring the Challenges of Development in the AEC Industry. In L.F. Alarcón & V.A. González (Ed.), Proceedings of the 29th Annual Conference of the International Group for Lean Construction (pp. 207–216). https://doi.org/10.24928/2021/0181 Kolberg, D., & Zühlke, D. (2015). Lean Automation Enabled by Industry 4.0 Technologies. IFACPapersOnLine, 48(3), 1870–1875. https://doi.org/10.1016/j.ifacol.2015.06.359 Koskela, L. (1992). Application of the New Production Philosophy To Construction (Volume. 72). Stanford University, Stanford. Koskela, L. (2000). An Exploration Towards a Production Theory and its Application to Construction. VTT Publications. Kotter, J. P. (1995). Leading Change: Why Transformation Eforts Fail. Harvard Business Review, 2(1), 1–10. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hofmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334-4 Liker, J. efrey, K., & Meier, D. (2006). Toyota Way Fieldbook: A Practical Guidr for Implementing Toyota’s 4Ps. McGraw-Hill Education. Mansouri, S., Castronovo, F., & Akhavian, R. (2020). Analysis of the Synergistic Efect of Data Analytics and Technology Trends in the AEC/FM Industry. Journal Construction Engineering Management, 146(3), 04019113. https://doi-org.login.ezproxy.library.ualberta.ca/10.1061/(ASCE)CO.1943-7862.0001759 Mayr, A., Weigelt, M., Kühl, A., Grimm, S., Erll, A., Potzel, M., & Franke, J. (2018). Lean 4.0-A Conceptual Conjunction of Lean Management and Industry 4.0. Procedia Cirp, 72, 622–628. https:// doi.org/10.1016/j.procir.2018.03.292 Oztemel, E., & Gursev, S. (2020). Literature Review of Industry 4.0 and Related Technologies. Journal Intelligent Manufacturing, 31(1), 127–182. https://doi-org.login.ezproxy.library.ualberta.ca/10.1007/ s10845-018-1433-8

Vicente A. González et al. Pagliosa, M., Tortorella, G., & Ferreira, J. C. E. (2019). Industry 4.0 and Lean Manufacturing: A Systematic Literature Review and Future Research Directions. Journal Manufacturing Technology Management, 32(3), 543–569. https://doi-org.login.ezproxy.library.ualberta.ca/10.1108/JMTM12-2018-0446 Pekuri, A., Herrala, M., Aapaoja, A., & Haapasalo, H. (2012). Applying Lean in Construction– Cornerstones for Implementation. Proceedings of the 20th Annual Conference of the International Group for Lean Construction, 18–20. Porter, M. E., & Heppelmann, J. E. (2014). How Smart, Connected Products Are Transforming Competition. Harvard Business Review, 92(11), 64–88. Porter, M. E., & Heppelmann, J. E. (2017). Why Every Organization Needs an Augmented reality strategy. HBR’S 10 MUST, 85. Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2020). Impacts of Industry 4.0 Technologies on Lean Principles. International Journal Production Research, 58(6), 1644–1661. https://doi.org/10.1080/002 07543.2019.1672902 Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The Future Of Productivity and Growth in Manufacturing Industries. Boston Consulting Group, 9(1), 54–89. Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction With Digital Twin Information Systems. Data-Centric Engineering, 1. https://doi.org/10.1017/dce.2020.16 Satoglu, S., Ustundag, A., Cevikcan, E., & Durmusoglu, M. B. (2018). Lean Production Systems for Industry 4.0. In A. Ustudung & E. Cevivkan, Industry 4.0: Managing the Digital Transformation (pp.43–59). Springer. Sawhney, A., Riley, M., & Irizarry, J. (2020). Construction 4.0: An Innovation Platform for the Built Environment. Routledge, Taylor & Francis Group. https://doi.org/10.1201/9780429398100 Schia, M. H., Trollsås, B. C., Fyhn, H., & Lædre, O. (2019). The Introduction of AI in the Construction Industry and its Impact on Human Behavior. Proceedings of the 27th Annual Conference of the International Group for Lean Construction, 903–914. https://doi.org/10.24928/2019/0191 Shingo, S., Dillon, A. P., & Bodek, N. (2005). A Study of the Toyota Production System: From an Industrial Engineering Viewpoint (1st ed.). Routledge. https://doi.org/10.4324/9781315136509 Swenson, A., & Chang, P.-C. (2020). Construction. Encyclopedia Britannica. Taiichi, O. (1988). Toyota Production System: Beyond Large-Scale Production (1st ed.). Productivity Press. Tortorella, G. L., & Fettermann, D. (2018). Implementation of Industry 4.0 and Lean Production in Brazilian Manufacturing Companies. International Journal of Production Research, 56(8), 2975–2987. https://doi.org/10.1080/00207543.2017.1391420 Tortorella, G., Sawhney, R., Jurburg, D., de Paula, I. C., Tlapa, D., & Thurer, M. (2021). Towards the Proposition of a Lean Automation Framework. Journal Manufacturing Technology Management, 32(3), 593–620. https://doi.org/10.1108/JMTM-01-2019-0032 Womack, J. P., & Jones, D. T. (2003). Lean Thinking: Banish Waste and Create Wealth in Your Corporation (2nd ed.). Productivity Press. Womack, J. P., Jones, D. T., & Roos, D. (1990). The Machine that Changed the World. Macmillan Publishing Company. Xu, L. Da, Xu, E. L., & Li, L. (2018). Industry 4.0: State of the Art and Future Trends. International Journal Production Research, 56(8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806 Zhang, Y., Liu, H., Kang, S.-C., & Al-Hussein, M. (2020). Virtual Reality Applications for the Built Environment: Research Trends and Opportunities. Automation Construction, 118, 103311. https:// doi.org/10.1016/j.autcon.2020.103311 Zhou, Z., Goh, Y. M., & Shen, L. (2016). Overview and Analysis of Ontology Studies Supporting Development of the Construction Industry. Journal Computing Civil. Engineering, 30(6), 4016026. https://doi-org.login.ezproxy.library.ualberta.ca/10.1061/(ASCE)CP.1943-5487.0000594 Zimina, D., Ballard, G., & Pasquire, C. (2012). Target Value Design: Using Collaboration and a Lean Approach to Reduce Construction Cost. Construction Management Economics, 30(5), 383–398. https://doi.org/10.1080/01446193.2012.676658 Zuehlke, D. (2010). SmartFactory—Towards a Factory-of-Things. Annual Reviews in Control, 34(1), 129–138. https://doi.org/10.1016/j.arcontrol.2010.02.008

PART 2

Teoretical and Practical Perspectives for Lean Construction 4.0

2 TOWARDS LEAN CONSTRUCTION SITE 4.0 Integrating Lean and Digital Technologies Kevin McHugh, Bhargav Dave, Algan Tezel, Lauri Koskela, and Viranj Patel Introduction Construction 4.0 Industry 4.0 refers to a new phase and vision in the Industrial Revolution that emphasises interconnectivity, automation, Machine Learning (ML), and real-time data to create a more efcient and better-connected production and supply-chain ecosystem (Santos et al., 2018). The counterpart of Industry 4.0 in the Architecture/Engineering/Construction (AEC) and Facilities Management (FM) industry is known as Construction 4.0. The term was coined in the second half of the 2010s and has gained much attention from both academics and practitioners. It has recently been ofered as a transformative framework for the industry covering (Forcael et al., 2020; Richard et al., 2021) (i) industrial production and construction– industrialisation of construction, (ii) cyber-physical systems, and (iii) digital technologies. There is little consensus on what technologies are included when Construction 4.0 is discussed. In one of the earlier articles to be published on the topic, Andulkar et al. (2018) mention digital data, automation, connectivity, and digital access as the key components behind Construction 4.0. Tortorella et al. (2020) include Building Information Modelling (BIM), Common Data Environment (CDE), cloud-based technologies, artifcial intelligence (AI)/ ML, and Big Data as the key to Construction 4.0. In a review of Construction 4.0, Forcael etal. (2020) fnd the following key technologies being mentioned by a wide range of selected journal publications on Construction 4.0: Internet of Things (IoT), Computer Aided Design (CAD)/BIM, 3D printing, Big Data, AI and robotics, virtual reality (VR) and augmented reality (AR), and new materials related to industrialisation. The connected use of these technologies and industrial production and construction approaches is deemed to have a great potential in improving the performance and competitiveness of the AEC/FM industry. From a management perspective, Construction 4.0 promises a more agile, comprehensive, and accurate understanding of the status of the project, embedding ‘smartness’ for self-confguration of managerial systems, and fuelling the bottom-up processes with the close and almost real-time control they need. The concept therefore implies a profound transformation of the project management in the industry, putting the self-confguration of systems and real-time data at its core (Dallasega et al., 2018). DOI: 10.1201/9781003150930-4

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Te Underlying Concerns for Construction Site 4.0 The positive outlook from the technology is not without its concerns. Most publications take the approach that technology in itself will solve the problems plaguing the industry rather than providing a socio-technical framework to consider people and process aspects. As the sensor networks ofer the possibility to track resources including workers, materials, and equipment in an accurate and real-time manner, several researchers are proposing solutions to support time and motion studies frst proposed by Gilbreth and Kent (1911) and Taylor (1947). Calvetti et al. (2020) put forward an argument that it is useful to track individual task productivity in order to improve efciency. Similar approaches have been made by other researchers. However, focusing on the micro-level processes instead of holistic systems can be likened to taking a partial transformation-based approach when viewed from the Transformation, Flow, and Value (TFV) theory of production standpoint (Koskela, 2000). Combined with a top-down transformation-based production, these notions of micro analysis of craft productivity can lead to negative efects and can even demoralise workers. An elite core group of data gatekeepers may end up controlling and monitoring transactions, rendering the perks of Construction 4.0 accessible by a select few rather than all. Indeed, there is a risk of Construction 4.0 creating more chaos and complexities rather than solving them in an industry that has been scrambling to keep up with the pressure for change.

Lean Construction as a Backbone for Technology To mitigate those risks, a management framework advocating the reconciliation of topdown management with the ground-level production planning and control is needed. Lean Thinking can provide this, as unlike the prevalent traditional production management, which views production simply as a transformation of inputs into outputs, it addresses the management of production fows – the dynamics of how work progresses through the production system (Bertelsen & Koskela, 2002; Dave et al., 2016). A seamless linking of value-added steps and preconditions (i.e., information, components and materials, equipment, space, workforce, connecting works, and external conditions) is needed; this is where system value and waste are actually generated. Adopting this kind of holistic production mindset will reduce the risk of compartmentalisation of works and systems in project management in connection to the implementation of Construction 4.0. One of the principles of Lean concerns the fulflment of the needs of both the end customer and the next customer, in the sense of the next workstation. The involvement of people into decision making, in the spirit of the mentioned principle, will reduce the potential alienation of workforce and customers in this real-time, data centric, self-regulating environment. Construction 4.0 can also facilitate achieving other Lean goals and realising Lean methods such as Just-in-Time production, reduced fow variation, pull-based control, taktbased production, automation of value-adding activities, and necessary non-value adding activities, in-station quality, and visual management (VM) (Salem et al., 2005). There is also a distinct scope for waste identifcation as well as safety-related measures, if the 4.0 technologies are used appropriately. The most important aspect would be to understand the process as a whole rather than making point-wise observations. Further, 4.0 technologies should be supportive in production rather than stand-alone implementations on site. They should ease the processes and the workload for the team on site. Although gaining the efciency and optimising the whole process would be the prime focus here, the resultant framework must harmonise human values, their needs, free will, sustainability in line with construction processes (Hamzeh et al., 2021). 18

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The rest of the chapter explores this synergy between Lean and Construction 4.0. First, the state of the art of fundamental technologies for Construction 4.0 in conjunction with Lean processes is reviewed. Then, the topic is examined through a case study. Discussion and conclusions complete the chapter.

Technological Architecture Before discussing potential solutions to Construction 4.0, it is important to understand the state-of-the-art of relevant technologies and how they ft in the wider context of construction and especially site-based management processes. This section provides a brief overview of what technologies are currently available and how they could be used. The use-cases for technologies are mapped throughout the section and fnally tabulated for summary (Table 2.1).

Main Technologies Building Information Modelling The emergence of BIM has captured the imagination of researchers and practitioners alike as it provides a rich parametric platform to represent the built environment. Research in this feld has cut across design, manufacturing, construction, handover, and FM, i.e., the entire lifecycle of the construction project (Begić & Galić, 2021). Given the parametric, intelligent, and contextual information that BIM represents about the built environment, it lends itself as a platform of choice representing the physical world in the virtual domain, i.e., hosting digital twins (Tao & Zhang, 2017). There is an opportunity here to collect data from the inception to handover and demolition of a facility (cradle to grave/lifecycle management) which will be highly valued for future design, planning, construction, and operation of projects (Chowdhury et al., 2019; Megahed, 2015). With the emerging popularity of the BIM, it can be leveraged as a natural synchronisation and visualisation platform that will interact with other construction 4.0 technologies such as sensor networks, automated manufacturing and 3D printing systems, reality capture (RC) and AR/VR systems, and AI/ML platforms. Some examples already exist that showcase the capabilities of BIM in relation with the Construction 4.0 technologies.

Artifcial Intelligence The TFV theory for production, developed tools and systems as the Last Planner System® (LPS®) that require signifcant eforts for balancing and optimising the fows and values within the production system (Habchi et al., 2016). In many cases, systematic use of these tools become taxing and may be abandoned due to lack of control (Thorstensen etal., 2013). In reality, there are too many variables to balance. For instance, what dimensions the team are covering to optimise fows: Location, workforce, product, fnance, equipment, design, or external factors and conditions? Realistically, humans have limitations in terms of identifying, processing, and recognising patterns to produce the most optimal production system design. Though, some of the Lean tools can manage few of these fows parallelly. For example, location-based scheduling (LBS) and takt planning are heavily focused on balancing workfows in spaces for trades. Not only the listed variables infuence the reliability of planning, but also the interconnectedness between the factors impacts the delivery of production in 19

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unexpected ways. As a result, even when a highly experienced and mature group of production planners are collaborating and formulating the production plan, they cannot address all the variables at once. Coming from the Industry 4.0 framework, AI leverages the purposefully developed intelligence machines or tools to carry out complex calculations, multi-dimensional simulations, and pattern recognitions to solve similar problems ( Joiner, 2018). The same analytical, predictive, and cognitive capabilities can be leveraged to reinforce the production systems. In construction, there are multiple possibilities of deploying AI solutions, examples include ML-based prediction systems for project scheduling, machine or computer vision (CV) to track progress and identify defects, combination of sensors, and AI to alert safety hazards, among others (Akinosho et al., 2020). However, the examples are still too few and there are many gaps and thus opportunities to develop systems to better support the construction process (Klinc & Turk, 2019). AI/ML-assisted production planning and forecasting: From the production planning context, the fow and value management can be simplifed using ML a subsidiary branch of AI (Amer et al., 2021). ML prediction algorithms identify patterns based on the richness and soundness of the dataset available from production. Importantly, the larger and more relevant dataset one has, the more accurate and logical patterns one can identify. Evidently, the commonly used prediction validation labels in the prevailing prediction systems are more focused on earned value management (EVM) and productivity (i.e., schedule performance index and cost performance index), which are solely focused on budgeting, sequencing, and resource management. Contrarily, the new Construction Site 4.0 (CS 4.0) will require to run based on broader indices, i.e., Flow index, Reliability index, Maturity index, and Percent Plan Complete (PPC) (Sacks et al., 2017). The future sites are expected to have purposefully trained prediction engines deployed that have ever-evolving ML algorithms self-improving based on the Lean metrics (AI#1). Apart from becoming a prediction and forecasting aid, the use of generative design is rapidly increasing in the design processes to fgure out optimal space allocation and management (AI#2). In fact, the same underlying concept can be applied to on-site logistics and layout planning (AI#3). Moreover, AI-ML is expected to emerge as a data driven and proactive Decision Support System which is fundamentally operating based on Lean decision tools like Choosing by Advantages (CBA) (AI#4). AI in production monitoring and control: Most prevailing use cases for the AI in construction are for production monitoring and control. Ultimately, process mapping and monitoring is one of the critical aspects that contributes to efective progress review and feedback mechanism (AI#5). Though the methods of collecting data are getting improved by technological interventions, i.e., IoT and RC; the existing Lean Production monitoring processes still require heavy reliance upon the human interventions to process the collected information for converting it into insights and wisdom. However, these processes are now getting transformed to be more efcient and less labour-intensive by leveraging AI-ML assistance. Especially the solutions which are developed based on CV, namely image processing and object detection, are contributing heavily to the following areas: 1 2 3

As-is progress detection using image processing. (Discussed in Reality Capture section) (AI#6) Identifying and fagging safety hazards using object detection. (AI#6) Identifying discrepancies and defects (quality of delivery). (AI#7) 20

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Apart from detecting the defects and discrepancies of the product, AI-ML can also be useful to do the same for the process. Commonly available tools like business process automation for reporting or triggering specifc actions can be used to setup a process fow-detection system (AI#8) and further to be linked with supervised deep learning, i.e., cognitive neural networks (CNN) for further optimisation (AI#9). These CNN algorithms can convert construction information collected into actionable insights that are transferable across diferent production environments (AI#10).

Reality Capture Following the Deming’s cycle of Plan-Do-Check-Act (PDCA), Lean Production management comprises dedicated activities to reviewing the current practices and constantly driving for improvement. However, it requires meticulous data collection from production environment. Collecting data from production, managing the information in multiple places and multiple formats, and analysing them to present the clear picture of site to the team is a very tedious but important process in construction (Golparvar-Fard et al., 2009). A collection of ad-hoc photographs and video recording are being captured from site to report issues, and ultimately being utilised for progress reporting, snagging and for the handover processes. When the introduction of remote working has limited the feld supervision and site walks, the need for visualising the site during weekly planning meetings became even more critical for making the contextually informed decisions for managing the production remotely. Using RC technology, the entire process of visual data collection and reporting from site can be made faster, seemingly visual, and less labour intense (Golparvar-Fard et al., 2011). Using either photogrammetry or Light Detection and Ranging (LiDAR) advanced distance measurement methods as its technological core, RC can capture entire sites as point cloud models rapidly for accurately depicting the as-is site condition (RC#1). The process can be further accelerated by using unmanned aerial vehicles (UAVs) and 360° cameras increasing the accessibility and reach of data collection from site. RC in production planning: For the site logistic planning, as-is condition model produced using RC tools provide signifcant aid in building spatial awareness within the production team (RC#2). Having spatial information available, the production team can produce more realistic, systematic construction logistics and site planning (RC#3). Maintenance and building retroftting activities are also key area of work in construction where the industry is heavily invested yet struggling due to the lack of as-is data and inadequate data collection approaches to create BIM models (RC#4). RC has made remarkable contribution to the Scan-to BIM process by reducing the time and efort being consumed in the process and the level of details captured through models (Golparvar-Fard et al., 2011). Further to the production planning, constructability reviews with RC can be improved tremendously as the teams can discuss their make ready needs, space/logistic needs, safety protocols, on top of the live three-dimensional representation of their sites (RC#5). Moreover, the ability to visualise the as-is or as-built information, together with planned deliverables (BIM models), allows to identify gaps and discrepancies in production visually (RC#6). In fact, the as-is capturing system is especially benefcial in weekly contractor coordination meetings as now more time can be spent on decision-making tasks as opposed to describing and explaining the situation using traditional 2D representation tools (RC#7). RC for production monitoring and control: In contrast to the prior situation, where countless hours were spent on manual progress monitoring, image-based point cloud models provide an opportunity to automatically extract semantic information from the as-built (i.e., 21

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progress, productivity, quality, and safety) through the content of the images. Accordingly, the physical progress monitoring can be accelerated and automated signifcantly when RC is being used in conjunction with BIM models and AI to automatically recognise the BIM elements and their progress based on image processing to be discussed in the case study section (RC#8). Additionally, RC and BIM overlays help in identifying and evaluating the deviations, defects, and discrepancies during quality or safety reviews (RC#9). The constraints and issues can be marked specifcally on the RC model space or component, aiding to visually manage the constraints and issues related to production (RC#10). Further, periodically collected RC data provides historical snapshots of the project. This is useful while fguring out the root causes of failures and discrepancies (RC#11). Additionally, with the help of IoT-based real-time sensors, the collected RC data can be further enhanced and ultimately transformed into a digital twin (DT).

Virtual Reality-Artifcial Reality –Merged/Mixed Reality Essentially developed to break the physical boundaries and to innovate beyond the existing reality, be it in completely diferent (digital) environment or augmented in the real environment, VR, AR, and MR have a pivotal role specially to enable immersive collaboration and VM. Virtual and immersive collaboration: Collaboration is one of the fundamental principles of Lean Construction. It is defned as a process to leverage production participants’ skill and knowledge to produce innovative solutions through interactions to increase value and minimise waste during production. Conducting structured interaction sessions and creating big rooms are the commonly used tools to create a collaborative environment for the production team. However, constraints such as availability of time/space/resources, mandatory physical presence, and lack of communication and information constantly hinder this collaboration. Overcoming these barriers, new CS 4.0 will be equipped with VR-conferencing solutions that facilitate real-time conferencing with multiple teams to collaborate virtually irrespective where they are (AVMR#2). Furthermore, the setup can now be leveraged to create virtual and immersive big rooms for CS 4.0. The Integrated Concurrent Engineering (ICE) sessions, which are designed to discuss and solve production problems with focused groups, can also get benefts from the same (Chuquín et al., 2021). With the ideation, brainstorming, problem-solving, process mapping, root case analysis, and many more tools available within the virtual workspace, the technology has shown considerable promises for making collaborative processes more efective. VR-AR-MR-based visual management: VM has been an intrinsic ingredient of the Toyota Production System (TPS) and its derivatives as Lean Production systems (Tezel & Aziz, 2017). It has helped developing common ground, situational awareness, and shared understanding within the production teams using various visual tools having one of the listed characteristics (AVMR#1): (i) information giving; (ii) signalling; (iii) response limiting /controlling, and (iv) response guaranteeing (Galsworth, 2017). The vision of CS 4.0 hence requires advanced VM mediums that can provide the true sense of visual awareness and control over production. On the similar front, the VR-AR-MR approaches are increasingly leveraging BIM and gaming technologies to reinforce the visualisation, coordination and immersive experience among project participants and stakeholders (Karmakar & Delhi, 2021). Outrunning the conventional visualisation tools (i.e., visual performance boards or digital dashboards, Obeya rooms, BIM

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viewer and colour-coded health and safety information), VR-AR-MR can provide more immersive visual feedback and control over production. They allow to leverage combined senses to visualise and experience the product, process, and production environment efectively, irrespective of actual physical presence on site. For example, one of the most prominent use cases of VR-AR-MR is as training technology for the site team. Virtual prototyping done based on the VR-AR setup has considerable potential to reduce rework and making-do waste in the production processes (Li et al., 2018). Moreover, site walkthroughs in VR are now commonly used to train production team for safety and hazard prevention and recovery drills, without risking any personal or physical assets (AVMR#3). AR-MR is also being utilised as virtual guidance systems providing assistive support in the case of assembling complex products and difcult processes. Moreover, AR technology can be used to establish a real-life construction model by overlaying virtual object data against the actual background of a project site on a computer, aiding to progress visualisation and monitoring for production. (AVMR#4). By visualising construction elements against the background of an actual project site, it is easier to understand the work process, and the technology can be also used in the inspection stage to save costs associated with error and rework. For complex maintenance jobs, virtual view overlays using AR are being used to check services and utilities maintenance jobs, avoiding any accidental or physical damage to the services. Moreover, AR-MR are very useful when feld maintenance is required, by successfully accessing the expertise from remote teams. Virtual As-build reviews with BIM overlays using VR-AR-MR provide very good accuracy in terms of determining non-conformances, defects, deviations, and quality discrepancies (AVMR#5).

Sensor Networks Efective production planning and control requires reliable, timely, accurate, actionable, and factual information on top of meaningful collaboration. Conventionally, the sources of information are production reports captured manually or through distributed data collection points. The construction industry is still lacking an interconnected channel that can efectively help collecting data from production. Data collection requires signifcant manual processes, and the production teams are struggling to process the signifcant volume of data. To aid the production planning and control, the teams need to know precisely who is on-site, when they arrive, and when they leave. This is critical to efectively manage the site and coordinate the resources for streamlined production. Paper timesheets are tedious, error-prone, and trap valuable information in a non-collaborative format that is difcult to compile, analyse, and report on (S#5). Therefore, CS 4.0 requires a data collection system with the following characteristics: (1) minimal human intervention, (2) focused, (3) error proof, (4) minimal latency, and (5) continuous. Sensor technology deals with collecting information by sensing or detecting changes (i.e., physical, biological, chemical) and providing data on these as readable signals. These are devices that can continuously collect purposeful data from the production environment for generating insight for production management. This data collection medium can gather data depending upon the use cases listed below: Checking condition of product and production: With tight schedules and being heavily dependent on the production line, the hardening rate of concrete, a key component, could become critical to maintain the cycle time during production (S#1). Real-time data

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from the sensors embedded in concrete allow making faster decisions about changing the mix or the specifcations if the performance is not found as expected (Catherine, 2021). Equipment maintenance and trackers for avoiding breakdowns (S#2): Production equipment breakdown and failure can lead to high inventory levels and low productivity especially in infrastructure projects. One of the Lean tools called Total Productive Maintenance (TPM) aims at optimising equipment performance and workfows (Borris, 2006). Advanced sensors enable predictive maintenance where equipment is serviced based on their condition which is identifed from sensor-based data (Stephen & Brian, 2020). Tracking resources, assets, and their logistics: They measure the time spent and track movement in locations and spaces. Wearable sensors may also be used for the same purpose. The recorded information provides the timeline for the actual time spent by each team member working on a specifc activity, which further helps identifying the potential wastes in the activities (S#3). Safety and hazard indicators (S#4): Sensors that detect potential safety hazards and provide alerts to the production team. Also, they are extremely useful in capturing worker health and safety conditions to maintain productive and healthy workspaces (Park et al., 2016).

Robotics Robotics systems for construction have been developed since the 1960s, at the same time when other industries started their automation; however, the adoption of robotics in the construction industry has been slow and lagged (Kim et al., 2015). Robotics is a key element of Construction 4.0. The current landscape of robotics in construction is composed of four main categories (Davila et al., 2019): (1) of-site prefabrication systems (e.g., building component manufacturing [BCM], large-scale prefabrication [LSP], and additive manufacturing [3D printing]); (2) on-site automated and robotic systems (e.g., single task robotic systems for bricklaying, welding, robotic on-site factories, and swarms for on-site assembly); (3) drones and autonomous vehicles (e.g., access to extreme or dangerous environments, surveying, inspection and monitoring, automated drilling, excavation and earth moving); and (4) exoskeletons – wearable devices that work together with the user to reduce fatigue, injuries and increase productivity. Construction is a labour-intensive industry. Robotic systems have proved to be efective in other industries for reducing labour costs while improving productivity, quality, reducing injuries, and freeing workers from conducting dangerous tasks (Carra et al., 2018). These are in line with the Lean principles of reducing wastes by automating necessary non-value adding tasks, reducing overburdening in the system, and increasing production stability.

Synthesis of Technologies in Relation to LEAN As highlighted, the 4.0 technologies should be supportive; they should ease the processes and workload for the production team. Accordingly, the Construction 4.0 technologies are expected to fundamentally support the implementation of the TFV theory for production and associated tools, such as the LPS®, which operates based on the underlying TFV principles. In Table 2.1, some of the major 4.0 technologies and their use cases are mapped against the LPS® process elements. Note: The reference codes for use cases have been explained above in this section.

Towards Lean Construction Site 4.0 Table 2.1 The potential of main technologies to support the Last Planner® System Process

AI-ML (AI#)

Key process elements

Process description

PS-1 Master Planning (what should occur)

Preparing the master schedule with the key milestones.

Reality capture (#RC)

VR-AR-MR (AVMR#) Sensors (S#)

AI#1, AI#4

PS-2 Pull Planning (what should occur)

Planning a segment of Works and focusing on handovers between trades to achieve the milestone.

AI#1, AI#4

PS-3 Make-ready planning (what can occur)

Identifying constraints between teams to perform work, agree on the lookahead plan.

AI#3

PS-4 Weekly work planning (what will occur)

Reviewing open constraints, planning each day’s work, checking resource availability.

PS-5 Daily huddle meetings (what is occurring)

Checking to-dos for the day; Discussions on what is needed to maintain the plan?

PS-6 Percent plan complete (PPC – what did occur)

Checking what was completed and what failed. Indicate reasons for failures.

AI#5, AI#6,

RC#1, RC#6, RC#9

PS-7 Reason for variance (why did it occur) PS-8 Team health, maturity and efectiveness

Focusing on where to improve.

AI#6, AI#7

RC#9, RC#11

Determine the positive and AI#8, negative factors afecting AI#9, PPC between teams; what AI#10 needs more attention.

AVMR#1, AVMR#4

RC#2, RC#5, RC#10

AVMR#1, AVMR#2, AVMR#4

S#2

RC#2, RC#3, RC#5, RC#7, RC#10

AVMR#4

S#1, S#2

AVMR#3

S#1, S#4

AVMR#5

S#5

RC#11

S#3

Case Study Background The study was a site-based initiative carried out on the construction of hyperscale data centre. The building is an 86,000 square metre structure consisting of eight single-storey data halls and an administration building. This is the third stage of construction for the site team, which has been working on the campus since 2015. The current project commenced early in 2019 and is expected to be completed by mid-2022. The project team has matured in Lean Production practices and had successfully implemented this process on previous phases undertaken on the campus. During the latest phase of the project, the construction team

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wanted to experiment further with the implementation of digital tools to support the Lean practices. As a result, a combination of digital platforms was deployed to enable aspects such as LPS®, digital ‘Big Room’, and RC using CV techniques and sensors to locate resources among others. Additionally, the emergence of the Covid-19 pandemic in late 2019 afected the project in early 2020 when a national lockdown was implemented to control the spread of the virus. This closed all non-essential workplaces including all construction projects in the country. A reopening strategy was adopted to facilitate a blended workplace of site-based and remote-working teams. This study will highlight how digital tools supported the Lean process disrupted by the pandemic. Construction teams were dispersed and fragmented due to the health restrictions. This disrupted the social interactive site-based collaborative processes. Digital tools were identifed to support the Lean Construction methods. In particular, RC was increasingly used to provide support for the LPS® by increasing the situational awareness of participants. The frst author conducted participatory action research to apply and combine digital tools to support the Lean practices and VM tools that were relied upon thus far on the project. The frst author managed the production control system for the project and was involved in the design and implementation of it pre and post pandemic (McHugh et al., 2022).

Introduction Team fragmentation was observed as a practical gap in the collaborative planning process, which was established prior to the pandemic. Teams working remotely were disconnected from the site operations and were relying on ad hoc semi structured feedback from the site teams. The ability to collaborate was afected by construction teams migrating to remote working practices. This reduced the quality of the information and therefore reduced the efectiveness of the collaborative planning process. Commitments were declared without the required quality of information. This reduced the efectiveness of the project production control system that relied on collaborative and social interaction for the preparation and execution of commitments. There was increasing frustration between the disciplines to fully understand why the commitments were failing and where the unrealistic commitments were declared. As a countermeasure, digital tools were increasingly used to provide virtual collaborative platforms and near real-time information to connect the fragmented teams. Collaborative meetings were hosted with online meeting platforms. Further integration of digital media was facilitated which introduced BIM, RC, and laser scanning to the production control process. RC had been utilised on the previous phases using drone technology to capture high-defnition (HD) images and map them to the coordinates on the project. Integrating drone imagery with 2D model information provides a situational picture that improves site integration.

Drone Imagery Drone had been used extensively to assist the management of structural and civil works on the campus. This began with scheduled fy-over videos that periodically document progress using drone imagery to map the site utilising a mapping software (DroneDeploy™) to process and manage the imagery (Figure 2.1). The software facilitated marking up 2D drawings

Towards Lean Construction Site 4.0

Figure 2.1 Drone image comparison at diferent moments of time for progress reviews

to be coordinated with the mapped images to increase the quality of information, improving the efectiveness of collaboration. Drone imagery and laser scanning has been used on the project to assist with the external infrastructure planning. Site location plans were overlayed onto the mapped images to increase coordination. 2D drawings were also overlayed to provide production insights. These images were used in the production lookahead meetings to highlight the interfaces and to report on the planned versus actual production. This information improved the coordination between the trade contractors and increased the understanding between the trades. Comparing the short-term lookahead plans with the drone imagery and design drawings allowed the teams to collaborate efectively and highlight make ready needs, which resulted in more reliable trade-to-trade handovers. Images were also used to review the PPC. This improved the interaction between the contractors and increased the trust between the trade contractors. Opportunities for improvements could be identifed where the PPC was lower than expected. The high degree of VM enhanced the discussions where multi-disciplines can communicate with common information.

360° Photography To improve the coordination of the internal work scheduling meetings, RC was identifed as an opportunity to improve the communication with the of-site design and construction teams. OpenSpace™ was identifed as a platform for integrating the 360° images with 2D location plan drawings that can be aligned with the BIM model. Internal images were captured by mounting a 360° HD camera on a supervisor’s hardhat and syncing one’s location on a foorplan to OpenSpace. Daily schedule of internal image capture walks quickly provided an extensive catalogue of 360° images. These images were processed by OpenSpace™ and displayed on a project plan for navigation and aligned to a BIM viewer for comparison. Progress images were used to validate the weekly work plans and review the progress on site (Figure 2.2). Opportunities and risks were highlighted with the teams during the collaboration meetings. This increased the quality of the work planning where the interface between the teams could be expressed which identifed the correct sequence and fow of activities.

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Figure 2.2 3D photograph and BIM integration for as-is vs. as-planned comparison

Augmented Reality Mixed-reality technologies provide solutions to integrate fragmented teams by allowing people to come together in a simulated environment that allows them to see and interact with fellow participants and the simulated environment, regardless of geographical location (Volkow & Howland, 2018). AR process was utilised to provide real-time immersive interaction between the teams. Virtual site walks were carried out using the Hololens® coupled with the Microsoft Teams and Zoom video conferencing platforms. Information sharing and collaboration were facilitated virtually with the site teams interacting with the remote teams. Validation and inspection functions were more efcient as the multi-disciplined stakeholders could review and interact with the live site demonstrations (Figure 2.3). This increased collaboration, provided certainty with the constructed elements, and increased the trust between the project stakeholders. The immersive experience of viewing the as built and integrating the design model virtually improved communication between the stakeholders.

Digital Lean Construction The technology used to support construction management is evolving. Information can be collected from diferent media and displayed to provide insights for construction managers to make informed decisions. Integrating the systems to complement Lean workfows has improved the quality of information. The digital systems were combined to be used as management tools. Sharing information between the construction teams increased the value of the information. Information-sharing promotes integration of project teams, identifying common objectives, and promoting information-sharing itself. In this case, the information from multiple sources that was presented to improve the collaboration between the multi-disciplined teams. Progress monitoring of construction works is mainly performed manually. Weekly feld updates are collated by hand by site-based supervisors. This procedure requires an adequate frequency to provide efective updates, as a result it is a laborious process which can often 28

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Figure 2.3

AR in the feld for augmented model comparison with reality

be interrupted by time constraints imposed on site supervision. Internal fnishing works is a stage sensitive to schedule uncertainty with the involvement of several disciplines. Digitising the reporting process using RC reduces the workload and provides a platform to review the current state of the project more efciently (Kropp et al., 2018). Capturing information from multiple users working on a shared digital platform has increased the transparency between the project stakeholders. This, in turn, increases the reliability and accountability of the trade contractors. Near real-time information improves the situational awareness, which provides a basis for a more accurate progress assessment, in turn, improving the quality of team interactions. Business Intelligence (BI) tools have been developed to link to multiple sources of digital information. Information can be arranged and formatted to provide an integrated visual display of live information. Collaboration between the disciplines is increased where multiple levels of information can be presented to enhance the situational picture by providing accurate progress information (Figure 2.4). Near real-time project data improves the accountability of project teams, which in turn increases the reliability of their commitments. The availability of multi-disciplined information improves the understanding across managerial functions, and the presentation of live data improves the transparency and therefore the trust between involved parties. This changes the focus from questioning the information received 29

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Figure 2.4 Advanced analytics with Business Intelligence (BI) digital tools

to using it to collectively highlight project risks and search for opportunities for performance improvement. Also, a common reporting template will become much easier to implement, once cyber-physical systems are introduced (Schuh et al., 2014).

Findings Digital information management has evolved to include information that can be accessed from site. Digital integrated systems have helped to increase the co-operation between project teams. Collaboration can be facilitated digitally utilising multiple sources of information. This focus can improve the quality and the reliability of committed activities. The increased reliability of information received from the feld and near real-time reporting helps deal with project complexity in a better way. This combined with collaborative frameworks such as the LPS® demonstrates how cyber-physical systems provide a function to increase productivity. Social presence in virtual worlds has been shown to create a sense of realism and immersion that enhances learning beyond face-to-face or traditional online interactions (Biocca& Harms, 2002). Detailed and complex conversations were observed between participants using AR tools, connecting site-based users and ofce-based managers and designers. This encourages team collaboration that reduces errors and increases the quality and amount of work completed of activities. Using mixed reality as part of the site collaborative planning process increases productivity and safety by clearly defning planned works and increased collaboration identifying the correct size and sequencing of works.

Discussion and Conclusion Now, it is possible to present a synthesis of the opportunities and challenges related by ‘Construction 4.0’ for site management. We divide the discussion temporally: what will be done before the productive act, and immediately after it. This corresponds to the two pillars of Lean: Just-in-time, and jidoka. 30

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Preparing production on Construction 4.0: towards just-in-time: Digital tools are being used to capture, collect, and manage all the information and data needed for preparing the tasks and plans for diferent time periods. This preparation and planning are done in a collaborative mode. A genuine social interaction is targeted, be it in situ or computer-mediated. There is an excellent visibility to process (plans, instructions, standard work sheets) and product (BIM providing visual representation and textual and numerical information). Based on better preparation and planning, the compliance with plans, schedules, and specifcations improves. Jidoka: However, the fundamentals of site work remain unchanged, there are many more sources of disturbances to work than in factories, and the prospect of all kinds of deviations to plan and work output remains. Here, digitally supported jidoka comes into the picture, on one hand, providing transparency to the achieved quality of the output and, on the other hand, to the general progress of tasks (situation picture). Based on almost real-time observation of deviations, efective countermeasures can be initiated. The technical solutions for CS 4.0 exist, and they will be incorporated into current digital site management solutions and also embodied into new solutions. There will be a ferce competition between diferent oferings, sifting over time the most efective solutions to the fore. However, it will be difcult for construction companies and construction projects to evaluate available oferings and decide over their implementation. Ultimately, all the technological enhancements discussed here are just a medium to realise, analyse, and cure the underlying inefciencies of the production processes. These are socio-technical systems, where the technical subsystem is composed of equipment and processes, while the social subsystem consists of people and relationships (Manz & Stewart, 1997). The importance of retaining the role of humans will always be there, alone for intervening when the technology errs or fails. However, most certainly, this transformation towards an increased use of technology will cause many of the existing roles to be changed. Also, this transformation entails jobsites becoming more collaboration oriented for fulflling the irreplaceable aspects of innovation, creativity and problem solving. Therefore, the technology should be perceived as an enabler for insights, decision making on near real-time data, extensive collaboration, and relieving people of tedious tasks to allocate more resources for value-adding activities and continuous improvement. Lean Construction methods improve the quality of planned work assignments and continuously improve the production process. Digital platforms and tools can be used to support this process of producing certainty and managing complexity in modern construction projects. Gathered data enhance the situational awareness of managers, which facilitates the interactions of participants to improve the quality of planned works. As generally in the digitalisation of construction, it is advisable to start the switch to CS 4.0 from the core activities, and only then to move to more peripheral activities. This often entails utilising multiple platforms and integrating digital information, which improves the decision-making process. In a more interlinked world, the function of employees will shift away from simple operators towards decision-makers that are actively involved in the decision-making process, which not only focuses on selective optimisation but also considers the overall context (Frazzon et al., 2013).

References Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827. https://doi.org/10.1016/j.jobe.2020.101827

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Towards Lean Construction Site 4.0 Journal of Construction Engineering and Management, 137, 1099–1116. https://doi.org/10.1061/(ASCE) CO.1943-7862.0000371 Habchi, H., Cherradi, T., and Soulhi, A. (2016). Last Planner system® implementation in a Moroccan construction project. Proceeding of 24th Annual Conference of the Int’l. Group for Lean Construction, Boston, MA, sect.6, 193–202. Hamzeh, F., González, V. A., Alarcon, L. F., & Khalife, S. (2021). Lean construction 4.0: exploring the challenges of development in the AEC industry. Proceedings of the 29th Annual Conference of the International Group for Lean Construction (IGLC), 207–216. https://doi.org/10.24928/2021/0181 Joiner, I. A. (2018). Emerging Library Technologies: It’s Not Just for Geeks. Elsevier Science Karmakar, A., & Delhi, V. S. K. (2021). Construction 4.0: What we know and where we are headed? Journal of Information Technology in Construction, 26(May), 526–545. https://doi.org/10.36680/j. itcon.2021.028 Kim, M. J., Chi, H. L., Wang, X., & Ding, L. (2015). Automation and robotics in construction and civil engineering. Journal of Intelligent and Robotic Systems: Theory and Applications, 79(3–4), 347–350. https://doi.org/10.1007/s10846-015-0252-9 Klinc, R., & Turk, Ž. (2019). Construction 4.0 – Digital transformation of one of the oldest industries. Economic and Business Review, 21(3), 393–410. https://doi.org/10.15458/ebr.92 Koskela, L. (2000). An Exploration Towards a Production Theory and its Application to Construction. https:// aaltodoc.aalto.f:443/handle/123456789/2150 Kropp, C., Koch, C., & König, M. (2018). Interior construction state recognition with 4D BIM registered image sequences. Automation in Construction, 86, 11–32. https://doi.org/10.1016/j. autcon.2017.10.027 Li, X., Yi, W., Chi, H.-L., Wang, X., & Chan, A. P. C. (2018). A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Automation in Construction, 86, 150–162. https://doi.org/10.1016/j.autcon.2017.11.003 Manz, C. C., & Stewart, G. L. (1997). Attaining fexible stability by integrating total quality management and socio-technical systems theory. Organization Science, 8(1), 59–70. https://doi.org/10.1287/ orsc.8.1.59 McHugh, K., Dave, B., & Koskela, L. (2022). On The Role of Lean in Digital Construction. In M. Bolpagni, R. Gavina, & D. Ribeiro (Eds.), Industry 4.0 for the Built Environment: Methodologies, Technologies and Skills (pp. 207–226). Springer International Publishing. https://doi. org/10.1007/978-3-030-82430-3_9 Megahed, N. A. (2015). Towards a theoretical framework for HBIM approach in historic preservation and management. Archnet-IJAR, 9(3), 130–147. https://doi.org/10.26687/archnet-ijar.v9i3.737 Park, J., Kim, K., & Cho, Y. (2016). Framework of automated construction-safety monitoring using cloud-enabled BIM and BLE mobile tracking sensors. Journal of Construction Engineering and Management, 143, 05016019. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001223 Richard, S., Pellerin, R., Bellemare, J., & Perrier, N. (2021). A business process and portfolio management approach for industry 4.0 transformation. Business Process Management Journal, 27(2), 505–528. https://doi.org/10.1108/BPMJ-05-2020-0216 Sacks, R., Seppänen, O., Priven, V., & Savosnick, J. (2017). Construction fow index: A metric of production fow quality in construction. Construction Management and Economics, 35(1–2), 45–63. https://doi.org/10.1080/01446193.2016.1274417 Salem, O., Solomon, J., Genaidy, A., & Luegring, M. (2005). Site implementation and assessment of lean construction techniques. Lean Construction Journal, 2(2), https://www.researchgate.net/ publication/228676008 Santos, L., Brittes, G., Fabián, N., & Germán, A. (2018). International journal of production economics the expected contribution of industry 4. 0 technologies for industrial performance. International Journal of Production Economics, 204( July), 383–394. https://doi.org/10.1016/j.ijpe.2018.08.019 Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. R., & Prote, J.-P. (2014). Collaboration mechanisms to increase productivity in the context of industrie 4.0. Procedia CIRP, 19, 51–56. https:// doi.org/10.1016/j.procir.2014.05.016 Stephen, L., & Brian, K. (2020). Digital Lean Manufacturing Industry 4.0 Technologies: Transform Lean Processes to Advance the Enterprise - A Report from the Deloitte Center for Integrated Research. www. deloitte.com/us/cir. Tao, F., & Zhang, M. (2017). Digital twin shop-foor: A new shop-foor paradigm towards smart manufacturing. IEEE Access, 5(October), 20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069

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3 THE IMPLICATIONS OF THE 4.0 REVOLUTION IN THE AEC INDUSTRY ON THE LEAN CONSTRUCTION PARADIGM Identifying the Status Quo and Drawing the Path Forward Evangelos Pantazis, Eyuphan Koc, and Lucio Soibelman Introduction The stagnant productivity in architecture-engineering-construction (AEC) industry has been an issue for at least the last three decades while other industries, e.g., manufacturing, have managed to double their productivity over the same period, and create continuous improvement in their processes. The lack of productivity manifests itself in budget overruns and project delays. These efects are especially signifcant in megaprojects as according to a McKinsey research 77% of such projects tend to be delivered at least 40% later than scheduled (Changali et al., 2015). In addition, construction activities are waste intensive, and the built environment overall boasts a large carbon footprint. A multiplicity of factors which are tightly interconnected are obstacles to improvement in the industry across the mentioned dimensions, and they are rooted to all project phases. Factors including but not limited to poor organization, inadequate communication among project teams, fawed performance management and contractual misunderstandings appear in the conceptual/design phases, extend into the contracting and procurement phase and are revealed most clearly at the execution phase. For example, the lack of readily available accurate project data results in decision making and procurement processes that are not fully informed, while inadequate communication and lack of transparency contribute to owners not having a full understanding of project status at a given time. Contractual misunderstandings and competing incentives across project stakeholders and unclear defnition of roles and responsibilities lead to issues being resolved much slower than they could be as a project progresses. Additionally, syncing of project-level planning and day-to-day task management require that managers and schedulers monitor task completion and connectivity so that they can update priorities in ‘real time’, an ability which is often not attained due to poorly structured reporting mechanisms sufering from the traditional Critical Path Method (CPM)-based views on construction management. Consequently, although there is DOI: 10.1201/9781003150930-5

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a relatively good understanding on how a project should proceed in the upcoming months, when a mishap occurs, there is very little fexibility to mitigate losses. Seemingly trivial issues such as having the necessary equipment or teams in place at the right time becomes increasingly challenging in such settings. Furthermore, although long-term risk management receives considerable attention, a risk assessment at a more granular ‘task’ level is missing. Most of the problems mentioned above are well-known and common and have become systemic and inherent to the industry that created a culture that assumes that they are hard to address (Changali et al., 2015). Lean Construction, understood both as a theory of production of its own as well as the set of tools/techniques to translate Lean Manufacturing principles to AEC, has been an attempt at resolving the mentioned management challenges. As early as 1990s, it ofered a new perspective on the management of AEC projects that combined existing views of production, namely the transformation, value and fow views (Tzortzopoulos et al., 2020). This new synergetic view demanded that (on top of conventional task management), the client’s needs and requirements are fully understood and managed from the outset to the end (i.e., value), and waste is reduced or eliminated through continuous improvements in the fow processes (information fows, materials fows, process fow) of a project. In addition, scholars in this area ofered tools such as the Last Planner System (LPS) to implement the mentioned principles. Yet, despite proving its efectiveness in diferent contexts and the growing academic attention (Figure 3.1), Lean has not achieved the scale of improvement seen in the other examples of shifts in production paradigms outside the AEC. It is not widely adopted in the industry today and most of the problems mentioned earlier still exist to a large extent across projects of virtually all types and sizes. It is argued that these problems need to be reconsidered, given today’s circumstances that ofer a new potential to address them through a smarter deployment of more mature and readily available technology. A recent report based on data from almost 4,000 global industry stakeholders shows that there has been a dramatic increase of technology adoption, naturally leading to the generation of a substantial amount of project data. However, more than 80% of the responders, who range from project owners, architects

Figure 3.1

Number of publications coming out of IGLC over the years

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and engineers to general contractors and special trade contractors, claim that at least 25% of their project data are unusable (Thomas & Bowman, 2021). In most cases, the data are unusable either due to errors in collection or outright inaccuracy. In other cases, the data are unusable because they are incomplete due to missing pieces or basically due to lacking processes that make data readily accessible. Yet another challenge voiced by the participants is the complexity of aggregating and fusing data from diferent sources. More and more companies are trying to address the mentioned data-related problems by forming in-house IT teams or receiving outside help. The report concludes that it is hard to pinpoint a single reason why the data are deemed unusable, and that the problem arises from a multiplicity of factors. What becomes evident thanks to this report and others (Aminov et al., 2019) is that there is an ever-increasing need in AEC industry to make quicker decisions and that poor data quality prevents decision-makers from efectively addressing this need. In return, insufciently informed decisions cause waste (in material or otherwise) that is refected in design changes, delays and increasing rework costs. At this junction, it becomes evident that the problem lies not in the available tools and technologies themselves, but in integrating and managing them towards enhanced use of project data that will help decision-makers across the board.

Background: Emerging Symbiosis between Construction 4.0 and Lean The so-called Fourth Industrial Revolution is characterized by the paradigm-shifting changes in manufacturing since the introduction of programmable logic controllers, underlining the concepts of increased digitization and automation, modularization of production, mass customization and self-organization. We use the term ‘Construction 4.0’ to refer to the set of Industry 4.0 tools, methods and technologies that contribute directly to the outcomes of AEC projects (Koc et al., 2020). As mentioned, the AEC industry has been less perceptive of these advancements due to well-known limiting factors relating to uniqueness/complexity of projects, fragmented supply chains and production, and the culture that takes little innovative risks. Another signifcant factor that slowed down the difusion of the Industry 4.0 innovation in AEC has been marginal and isolated use of technology, to automate or digitize the execution/control of specifc tasks (often low hanging fruits) instead of unleashing the potential of technological advancements by pursuing a paradigm shift in the production framework itself (i.e., modular innovation instead of integral innovation). At this junction, Lean Construction ofers an avenue, given the guiding principles established by its research and practice over the years (Hamzeh et al., 2021). Hamzeh et al. (2021) identify this potential precisely and describe the ways in which Construction 4.0 implementation could beneft from the ‘waste hunting’ and ‘adding value’ production environment created by Lean. On the other hand, successful implementation of Lean Construction demands a data-driven approach to project development and execution, emerging as a natural requirement of the tools that it recommends. For example, LPS requires that planning is done at various levels instead of traditionally centralized project planning and controls, which calls for seamless sharing of data across teams and organizational levels. This data will increasingly come from the deployment of Construction 4.0 concepts and technologies across various phases of an AEC undertaking. Despite this emerging potential for a symbiotic relationship, very little research has been carried out on the convergence of the two. In an efort to understand the attention given by Lean literature to the tools, methods, and techniques classifed under Construction 4.0 today, the authors analyzed the publications coming out of the International Group for Lean Construction (IGLC)’s annual conference series over the last two decades. 37

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Figure 3.2 Sum of records for each cluster keyword. Clustering is based on a classifcation by Alves and Tsao (2007)

By following a classifcation provided by Alves and Tsao (Alves & Tsao, 2007), we clustered the 1718 publications based on their keywords in 14 topical groups in an attempt to reveal the underlying themes that Lean Construction research has focused on (see Figure 3.2). Figure 3.3 lists the keywords and the clusters they belong to. Figure 3.2 shows that the cluster with most publications is the expectedly the one that relates to Lean Construction as a theory itself. The second-largest cluster is the one dealing with information technology (IT). A further analysis of the keywords under the IT cluster indicates that researchers predominantly focused on BIM followed by topics such as LPS, Lean Design, and visual information systems (VIS). It becomes evident that the Lean Construction community has been following technologies which are included in Construction 4.0 framework today. However, what is interesting is that topics such as autonomous robotics, big data analysis, cloud computing and artifcial intelligence are not very prominent in the Lean Construction literature. Refecting on recent work that explores Lean Construction 4.0 developments in the AEC industry and particularly answering the questions raised by Hamzeh et al. (2021), the authors support the view that Lean Construction as a theory of production is necessary to orchestrate the emerging concepts and technologies that are included in the Construction 4.0 framework. This is because Lean ofers a way of thinking accompanied by a set of tools and methods for problems on which we see parts and pieces of Construction 4.0 is deployed. The contrast between the two lies in their approach: Lean is a production management theory and views the broad problems of the industry holistically, whereas the Construction 4.0 framework emerges from technological advances and the idea that such advances could be used to address specifc problems. The authors, in this context, view Lean Construction as the train and Construction 4.0 as its new fuel. Construction 4.0 can help Lean Construction reach further destinations at a higher speed (i.e., increased productivity, cheaper project development costs including environmental externalities and better-quality buildings) and certain technologies can help lay new tracks that can bring AEC to new destinations (sustainable development and circularity).

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Figure 3.3

List of keywords appearing the literature of Lean Construction Community (IGLC) which are related to the information technologies. The classifcation and clustering scheme is based on Alves and Tsao (2007)

To achieve this, the Lean Construction (LC) community needs to work at more granular levels in mapping the theory and the methods such as value stream mapping to specifc emerging technologies such as autonomous robotics, cloud computing and big data analytics to name a few. Here, let us consider the LPS, a method which was introduced into Lean Construction by Ballard (2000) as a response to the inefectiveness of the critical path method widely used in traditional construction management (CM). LPS seeks improvements by attempting to reduce the probability of failure in activities by establishing weekly updates and by allowing the people on site to afect the schedule in contrast with centralized planning in traditional CM. It was successfully applied in complex projects, e.g., for the construction of Terminal 5 at the Heathrow Airport, where due to the complexity as well as the scale of the project, meetings were held daily instead of weekly. Daily meetings and constant improvements to the schedule in such complex projects result in a huge amount of data generation, most importantly on why and how the schedule has been changed. This could open doors to carry out data-driven analyses that create insights that could be defning for the success of that project or new projects to follow. Specifcally, the deployment of tools such as the LPS at that scale and complexity could be a learning opportunity in terms of the efciencies the tool has created and the challenges that it could or could not address. Achieving this over a sufciently long horizon has tremendous potential for frms as each new project undertaken could kick of based on the cumulative insight obtained through such an exercise. However, the mentioned data-centric approach needs a conscious efort from the organizations, from data collection and cleaning to analysis and storage. The authors’ personal experience with 39

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the Heathrow Terminal 5 project showed that despite the large amount of data collected via meeting minutes, trying to do analytics on the data proved to be challenging due to the lack of consistency and accuracy. This gap between Lean Construction and information technologies needs to be researched extensively. We discuss some directions in the following section.

Minding the Gap: Converging Teory and Technology To emphasize the signifcance of dealing with the gap between production management and developments in technology, let us consider an example of additive manufacturing that has gained popularity in the AEC industry recently, contour crafting (Khoshnevis, 2004). Contour crafting is a 3D printing technology for large-scale additive manufacturing of concrete structures which was initially introduced 20 years ago (almost coincident with the birth of Lean Construction) but it has not been widely adopted. One might argue that the technology is still premature and therefore not accepted as a viable mode of construction. A more critical argument is that the technology deals with automating a problem that is highly complex but with relatively low weight in terms of overall project cost. Seen from a Lean perspective, the value proposition of such a technology within the broader picture of project delivery is not that high, as the building envelope in a typical project generally pertains to 15%–20% of the cost. It follows from this argument that a technology initially viewed revolutionary cannot bring a signifcant change in increasing the productivity of construction by focusing only on one task. AEC frms are facing similar problems today on what technology to adopt and in fnding ways to evaluate automation trade-ofs, meanwhile trying to sustain their proftability leaning on their existing ways of operation. Taking this trade-of understanding further, instead of additive manufacturing of building envelopes, one could see more value in developing demolition robots that can help take apart old structures without sacrifcing the safety of the workers while saving money and time. Another example relates to the information loss during project execution, particularly on the decisions taken at various stages of a project. In the delivery of buildings, a lot of times design decisions follow a design rationale which is not recorded and explicitly shared. Architects and engineers are often making decisions when they are faced with a problem, yet the rationale behind their decisions is not subject to a data collection efort. Most of this information is discussed during meetings and lost due to a lack of structured methodology to collect it. For instance, a designer decides to paint the western wall of a building white based on aesthetic considerations but also due to the color characteristic to refect heat in the afternoon hours. If this rationale is not recorded, a future remodeling of the same wall can use black paint, therefore resulting in a lot more heat absorption and making the interior space uncomfortable as well as the whole building less energy-efcient. The question arises, how can we avoid losing information on such decisions or how can data created in design charrettes, and construction meetings be retained? How can we establish shared data environments for collecting relevant project data in a structured and standardized manner that will allow all stakeholders achieve an accelerated and informed decision-making process? The authors argue that it falls on the Lean Construction community to bridge the mentioned gap. Apart from the broad objective of Lean Thinking to change existing behaviors and paradigms in construction seeking continuous improvement, it did not yet address the data issues arising from the tools and techniques it recommends at a fner level of detail. More seamless deployment of 4.0 technologies moving into the AEC domain depends on this advancing Lean Construction in this direction. This need becomes more evident when 40

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one realizes that despite the introduction of new technologies in the AEC industry during the last 20 years, there has been no signifcant impact in the improvement of productivity in construction.

A Preliminary Roadmap Lean can be considered as a Swiss army knife of capabilities and it has been proven in the past 20 years that it can have a tangible impact on many diferent types of problems and environments (Wood et al., 2021). The authors assert that the characteristic of Lean Construction to identify root causes of problems ofer an opportunity to explore and study the trade-ofs related to the adoption of Construction 4.0 concepts and technologies, and the understanding to continually address to identifed root causes through technology (where it applies) needs a roadmap, ideally laid out by LC research. The authors adopt the Lean Project Delivery System (LPDS) concept from Koskela et al. (2002) to provide a context for the ideas that can shape the mentioned roadmap. Figure 3.4 shows the LPDS in terms of its phases and the connectivity between them. Notice that in contrast to the traditional CM view on project phases, LPDS suggests that there is a lot more room for feeding information back and forth while ensuring that transformation, fow and value principles of Lean Construction are applied. To manage this seemingly more complex delivery method (due to the integration across phases involved, and partially why such delivery systems have not been widely adopted), technology could and should help. In what follows, we discuss ways that Lean Management could be enhanced through Construction 4.0 in two subsections frst focusing on project defnition and Lean Design, then discussing Lean Supply and assembly aspects. We reserve that the LPDS conceptualization by Ballard (2000) misses on the operations phase of a project, which we include in the discussion without allocating a separate subsection for it.

Figure 3.4 Lean project delivery system (adapted from Ballard, 2000)

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Project Defnition and Lean Design In the project defnition phase of LPDS, there is an efort to capture the needs and values of the client and formulate design criteria that lead to the design concepts. At this stage, a wide spectrum of discussions and interviews take place with the client, the size of the efort largely depending on the client’s ability to communicate his needs and how he defnes value for the given project. Today, VR/AR tools are being introduced into these processes where the clients could see alternative design concepts created for the project or from elsewhere (other ongoing or past projects). Think of an ofce building project where the end product will be used by hundreds of employees virtually every day and whose input (if valued by the owner) could prove invaluable at this stage. Cloud computing technologies paired with VR/ AR or other visualization tools could enable a much wider reach than owner only, and the design team could interact with the users to survey them, show them alternatives and their respective performances across a variety of metrics (from daylight and energy simulations to accessibility concerns, use of common areas, etc.). This user or customer-centric concept is essential to the Lean Construction’s view of value. Pursuing such an activity could then enhance transparency throughout the project as it evolves to start delivering what was convincingly agreed upon. In traditional CM, the architect talks to the owner, locks a design once the owner is convinced and more than often the end users only get to experience the product once it is nearly or fully complete. As shown by Figure 3.2, most of the IT-related research investigated within the Lean Construction community was BIM. There is very little attention to generative design methodologies in the community. Although the use of computational design methods could be leveraged by methods recommended by Lean Construction such as Set-based Design (SBD), Choosing by Advantages (CBA), Force Field Diagramming (FFD), and Target Value Design (TVD), this interconnection has not been explored enough. For instance, agent-based modeling and simulation methods could be used for simulating construction processes similar to their implementation in manufacturing. BIM tools today are not agile to the extent that allows for design exploration and their focus is more on the product than the process (both for logistics and assembly). Thus, in a Lean Management approach, using custom computational design solutions could prove valuable early in the design phase thanks to their fexibility. This fexibility ofers the capability to defne performance-based design metrics that are driven by client’s needs and values. BIM was intended solely as a design tool in its origins, but recently it has evolved as a tool that is used in other tasks such as the execution and controls of a project. Currently, frms in the AEC industry have an increasing number of projects completed using BIM. This means there exist a large amount of data isolated within the digital project that was generated in and is inaccessible for a more general use within a frm. Instead, BIM could be envisioned (in a ‘Construction 4.0’ future) as a data repository of many projects accumulating models through time, enabling designers and other stakeholders to query against a well-structured database. At this point, the specifcations and standards for creating the mentioned well-structured data environment have not been defned. Once achieved, such a capability could enable designers to identify problems related to their conceptual design and the production processes that it requires before proceeding with design development. Broadly, having this information fow working in a feedback manner within and across projects could support more informed design rationale. Furthermore, identifying problems before they are physically manifested supports a better alignment of interest between owners, designers, contractors and building managers as well. This shared objective among stakeholders need to be supported by transparency, e.g., via multi-party contracts, shared risks and responsibilities. 42

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Lean Supply and Delivery In the context of supply and delivery, the fow principles of Lean Construction mainly focus on adding value and eliminating waste whether it is material waste or others. In a traditional sense, waste is commonly regarded to be material waste. For example, a badly designed formwork can result in requiring more concrete for each slab which becomes more signifcant with project size. A poor site layout can create inefciencies related to material delivery and assembly. Today, 4D simulations and generative design approaches can be deployed to allow for the exploration and simulation of diferent site layouts. An entirely diferent mode of production is of-site manufacturing which relies on vertical integration, where prefabricated components can arrive ‘just-in-time’ before their assembly not requiring on site storage. For such a supply chain to operate seamlessly, the data from production and on-site project control need to be synced. Today, this could be achieved with more readily available sensing and tracking technologies such as RFID, GPS and computer-vision-based tracking. In a successful implementation of of-site manufactured construction, installation of building components is less complicated which in turn fulflls the Lean objective of attaining a more efective fow of material and other resources on site. Expanding on the discussion on site layouts, an example scenario is the management of the ‘shake out area’ in construction sites, an area where steel structural members arrive on site as they are manufactured by the factory. The name ‘shake-out’ comes from the fact that members that are stored there are constantly shufed based on their assembly sequence. The need for re-shufing arises from the mismatch between the on-site scheduling of assembly and the of-site production at the factory. The sensing and tracking technologies mentioned above could play a role in addressing this problem but are not by themselves solving this problem. However, a consideration of the assembly processes while developing the design of the site layout via 4D simulations along with transparency in communication between manufacturers and general contractors via data sharing, could result in more efective manufacturing, delivery, and assembly of the structural members on site. The examples clearly illustrate the potential benefts of a more efective orchestration of production and highlight the technological potential that exists today to achieve such benefts through the increased level of digitization and the implementation of Lean methods. The design considerations introduced in the previous subsection have impacts on Lean supply and delivery as well. Following up on the BIM discussion above, it is signifcant to note that during project delivery where detailed engineering is required, it is a common practice today that entirely new BIM models are developed. This is due to multiple reasons such as separate contracts, diferent BIM authoring tools, the absence of common modeling and data standards to name a few. This naturally creates waste in labor relative to the case where a single digital model is used from initial stages of design until fnal delivery of a project. If data standards facilitating BIM (i.e., Industry Foundation Classes or IFC) were fully established, a designer or engineer could extract the part he/she is tasked to work on, complete the task and commit back to the database similar to processes in agile software development. Unfortunately, today in most cases, each contractual entity develops their own model supported by their own standards (e.g., model templates), creating more ‘waste’ in the delivery process. Most of the Construction 4.0 technologies existed in the 1990s at a level of ‘awareness’ and ‘interest’ (Koc et al., 2020), with exceptions such as CAD being more mature at a level of ‘adoption’. The diference today is that many of these technologies have advanced towards adoption and have come to a level maturity where they are used more synergistically. This 43

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Figure 3.5

The evolving relationship of Lean construction and information technologies

synergy resulted from several developments in the industry including the introduction of cloud computing, mobile interfaces, sensors, etc., enriching today’s more digitized construction sites. In Figure 3.5, the authors illustrate the mentioned advancements in the digital technologies themselves (becoming cheaper, more accessible, and mature) and their use in the AEC industry by contrasting the settings of 1990s and today. Advancements in individual technologies are demonstrated by lengthening branches of the tree, and their synergetic use is shown by links in dashed (grey) lines that connect them, forming a network. The argument is that this new setting manifested by more available and integrated technologies ofers Lean Construction a new playing feld in terms of creating a larger impact when compared to two decades ago during its conception. In other words, Lean Construction methods and principles are further enabled through this new setting of advanced digital technology and emerging connections between them and Lean Construction, e.g., digitally enabled LPS that can track a part and its assembly through sensors. The tracking of physical components has a digital facet in the form of a synchronous BIM. Increasing number of connections between Lean Construction methods and advanced digital technologies is illustrated with the dark grey lines in the fgure. For an AEC frm, fnding its way within this new setting (choosing a partition of the emerging network) could prove challenging, particularly in terms of exploring the trade-ofs for digital innovation. In what follows, we provide examples for such trade-ofs.

Te Technology – Value Trade-of As mentioned earlier, many AEC frms are becoming more aware of the digital turn and deploying resources to create value through data acquisition (Carpo, 2013). The challenge, as found by the Autodesk report (Thomas & Bowman, 2021), is that most of the data collected over the recent years proved unusable in the efort of achieving informed decision 44

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making. Firms deploy an ever-increasing number of technologies to generate and gather data. Therefore, the decisions on what data to collect/analyze or what technology to deploy are connected, and this connection leads to value trade-ofs that need to be explored. The authors argue that Lean Construction (as a mature theory of production) ofers the guiding principles in analyzing the mentioned trade-ofs. The following discussion ofers several examples to underline this idea. Prefabrication will have a huge impact on construction: Despite multiple unsuccessful attempts to apply prefabrication in construction at scale since earlier decades of the 20th century, there is considerably more investment today. Yet it is still a very challenging task to achieve due to the existing modes of construction and the associated supply chains as it is not easy to reach break-even points to by-pass them. Demand in construction is typically inconsistent for a given frm which makes it hard to make investments in a factory of fabricated parts. This problem is further complicated by the service area of a prefabrication facility due to included transportation costs. Lean Construction always had an interest in prefabricated construction due to its characteristics to improve ‘fow’ and remove waste. For an AEC frm trying to decide between on-site or of-site mode of construction, the following questions could be relevant from a Lean Construction perspective: (1) What parts of the project can be realized through of-site manufacturing? (2) What is the added value of choosing of-site for parts or the entirety of the project? (3) How is the value delivered to the client afected by this trade-of? More questions that afect the mentioned trade-of could be listed and included in the fnal decision. Emerging technologies should be deployed at the appropriate scale of the task: In construction, tasks vary signifcantly in terms of their complexity, physical scale, created value and the characteristics of the location in which they are performed. As an example, the invention of drywall replaced the process of plastering walls, reducing the amount of labor required for wall fnishing. An example of automation in this context is the use of humanoids for the installation of drywall boards (Kumagai et al., 2019). The proposed value of the robot is improved safety and efciency. Having a humanoid on a construction site for this task could indeed improve safety and efciency within the task. The questions that need an answer for an AEC frm considering such a technology from an Lean Construction perspective are (1) ‘What is the value of the humanoid robot when compared to manual installation?’; (2) ‘What other (robotic) technologies are available to that could deem the same task safer and more efcient?’ or (3) ‘What are the potential issues in integrating the robotic system with resources on site working on other tasks?’ Taking the second question further gives rise to the analysis of alternative technologies. For example, instead of automating the task itself with a complex robotic system, could a simpler yet autonomous robotic system move around on site to present visualized information to the workers on how to correctly install the part? If the inefciency in this task emerges from workers not being familiar with the design and the variations in the assembly of the same part in diferent locations, this simpler system could potentially address the problem at its root cause. Flow-focused tools of Lean Construction such as Kanban could provide the framework around this type of decisions. There is a limit to the value of added automation: Construction 4.0 framework ofers a variety of tools and technologies across the automation spectrum. Taken to its furthest limit, one could envision a project where the entirety of the constructions tasks is undertaken by a robot (Keating, 2016). A Lean Construction view on the mentioned spectrum of automation will need to consider the added value of increasing automation. An example that relates to this aspect is the invention of the concrete hose pump as a transformative technology in terms of the efciency it brought about, when compared to the former mode of concrete 45

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pouring including multiple workers shuttling wheelbarrows flled with concrete by a single operator. Automating the concrete pouring process even further could be with autonomous hose pumps or autonomous trucks as the platform, taking out even more labor costs associated with the task. In mining, autonomous hauling trucks are already being engineered and deployed by numerous frms in that industry. However, mines are controlled environments where routing of autonomous vehicles is much easier than, for example, a densely populated urban area. This could prove the innovation not viable as the AEC undertakings take place in very diverse settings that complicate it. In the case of the hose pump, the added automation would not bring in the level of value brought in by the original innovation.

Lean Construction 4.0 in Context Virtually all trade-ofs raised as examples in the previous section and other ones arising from similar concerns must include the context in which Lean Construction 4.0 is to be achieved. By context, the authors signify the social, cultural, and environmental boundary conditions within which the AEC undertakings take place. These boundary conditions alter signifcantly based on the geographic region of interest, an aspect that has tremendous impact on the adoption of digital innovation, and this has been overlooked by the literature on Lean Construction (see Figure 3.6). Firstly, the technological advances themselves are tightly connected to existing socioeconomic circumstances, i.e., a technology readily available in country X could be much behind in terms of its development in country Y. Other resources such as labor also become defning factors. In developed economies, scarcity of construction labor or the negotiations with trade unions prove to be obstacles for frms. In addition, AEC wages (in design/construction/operations) difer across regions of the world, which ties directly into the technological trade-ofs discussed. For example, automating bricklaying using

Figure 3.6

Triad of Lean implementation (Hamzeh et al., 2021) in the current view (left) and in the 4.0 era given environmental stressors (right)

The Implications of the 4.0 Revolution in the AEC Industry

industrial robotic arms could be fnancially viable in a developed economy such as the UK, but could entirely be dismissed in a developing economy. In those settings, value proposed by automation often do not reach the fnancial or functional break-even points that would deem it feasible. Apart from the spatial variations in digital and smart innovation, the rate of innovation difusion for a specifc digital technology varies across types of AEC stakeholders (designers, contractors, subcontractors, owners, operators) (Koc et al., 2020). The difusion often depends on the culture of the organization or the entity. For instance, the widespread use of mobile interfaces on construction sites allowed the subcontractors (typically behind on the adoption curve) to experience the benefts of having digital models of reference. This was followed by a faster adoption of BIM on the construction site, thus across more stakeholders involved in AEC. Over time, integrated use of technologies such as these should be expected to change the culture around digital innovation in the industry. Moving towards a future of Lean Construction 4.0, researchers and practitioners need to be aware of and work within such cultural aspects arising from existing challenges of fragmentation. This task calls for additional research on how to bridge the gaps arising from social and cultural characteristics. In addition, stressors arising from a changing climate and the quantifcation of the energy use and carbon footprint from the built environment result in a need to consider environmental boundary conditions as well. Hamzeh et al. (2021) illustrated the triad of Lean implementation with three main pillars, namely People/Culture, Philosophy/Processes and Technology. Given the rise of concerns around environmental sustainability, it is argued that the path toward Lean Construction 4.0 needs to include environment as a fourth pillar which is demonstrated by Figure 3.6. The governing trend of complying with environmental sustainability concerns already manifests itself in the form of compliance measures (e.g., EUETS or the European Union Emissions Trading System). Moreover, an increasing number of global corporations within or outside the AEC industry are committing to net-zero emission targets. For AEC frms around the world, to prepare for a future of stricter guidelines around environmental waste and emissions demands that Lean Construction methods and principles are evaluated within this new set of circumstances. Lean Construction 4.0, implemented in a manner that advances holistic and data-driven decision making, creates the opportunity for accurately quantifying and mitigating the environmental footprint of the built environment across spatiotemporal scales (project level, local, regional, and global). As it concerns an AEC frm, it also provides the framework to test various scenarios for project development in terms of their design parameters sensitive to these newly emerging environmental objectives. Until now, this genre of eforts in the buildings space leaned on green building standards such as LEED or BREEAM that deal with how green a building is without considering the environmental externalities holistically, such as the type of electricity generation (e.g., coal, solar) powering its operation or the carbon emissions associated with that power. In a future of globally implemented compliance and voluntary carbon markets, the carbon intensity of construction materials will also have to be accounted for. The proven tools, methods and technologies in the Lean Construction 4.0 framework ofer the best currently existing roadmap for addressing such challenges.

Discussion Lean Construction since the 1990s tries to address the chronic issues of the AEC industry to achieve higher levels of productivity and value delivered while decreasing waste. 47

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Meanwhile, the Construction 4.0 framework has been emerging as a set of concepts, tools and technologies gaining increasing interest in the current trend of digitization and automation across various project phases. The main argument presented in this chapter is that there is an evident potential for a symbiosis between theory (Lean Construction) and the technology (Construction 4.0). This has already shown itself in the industry observing the eforts to collect and analyze more data, despite a lack of current success in terms of deriving value. The authors highlight that the gap in between should be bridged by the Lean Construction community through addressing the research problems motivated by the genre of examples presented here. This convergence efort between the theory and the technology must consider the context in which it is to be achieved, by working across social, cultural, and environmental dimensions beyond the spatiotemporal scales. Furthermore, the convergence will be afected by the level of innovation difusion attributed to specifc technologies, and how existing Lean Construction methods can be used to leverage them. One of the most essential components in the envisioned paradigm shift will be the workforce and their fexibility to adapt. It is critical that designers and engineers are educated on computer science, principles of algorithm design, general systems theory (GST) and given a good understanding of complex adaptive systems (CAS) as well as production management theory. Most of the aforementioned domains are marginally touched upon in current academic curricula or in the industry. At this point, we need to stress that acquiring such skills will position the AEC workforce in a strategic position to lead the transformation of their industry instead of outsiders (i.e., players from the tech industry). In addition, instead of forecasting the loss of jobs brought about by the difusion of disruptive innovations both in design and engineering (automation of design tasks) as well as on the construction site (i.e., robots substituting workers), the authors support that under the correct circumstances, Construction 4.0 innovations could allow architects and engineers shift the focus to more creative tasks such as problem solving and decision making rather than drafting and annotating drawings. The shift of focus can also help in the development of smarter production management which reduces waste in existing processes of the traditional design-build paradigm. The path to Lean Construction 4.0 requires us to emphasize the perception of Lean as a ‘process’ and not a ‘project’ (Wood et al., 2021). There needs to be multi-stakeholder buy-in for embracing the principles of Lean Construction 4.0 in the long term, especially on the side of owners and higher AEC management.

References Alves, T. D. C. L., & Tsao, C. C. Y. (2007). Lean construction–2000 to 2006. Lean Construction Journal, 3, 46–70. Retrieved from www.Leanconstructionjournal.org Aminov, I., De Smet, A., Jost, G., & Mendelsohn, D. (2019). Decision making in the age of urgency. Retrieved from https://www.mckinsey.com/ Ballard, H. G. (2000). The last planner system of production control: (Ph.D.) University of Birmingham. Carpo, M. (2013). The digital turn in architecture 1992–2012: John Wiley & Sons. Changali, S., Azam, M., & Van Nieuwland, M. (2015). The construction productivity imperative. Retrieved from www.mckinsey.com Hamzeh, F., González, V. A., Alarcon, L. F., & Khalife, S. (2021). Lean construction 4.0: exploring the challenges of development in AEC industry. Paper presented at the 29th Annual Conference of the International Group for Lean Construction (IGLC29), Lima, Peru. iglc.net Keating, S. (2016). From bacteria to buildings: Additive manufacturing outside the box. (Ph.D. Doctor of Philosophy in Mechanical Engineering): MIT, Boston, MA. Khoshnevis, B. (2004). Automated construction by contour crafting—related robotics and information technologies. Automation in Construction, 13(1), 5–19.

The Implications of the 4.0 Revolution in the AEC Industry Koc, E., Pantazis, E., Soibelman, L., & Gerber, D. J. (2020). Emerging trends and research directions. In A. Sawhney, M. Riley, & J. Irizarry (Eds.), Construction 4.0: An innovation platform for the built environment: Routledge, Taylor & Francis Group. Koskela, L., Howell, G., Ballard, G., & Tommelein, I. (2002). Design and construction: Building in value. In R. Best, & G. De Valence (Eds.), The foundations of Lean construction (pp. 211–226). Routledge. Kumagai, I., Morisawa, M., Sakaguchi, T., Nakaoka, S., Kaneko, K., Kaminaga, H., & Kanehiro, F. (2019). Toward industrialization of humanoid robots: Autonomous plasterboard installation to improve safety and efciency. IEEE Robotics & Automation Magazine, 26(4), 20–29. Thomas, E., & Bowman, J. (2021). Harnessing the data advantage in construction: Why adopting a data strategy can bring frms a competitive edge. Retrieved from www.autodesk.com Tzortzopoulos, P., Kagioglou, M., & Koskela, L. (2020). Lean construction: Core concepts and new frontiers: Routledge. Wood, A., Rai, A., Seltzer, A.-L., & Roche, C. (2021). Adopting Lean principles. Retrieved from https:// discover.3ds.com

4 PROPOSING A HOUSE OF LEAN CONSTRUCTION 4.0 Makram Bou Hatoum and Hala Nassereddine

Introduction For the last decades, the architectural/engineering/construction (AEC) industry has been shaped by the adoption of Lean Thinking and the implementation of Lean Construction. More recently, concepts of Industry 4.0 in the AEC industry, known as Construction 4.0, have been regarded as the impetus to transform the industry (Ammar et al., 2022). While each movement is powerful in and of itself, their synergy results in manifold gains. Thus, the term ‘Lean Construction’ 4.0 emerged. The vision of Lean Construction 4.0 comes at a time when the construction industry is in need of change. While the construction industry accounts for around 13% of the world’s gross domestic product (GDP), its annual productivity growth has barely increased 1% annually over the last two decades, requiring nearly $1.6 trillion in opportunities to close the productivity gap with competing industries (Barbosa et al., 2017). The root cause of this gap can be attributed to the traditional business-as-usual ways in which the construction industry has reached a stagnation point – there is a pressing need to increase productivity, improve project performance, address the labor shortage, reskill workers, reduce fragmentation, introduce standardization, address resistance to change, improve procurement, and increase collaboration (Barbosa et al., 2017; Hatoum & Nassereddine, 2020; Lau et al., 2019; Sawhney et al., 2020). With the manufacturing-style innovations that a vision like Lean Construction 4.0 will bring, the needs can be addressed, and the industry could witness an expected productivity boost by 50% to 60% (Barbosa et al., 2017). The signifcance of Lean Construction 4.0 vision makes it important to bring awareness to the subject to both academicians and practitioners in the AEC industry. To do that, this chapter proposes a conceptual ‘House of Lean Construction 4.0’ framework that centralizes all the vision aspects together, so it can be used as an educational tool by academicians and a digital support plan for practitioners. The depiction of the Lean Construction 4.0 vision as a house is inspired by the ‘Toyota Production System House’ introduced by (Liker, 2021) who explained that: ‘A house is a structural system. It is strong only if the roof, the pillars, and the foundations are strong, and any weak link weakens the whole system’. Therefore, presenting Lean Construction 4.0 as a structural house can show the importance of all the aspects discussed in the chapter in creating a successful Lean Construction 4.0 vision. In specifc, the components of ‘House of Lean Construction 4.0’ will answer the following questions: 50

DOI: 10.1201/9781003150930-6

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1 2 3

What are the major transformations that support the Lean Construction 4.0 vision? The answer to this question is portrayed by the beam of the house. What are the pillars that support and enable these transformations? The answer to this question is portrayed by the columns of the house. What are the factors that AEC frms must consider strengthening to support the Lean Construction 4.0 vision? The answer to this question is depicted by the foundations of the house. What role do people play in supporting the Lean Construction 4.0 vision? The answer to this question is outlined in the subgrade of the house.

Research Approach To envision the conceptual ‘House of Lean Construction 4.0’ framework, this study synthesized the existing research corpus for fve main tasks (Figure 4.1): Task 1 (T1) describes the transformations of a Lean Construction 4.0 vision; Task 2 (T2) presents the culture, principles, methods, and tools of Lean Construction; Task 3 (T3) presents the design principles capabilities, concepts, and technologies of Construction 4.0; Task 4 (T4) investigates the synergies between Lean and Construction 4.0; Task 5 (T5) summarizes the technologyorganization-environment (TOE) factors that afect AEC frms’ decisions to implement Lean Construction 4.0 innovations; and Task 6 (T6) describes the importance of people.

Conceptual House of Lean Construction 4.0 Framework The proposed House of Lean Construction 4.0 is shown in Figure 4.2. Applying the reverse planning method, the following sections discuss the house backward starting with the roof and ending with the subgrade.

Te Transformations of Lean Construction 4.0 (T1) The success of the Lean Construction 4.0 vision can be translated into four major industry transformations: product transformation, delivery transformation, digital transformation, and mindset transformation.

Figure 4.1

Schematic representation of the methodology

Makram Bou Hatoum and Hala Nassereddine

Figure 4.2

‘House of Lean Construction 4.0’

The main output of any construction project is the physical built environment asset that needs to be constructed for the client or owner – whether a residential, non-residential, infrastructure, or industrial facility (Barbosa et al., 2017). Consequently, a Lean Construction 4.0 vision will have a direct impact on the end-product, and thus prompting a product transformation. One major example of product transformation that Lean Construction 4.0 can embrace is ofsite construction (Sawhney et al., 2020). In the last few years, the construction industry has been witnessing a rise in prefabrication and modular construction, moving it gradually away from the traditional ‘stick-built’ on-site construction (Razkenari et al., 2020). This movement places projects in an ideal ‘factory-like’ environment where the application of Lean management principles and automation systems like modeling, simulations, and robots becomes easier (Brissi et al., 2021). Another example to highlight is the use of additive manufacturing or 3D printing (Sawhney et al., 2020). 3D printing is one of the core technologies of Construction 4.0 that can bolster the Lean philosophy goals including lead time reduction, waste eradication, quality or Jidoka improvements, and cost savings (El Sakka & Hamzeh, 2017; Muñoz-La Rivera et al., 2021). In addition to the physical asset, a construction project should be completed safely without exceeding its planned budget or running behind schedule; the project should also provide the value that its owner paid for (Han et al., 2012). Thus, the delivery of the project is important, and that is where a Lean Construction 4.0 vision will prompt a delivery transformation. For example, the use of key concepts such as Building Information Modeling (BIM) throughout the entire project lifecycle can be enabled by Lean Project Delivery Systems (LPDS) and Integrated Project Delivery (IPD) (Babalola et al., 2019; Fakhimi et al., 2016; Nassereddine et al.; 2022a). Conversely, Lean planning and control methods, such as Last 52

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Planner System (LPS), location-based management system, (LBMS), and value stream mapping (VSM), can be enhanced using Construction 4.0 technologies such as augmented reality, laser scanning, and wireless sensors (Brahmi et al., 2021). As for the digital transformation, it is notably represented through linking the physical world to the cyber one and creating resilient cyber-physical systems (CPS) in the construction industry. CPS is a ‘system with a seamless automatic connection between the material world and smart digital components, capable of perceiving, directing, and controlling the physical world’ (Klinc & Turk, 2019). There are fve levels of CPS implementation – denoted as the 5Cs, that Lean Construction 4.0 should enhance: connection, conversion, cyber, cognition, and confguration (Lee et al., 2015). Figure 4.3 explains the 5Cs and mirrors their current application in the construction industry. Finally, a mindset transformation can also be achieved with Lean Construction 4.0. Projects for example should be looked at as engagement platform that allows parties to encourage dialogue, develop information-sharing practices to provide access to valuable data, understand and share both risks and rewards, and promote transparency ( Jacobsson & Roth, 2014). Team leaders such as project managers should develop a growth mindset that enables lifetime learning and allows them to embrace challenges, accept feedback and criticism, learn from mistakes, and believe in their teams’ ability to develop intellectual skills (Owusu-Manu

Figure 4.3

Levels of CPS and their applications in the construction industry

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et al., 2020). Another example is ‘scientifc thinking’ which was added at the center of the 2021 version of Toyota’s 4P model (Liker, 2021). Scientifc thinking should expand the industry’s level of knowledge by emphasizing several scientifc behaviors such as deep observations, iterative learning, alignment of plans and goals with policies, and bold strategies with few big leaps and big small steps (Liker, 2021).

Te Pillars of Lean Construction 4.0 (T2, T3, T4) A Lean Construction 4.0 vision is inspired by the synergies of two main pillars: Lean Construction and Construction 4.0 (Hamzeh et al., 2021).

Lean Construction (T2) Lean Construction emerged as a new concept in the mid-1990s as a novel theory-based approach to the construction industry with a kit of principles, tools, and methods from Toyota’s Lean Production (Koskela et al., 2002). It is a ‘respect- and relationship-oriented production management-based approach to project delivery’ which changes the traditional way of designing, building, supplying, and delivering construction projects (Seed, 2015). Lean Construction aims to ‘optimize the whole’ in construction projects by removing waste, focusing on processes and fow, generating value, and continuously improving (Seed, 2015). Lean Construction embraces an unconventional culture that is diferent from the traditional environment of the construction industry and is built on 14 principles inspired by Toyota and supported by a set of practices and tools. Compiled comprehensively from various studies, Figure 4.4 provides a summary of the characteristics of the Lean Construction culture, alongside the 14 Lean principles and the most-common Lean practices and tools (Ansah et al., 2016; Babalola et al., 2019; Demirkesen & Bayhan, 2019; Gao & Low, 2014; Liker, 2021; Pekuri et al., 2012).

Figure 4.4 Culture, principles, practices, and tools of Lean Construction

Proposing a House of Lean Construction 4.0

Construction 4.0 (T3) Modeled after Industry 4.0, the concept of Construction 4.0 is inspired by the convergence of trends and technologies, both physical and digital (Sawhney et al., 2020). Construction 4.0 aims to (1) link the physical and digital layers of a built environment asset, (2) leverage the use of technology in construction processes to assist people through the project lifecycle, and (3) enable horizontal integration, vertical integration, and end-to-end through engineering (FIEC, 2020; Muñoz-La Rivera et al., 2021; Sawhney et al., 2020). Like Lean Construction, Construction 4.0 is set on design principles and enables major capabilities through diferent technologies and technological concepts. Based on the literature, Figure 4.5 provides a comprehensive summary of the design principles, capabilities, and key technologies and concepts that Construction 4.0 enables (Hossain & Nadeem, 2019; Karmakar & Delhi, 2021; Klinc & Turk, 2019; Prieto, 2021; Sawhney et al., 2020).

Synergies between Lean Construction and Construction 4.0 (T4) As seen in Figures 4.4 and 4.5, Lean Construction and Construction 4.0 share several common characteristics that refect positively on the construction industry. In fact, the interaction between Lean and the fourth industrial revolution has been investigated in several studies, some of which are shown in Figure 4.6 (Buer et al., 2018; Ciano et al., 2021; Ejsmont et al., 2020; Küpper et al., 2017; Rosin et al., 2019; Sanders et al., 2016; Sony, 2018). As for Lean Construction 4.0, it has yet to be directly addressed in the literature (Hamzeh et al., 2021). However, some studies have investigated synergies between Lean ideologies and Industry 4.0 technologies in the construction industry. Examples of such investigations: Lean with augmented reality (Nassereddine, 2022b); Last Planner System with BIM, augmented reality, and virtual reality (Dallasega et al., 2018); Lean management with BIM and

Figure 4.5

Design principles, capabilities, technologies, and concepts of Construction 4.0

Makram Bou Hatoum and Hala Nassereddine

Figure 4.6

Examples of major studies investigating Lean and Industry 4.0

Big Data analytics (Demirdöğen et al., 2021); Lean with 3D printing (El Sakka & Hamzeh, 2017); BIM with IPD (Brahmi et al., 2021; Fakhimi et al., 2016); Lean practices with UAVs (Ersoz et al., 2019); Lean scheduling with BIM and AI (Li et al., 2020); Lean with cloudbased IoT in prefabricated construction (Xu et al., 2018); Lean principles with robotics in ofsite construction (Brissi et al., 2021); Lean principles with BIM and digitalization in ofsite construction (Barkokebas et al., 2021). Additionally, some studies presented implementation frameworks based on Construction 4.0 and Lean Construction. For example, Lekan et al. (2020) investigated the Lean Thinking areas and disruptions caused by Industry 4.0 technologies to propose a hybrid model for achieving Construction 4.0. Another study by Hatoum et al. (2021) presented a ‘Construction 4.0 Process Reengineering’ (CPR4.0) framework to assist AEC frms in rethinking processes to integrate Construction 4.0 technologies. The Lean-based framework was built on existing reengineering methodologies, people-process-technology methodology, and Lean principles (Hatoum et al., 2021).

Underlying Factors that Afect Change Decisions Within AEC Firms (T5) Implementing the Lean Construction 4.0 vision requires AEC frms to be strategically positioned for this change efort. Therefore, it is essential to understand the factors that can 56

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infuence an organization’s decision toward Lean Construction 4.0. The TOE methodology was adopted to compile these factors from the existing literature. Technology factors are related to the innovations ofered by the Lean Construction 4.0 vision. Organization factors include the organizational features of AEC frms. Environment factors refect on the external environment surrounding the organization and the innovations. A summary of the factors is presented in Figure 4.7 and discussed below.

Technology Factors Compatibility The compatibility of the innovation represents the degree to which the innovation is ‘being consistent with the existing values, experiences, and needs of the company’ (Rogers & Shoemaker, 1971). It can be either normative indicating the innovation’s compatibility with how people feel about it, or practical and operational indicating the innovation’s compatibility with people’s tasks and responsibilities, or both (Tornatzky & Klein, 1982).

Complexity The complexity of the innovation is the degree to which it is perceived as relatively difcult to understand or use (Rogers & Shoemaker, 1971). Higher complexity can lead to problems around information availability and innovation usage. Additionally, the time taken to utilize the innovation’s interface forms a cost of adoption. New technologies, for instance, are more easily adopted when they are simple or at least easy to interact with (Mabad et al., 2021).

Observability The observability of the innovation is the extent to which the results can be visible to others (Rogers & Shoemaker, 1971). This factor can afect the general reception of the innovation, through communicability and social approval (Tornatzky & Klein, 1982). The existence of successful cases of the innovation can demonstrate the efectiveness, benefts, and practical applications in the AEC industry (Wu et al., 2018).

Relative Advantage The relative advantage of the innovation can be described as the degree that an innovation is seen better than the idea, program, or product it is replacing (Rogers & Shoemaker, 1971). It can be evaluated using several indicators, including the change in profts, time savings, social benefts, and/or hazards removed (Tornatzky & Klein, 1982).

Divisibility and Trialability The divisibility and trialability aspects of an innovation, while diferent, can be related. Divisibility is the extent to which the innovation can be tried on a small-scale prior to adoption. Trialability is the extent to which the innovation may be experimented with on a limited basis (Rogers & Shoemaker, 1971). While a highly divisible technology can be ‘trialable’, not all trialable technology are divisible since some trialable technology can be relatively small and easily reversible (Tornatzky & Klein, 1982). 57

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Industry Standards With the lack of standardization being a major barrier for change in the construction industry, the existence of industry standards is an important incentive for AEC frms to adopt an innovation (Elghdban et al., 2020). Industry standards are a set of policies, regulations, or best practices presenting a structured adoption and implementation plan of an innovation (Wu et al., 2018).

Privacy and Security Privacy and security of data are critical to organizations and key issues when adopting innovations (Mabad et al., 2021). An innovation should address security authentication, authorization, accountability, data protection and disaster recovery, reluctance to share internal data with external partners, and privacy of users (Gangwar et al., 2015). Addressing these concerns facilitates innovation implementation for AEC frms (Arabshahi, 2021).

Virtual Community A virtual community can be defned as an ‘online social network where people with common interests, goals, or practices interact to share information and knowledge while engaging in social interactions’ (Chiu et al., 2006). It should provide ‘rich innovation of interest, with members both sharing and receiving valuable information’ (Chiu et al., 2006). Such communities can centralize information on any innovation and act as an open source to promote the innovation’s adoption (Tsai & Yeh, 2019). Some factors to evaluate a virtual community include its knowledge platform, collaborative approach, being open-source, and the nature and security of the information it provides (Tsai & Yeh, 2019).

Organization Factors Organization Scale The size of an organization plays an important role in the adoption of innovations. Larger frms are perceived to have the capacity and capabilities to take risks and invest in innovations compared to smaller frms (Mabad et al., 2021). While size is important, other organizational factors must be considered to defne the scale of the organization including age (i.e., legacy or not), origin, geographical location, scale of operations, and number of customers and projects (Chandra & Kumar, 2018; Elghdban et al., 2020; Ukobitz, 2021).

Scope of Operations The scope of operations represents the type of work performed by the organization and is associated with innovation adoption (Tsai & Yeh, 2019). Examples in the AEC industry include the nature of construction projects such as residential, commercial, infrastructure, and industrial, and the type of the project-delivery system (Killough, 2021; Seaden et al., 2003; Tsai & Yeh, 2019).

Financial Resources The cost needed to adopt any innovation is one of the most signifcant factors that impact adoption decisions (Elghdban et al., 2020). Cost is considered throughout the lifecycle of the innovation. Starting with procurement and acquisition for the initial required 58

Proposing a House of Lean Construction 4.0

investments such as people, training, hardware, software, system integration, and/ or consultants; then operations and management for the cost to operate, maintain, and/ or insure the innovation to successfully use it; and fnally costs for retirement and disposal (Eiris & Gheisari, 2017).

Human Resources The demographic variables of the company’s employees such as age, education, position, years of experience, type of experience, skills, and digital literacy can infuence the company’s overall knowledge and innovation adoption decisions (Schuh et al., 2020; Tsai & Yeh, 2019).

Information Systems Information systems (IS) allow the organization to manage the information generated by its people, machinery, equipment, tools, materials, and projects (Schuh et al., 2020). IS play a key role in the company’s internal and external response to changes in the environment (Laudon & Laudon, 2006). Information technology (IT) infrastructure is also essential for innovation adoption because it is the platform for the organization’s specifc information system applications (Laudon & Laudon, 2006; Schuh et al., 2020). Thus, a strong IT infrastructure increases organizational likelihood to adopt innovations ( Jeyaraj et al., 2006; Mabad et al., 2021). The strength of an IT infrastructure depends on its resources, technical (e.g., hardware, software, network, and other tangible resources) and non-technical (e.g., people, procedures, data policies and governance, know-how, and nature of collaboration) (Elghaish et al., 2020; Mabad et al., 2021).

Management Qualities Organizational leadership through its support to change initiatives is key for innovation adoption (Ukobitz, 2021). Leadership perceptions of the usefulness of innovations promote a long-term innovative mission and vision, reinforce organizational values, manage resources optimally, and cultivate a favorable organizational climate (Gangwar et al., 2015). Decision-makers should also cultivate trust, open communication, and social collaboration throughout the organization and ensure an innovation does not result in job losses (Muylle, 2019; Schuh et al., 2020).

Decision Bureaucracy The decision-making process within an organization infuences innovation adoption decisions and depends on the organizational culture (mechanistic vs. organic) and structure (technical vs. social) (Adler, 1999; Reigle, 2001). Decision-making in mechanistic cultures happens centrally and is formalized with more vertical communication. Organizations with organic cultures have a decentralized decision-making process with more lateral communication (Reigle, 2001). The nature of this process determines whether decision-makers are forcing innovations on the organization or are pulling innovations as needed (Dixon, 2001). An organization is encouraged to cultivate an enabling bureaucracy, where rules and procedures can be tools that empower employees to engage in decisions on the adoption of innovations (Liker, 2021). 59

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Readiness and Commitment Organizational readiness can be defned as the degree to which an organization ‘has the awareness, resources, commitment, and governance to adopt’ an innovation (Tan et al., 2007). Readiness is also infuenced by the employees’ beliefs of the practicability of the innovation, and the alignment of the innovation with the organization’s structure and people (Holt etal., 2007). Three dimensions – namely technological, fnancial, and human– describe the readiness and commitments needed from an organization to adopt the innovation (Elghdban et al., 2020).

Champions Identifying a ‘champion’ to lead innovation adoption is a critical ingredient (Elghdban et al., 2020). A champion should excel in their knowledge and role, have a comprehensive understanding of the perceived benefts, communicate advantages with stakeholders and professional teams, convince resistors with the new change, take the lead in the adoption process and work closely with the related teams, assist in training present staf, and collaborate with the IT department to establish the needed IT resources (Mabad et al., 2021).

Availability of Training Training is described as the degree to which a company: instructs its employees to use an innovation in terms of quality and quantity, reduces employees’ anxiety and stress, provides motivation and a better understanding of benefts, reduces ambiguity, improves the perceived ease of use and usefulness of an innovation, and opens the door for future improvement (Gangwar et al., 2015; Schillewaert et al., 2005).

Figure 4.7

The technology-organization-environment (TOE) factors

Proposing a House of Lean Construction 4.0

Environment Factors Project Stakeholders and Trading Partners As a construction project requires the involvement of many players, the readiness and support of those impacted by an innovation must be considered (Elghdban et al., 2020). An AEC frm is said to be more likely to adopt an innovation if its partners have already adopted it (Chen et al., 2019). Pressure from trading partners across the supply chain (from upstream or downstream) can incentivize others in the supply chain to adopt the innovation (Pan & Pan, 2020).

Vendors Access to innovation vendors is needed to adopt an innovation. Vendors providing technology solutions need to collaborate with AEC frms to facilitate and expedite the adoption of innovations (Mabad et al., 2021). Vendors can also provide frms with licenses and partnering arrangements, software updates, and track records to prove the value of an innovation (Arabshahi, 2021).

Customer/Owner A frm’s innovative behavior is afected by the customer, and construction industry frms are no exception. Innovation-driven frms improve customer value across the entire project lifecycle, and thus, gain customer loyalty (Chen et al., 2019). Additionally, customers’ awareness of an innovation can incentivize AEC frms to adopt this innovation, and thus improving business relations and project delivery (Chen et al., 2019).

Labor Unions Labor unions are key stakeholders to consider when innovating the AEC industry because workers’ jobs, roles, responsibilities, and skills may be impacted ( James Manyika et al., 2017). Unions can engage with the rest of the industry and play an active role in leading the transition by ensuring access to programs that meet innovation demands and working with employers for efective employment transitions (Green, 2019).

Competition The pressure resulting from the practices of competitors and the need to gain competitive advantage can drive frms to change their business-as-usual and innovate (Chen et al., 2019; Pan & Pan, 2020). Research showed that in a competitive environment, frms innovate to ‘alter the rules of the competition and change the competitive playing feld’ (Chen et al., 2019; Martins et al., 2015). Examples of competitive strategies include the quality of the project, innovativeness of the project delivery process, cost savings and low end prices, fexibility in designing and customizing the project, short delivery times, and high customer service performance (Kinkel et al., 2021).

Social Responsibility Construction projects are dynamic in nature – they have diferent sizes and locations and serve diferent purposes. Additionally, AEC frms serve diverse communities and thus may either feel a voluntary obligation to societies based on social expectations, norms, and codes 61

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of conduct, or be placed in situations where they cannot ignore the social community due to rising public pressures (Hwang et al., 2016). Such social responsibilities can lead frms to innovate (Elghdban et al., 2020). Climate change is a great example. The rising pressure from the public created a growing need for sustainable and green construction (Clifton et al., 2021).

Government Governmental agencies and authorities can encourage the spread and difusion of multiple innovations (Chen et al., 2019). The efect of existing rules, policies, and regulations can also help create a perception of the values associated with innovations (Chaurasia & Verma, 2020).

Market Demands and Trends The infuence of market demand on innovation has been highlighted from the market pull perspective in innovation studies, especially in an innovative generation (Di Stefano et al., 2012). For the construction industry, the adoption intention also arises from the need to address present challenges facing the market, such as workforce replenishment, productivity improvement, and environmental impact reduction (Pan & Pan, 2020).

People as a Subgrade to ‘House of Lean Construction 4.0’ (T6) Any house needs a strong subgrade to support it, and the most important subgrade of ‘House of Lean Construction 4.0’ is ‘People’. The people aspect is ingrained into every element of the conceptual framework. First, every technology, organization, or environment factor that afects change in an AEC frm is directly or indirectly related to the people involved: people develop, use, and defne characteristics of the technology; people are also at the core of any organization, they make the change decisions and they are the frst to be afected by them; and people are responsible for creating the supportive outside environment to help organizations seeking change (Elghdban et al., 2020). Second, people are at the center of the Lean philosophy and ideologies, where training in scientifc thinking and respecting people is important to embrace change and continuously improve (Liker, 2021). People should work in an organization that provides a safe environment for constructive confict, recognizes their added value and contribution, challenges them to improve their capabilities and grow, ofers communication and collaboration means, and cultivates trust (Seed, 2015). Technologies ofered by Construction 4.0 also need the support of humans, where a company cannot successfully implement technologies if its people feel threatened by them (Gallo et al., 2021). On the contrary, people in an organization should be treated as the ‘authors of the innovation’, as they will be the ones using Construction 4.0 technologies and adapting their skills to get the maximum benefts out of it (Gallo et al., 2021). Thus, the innovations that Lean Construction 4.0 will bring to construction processes should meet human needs and respect the people involved, and is core to creating the desired harmony between people, technology, and the change in processes (Hamzeh et al., 2021). Finally, the manifestation of people under Lean Construction 4.0 can be explained by the people (1) applying Lean principles, tools, and practices to be empowered and respected and (2) using Construction 4.0 technologies to support their work and performance (Hatoum

Proposing a House of Lean Construction 4.0

Figure 4.8 AR-PSP prototype

etal., 2022). An example that embodies people under Lean Construction 4.0 is illustrated in Figure 4.8 where a user applies an AR-enabled Lean practice, the AR-enabled Production Strategy Process (PSP), to plan the project and produce a production strategy (Nassereddine, 2022b).

Conclusions The purpose of this chapter was to present the conceptual ‘House of Lean Construction 4.0’ framework, a holistic structure that summarizes the goals, pillars, and foundations of the Lean Construction 4.0 vision. The house can be used as a tool to bring awareness around this vision to both academicians and practitioners. AEC frms can consider this house as part of the digital strategic plan. The house is theoretic in nature and its fndings are limited to the existing body of knowledge. Future studies can build on this work to provide empirical evidence to support the elements of the house. Additional research can be conducted to discuss the house with practitioners and academicians to expand on the current version of the house.

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Makram Bou Hatoum and Hala Nassereddine Karmakar, A., & Delhi, V. S. K. (2021). Construction 4.0: What we know and where we are headed. Journal of Information Technology in Construction (ITcon), 26(Next Generation ICT-How distant is ubiquitous computing?), 526–545. https://doi.org/10.36680/j.itcon.2021.028 Killough, D. (2021). Common construction project delivery methods: A breakdown. Construction Payment Blog. https://www.levelset.com/blog/construction-project-delivery-methods/ Kinkel, S., Baumgartner, M., & Cherubini, E. (2021). Prerequisites for the adoption of AI technologies in manufacturing – Evidence from a worldwide sample of manufacturing companies. Technovation, 110, 102375. https://doi.org/10.1016/j.technovation.2021.102375 Klinc, R., & Turk, Ž. (2019). Construction 4.0—digital transformation of one of the oldest industries. Economic & Business Review, 21(3), 393–410. https://doi.org/10.15458/ebr.92 Koskela, L., Howell, G., Ballard, G., & Tommelein, I. (2002). The foundations of Lean Construction. In R. Best & G. De Valence, (eds.), Design and Construction: Building in Value, 211–226. Routledge. Küpper, D., Heidemann, A., Ströhle, J., Knizek, C., & Spindelndreier, D. (2017). When Lean meets industry 4.0: The next level of operational excellence. Boston Consulting Group. https://www.bcg. com/publications/2017/Lean-meets-industry-4.0 Laudon, K. & Laudon J. (2006). Management Information Systems: Managing the Digital Firm. Prentice Hall, Pearson. Lau, S. E. N., Zakaria, R., Aminudin, E., Saar, C. C., Abidin, N. I. A., Roslan, A. F., Abd Hamid, Z., Zain, M. Z. M., & Lou, E. (2019). Identifcation of roadmap of fourth construction industrial revolution. IOP Conference Series: Materials Science and Engineering, 615(1), 012029. https://doi. org/10.1088/1757-899X/615/1/012029 Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j. mfglet.2014.12.001 Lekan, A., Clinton, A., Fayomi, O. S. I., & James, O. (2020). Lean thinking and industrial 4.0 approach to achieving construction 4.0 for industrialization and technological development. Buildings, 10(12), 221. https://doi.org/10.3390/buildings10120221 Li, B., Schultz, C., Melzner, J., Golovina, O., & Teizer, J. (2020). Safe and Lean Location-Based Construction Scheduling. In Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020), 1409–1416. https://doi.org/10.22260/ISARC2020/0195 Liker, J. K. (2021). The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer (2nd ed.). McGraw-Hill. Mabad, T., Ali, O., Ally, M., Wamba, S. F., & Chan, K. C. (2021). Making investment decisions on RFID technology: An evaluation of key adoption factors in construction frms. IEEE Access, 9, 36937–36954. https://doi.org/10.1109/ACCESS.2021.3063301 Martins, R., Oliveira, T., & Thomas, M. A. (2015). Assessing organizational adoption of information systems outsourcing. Journal of Organizational Computing and Electronic Commerce, 25(4), 360–378. https://doi.org/10.1080/10919392.2015.1087702 Muñoz-La Rivera, F., Mora-Serrano, J., Valero, I., & Oñate, E. (2021). Methodological-technological framework for Construction 4.0. Archives of Computational Methods in Engineering, 28, 689–711. https://doi.org/10.1007/s11831-020-09455-9 Muylle, L. (2019). Technology readiness and adoption of 3D printing in the construction industry. Master’s Thesis, Master in Business Engineering: Operations Management, Universiteit Gent. Nassereddine, H., Hatoum, M.B., and Hanna, A.S. (2022a). Overview of the State-of-Practice of BIM in the AEC Industry in the United States. In Proceedings of the 39th International Symposium on Automation and Robotics in Construction (ISARC 2022), 524–531. https://doi.org/10.22260/ ISARC2022/0074 Nassereddine, H., Veeramani, D., and Hanna, A.S. (2022b). Design, Development, and Validation of an Augmented Reality-Enabled Production Strategy Process. Frontiers in Built Environment, 8, 730098. https://doi.org/10.3389/f buil.2022.730098 Owusu-Manu, D.-G., Debrah, C., Amissah, L., Edwards, D. J., & Chileshe, N. (2020). Exploring the linkages between project managers’ mindset behaviour and project leadership style in the Ghanaian construction industry. Engineering, Construction and Architectural Management, ahead-of-print. https:// doi.org/10.1108/ECAM-03-2020-0149 Pan, M., & Pan, W. (2020). Understanding the determinants of construction robot adoption: perspective of building contractors. Journal of Construction Engineering and Management, 146(5), 04020040. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001821

Proposing a House of Lean Construction 4.0 Pekuri, A., Herrala, M., Aapaoja, A., & Haapasalo, H. (2012). Applying Lean in construction–cornerstones for implementation. Proceedings of the 20th Annual Conference of the International Group for Lean Construction (IGLC), 18–20. https://iglc.net/Papers/Details/821 Prieto, R. (2021). Construction 4.0 (Technology). NAC Executive Insights. https://www.researchgate. net/publication/348690890_Construction-40 Razkenari, M., Fenner, A., Shojaei, A., Hakim, H., & Kibert, C. (2020). Perceptions of ofsite construction in the United States: An investigation of current practices. Journal of Building Engineering, 29, 101138. https://doi.org/10.1016/j.jobe.2019.101138 Reigle, R. F. (2001). Measuring organic and mechanistic cultures. Engineering Management Journal, 13(4), 3–8. https://doi.org/10.1080/10429247.2001.11415132 Rogers, E. M., & Shoemaker, F. F. (1971). Communication of Innovations; A Cross-Cultural Approach. The Free Press. Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2019). Impacts of industry 4.0 technologies on Lean principles. International Journal of Production Research, 58(6), 1644–1661. https://doi.org/10.1080/00 207543.2019.1672902 Sanders, A., Elangeswaran, C., & Wulfsberg, J. P. (2016). Industry 4.0 implies Lean manufacturing: Research activities in industry 4.0 function as enablers for Lean manufacturing. Journal of Industrial Engineering and Management ( JIEM), 9(3), 811–833. https://doi.org/10.3926/jiem.1940 Sawhney, A., Riley, M., & Irizarry, J. (2020). Construction 4.0: An Innovation Platform for the Built Environment (1st ed.). Routledge. Sawhney, A., Riley, M., Irizarry, J., & Pérez, C. T. (2020). A proposed framework for construction 4.0 based on a review of literature. EPiC Series in Built Environment, 1, 301–309. https://doi. org/10.29007/4nk3 Schillewaert, N., Ahearne, M. J., Frambach, R. T., & Moenaert, R. K. (2005). The adoption of information technology in the sales force. Industrial Marketing Management, 34(4), 323–336. https://doi. org/10.1016/j.indmarman.2004.09.013 Schuh, G., Anderl, R., Dumitrescu, R., Krüger, A., & Hompel, M. ten. (2020). Industrie 4.0 maturity index: Managing the digital transformation of companies – Update 2020. Acatech STUDY. Seaden, G., Guolla, M., Doutriaux, J., & Nash, J. (2003). Strategic decisions and innovation in construction frms. Construction Management and Economics, 21(6), 603–612. https://doi. org/10.1080/0144619032000134138 Seed, W. R. (2015). Transforming Design and Construction: A Framework for Change. Lean Construction Institute. https://Leanconstruction.org/media/learning_laboratory/new/old/TDC-Book.pdf Sony, M. (2018). Industry 4.0 and Lean management: A proposed integration model and research propositions. Production & Manufacturing Research, 6(1), 416–432. https://doi.org/doi.org/10.1080/2 1693277.2018.1540949 Tan, J., Tyler, K., & Manica, A. (2007). Business-to-business adoption of ecommerce in China. Information & Management, 44(3), 332–351. https://doi.org/10.1016/j.im.2007.04.001 Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of fndings. IEEE Transactions on Engineering Management, EM-29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463 Tsai, C.-A., & Yeh, C.-C. (2019). Understanding the decision rules for 3D printing adoption. Technology Analysis & Strategic Management, 31(9), 1104–1117. https://doi.org/10.1080/09537325.2019.1 584287 Ukobitz, D. V. (2021). Organizational adoption of 3D printing technology: A semisystematic literature review. Journal of Manufacturing Technology Management, 32(9), 48–74. https://doi.org/10.1108/ JMTM-03-2020-0087 Wu, P., Zhao, X., Baller, J. H., & Wang, X. (2018). Developing a conceptual framework to improve the implementation of 3D printing technology in the construction industry. Architectural Science Review, 61(3), 133–142. https://doi.org/10.1080/00038628.2018.1450727 Xu, G., Li, M., Chen, C.-H., & Wei, Y. (2018). Cloud asset-enabled integrated IoT platform for Lean prefabricated construction. Automation in Construction, 93, 123–134. https://doi.org/10.1016/j. autcon.2018.05.012

5 A SHARED RESPONSIBILITY Ethical and Social Dilemmas of Using AIintheAEC Industry Paz Arroyo, Annett Schöttle, and Randi Christensen

Introduction A signifcant part of Lean Construction 4.0 is the extensive use of smart and digital technologies which will lead to growth in data and use of artifcial intelligence (AI) to make sense and take advantage of the data. At the same time, Lean Construction advocates for respect for people. Therefore, there is a need to study the ethical and social considerations for employees in the architecture, engineering, and construction (AEC) industry and for society in general. AI has been on the horizon for our society since the late 1950s and recently projects and companies in the AEC industry have incorporated it to support daily work routines. Recent debates in the media have covered the use of AI in social media. For example, Facebook (now Meta) has been accused of using biased and untransparent algorithms, which have led to considerations on how AI is applied and used in the AEC Industry (Arroyo et al., 2021). At the same time, it is worth noting that the World Economic Forum perceives barriers to digital inclusivity as one of the main risks in their 2021 Global Risk report (McLennan, 2021), and the risk from automated biases is assumed to accelerate as the amount of data generated is expected to nearly quadruple by 2025 to 175 Zetta Bytes (Reinsel et al., 2018). The AEC industry should both engage in this debate and take a proactive standpoint on how to use and improve the use of AI in the industry. As a start, we see the need to pose questions to us, the international AEC community, to discuss how ethical and social dilemmas related to the use of AI will afect people in our industry. AI is used in several aspects of our daily private and business lives. What we choose to hear and what we see in the media is somehow connected to an algorithm that can infuence our information access and thus, our opinions. As Lean practitioners, we seek to optimize processes, to work efciently and to utilize technology where it enables us to either deliver more value or reduce waste, by focusing on fow efciency based on the mantra ‘respect for people and resources’. Therefore, we need to make sure we preserve this respect in the future of the AEC Industry, as Lean Construction 4.0 and AI (Schia et al., 2019) will transform our interactions. We believe that as a society we should discuss all perspectives on using AI to both obtain benefts and at the same time still keep asking the more fundamental and ethical questions. Data is not an objective, cold, unbiased fact, it is created and designed by (fallible) humans (Ely, 2015). Thus, 68

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this should not be a discussion limited to AI developers who might be trying to maximize their commercial value. Although developers might have the best of intentions in aiming to increase the productivity of the AEC industry, there might be unintended or unforeseen consequences resulting from their actions. In this chapter, we explore several questions associated with the current and future application of AI in the AEC industry and we show some of the ethical and social dilemmas that can easily occur. An earlier version of this chapter was previously published in the Proceedings of the 29th Annual Conference of the International Group for Lean Construction (IGLC) (Arroyo et al., 2021). We hope this exploratory work sparks other researchers and practitioners to have an honest and critical conversation about AI uses within the AEC community.

Research Approach In this chapter, exploratory research is used, which is open-ended and interactive in nature; the structure is not predetermined, as opposed to confrmatory research (Stebbins, 2001). This type of research is appropriate for questions as to how and why, in felds where there is an absence of previous research data, as is in the case of this chapter. The research question is how AI-based decisions in applications in the AEC industry can present social and ethical dilemmas. Our purpose is to discuss the ethical and social dilemmas of using AI in the AEC industry and the implications to preserving the respect for people working in construction and society. The authors have reviewed the literature to frst defne AI as a feld of study, and to explain the ethical and social dilemmas. Then, we summarized the current and potential future uses of AI in the AEC industry. Finally, a discussion section is presented based on questions that emerged by associating AI applications in the AEC industry with ethical and social dilemmas in decision-making. In addition to the literature review, the authors interviewed AI experts, one from DPR Construction and one from COWI.

Background Information How AI impacts humans and human behavior is of high importance. At the moment, there is little discussion regarding the efects of AI on human behavior and especially so for AIbased decisions in the AEC industry. In this section, AI is defned through literature and the distinction between ethical and social dilemmas is discussed in the context of applications of AI to the AEC industry.

What Is AI? Many AI defnitions are available in the literature. Russell and Norvig (2009) studied several defnitions of AI and classifed them into systems (including machines) which think or act like a human, and systems which think or act rationally. These defnitions create a need to study the defnition of human thinking and acting, and how we defne what rational acting or thinking is. Therefore, they stated that artifcial intelligence or AI, attempts to understand intelligent entities. Thus, one reason to study it is to learn more about ourselves. But unlike philosophy and psychology, which are also concerned with intelligence, AI strives to build intelligent entities as well as understand them. 69

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To provide a satisfactory operational defnition of intelligence to judge whether a system is acting like a human or not, Alan Turing (1950) designed the so-called Turing test. The test is passed if the system can demonstrate the following capabilities: • • • •

Natural language processing to enable it to communicate successfully in English (or some other human language). Knowledge representation to store information provided before or during the interrogation. Automated reasoning to use the stored information to answer questions and to draw new conclusions. Machine learning to adapt to new circumstances and to detect and extrapolate patterns.

AI uses machine learning (ML), logical systems, and knowledge-based systems to reason about the world in many applications, such as driving a robot or recognizing images. AI algorithms have goals to predict how actions will afect the model of the world and make decisions on actions that will best achieve that goal (Dreyfus et al., 2000). In a famous critique of AI research, Dreyfus (1972) published What Computers Can’t Do. Dreyfus (1972) argued that human intelligence and expertise depend primarily on unconscious processes rather than conscious symbolic manipulation, and that these unconscious skills can never be fully captured in formal rules. Dreyfus’ point is still valid to date: How can we use algorithms that try to replicate human behavior if we have not yet understood it? On the other hand, some AI algorithms are so complex that we cannot even understand them fully in retrospect. Russell and Norvig (2009) also point out that AI has produced many signifcant and impressive products, even at this early stage in its development. Although no one can predict the future in detail, clearly, AI will have a huge impact on our everyday lives and on the future course of civilization. Harari (2018) raises questions about the diference between humans and AI, and asks what would happen if AI achieved superhuman intelligence: Would AI then be more valuable than humans?

What Are Ethical and Social Dilemmas? An ethical dilemma occurs when a decision has to be made between two alternatives in which both alternatives are not fully acceptable ethically. For example, you must choose between two road designs. One alternative is perceived as safer by the road users, but involves eliminating a protected natural area (e.g., an ancient forest). Which are you going to choose, perceived safety or preserving a natural resource? How would an AI algorithm judge these ethical decisions? ‘Ethical dilemmas will often result in unethical behavior’ (Sims, 1992, p. 510). Ethical behavior means we are following the values, norms, and rules of our society. Schermerhorn (1989) introduced four perspectives on ethical behavior: (1) Justice (act based on fundamental rights); (2) Moral rights (fair treatment); (3) Individualism (long-term self-interest); and (4) Utilitarian (best for most people). Those four perspectives can create dilemmas based on long-term vs. short-term advantage and which perspective is prioritized. In addition, the American psychologist Lawrence Kohlberg defnes the highest level of moral reasoning, ‘Postconventional moral reasoning’, as the ability to question ‘What is ethically right?’ and ‘What are the wider long-term consequences?’ An example is the importance of applying the spirit of the law rather than the letter of the law. Thus, when we use AI in the AEC industry, we need to consider how the AI is designed, how it operates and learns, and how the algorithm works in the context of ethical and social problems. AI must cope with 70

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dilemmas and, in comparison with human decision-making, which can be quick to make judgments, are we critically assessing the information AI delivers? Also, when talking about ethical behavior, we talk about the ethical standards of a society (Siau, 2020), but an ethical standard for one country can be diferent for another county. So which standard will be the ultimate standard? An AI algorithm that works in every country must overcome many cultural challenges. A social dilemma occurs in a situation where there is a confict between self and collective interest (Dawes & Messick, 2000; Van Lange et al., 2013). For example, a social dilemma exists if an architect and a mechanical and electrical designer are working on the same project, but locally optimizing based on each separate perspective. The two designs are interdependent and therefore the fragmented views may result in a suboptimal and inefcient building. If a generative design is used to optimize only one perspective, it may not be the best for the whole project. To sharpen our way of thinking, we will in the next sections raise questions on potential ethical and social dilemmas in relation to use of AI in the AEC industry. The purpose of the questions is not to undermine the use of AI, or research within the area, and there might be perfectly good answers to the questions. The purpose is to bring awareness to the reader, by listing some examples of good questions about AI to ask oneself. If we as researchers and practitioners in the industry will apply and use AI as part of Lean Construction 4.0, we should also be willing to ask critical questions and refect on potential negative impacts. We need to train our ability to think outside the immediate use and beneft of AI and also assess what ethical and social dilemmas the use of AI might bring.

Current Uses of AI in Construction This section presents diferent current uses of AI in the AEC industry. Uses include but are not limited to automatic schedule generation for planning and control, design automation techniques as generative design, contractual document analysis, and facility management. These applications could be characterized as narrow AI, meaning they use a relatively simple ML algorithm to reach an outcome.

Generative Design Diferent AI algorithms have been used in construction projects, with the purpose of optimizing design. This type of AI application aims to generate numerous design options, which are not only optimized for aspects such as aesthetic, but also for engineering performance. Oh et al. (2019) used generative models to create design alternatives and topology optimization to help designers choose a design alternative in an iterative manner. According to Oh et al. (2019), their framework represents better aesthetics, diversity, and robustness compared to those resulting from previous generative design methods. Newton (2019) argued that Generative Adversarial Networks (GANs) are an emerging research area in deep learning that have demonstrated impressive abilities to synthesize designs; however, their application in architectural design has been limited. Newton (2019) tested the creation of 2D and 3D designs from specifc architectural styles and experimented on how to train algorithms to a desired design to control the ‘fdelity’ and ‘diversity’ of the design. Our questions here are related to generative design, not only to Newton’s research, but to all future research in this feld: How do you defne a successful design? What do ‘fdelity’, ‘diversity’ or any other design’s attribute mean? How do you measure them? And who is the 71

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judge of those designs? Can designs be transferable from project to project? Again, this raises the question of how biased the algorithm is. How does the optimization algorithm make trade-ofs between designs and who decides which factors are considered in the decision? Usually, optimization algorithms seek to optimize one or two parameters or have a priority system. How aware are designers of the AI Algorithm’s assumptions? More broadly, what is the role of designers if the design is generated by an algorithm?

Claim Analysis According to Riad, Arditi, and Mohammadi (1991), delays are the major cause of construction disputes; mediation is usually an efective solution, but a preventative and comprehensive approach is lacking. Riad et al. (1991) developed an AI algorithm for time-based claim management, which analyzes disputes that arise due to diferent types of delays (excusable/compensable, excusable/non-compensable, non-excusable; independent, concurrent, serial) and helps determine the responsibility of each party. The algorithm utilizes a procedure called ‘Time Impact Analysis’ and involves the use of network-based scheduling tools to identify, quantify, and explain the cause of a schedule variance (Riad et al., 1991). We propose some questions to invite refection. Who developed the AI algorithm used in case of a claim? Is the algorithm trustworthy? Finally, is the AI algorithm carrying the bias of its creator?

Environmental Performance and Sustainability Fernandes et al. (2019) presented the development of an artifcial neural network-based model to predict the environmental performance of buildings in Brazil, in terms of energy, water, and waste generation. Fernandes et al. (2019) argued that these equations help managers obtain a benchmark based on the current building stage and how they can promote improvements in its future environmental performance. We would like to induce refection on potential ethical issues, given that the algorithms are based on data from other buildings’ performance. For example, is the benchmark appropriate based on current performances? Also, how big should a data set be required to generate the benchmarks? Should building design aim to perform well based on the goals of society, in terms of the use of natural resources available and social comfort? With the rapid development of the sustainability agenda across the world, it is important that the parameters by which we judge diferent solutions also change to avoid data drift. The Paris agreement committed participating countries to assess their targets for Carbon Emissions every fve years. Therefore, taxes on carbon are expected to raise and play a signifcant part of design and selections for bids. Also, other parameters such as biodiversity and circularity are expected to become important factors in design decisions in the years to come. When AI are using old datasets for making decisions on our behalf, we should ask whether this will be in correlation with the demands of society. Should ‘new’ data have a higher impact than ‘old’ data sets for the ML? What we defne as adequate or good today might be inadequate or unacceptable next year. Will AI really be able to adapt to new situations? Who can ensure that the algorithm will be adjusted?

Robotics and AI Robotics is the physical use of AI. They are mechanical systems that can help to remove tedious and repetitive tasks. For example, Dusty Robotics is a layout robot that uses AI to 72

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optimize the path of the layout printing process. The robot also decides where to start the layout. It uses the inputs of coordinated drawings among diferent specialties (DPR Construction, 2021). Another example is Canvas, a robot that performs mudding and sanding in drywall surfaces. The operator decides which walls and part of the wall the robot will work on, and the AI algorithm decides the spray pressure, path, and fow rate. The robot AI can recognize the surface and decide which area to work on, which can go up to Level 5 fnish, which is the highest standard in terms of obtaining a smooth surface (Hedmond, 2021). According to Roedel (2021), labor shortage is an existential problem for the AEC industry, one that robots can help ease, by allowing scaling and accelerating repetitive processes, and reducing costs. In terms of safety, some people argue that robots can avoid repetitive unsafe tasks for personnel, such as working at height, or in a multitude of stressful situations. This also has the potential to allow for a more diverse workforce on site, since people with disabilities may now be able to perform tasks otherwise impossible for them. Roedel (2021) also argues that just the use of robots itself could motivate younger generations to work in the AEC Industry. To better use robots on site, we need to analyze the status of work they can do and the interaction they need with site workers. Which kind of work activities can be done by robots? How does the job description of a worker on site change?

Use of Digital Twins ‘Digital Twin’ is a term widely used in the AEC industry, but it is recognized that it consists of three elements: a physical artifact, a digital representation, and some connections between the two (Tao, 2019). Thus, a digital twin is more than just a BIM model, and any change in the physical twin also means a change in the digital twin (Sacks et al., 2020). By having digital representations of physical assets and the comparison of monitoring data with digital simulations, we can compile knowledge of likely behaviors of assets and use it in scenario-testing before making non-reversible decisions. Digital twins can be used, for instance, in efective decision-making in relation to planning and detailed design during construction (Sacks et al., 2020), and as input to design by assessing the structure’s resilience to, e.g., climate change (Faber, 2020). Based on the developed digital twin, it is possible to track the operation and maintenance of the building. At the same time, this is an ethical dilemma, because a continued electronic access to a large and complex computerized model might require a large amount of energy and is therefore, against the trend of energy saving. In general, the more data volume we produce and use, the more data centers need to be built and thus, the more electrical energy is needed to support it. It seems likely that tech giants could see a commercial model in collecting data from our built environment, e.g., from street views, and through ML reach conclusions on how we should plan and build our societies in the future. When non-public bodies own digital infrastructure and interpret data through AI to create knowledge for the support of decisions, we might face a democratic problem. How would it impact on our democracies and ability to make important societal decisions, if investment choices are to be based on AI provided by private institutions with commercial interests?

Planning and Control There are several AI applications currently being used in construction projects, especially around project scheduling analysis performed based on ML algorithms (ENR, 2021). One of the software vendors to optimize schedules (ALICE) states in an ENR article published in 73

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2021 that the aim of this technology is to help teams avoid tedious planning tasks. This article pointed out ‘Why would anyone in their right mind want to spend time crunching all the constraints on a project? It’s mind-numbingly boring’. This technology, based on commercially available AI algorithms, extrapolates thousands of possible ways of executing a project by running simulations of a project’s 4D schedule and BIM, readjusting input variables, to be tweaked within the project ‘recipe’. Users adjust the inputs, and the AI algorithm tells them how it will afect the construction schedule. The software developer says that the idea is not to provide ‘decision-making capabilities’ to the algorithm; rather, it is about automating the process of generating possible alternate schedules (ENR, 2021). These algorithms can help optimize the project schedule based on certain parameters; for example, crane placement and task sequencing. But how does the algorithm work? Since these companies have commercial interests, the detailed understanding of how it works is not accessible to most people. Schia et al. (2019) carried out interviews and a case study where AI was used for planning of a project in Norway (implementing ALICE). The study concluded that, when it comes to AI, the human-AI trust will be the most decisive factor for a successful implementation. Furthermore, it will be difcult for a worker to understand how ALICE arrives at the output, and further trust the output itself. Currently, the algorithm depends on human input data, but in the future when the AI algorithm has enough historical data, human input will no longer be necessary (Schia et al., 2019). This sparked other questions such as: Is the schedule optimization algorithm based on the critical path method? Does it use Lean principles? How collaborative is it? How does the algorithm balance diferent interests? Can workers input their preferences for task sequencing? How do we know whether it is successful?

Construction Progress Monitoring Many technology companies are trying to create products that help monitor construction progress and compare it with the plan and with the associated BIM model, many appealing to the notion of getting quick information without a tedious manual process. Some of these companies use AI algorithms for image recognition and to make rapid predictions of accuracy. An example is Buildots, a British-Israeli startup, that is developing an overall AI site inspector that matches images taken on-site against a digital plan of the building, by using video footage from GoPro cameras mounted on the hard hats of workers (Heaven, 2020). This is not the only company doing this type of monitoring. The benefts for construction management are evident, with no need for managers to walk the site to have detailed progress monitoring compared with the plan. However, there are some ethical considerations that appear, such as whether this would be seen as a form of surveillance for construction workers? Is that consistent with respect for people? What decisions will be made based on productivity data and will workers’ opinions be considered, if they fnd a better or more helpful way of improving their productivity? Is monitoring the new go to Gemba? How will the tacit knowledge of the workers be able to qualify the explicit data?

Safety Applications Another emerging AI use in construction is the use of image recognition algorithms to predict the probability of accidents. The basic idea is that a construction project shares pictures of the process, and an AI algorithm will predict the probability of accidents happening in the project based on historic injury data. The more data the algorithm has, the better a deep learning algorithm will work (Knight, 2019). A commercial example is Smart Vid, which 74

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makes use of a deep-learning algorithm trained on construction site images and accident records. Smart Vid monitors a new construction site and fags situations that seem likely to lead to an accident, such as a worker not wearing gloves or working too close to a dangerous piece of machinery (Knight, 2019). This has several ethical aspects. On one hand, preventing safety incidents is very important, but on the other hand, it requires detailed surveillance of the construction workers. However, as the article points out, the end goal is ‘using AI to monitor, quantify, and optimize work life. Increasingly, companies are fnding ways to track the work that people do and are using algorithms to optimize their performance’ (Knight, 2019). This poses several ethical challenges, since in the future, AI algorithms will supervise feld workers. Does the need for increased safety justify potential worker’s surveillance?

Discussion In this section, we discuss the thoughts and concerns the authors have around AI and how AI uses could lead to ethical and social dilemmas. We acknowledge the potential benefts of current and future uses of AI. However, in this discussion, we are attempting to articulate our point of view, not as a defnitive conclusion, but as a starting point. We need to learn more, and we hope to encourage others to explore the challenges of using AI in the AEC industry and to consider the consequences for the people that are afected by it. Table 5.1 presents a relationship between the dilemmas presented in AI uses and the discussion questions.

What Is the Source of Data? AI algorithms need data to learn. The data AI uses to allow, for instance, the design of solutions or decision-making, might be created and selected based on biases (Leavy, 2018). In a world where society is being more dependent on big data, blind spots or missing details in datasets could lead to half-truths in the best-case scenario, or our biases can be reinforced in the worst case (Perez, 2019). As these algorithms fnd more uses in the AEC industry, they will also need large amounts of data to be trained. Having access to data means having Table 5.1 Social and ethical dilemmas and discussion questions Dilemma

Discussion questions and key points

Ethical dilemma (alternatives are not fully acceptable ethically)

What is the source of data? Artifcial intelligence (AI) applications risk trade-ofs among ethically unacceptable alternatives.

Ethical behavior (following values, norms and rules of the society)

Can we trust AI decisions? AI applications should follow society’s values and norms as they evolve. There is a risk that AI cannot adjust at the same pace as ethical norms.

Social dilemma (confict between self and collective interest)

Are AI algorithms biased? AI applications risk refecting their creators’ biases when prioritizing between conficting interests. Do we need to please the algorithm? AI algorithms can infuence behaviors when rewarding them to get a desired outcome. Does AI impact project team motivation? We may be creating a problem regarding empowerment and motivation if AI makes more decisions for us.

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access to better algorithms and the more data is used the more powerful the algorithm will be. Additionally, the AI decision-making process is impacted by the political, economic, social, technological, environmental, and legal dimensions, as well as by ethical boundaries and ethical codes of conduct (Brendel, 2021). ‘To make an ethical decision [the machine] must know what an ethical confict is [a situation where ethical rules clash with an agent’s own self-interest]’ (McDermott, 2008, p. 6). Furthermore, based on the database used and diferent techniques used, the self-learning quality of AI will vary between diferent organizations (Brendel, 2021). AEC companies will need to decide which data to collect, and for what purposes. Finally, ‘When we assume information is objective, we forget that information doesn’t create itself ’ (Flores, 2012, p. 43). Often there is a lack of consideration of whether all of a data set is useful and necessary. One point of interest can be that we do not really know if we might need the data in the future. Another point is the fear of missing something by not seeing the whole picture, having information asymmetry or by being limited by our cognition. But can AI consider everything a decision requires, such as diferent political, moral, and social interests as well as biases? Who decides which algorithm will be more successful? Which algorithm has more commercial value? Which algorithms are going to be backed by venture capital? Which companies will lead the competition on AI development?

Can We Trust AI Decisions? AI replicates patterns because it is learning from patterns that exist. The algorithm is learning based on the status quo of knowledge, but we discover and learn new things on a daily basis. Thus, we are imperfect, and our knowledge is not all-embracing, and neither is AI. Thus, new knowledge and experiences change our values and norms and need to be considered in the algorithm. So, are we trusting AI too much? Should we be more critical? How does it impact our daily work and businesses? What happens if someone believes that the algorithm is incorrect? What happens if you do not follow a recommendation made by the algorithm on a task sequence or schedule? What happened to those critical of AI in an organization? Will designers be ultimately responsible for the design, or will they be responsible only for providing data to feed the algorithm? Does the algorithm know what is best for us? There are no guidelines to ensure AI is trustworthy, but making sure AI can be falsifed is an essential factor to improve trustworthiness (Floridi et al., 2020). As AI is applied where the amount of data makes it almost impossible for the human brain to process it, it will be impossible to test AI for all scenarios. But to test some extremes, e.g., in relation to safety, should be mandatory as part of approval procedures. In addition, the result should be tested against the assumptions of the AI. But how often do we know the assumptions and simplifcations included in the software we use? Thinking that the algorithm is objective may not be correct if the data that was used to train it perpetuates subjective behaviors based on outdated belief systems. For example, if you want to select a successful project team, you will only be able to judge project teams based on the data available regarding their past performance and interactions, not on their human potential.

Are AI Algorithms Biased? Many people think that AI can prevent or avoid biases and create an objective decision, with data that is fully transparent and traceable. It is well-known that humans carry biases: if you think, you have biases. For example, we tend to more easily believe the opinions from 76

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people that have similar backgrounds and life experiences to ourselves (Nickerson, 1998). Even when we may be aware of our biases, we still cannot get rid of them; intensive training and a diverse group is needed to counteract them. AI is created by humans and, therefore, biases of its creators are applied to a greater scale when using the algorithms (Cassel, 2016). An example of this is that AI for face recognition does not work for people of color as they do for white people. In a review of 189 facial recognition algorithms, representing the majority in the industry, researchers found that black and Asian faces were 10 to 100 times more often mistaken than white faces, and it did worse for black women (Rauenzahn et al., 2021). One very well-known bias is groupthink. Groupthink occurs when group members avoid disagreement and thus adjust or subordinate their individual opinion to achieve consensus ( Janis & Mann 1979, 1982; Johnson & Johnson, 2009). This results in weak decisions as alternatives are not considered, being ‘caused by a lack of diverse thinking’ (Schöttle et al., 2019, p. 799). Is the algorithm catalyzing groupthink? Are we going to avoid difcult conversations by relying on solutions from AI? Will it lead us to overlook important ethical dilemmas, only because we do not see the confict? Are we able to have authentic discussions and productive conficts in the workplace if we rely on AI? McDermott (2008, p. 6) argued that the mere fact that the program has an explicit representation of the ethical rules, and that the humans who wrote or use the program know the rules are ethical does not make an ‘explicit ethical reasoner’ an ethical agent at all. For that, the agent must know that the issues covered by the rules are ethical. In addition, the creators of AI should pay attention to and consult with users of the AI as both groups, users and developers, might not rely on the same set of values and the AI might therefore lead to undesired outcomes, which the users might not be allowed to ignore or modify (Floridi et al., 2020). So, is an algorithm able to make an ethical decision without free will and emotions? Is the algorithm able to decide in a social complex setting? Does the person programming the algorithm have a full understanding of the social complexity and are they able to program such an algorithm? Is the solution creative? Does the solution consider new innovations or produce an innovative solution? As written above, decision-making often results in trade-ofs. But how does the AI decide in terms of trade-ofs and does an AI have decision-making autonomy?

Do We Need to Please the Algorithm? Humans have the power to develop algorithms, but people that work for an algorithm often do not understand the outcomes. For example, Harari in his book Homo Deus (2018) describes how Google’s search algorithm is so complex that we cannot predict what the search result will be. This applies even more if someone wants to create a successful website. They have to do it, so it is promoted by the algorithm (the website has to please the algorithm, not directly the humans that it is trying to reach) and people then just see what the algorithm likes. Thus, our world is shaped by how we pleased the algorithm. There is a clear risk with AI that if the outcome is easy to predict based on a set of inputs, it could lead to manipulation of data (Floridi et al., 2020). In the AEC industry, this issue can be created when a schedule is decided by an algorithm, and now the subcontractor has to please the algorithm to get a good evaluation. There may be the case that circumstances change and following the original plan may no longer be optimal. Another example that often occurs in practice is that structural engineering software prescribes an amount of reinforcement in a structure, which could lead 77

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to the use of more steel than necessary. Although structural engineers know this, they please the algorithm and thus produce waste. This causes a dilemma regarding sustainability and costs, and sometimes limits human creativity. Thus, could the urge to reach a desired outcome impact selection of input data? How do we ensure that the focus is on value creation and not on short-term success based on the desired outcome?

Does AI Impact Project Team Motivation? Another point that needs to be considered arises in terms of motivation. As known from the self-determination theory, autonomous motivation, necessary to accomplish engagement and self-interest for creative problem solving, is based on the fulfllment of the three psychological needs: autonomy, competence, and relatedness (e.g., Deci et al., 2017; Deci & Ryan,1985, 2014; Gagné & Deci, 2005; Ryan & Deci, 2000). All three factors will be impacted by AI. If we trust AI, are we really making decisions autonomously? If we rely on AI, are we going to have productive conficts from which we grow and strengthen our relationships? Are we going to learn from failure or is AI constraining humans from improving? What does collaboration in a project team look like when AI takes over? If we let AI decide for us, what does this do to our motivation and the performance of a project team (e.g., Schöttle, 2020)? Will AI take over all the tedious repetitive and unsafe work, and free up humans to deal with creative thinking? Or will humans rely on algorithms’ decisions about what to do next without questioning them, losing motivation, and transforming AI into a new superhuman?

How May Dilemmas Impact Lean Construction 4.0? It might not be possible to answer all the questions and dilemmas above. However, we have an obligation to refect on the questions and spend time considering the direct and indirect impact the use of AI has on our project, on our teams and society. We will need to constantly evaluate whether our AI systems deliver value and work efciently on behalf of our teams and society when moving to Lean Construction 4.0. In the digital age, we have access to enormous amounts of data and information, but it comes with a cost. As the Nobel Prize Laureate in economics Herbert Simon said in 1971, ‘In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients’ (Hendricks& Vestergaard 2019). Relying more on Big Data and the use of AI and ML leads to an extended responsibility for the industry to keep scrutinizing the data input, the algorithm, the outcome, and how these impact society. Even though most examples we presented were mainly related to ML, we need to also be prepared for other uses of AI and we need to question, not only what the benefts are, but also what may be the unintended consequences.

Conclusion This chapter presents examples of AI-based decisions in applications in the AEC Industry that can contain social and ethical dilemmas. There are many potential benefts to the use of AI in the AEC industry, from supporting better decisions, to optimizing schedules and reducing environmental impacts. As Lean practitioners, we want to make the design, planning, and construction process as efcient as possible, and, if AI can help do this, it should 78

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be part of our toolkit. But we should also be skeptical and ask questions to make sure we do not end up with an inappropriate solution, simply because it is too complicated to understand the process behind it. When we are busy, we are more likely to overlook potential conficts and biases. So, when using AI to work more efciently, we might fall into the trap of a social or ethical dilemma without exploring it, and risk ending up with an inappropriate solution despite our intentions. The authors believe that the future of the AEC industry will include more and more AI, as we get better at collecting data and training the algorithms. Setting standards for AI and how it is applied in our industry is not a matter for software developers alone. As we also know from the use of the Last Planner System, it is when we start to respectfully question other disciplines that we proactively identify waste and build on each other’s ideas to drive real innovative thinking. AI and ML will increasingly be applied in our industry, and we have an obligation to ensure this is done with respect to users and their individual uniqueness and integrity. This is a shared responsibility, and we all play a role in this whether we are designers, contractors, managers, or employees. If we collectively ensure a balanced and transparent design and construction process, we will allow ourselves and future generations to learn from our successes and failures. We want to invite the industry and the Lean community to engage in debating the benefts and potential pitfalls of using AI to improve the AEC Industry, to ensure the optimization is balanced, and consider benefts for the wider society. What is ethically right? What are the wider long-term consequences?

References Arroyo, P., Schöttle, A., & Christensen, R. (2021). The Ethical and Social Dilemma of AI Uses in The Construction Industry. Proceedings of the 29th Annual Conference of the International Group for Lean Construction (IGLC29), Lima, Peru, 227–236. https://doi.org/10.24928/2021/0188 Brendel,A.B., Mirbabaie, M., Lembcke, T.-B., & Hofeditz, L. (2021). Ethical Management of Artifcial Intelligence. Sustainability, 13, 1974. https://doi.org/10.3390/ su13041974 Cassel, D. (2016). Artifcial Intelligence Research is Awash in Dudes, and That Could Be a Problem. The NewStack. Accessed: 10/18/21. Dawes, R. M., & Messick, D. M. (2000). Social Dilemmas. International Journal of Psychology, 35(2), 111–116. https://doi.org/10.1080/002075900399402 Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2017). Self-Determination Theory in Work Organizations: The State of a Science. Annual Review of Organizational Psychology and Organizational Behavior, 4(1), 19–43. Deci, E. L., & Ryan, R. M. (1985). Intrinsic Motivation and Self-Determination in Human Behavior. New York: Plenum Press. Deci, E. L., & Ryan, R. M. (2014). The Importance of Universal Psychological Needs for Understanding Motivation in the Workplace. In M. Gagné (Ed.), The Oxford Handbook of Work Engagement, Motivation, and Self-Determination Theory (pp. 13–32). Oxford University Press. DPR Construction (2021). DPR and Dusty Robotics Collaborate to Set Up Success for Craft. Retrieved from https://www.dpr.com/media/blog/dpr-and-dusty-robotics-collaborate-to-set-up-successfor-craft?utm_campaign=Construction%20Technologies&utm_content=185796124&utm_ medium=social&utm_source=twitter&hss_channel=tw-36725949. Accessed 11/10/2021. Dreyfus, H. (1972). What Computers Can’t Do: The Limits of Artifcial Intelligence. HarperCollins. Dreyfus, H., Dreyfus S. E., & Athanasiou, T. (2000). Mind Over Machine. Simon and Schuster. Ely, K. (2015). The World Is Designed for Men: How Bias Is Built Into Our Daily Lives. HH Design. Retrieved from https://medium.com/hh-design/the-world-is-designed-for-men-d0664065449. Accessed: 10/26/2021. ENR (2021). How Artifcial Intelligence Can Transform Construction. Retrieved from https://www.enr.com/ articles/51190-how-artifcial-intelligence-can-transform-construction. Accessed: 10/26/2021.

Paz Arroyo et al. Faber, M. H. (2020). Towards a New Paradigm in the Governance and Management of the Built Environment. IABSE Conference Seoul 2020: Risk Intelligence of Infrastructures. International Association for Bridge and Structural Engineering, 10–17. Fernandes, L. L. A., Rocha, M. J., &, Costa, D. B. (2019). Prediction of Environmental Performance Indicators for Construction Sites Based on Artifcial Neural Networks. Proceedings of the 27th Annual Conference of the International Group for Lean Construction (IGLC), Dublin, Ireland, 1413–1424. https://doi.org/10.24928/2019/0248 Flores, F. (2012). Conversations for Action and Collected Essays: Instilling a Culture of Commitment in Working Relationships. CreateSpace Independent Publishing Platform. Floridi, L., Cowels, J., King, T.C., & Taddeo, M. (2020). How to Design AI for Social Good: Seven Essential Factors. Science and Engineering Ethics, 20, 1771–1796 Gagné, M., & Deci, E. L. (2005). Self-Determination Theory and Work Motivation. Journal of Organizational Behavior, 26(4) 331–362. https://doi.org/10.1002/job.322 Harari, Y. N. (2018). Homo Deus: A Brief History of Tomorrow. Random House. Heaven, W. D. (2020). AI That Scans a Construction Site Can Spot When Things Are Falling Behind. MIT Technology Review. Retrieved from https://www.technologyreview.com/2020/10/16/1010617/ ai-image-recognition-construction-computer-vision-costs-delays/. Accessed 11/12/20201. Hedmond, S. (2021). Meet Canvas, the Drywall Finishing Robot Ofering Level 5 Quality. Retrieved from https://www.constructionjunkie.com/blog/2021/2/9/meet-canvas-the-drywall-fnishing-robotofering-level-5-quality. Accessed 11/12/20201. Hendricks, V. F., & Vestergaard, M. (2019). Reality Lost: Markets of Attention, Misinformation and Manipulation. Springer Nature. Janis, I.L., & Mann, L. (1979). Decision Making: A Psychological Analysis of Confict, Choice, and Commitment. Free Press. Janis, I.L. (1982). Groupthink: Psychological Studies of Policy Decisions and Fiascoes. 2nd ed., Houghton Mifin. Johnson, D.W., & Johnson, F.P. (2009). Joining Together: Group Theory and Group Skills. 10th ed., Pearson. Knight, W. (2019). Artifcial Intelligence Sees Construction Site Accidents Before They Happen. MIT Technology Review. Retrieved from https://www.technologyreview.com/2019/06/14/134944/ artifcial-intelligence-sees-construction-site-accidents-before-they-happen. Accessed 11/09/2021. Leavy, S. (2018). Gender Bias in Artifcial Intelligence: The Need for Diversity and Gender Theory in Machine Learning. ACM/IEEE 1st International Workshop on Gender Equality in Software Engineering, 27 May–03 June. Gothenburg, Sweden, pp. 14–16. McDermott, (2008). Why Ethics Is a High Hurdle for AI. North American Conference on Computers and Philosophy (NA-CAP). Bloomington. Indiana. McLennan, M. (2021). The Global Risks Report 2021, 16th Edition. World Economic Forum. Newton, D. (2019). Generative Deep Learning in Architectural Design. Technology Architecture+Design, 3(2), 176–189. https://doi.org/10.1080/24751448.2019.1640536 Nickerson R. S. (1998). Confrmation Bias: A Ubiquitous Phenomenon in Many Guises. Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037%2F1089-2680.2.2.175 Oh, S., Jung, Y., Kim, S., Lee, I., & Kang, N. (2019). Deep Generative Design: Integration of Topology Optimization and Generative Models. Journal of Mechanical Design, 141(11). https://doi. org/10.1115/1.4044229 Perez, C. C. (2019). Invisible Women. Chatto & Windus. Rauenzahn, B., Chung, J., & Kaufman, A. (2021). Facing Bias in Facial Recognition Technology. Retrieved from https://www.theregreview.org/2021/03/20/saturday-seminar-facing-bias-in-facial-recognition-technology/ Accessed: 1/4/2022. Reinsel, D., Gantz, J., & Ryding, J. (2018). The Digitalization of the World, from Edge to Core. IDC White Paper, 1–28. Riad, N., Arditi, D., & Mohammadi, J. (1991). A Conceptual Model for Claim Management in Construction: An AI Approach. Computers & Structures, 40(1), 67–74. Roedel, H. (2021). Personal Interview on 10/19/2021. Russell, S. J., & Norvig, P. (2009). Artifcial Intelligence: A Modern Approach 3rd Edition. Upper Saddle River, New Jersey: Prentice Hall. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and Extrinsic Motivations: Classic Defnitions and New Directions. Contemporary Education Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020

Ethical and Social Dilemmas of using AI Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with Digital Twin Information Systems. Data-Centric Engineering, 1, e14-1-26, https://doi.org/10.1017/dce.2020.16 Schermerhorn, J. I. (1989). Management for Productivity. New York: John Wiley. Schia, M. H., Trollsås, B. C., Fyhn, H., & Lædre, O. (2019). The Introduction of AI in the Construction Industry and Its Impact on Human Behavior. Proceedings of the 27th Annual Conference of the International Group for Lean Construction (IGLC), Dublin, Ireland, 903–914. https://doi. org/10.24928/2019/0191 Schöttle, A., Christensen, R., & Arroyo, P. (2019). Does Choosing by Advantages Promote Inclusiveness in Group Decision-Making? Proceedings of the 27th Annual Conference of the International. Group for Lean Construction (IGLC), Dublin, Ireland, 797-–808. https://doi.org/10.24928/2019/0209. Schöttle, A. (2020). What Drives Our Project Teams? Proceedings of the 28th Annual Conference of the International Group for Lean Construction (IGLC), Berkeley, CA, 313–324. https://doi. org/10.24928/2020/0094Siau, K., & Wang, W. (2020). Artifcial Intelligence (AI) Ethics: Ethics of AI and Ethical AI. Journal of Database Management. Journal of Database Management, 31(2), 74–87. http://doi.org/10.4018/JDM.2020040105 Sims, R. R. (1992). The Challenge of Ethical Behavior in Organizations. Journal of Business Ethics, 11, 505–513. https://doi.org/10.1007/BF00881442 Stebbins, R. A. (2001). Exploratory Research in the Social Sciences (Vol. 48). Sage. Tao, F., Zhang, M., & Nee, A. Y. C. (2019). Digital Twin Driven Smart Manufacturing. Academic Press. Turing, A. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–460. Van Lange, P. A. M., Joireman, J., Parks, C. D., & Van Dijk, E. (2013). The Psychology of Social Dilemmas: A Review. Organizational Behavior and Human Decision Processes, 120(2), 125–141. https:// psycnet.apa.org/doi/10.1016/j.obhdp.2012.11.003

6 THE INTERPLAY BETWEEN CONSTRUCTION SUPPLY CHAIN AND BIM THROUGH KITTING A Lean-Based View Zakaria Dakhli and Zoubeir Lafaj Introduction Competition in the construction sector is ferce. While exploring continuous improvement techniques, Lean Construction leads most construction players to innovate and rethink their design and execution methods. In his book, LePatner (2007) explains that 49.60% of the onsite work represented non-value-added activities. There are many underlying causes: human error, production waste during execution, delays in decision-making and instruction, lack of communication and planning, bad weather, lack of a skilled workforce, and misuse of equipment or materials (Memon et al., 2011). Additional studies show that 6% of the total construction budget is dedicated to handling and moving materials and equipment during the trades [2], 30% to 40% is devoted to materials and equipment (Evaristo & Kalev, 2015), 1% is lost due to theft on-site (FFB Survey, 2007), and 2% to 4% is dedicated to waste elimination (FFB Survey, 2009). Social issues are also added: construction materials handling is at the origin of a third of the accidents in the construction sector, and workers are required to walk up to 15 km per day on a construction site (Internal BYCN study). All these challenges have led the scientifc community and practitioners to consider new approaches to improve the supply chain performance of the construction sector. The report, entitled Reinventing Construction: A Route to Higher Productivity (McKinsey, 2017), which highlights the problem of productivity in construction and proposes seven levers that can boost productivity by 60 %. A 7% to 8% improvement is attributed to construction site logistics and supply chain management. (Sundquist et al., 2018) also stressed the crucial vector of the construction supply chain and seemed to be an excellent vector for improving the performance of construction sites. Construction logistics are still rife with many challenges. For example, companies are not able to inform their customers of the availability of the goods they supply. One of the major challenges is identifying which materials have already arrived at the project site and fnding the precise material in the site’s inventory. Construction companies undertake several projects simultaneously, for which they need relevant supply chain information. There are also many materials and subcontractors whose performance should be monitored. In addition to

DOI: 10.1201/9781003150930-8

Interplay between Construction Supply Chain and BIM

monitoring for reporting and control purposes, performance measures can also be used to help the company improve its productivity over time. One of the under-investigated practices in construction logistics is “Kitting”. Kitting is a process in which the components are packaged and delivered together as a single unit. In other words, it is a method of supply of clustering elements intended to be assembled. It means delivering a package composed of construction products intended to be utilized on the site for construction.

Construction as a Supply Chain Process Logistics is defned as the process of planning, implementing, and controlling procedures for the efective and efcient transportation and storage of goods, including services and related information, from the point of origin to the point of consumption. Based on this defnition, logistics is part of supply chain management, including procurement, processing operations, and customer relations (Huart & Estampe, 2018). Construction logistics is an inherent component of construction practice. Moving in and out materials is the most apparent form of construction logistics. Financial and information fows are also more challenging to assess and track meticulously in a construction project because of the large number of transactions in a project lifetime (sometimes even in a single day). A signifcant number of construction managers do not perceive logistics as a critical aspect of construction (Caldas et al., 2014) but consider it as a by-product when managing a construction project. In an interview conducted with fve construction companies to evaluate how they manage construction logistics, we found that most construction managers are not aware of material management techniques and practices such as just-in-time ( JIT) and pull planning (Dakhli et al., 2016). Moreover, the responses highlighted the problem of tracking inventory on-site; inventory issues are common, such as broken products, stolen products, and unused products (Domingos et al., 2014). This study also showed that such issues should be addressed and well-prepared upstream, and construction managers’ time and focus should be dedicated to managing work progress and collaboration among stakeholders. The delivery of products on-site depends on the coordination agreed at the beginning of the project with the suppliers. How to source on-site demands is generally the responsibility of the product supplier; the latter should, with the support of the general contractor, fnd a way to ensure efective delivery of products to the site. In some cases, the products are stored on-site (if on-site storage space is available). If this is not possible, the supplier looks for a solution to reduce the transportation costs. Each supplier delivers its products to the construction site. Making several deliveries increases the cost for the supplier. As a result, the site contains many products that are stored, sometimes for several months. This action results in breakage, loss or stealing of products, and wasted time from moving products to the work location for use. In summary, this way of procurement is based on a massive storage on site for a long period of time.

Consolidation as an Enabler for Digital Technology A fair amount of research is conducted to explore the consolidation practice in construction. Court et al. (2006) implemented the Kitting system for a large mechanical and electrical project. They divided the components into three categories: A, B, and C. Category “A” comprises modular products that are directly shipped on-site. Category “B” is for components

Zakaria Dakhli and Zoubeir Lafaj

Figure 6.1

The Kitting supply chain is enabled by a third-party logistics “consolidation center”

from suppliers, and category “C” is for consumables. Those last two categories follow a fast-Kitting supply channel to the site. In various case studies, Kitting logistics are outsourced to third-party logistics providers via consolidation centers (Vaha et al., 2013). Consolidation centers are commonly adopted in the UK where the Kitting practice is supported by the government (EC Harris, 2013). This is due to difcult access to cities; the aim is to reduce transportation fows (in and out). Figure 6.1 describes the supply chain of the Kitting enabled by a consolidation center. Suppliers 1, 2, and 3 deliver their products to a consolidation center. The latter is responsible for the consolidation and storage until the construction manager requests the consolidated Kit delivery to the site. In parallel to this process, decision-makers decide which products not to include as part of the Kitting process. Those products could include modules or made-toorder products in case those products are needed at a specifc time during the project lifecycle. In Figure 6.1, this typology of products is associated with supplier 4.

What Kind of “Service” the Kitting Is? While Kitting could be viewed as an emerging practice in construction, it is commonly used within the manufacturing industry. Figure 6.2 presents the Product-Market Matrix, which expresses the relationship between product technology and the market regarding the strategy to be followed. The application of Kitting in the construction industry is considered “New”. As a result, Kitting practice could be assigned to the lower-left case of the Matrix entitled called Market Development. In this type of strategy, new uses in the market are enabled thanks to the introduced technology, and subsequent eforts should focus on clarifying the value-added opportunity of using this technology. While Kitting is not a “product” but rather a “service”, this strategy still applies.

Kitting System Confgurations Confgurations depend on the nature of stakeholders involved in the Kitting system and how materials are procured for the construction site. Figure 6.3 represents those kitting confgurations. The supply chain stakeholders include: 84

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Figure 6.2 The product-market matrix

• •

•

The supplier(s) of the general contractor is identifed as Supplier. Subcontractor(s): Subcontractors have their own suppliers. For the Kitting system, a subcontractor can either deliver materials to the consolidation centers directly from their factories or warehouses (if any) or ask their suppliers for direct delivery to the consolidation center. The Construction Company (CC): The construction company realizes a part of the work (stipulated in the contract) and subcontracts other parts.

The supplier is the entity that provides the necessary construction products, for example, windows and tiles. The supplier’s liability is limited to the supply and is therefore not responsible for the on-site installation. The construction company is responsible for installing the products supplied by its suppliers. The subcontractor takes over specifc aspects of the production in a construction project, commonly defned as a work package or trade, under the responsibility of the “general ‘contractor’” (in our case, the construction company). The construction company resorts to subcontracting for several reasons: lack of expertise to achieve the work package, insufcient human resources to carry the work package, and fnally, sometimes for cost reasons (it is less expensive to subcontract). The subcontractor specifcally deals with: • • •

The provision for its share of work. The creation of the installation plans, if necessary. Installation itself.

Overall, the construction company seeks to reduce costs by juggling diferent possibilities (Supply and installation/subcontracting). For Kitting, on-site implementation requires a clear defnition of the stakeholders involved and their respective supply chains for products provision. A total of four models are listed in Figure 6.3. In all those confgurations, the consolidation center is located of-site. In this example, three work packages are subjected to Kitting. Confguration 1 is composed of a single supplier for the three work packages. The supplier sends the necessary products to the consolidation center. The latter handles the packaging of kits and deliveries on-site based on the construction manager’s demand. Once the Kit is delivered on-site, the construction company starts the installation (execution of the work). 85

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Figure 6.3 Kitting system confgurations in construction

Confguration 2 is composed of multiple and separate suppliers. Each supplier delivers its products to the consolidation center. The latter handles the packaging and deliveries on-site. Finally, the construction company is responsible for the assembly/installation. Confguration 3 is applied when the construction company appoints a subcontractor for multiple work packages. In this case, a single subcontractor is responsible for (1) the delivery from the supplier to the consolidation center and (2) the assembly/installation on-site. The consolidation center is responsible for the delivery on-site. Confguration 4 is a variant of confguration 3 where several and separate subcontractors send their materials to the consolidation center. For a construction project, we’ll have a combination of those confgurations. For instance, a single supplier and multiple subcontractors involved in the Kitting system can be observed. 86

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Those confgurations imply that Kitting under a subcontracting practice approach makes the supply chain more complex. This is because subcontractors have their own set of suppliers. A supply chain is defned for each participant in the Kitting process (CC and its subcontractors). The latter comprises suppliers for the inbound logistics, the client-side for the outbound logistics and management of materials and equipment within the factory.

Lean Construction Concepts Linked to “Kitting” The implementation of the Kitting system requires knowledge of some concepts from the manufacturing literature. The kitting system is a catalyst for applying knowledge from production planning, for instance, supply planning synchronization with the construction schedule. Besides, providing a complete package (Kit) of materials requires designers to consider the construction process and involve the upstream construction actors, participants upstream and the third-party logistics as the components and accessories must all be included of-site. Finally, managing stocks by knowing precisely the quantity available at any given time and the forecast are required for a successful Kitting implementation. Some of the notions (by-products) related to Kitting are: a

Delivery to the point of use: This term means delivery to the exact location of use. In the case of building construction, the Kit will be delivered to the point of use in the building (foor number or zone number). Push and Pull systems: A push system is based on a projected production plan. The demand forecasting study needs to be as accurate as possible to do this. The concept behind Pull Planning (or Pull system) is to produce only the amount of goods that are susceptible to be sold while paying attention to avoid any stock-outs or overproduction (Galbraith et al., 1991). Contrary to a push system in which goods are produced and stocked before the client undertakes a purchase, the pull system is entirely dependent on demand. Just-In-Time: It is a method for organizing and monitoring the production (Polat & Arditi, 2005). It is well-known within the manufacturing sector and aims at reducing inventory and Work-In-Progress (WIP). This method is mostly used to manage the supply and consists of ordering the raw materials only when they are about to be used immediately. Accordingly, JIT’s purpose is to cut intermediate stocks. The JIT method is essentially a pull system. Thus, on the one hand, good quality coordination is required between the manufacturer and the client. On the other hand, good quality coordination is also required between the manufacturer and the supplier. Additionally, (1) a relatively precise forecast study of the needs in terms of production, (2) a reliable transportation and delivery network, and (3) strict management of the demands are needed for a good implementation of JIT.

Case Study 1: Renovation through Kitting without Consolidation Center Project Presentation The case study is a renovation project of 74 houses consisting of the main structure from the 1920 and 1930 and an extension from the early 2000s, including the sanitary part component (bathroom, WC, and kitchen). The project duration was 13 months.

Zakaria Dakhli and Zoubeir Lafaj Table 6.1 Interior and outside works Interior works

Outside works

From early February 2014 to late January 2015

From April 2014 to January 2015

Replacement of door frames

Coverage of tile roofng/complete renovation of the roofng

Plastering – insulation

Complete jointing of the facades + water repellent

Heating and ventilation

Painting of facades

Electricity Interior joinery

The work mainly consisted of replacing the roof, insulating the walls and ceilings from the inside, insulating the cellar ceiling, replacing the external joinery, repointing the brick facades, electrical compliance, replacing the heating and ventilation networks, and painting the rooms. The site required the temporary relocation of current tenants accompanied by a social mediator who provided a permanent link between the tenants and the work teams and met the tenants one by one to explain and anticipate the work that would take place in their homes. The work carried out can be divided into two parts, as shown in Table 6.1.

Logistics Planning The Kitting system in this project was planned as follows: • • • •

Weekly delivery of products to the site. Each supplier delivers on-site the necessary quantity for two houses. One week = delivery of two pallets for exterior carpentry, two pallets for Interior Carpentry and two pallets of plasterboards for two houses. Deliveries of Interior Joinery were grouped because of the limited work in the houses.

Three trades were involved in kitting: • • •

Interior joinery: the procurement and installation are realized by the construction company. External joinery: the procurement and installation realized by the construction company. Plastering-Insulation: Purchasing/Installation, with the purchase being realized by a subcontractor and the installation being done by another subcontractor.

The kitting system implemented did not use a consolidation center as shown in Figure 6.4. Interviews revealed that a consolidation center could: •

•

complicate the delivery management, consolidating deliveries and then delivering them to the construction site requires additional work. The project’s chosen suppliers may be able to consolidate their products in their facilities. add the additional costs. 88

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Figure 6.4 The kitting system confguration used in the case study

In conclusion, using a consolidation center should meet an identifed need. The critical insight here is that the consolidation center is not required to perform Kitting.

Scheduling The project team had one month of preparation and twelve months of work as stipulated in the contract. In practice, there were three months of preparation and nine months of work. • • •

The schedule allowed for the reception of two houses per week. One hundred percent of the units were delivered on time. Delivery of the 74 units without post-quality rework.

Te Kitting Stakeholders The construction manager accompanied the plastering trade to validate the necessary quantities per house. This work was particularly meticulous given several types of houses (exactly four types). For the order of Kits delivery, a notifcation is made by the construction manager one week before delivery to the suppliers (for the plastering/insulation and interior joinery work packages). For the windows work package, the construction manager gives the notice to start the manufacturing eight weeks before delivery on-site. Regarding the collaboration with suppliers and the plastering trade, the Kitting process is explained during the frst meeting (delivery/house of pallets). There is no need for signifcant coordination work with suppliers (e.g., Kit labeling work) as the kits arrive JIT for each house. The case study’s Kitting confguration does not require an external consolidation center: there is no cost inherent to the implementation of Kitting. It is not the case for other case studies where a cost related to the implementation of Kitting is inevitable.

General Analysis of the Case Study This renovation project did not use a consolidation center to prepare or send the Kits. The Kitting was not set up for all the trades. The reason for this choice was stated by the 89

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construction manager as “a distribution center was not necessary, but we could’ve used it”. The construction manager continued: “In my opinion, a consolidation center can be useful when several companies are involved in the kitting procedure, and especially if there are several suppliers per trade, which is the case in new construction projects rather than renovation ones”. The need to quickly deliver a home for new construction is not as crucial as it is for renovation projects, in which clients want their home renovated as quickly as possible. Kitting makes sense since it is necessary that the work be done in stages (in this case study, work was done per house). The team received deliveries in one week, with the correct quantity to deliver two houses per week. The Kitting was also necessary in this case study since not enough storage space was available in the working area. Moreover, direct delivery to each house helped limit handling times, and consequently, specialized workers were less fatigued and spent less time on non-value-adding activities. Finally, the construction manager also stated that kitting is privileged when the general contractor deals with many work packages that are not subcontracted and thus manages several supply chains. In summary, the kitting approach primarily answered an inherent need of the renovation site.

Case Study #2: Renovation through Kitting with Consolidation Centre Project Presentation The project involves the rehabilitation of fve houses in a downtown courtyard. Figure 6.5 shows a photo of the courtyard. Those houses are individual, two-story dwellings. The renovation was carried out in empty dwellings (without the presence of occupants) and in the middle of the city (parking conditions, storage, rubble removal, etc.). Access to the dwellings does not allow for the use of heavy vehicles. The site trafc must coexist with the activities of the inhabitants. These conditions lead the renovation company to ensure:

Figure 6.5

Photo of the renovation project

Interplay between Construction Supply Chain and BIM

• • •

An active clearance of the sidewalks and walkways throughout the day. A rigorous approach to the safety of local residents and courtyard inhabitants. An active awareness of closing and securing the empty dwellings.

The renovation company is responsible for any damage that may be caused.

Planning Kits Using BIM By exploration the diferent possibilities of taking quantities from a building information modeling (BIM) model, the following general diagram in Figure 6.6 has been designed to carry out the Kit supply planning thanks to two elements: the BIM model and the work schedule. The input data (BIM model and work schedule) are indicated at the top of the diagram, the output data (fnishing elements and kits) are in the middle of the fgure, and the flters (flter by category and flter by date) by large arrows. These arrows indicate the direction of information transfer. After specifying the products that will be included in the Kitting delivery, products with the same start date are consolidated into the same Kit. Delivery is scheduled one day before the start of the task. To prevent the delivery from being scheduled during weekends, the delivery date was automatically deduced using an algorithm; if the task’s start is a Monday, the delivery will be made on the previous Friday. After the reception, products are supplied at the point of use. Figure 6.7 depicts the main phases of the Kitting implementation of case study #2. The “Initiation” phase started from January 2016 until March 2016. The next phase, “Preparation”, lasted three months and ended in early June 2016. The “production” phase took place during the summer. The initiation phase revolved around aligning project goals and identifying the right stakeholders for the kitting. The Pull planning sessions were essential to the success of the kitting operation. The reason is that a slip in the schedule can cause the project to collapse. Pull sessions for Kitting help in detecting inconsistencies in the work schedule and thus avoiding potential problems on-site. The preparation phase took three months; kitting requires spending time on detailed planning before execution. The consolidation center representative (logistician) was involved

Figure 6.6

Principles of planning kits using BIM

Zakaria Dakhli and Zoubeir Lafaj

Figure 6.7

Main phases of the kitting implementation of case study #2

early in the project during the initiation phase. This involvement helped the team to prepare the Kitting and also beneft from the logistician’s opinion/experience. The latter had a network of suppliers who are used to Kitting. The construction company had the time to consult its supplier base and make adjustments as necessary. The products selected to be part of Kitting were identifed during the preparation phase thanks to product segmentation as shown in Table 6.2. Table 6.2 summarizes the product typology established. Four types of products were identifed. Type A refers to custom-made products, MTOs (Make-to-Order), such as window frames and electrical panels. Type B includes standard products with custom preparation, such as pre-cut plaster sheets. Type C is categorized as MTS (Make-to-Stock) with no preparation required. Finally, category D concerns consumable MTS products (screws, joints, etc.). When selecting products for Kitting, particular attention should be paid to MTO products. This type of product has a signifcant lead time since it requires a certain level of customization during the manufacturing process. For example, windows require a manufacturing time of four to six weeks. Therefore, the order should be placed before a minimum of eight weeks of work. Kitting helped in identifying products that needed this consideration. In this case study, 72 tasks were performed for the renovation. Forty-four tasks were realized using Kitting (61%). The kitting took place over six weeks, compared to two weeks and half a month in the traditional supply model. The kitting schedule included the number of people needed to complete each task. It should also be noted that this schedule was realized during the “preparation” phase and that a supply schedule for the kits was drawn up afterward in perfect synchronization with the work schedule. This supply schedule is one of the “manufacturing” phase deliverables. Table 6.2 Product typologies Product typology

Nomenclature

Description

Examples

MTO

Custom manufacturing

Frames/electrical panel/prefabrication

MTO

Standard products with custom preparation

pre-cut plaster boards

MTS

Standard products without preparation

Baseboards/tiles

MTS

Consumables

Screws/joints/nails

Interplay between Construction Supply Chain and BIM

Comparison between Conventional Planning and BIM-Based Planning The Kit supply planning was carried out based on the BIM model and the work schedule. We drew a comparison between this method and the traditional method that is based on 2D plans and paper documentation. At frst glance, estimating quantities to realize supply schedules in the BIM process is automatic. At the same time, it is made manually in the traditional process and therefore subject to several errors and uncertainties. Moreover, in a similar case study conducted by (Whang& Min 2016), the use of BIM for estimating quantities was proven to lead to an accuracy of 95%, while the accuracy of manual quantity take-ofs was 85%. Furthermore, each time a modifcation occurs within the work plans in a traditional process, a manual re-entry of the data is necessary to update the supply plans. In contrast, with the BIM process, entries are automatically assigned to the elements (objects) in the BIM model. Finally, in a traditional process, the information is scattered in several documents, while in the BIM-based process, all the information is grouped in a central document. Table 6.3 summarizes the diference between the two procurement planning processes with and without BIM.

Framework for BIM-Based Logistics A method is proposed for monitoring the kits and their delivery (Figure 6.8). This method consists of creating an object called a Kit with the following parameters: ID, level, part, Table 6.3 Comparison between a planning process without BIM and with BIM Planning without BIM

Planning with BIM

Manual quantity taking

Automatic quantity taking

Data re-entry after modifcation of plans Scattered documentation

Automatic modifcations assigned to the BIM model Documentation centralized in the BIM model

Figure 6.8

Framework for BIM-based logistics

Zakaria Dakhli and Zoubeir Lafaj

components, delivery state, delivery date, delivery time, and comments. The modifed database fle is imported into the BIM model, and the modifcations are automatically assigned to the Kits objects modeled in the BIM model. The modifcation of the delivery parameters mentioned in the previous diagram is in the form of a table. An example of this is given in Table 6.4.

4D Modeling of Kits through BIM In a more advanced stage, a fow diagram (Figure 6.9) can be designed to generate the Kits automatically by integrating the work schedule into the BIM model. First, the planning is linked to the BIM model using a 4D modeling tool. Following this step, each model element should be linked to the task to be executed and, therefore, will have a time associated as its fourth dimension. Each modifcation in the work schedule will be assigned to the objects in the BIM model. Subsequently, we can exploit this new dimension that has been added to generate supply schedules dynamically and update them each time the work schedule is modifed. By entering a supply frequency, the interface will extract products with their quantities and start dates of the tasks. This method can be adjusted

Table 6.4 Interface for receiving Kits using a database

Level

Room

Components

Delivery date

Delivery time

Etat de livraison

12345

Ground foor

Kitchen

1 door2 windows

21/07/2016

11: 30

Shipped

Figure 6.9

Framework for BIM-based logistics

Comments Badly packed door

Interplay between Construction Supply Chain and BIM

to the needs of the kitting application by adding a flter that will only take into account tasks related to specifc construction phases (e.g., fnishing works).

Considerations for a Successful Implementation of Kitting The objective of this last section is to identify key points to consider for a successful Kitting operation with regard to the After Sales Service. The results of this section are based on an interview with the after-sales service manager of the construction company involved in both case studies. Having a history of kitting deliveries makes it easier to identify the company/entity responsible in the event of an incident during the use phase. It is usually the way to install material that poses an issue rather than the defects of the materials. The work packages that are most subjected to maintenance are: exterior carpentry (about 30% of issues in maintenance) due to poor installation, infltration, etc. Then, we fnd the waterproofng (not included in the Kitting for this research project) and the wood joinery and frames. For tiles and parquet, the main concern is delamination. In general, the construction company did not receive any complaints about locksmithing. For plastering, in general, no particular problems occurred during maintenance; however, small issues were raised during the delivery phase. Finally, the after-sales service related to the baseboards is rare and without consequent fnancial risk. While issues in the electricity work package are rare, the consequences of an incident (e.g., fre) are critical. The warranty does not depend on the type of work but the nature of the damage: the tenyear warranty after delivery (commonly used in France) concerns the degradation of all the elements that hinder the functional use of the construction, such as water or air infltration, signifcant cracks (small ones not included), lifting of the parquet, faience detachment, etc. or degradations that present a risk to the user. It was crucial to have traceability for each dwelling’s supplies and installation tasks, the history of the orders, and certifcates of insurance for the installers because 80% of the maintenance issues concerned the installations carried out by the subcontractors. Subcontracting generates more complications during the maintenance phase because the general contractor (construction company) has to spend time looking for the responsible entity. Moreover, the construction company must bear the whole responsibility in case the subcontractor no longer exists (bankruptcy, for example). The choice and the fnancial guarantee of a subcontractor are primordial. Finally, a clear defnition of responsibilities should be set in case the general contractor is responsible for the installation or for the interface between the work packages. Figure 6.10 shows the “Risk-Occurrence” matrix of issues during the maintenance phase. The matrix is composed of four compartments. The risk can be either low or high (e.g., a fre or a large fnancial loss). The occurrence can also be either low (maintenance requests are rare) or high (problems are recurrent). We can see that the precautions concern the external joinery and the wood joinery (Waterproofng being outside the scope of Kitting). The kitting structure required the early purchase of products and delivery (if necessary) to the consolidation center. During the planning process, a supply/installation logic was required. Furthermore, the suppliers paid supply costs before the actual installation (construction). Furthermore, the bidding department required a price library to quantify the products in Kitting, thus providing a supply/installation price instead of a global price. Finally, kitting required a detailed schedule of the architectural/technical work package in addition to a tailored supply schedule.

Zakaria Dakhli and Zoubeir Lafaj

Figure 6.10

Risk matrix of kitting

Conclusion In this chapter, the deployment of Lean 4.0 has been experimented with through the synergy of Kitting and BIM. The chapter also introduces logistics as a key element to gaining in project performance, especially since logistics in construction are not seen as a performance lever but rather as a necessary constraint to work progress. Two case studies of Kitting applied to renovation sites are presented. The frst experiment explored the potential of the BIM digital model within a Kitting approach. BIM is an efcient means for establishing the list of materials and the generation of supply schedules for kitting. The second experiment investigated the implementation of Kitting on a renovation site of fve houses. The kitting model was identifed based on criteria such as site conditions. The kitting schedule was created by selecting the products to be kitted. Finally, maintenance considerations were presented. The result of those experiments led to the proposal of a Kitting supply schedule development and an automation scheme using the BIM model. The experiments gave us information about how to use digital procurement to get around the limitations of the traditional supply system.

References Caldas, C. H., Menches, C. L., Reyes, P. M., Navarro, L., & Vargas, D. M. (2015). Materials Management Practices in the Construction Industry. Practice Periodical on Structural Design and Construction, 20(3). https://ascelibrary.org/doi/full/10.1061/%28ASCE%29SC.1943-5576.0000238 Court, P., Pasquire, C., Gibb, A., & Bower, D. (2006). Design of a Lean and Agile Construction System for a Large and Complex Mechanical and Electrical Project. In IGLC (Ed.), 14th Annual Conference of the International Group for Lean Construction, 1–13. Corpus ID: 55958407. Dakhli, Z., Laf haj, Z., & Bernard, M. (2016). Novel Techniques in Materials Management for Construction : A Focus on the Kitting System. World Conference on Innovation, Engineering, and Technology, 1, 314–328.

Interplay between Construction Supply Chain and BIM Domingos, B. S. M., Ribeiro, R. B., Barros, J. G. M., de, Júni, A. H., de, A., & Sabbadini, F. S. (2014). Process Improvement and Reorganization of Kanban Inventory in an Industry of Machinery and Equipment: A Case Study. Journal of Mechanical Engineering and Automation, 4(2), 49–54. EC Harris. (2013). Supply Chain Analysis into the Construction Industry. A Report for the Construction Industrial Strategy. A Report for the Construction Industrial Strategy, (Issue 145), 1–127. Evaristo, D., & Kalev A., S. T. (2015). Sustainable Urban Consolidation Centres for Construction (EU Project H2020- grant No. 633338) : D2.1 Detailed Pilot Site Description – Project UCCESS; 1–70, EU-Ref. Ares(2015)4731011 Galbraith, L., Miller, W. A., & Greene, T. J. (1991). Pull System Performance Measures: A Review of Approaches for System Design and Control. Production Planning & Control, 2(1), 24–35. https://doi. org/10.1080/09537289108919327 Huart, D., & Estampe, D. (2018). Mesurer la Performance Financière de la Supply Chain - Modèle Théorique Mesurer la Performance Financière de la Supply Chain Modèle Théorique. Techniques de L’ingénieur, 33(0), 1–12. LePatner B. (2007). Broken Buildings, Busted Budgets: How to Fix America’s Trillion-Dollar Construction Industry. The University Chicago Press. McKinsey. (2017). Reinventing Construction: A Route To Higher Productivity. McKinsey & Company, February, 20. Memon, A. H., Abdul Rahman, I., Abdullah, M. R., & Abdul Aziz, A. A. (2011). Time Overrun in Construction Projects from the Perspective of Project Management Consultant (PMC). Journal of Surveying, Construction & Property. https://doi.org/10.22452/jscp.vol2no1.4 Polat, G., & Arditi, D. (2005). The JIT Materials Management System in Developing Countries. Construction Management and Economics, 23(7), 697–712. https://doi.org/10.1080/01446190500041388 Sundquist, V., Gadde, L. E., & Hulthén, K. (2018). Reorganizing Construction Logistics for Improved Performance. Construction Management and Economics, 36(1), 49–65. https`://doi.org/10.1080/0144 6193.2017.1356931 Vaha, P., Heikkila, T., Kilpelainen, P., Jarviluoma, M., & Heikkila, R (2013), Survey on Automation of the Building Construction and Building Products Industry, Julkaisija Utgivare, 1–86, ISBN 978-951-38-8031-6,VTT 109. Whang, S. W., & Park, S. M. (2016). Building Information Modeling (BIM) for Project Value: Quantity Take-Of of Building Frame Approach. International Journal of Applied Engineering Research, 11(12), 7749–7757.

7 IMPLEMENTING LEAN-BIM DUALITY Balance between People, Process, and Technology Daniel Heigermoser and Borja García de Soto Introduction The architecture, engineering, and construction (AEC) industry has been slow to adapt to the digital revolution that signifcantly improved productivity, cost-efciency, and sustainability in other industries (BCG, 2018; World Economic Forum, 2016). However, the AEC industry is in the throes of change. Investments in venture capital and capital investments in digital solutions have more than doubled over the past decade, and investment growth in construction technology has trended upward since 2008 (McKinsey, 2020). Primarily responsible for the signifcant digital revolution in the AEC industry is Building Information Modeling (BIM). Besides the technological transformation, construction is also changing drastically in how production is managed through the implementation of Lean Construction. BIM and Lean are both approaches with a profound impact on the industry but have been used separately to increase productivity and efciency. Some studies (e.g., by Sacks etal. [2010]) have indicated that existing synergies can efciently enhance the productivity of projects by simultaneously applying both approaches. Also, the American Institute of Architects expressed that ‘although it is possible to achieve integrated project delivery without building information modeling, it is the opinion and recommendation […] that building information modeling is essential to efciently achieve the collaboration required for integrated project delivery’ (Eckblad, 2007). This chapter explains how key Lean principles and BIM functionalities across the value chain of projects are related to one another and what gains and benefts can be expected by implementing them jointly. A BIM-based tool is introduced that includes Lean planning functionalities. Then, the authors propose a guide for AEC managers and leaders to implement the Lean-BIM duality in organizations. Finally, technological and organizational challenges in the digital transformation of corporations are provided.

Te Foundation of Lean Construction It took a long time for the AEC industry to adopt Lean Production methods. The excuse was that, unlike the state-of-the-art automobile industry, it is not stationary producing. Hence, Koskela (1992 and 2000) developed an all-embracing production management system by 98

DOI: 10.1201/9781003150930-9

Implementing Lean-BIM Duality

adapting the Toyota Production System to put Lean theory into the AEC industry. Nowadays, diferent approaches to integrate Lean into construction management exist, including Lean Project Delivery System, Integrated Project Delivery, Last Planner ® System (LPS), Lean Thinking, 5S, PDCA, and Continuous Improvement, which give practitioners diferent options to apply Lean. This chapter focuses on the LPS, a methodology that has proven to enhance construction management in various aspects by eliminating non-value-adding activities and creating a more reliable project delivery.

Last Planner System of Production Control In 1992, Ballard started to develop the LPS of Production Control (Ballard, 2000). Over the years, the LPS was established as a critical element of project production control in Lean Construction. The project planning of the LPS is divided into long-term and short-term planning stages (Ballard et al., 2002). The long-term planning stage specifes what should be done. It consists of the Master Schedule – covering project milestones – and the Phase Schedule – adding details to identifed project phases. The Lookahead Plan bridges the gap between long-term project planning and short-term execution planning. It decomposes activities to the operations level by identifying constraints, assigning responsibilities, and making tasks ready so that they can be done (Hamzeh et al., 2008). Identifed tasks ready for execution are pulled into the Commitment Plan (a.k.a. Weekly Work Plan), thereby specifying the work steps that will be done. The Learning phase is a measure for optimization by tracking planning reliability to continuously improve productivity.

Building Information Modeling The adaption of Industry 4.0 in the AEC industry (a.k.a. Construction 4.0) implies digitization and automation. BIM is considered the starting point of information technologies transforming the way of working. Although Charles M. Eastman frst introduced BIM in the 1970s, it has only been gradually used since the mid-2000s as a solution to reduce inefciencies. In the BIM handbook, Sacks et al. (2018) also recognize BIM as an enabler for ‘new construction capabilities and changes in the roles and relationships among a project team’ with the potential to disrupt the industry by changing the infuence of stakeholders and transforming the way of working.

Te Impact of BIM on Our Built Environment While today’s construction ecosystem is still based on a highly complex, fragmented, and project-based construction process based on unique customer specifcations and limited use of end-to-end digital tools, the future ecosystem will be much more standardized, consolidated, and integrated. Due to BIM, the construction process will be increasingly product-based, thereby utilizing of-site manufacturing and prefabrication and limiting the general contractor’s infuence and responsibility to rely on the on-site assembly of elements. Once BIM technology as a modeling and visualization tool is mature, its impact will be amplifed as more disruptive technologies dependent on and enabled by BIM will be integrated across the facilities’ lifecycle (Figure 7.1). These technologies include generative design solutions, virtual and augmented reality (VR/AR) visualization to facilitate early design decision-making, and 3D scanning. It also integrates trends with high impact on 99

Daniel Heigermoser and Borja García de Soto

Figure 7.1

Digitalization trends and digital technologies enabled by BIM technology (adapted from BCG, 2016)

production, such as prefabrication/modularization, robotics, and digital feld management solutions, including drone technology and Internet of Things (IoT) sensors.

Lean-BIM Duality Integration Roadmap The integration roadmap is split into three main sections. The frst gives a theoretical background on how key Lean principles and key BIM functionalities are linked, what benefts are achieved through its joint implementation, and which metrics should be used to measure its benefts. The second discusses the integration of the Lean-BIM duality from the perspective of information technology solutions. Due to the lack of available solutions, this part introduces a BIM-based prototype production management tool developed by the authors that supports the LPS. The third considers the increasing need of leaders to drive the digital transformation by implementing Lean and BIM. Hence, the third section acts as a Lean-BIM implementation guide and gives specifc recommendations for implementing the Lean-BIM duality. It also gives an overview of the implementation barriers by discussing multiple organizational and technological challenges. The information is based on the authors’ experience and knowledge gained through research, industry experience, and interviews with industry leaders.

Key Lean Principles Supported by BIM Technology Based on the concept of increasing the output value and reducing the share of non-valueadding activities, Koskela (1992) defned 11 heuristic principles forming the foundation of Lean Construction. Four key principles are selected and analyzed (below) based on their synergies with the BIM technology and focusing on key technology trends enabled by BIM.

Reduce the Share of Non-Value-Adding Activities A non-value-adding activity takes time and/or resources without adding value to the overall project. Reducing the share of non-value-adding activities (waste) is a fundamental Lean Construction principle (Koskela, 1992). According to Stalk and Hout (1990), only up to 20% of processes add value. Although this value seems outdated today, it still shows its validity 100

Implementing Lean-BIM Duality

due to the industry’s productivity stagnation. The BIM technology signifcantly reduces waste by shifting the collaborative design decision-making process to earlier project phases, keeping all stakeholders updated and reducing design rework.

Reduce Variability and Cycle Time Variability, respectively uncertainty, is present in any production process by default. There are two main objectives to reduce production variability. First, uniform products create a higher customer value, and second, variability increases the share of waste due to its imperfect value creation process (Koskela, 1992). Reducing variability and cycle time is closely related to reducing waste and can be a key driver to meet this objective. Sacks et al. (2010) illustrated that integrating BIM into make-ready planning is essential to reduce process uncertainty, increase efciency for workers, and improve communication. Also, BIM advances the reduction of cycle time and uncertainty so that the shift towards a much more integrated product-based of-site manufacturing process is enhanced, with the positive side efect to increase output fexibility.

Increase Process Transparency The lack of process transparency is a critical factor regarding employees’ diminishing motivation for process improvement and the urge to reduce the visibility of errors. To better achieve the principle to increase process transparency, BIM represents graphical and non-graphical characteristics of a facility and ofers solutions along the entire project lifecycle by ‘making the main fow of operations from start to fnish visible and comprehensible’ (Stalk & Hout, 1990). During design, data are shared across diferent parties in real time, and models can be viewed using VR/AR. During construction, mobile workstations and handhelds allow site managers and Last Planners to access real-time model data and planning information on site.

Increase Output Flexibility The principle is associated with the ability to quickly react to changes and unexpected situations without the risk of an increase in non-value-adding activities or production cycle time. Lean reinforces teamwork and creates an increased relatedness of collaboration – internally and externally – to establish processes that consider each other’s needs and implies the necessity to eliminate any obstruction causing insufcient relatedness of project participants (Sacks et al., 2010). This behavior of an increased relatedness of collaboration and communication across disciplines is also required for the applications of BIM. For example, last-minute design changes require rework for checkers, planners, subcontractors, and suppliers. The increased collaboration and the direct information transfer of changes in BIM models are vital aspects to keep changeover time low and output fexibility high.

Key BIM Functionalities Contributing to Lean BIM technology was developed to improve the design, constructability, and coordination of design disciplines. While the degree of BIM implementation during design is increasingly established, its implementation during construction is less standardized and has yet to reach the same maturity. However, especially during the construction phase, an integrative 101

Daniel Heigermoser and Borja García de Soto

adoption of BIM functionalities and Lean theory is essential to efciently achieve the collaboration required to increase output value and reduce waste. This study focuses on fve key BIM functionalities (defned by Sacks et al., 2010, 2018) analyzed based on their correlation with Lean theory.

Visualization of Form BIM technology provides a powerful platform for visualizing the digital representation of facilities, workfows, and control systems that enable pull fow and deep collaboration between teams on and of-site. Sacks et al. (2010) identifed that integrating BIM through visualization and functional evaluation of workfow processes is essential to improving upstream fow variability from the early conceptual design stage onward. By integrating BIM and emerging technologies such as VR/AR, interaction and decision-making for design and construction are improved, thereby enhancing Lean principles such as increasing process transparency, reducing production uncertainty, and decision-making by consensus.

Maintenance of Design Integrity and Automated Design Generation BIM enables artifcial- and real-intelligence solutions using generative design tools to efciently develop design alternatives that maintain coherence with standards and regulations and transmits design changes to all drawings, thereby maintaining integrity at any time (Sacks et al., 2018). BIM also facilitates design analytics by identifying physical and clearance clashes between models of diferent disciplines and promotes an increased level of communication and coordination between designers. Hence, BIM supports Lean theory to reduce waste, rework, and production cycle time and improve the value creation process early in the lifecycle.

Cloud- and Object-Based Information Transfer and Communication Digital communication is turning the construction ecosystem upside down. BIM platforms are a substitute for outdated non-digital communication channels and becoming the norm. Also, research initiatives, such as the ASHVIN H2020 project (ASHVIN, 2020), strive to develop solutions that advance the power of digitalization by developing cutting-edge digital twin platforms that integrate IoT and real-time construction data. Those platform systems allow the direct information transfer of design and construction data and are essential to effciently enable prefabrication and additive manufacturing on a large scale. Digital platforms and tools that facilitate cloud-based information transfer and communication increase transparency among stakeholders, support standardization, and foster collaboration.

Collaboration in Design and Construction BIM facilitates the early participation and collaboration of diferent disciplines from the earliest project stage onwards, internally and externally: internal collaboration, by which stakeholders from the same organization but diferent disciplines and project phases work and collaborate on the same model; external collaboration, by which stakeholders from the same discipline but diferent contract parties exchange model information (Sacks et al., 2010). Hence, BIM contributes directly to Lean by increasing the relatedness of collaboration. The link can be exemplifed based on the integration of BIM during construction. Collaborative planning integrates BIM design models into construction through 4D visualization, helping 102

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participants get a deeper understanding of processes and detect constraints early to prevent late scheduling conficts and time loss.

Facilitating Real-Time Construction Tracking BIM technology serves to integrate the latest technology used to track construction productivity and progress. It enables site managers, planners, and Last Planners to exploit handheld solutions to visualize the model on-site, monitor site progress, and utilize LiDAR-based sensors and drone technology to track construction progress autonomously. By sharing retrieved data in the cloud and applying advanced analytics, BIM allows stakeholders to focus on essential value-adding activities. Thereby, it allows a more reliable construction process (Sacks et al., 2018) and delivers higher-value projects.

Gains and Benefts of the Lean-BIM Duality Realizing that the BIM functionalities and the Lean principles highlighted above are greatly supported by one another, it can be asserted that implementing the Lean management approach supported by the technical capabilities of BIM will beneft the overall transformation of construction projects towards Construction 4.0. Within the Lean Construction community, BIM is seen as the digital tool per se. Hence, the integration of digital solutions will become increasingly essential and an intrinsically tied component to the Lean methodology, thereby paving the way towards Lean Construction 4.0. The benefts of Lean Construction 4.0, particularly with regard to the Lean-BIM duality, are observable along all lifecycle stages of facilities, from design and engineering to construction to operations and maintenance (Figure 7.2). Key benefts from Figure 7.2 that contribute to improving project delivery and reducing time and costs are faster and more efcient decision-making processes based on improved information gathering, transmission, and utilization, as well as earlier and enhanced coordination and collaboration between stakeholders. When successfully implementing those elements as part of an integrated Lean-BIM approach, the UK Ofce of General Contractors estimated that cost savings up to 10% can be achieved on a single project and if lessons learned are transferred and continuous improvement is promoted over a series of projects, up to 30% of costs of construction can be saved (British Standards Institute, 2007). Table 7.1 highlights key performance indicators that should be used to measure and analyze advancements in the Lean-BIM implementation.

Figure 7.2 Business value of the BIM technology in Lean practices

103

Daniel Heigermoser and Borja García de Soto Table 7.1 Metrics measuring the gains and benefts of the Lean-BIM duality Percent Plan Complete (PPC)

PPC compares the planned to actual schedule compliance. An analysis by Love et al. (2003) and BCG (2018) revealed that PPC is only at about 50% in traditionally managed projects with little to no lean interventions. Reasons for plan failures are diverse, from incorrect time estimates to space and equipment conficts to poor coordination. PPC after implementing Lean improves to about 62% and after digital BIM-based Lean planning to 72% according to BCG (2018) and can decrease variability by 50% (Toledo et al., 2016).

Hours of rework

Rework causes project delays and cost overruns. Without realizing the benefts of Lean and BIM, studies by Love et al. (2003) indicated that 30% of construction and 35% of total construction costs is rework and contribute to 50% of the project’s cost overruns. Also, the McGraw Hill Construction Report (2012) estimated the value of BIM as high as 48% of reduced rework.

Physical conficts

Physical conficts can be fxed early without afecting the further course of the project. Visualization of design and construction processes using BIM empowers better coordination, thereby reducing design and planning errors by as much as 52% (McGraw Hill Construction Report, 2012).

Request for RFIs are used to clarify errors and unclear situations. The unambiguous information (RFI) visualization of project processes that BIM ofers allows stakeholders to work with a much higher degree of clarity and empowers better communication between teams. Toledo et al. (2016) indicated that BIM usage during planning meetings could reduce RFIs by more than 50%. Project participation

Toledo et al. (2016) measured that interaction between parties is focalized, although the duration of meetings could be shortened by 25%. Also, the interactive planning approach using BIM visualization attracted almost twice as many diferent project roles to be involved in planning meetings.

Introduction of a BIM-Based LPS Tool Although the Lean-BIM duality has been researched extensively, its combined integration has not fourished yet as many frms still show difculties in implementing either Lean or BIM individually. Also, even today, commercial building information technology software does not fully support common Lean tools, such as the LPS. When applying the LPS, it is still common to use sticky notes and self-created spreadsheets without using the added value of digital solutions. This lack of commercial BIM software integrating the LPS has been tackled in previous tools, e.g., Sriprasert and Dawood’s (2003) Lean Enterprise Web-based Information System, Dave et al.’s (2011) VisiLean tool, and Sacks et al.’s (2013) KanBIM Workfow Management System. All are database- and BIM-based production management tools that support the LPS linking scheduled tasks to the objects; however, they do not support more advanced planning capabilities, such as quantity take-ofs and as-built analyses, that would be regarded as essential for a production management system. Hence, Heigermoser et al. (2019) developed a BIM-based LPS tool, taking into account Sacks et al.’s (2010) fundamental requirements for an integral BIM-based Lean production management system and an extended list of requirements, including an automatic quantity take-of, clearly defned responsibilities, as well as tracking and analysis of as-built construction data. It was conceived as a construction management tool ofering planning and visualization features that imports building information models and construction schedules and stores data in an open-source database management system. 104

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Figure 7.3

(a) 3D visualization with assigned construction zones and (b) 4D visualization of the shortterm planning construction process

The tool facilitates resource allocation through the division of work zones and visualizes the objects according to their zone (Figure 7.3a), and simplifes the accessibility of non-graphical building information, e.g., quantity take-ofs fltered according to the data of interest (e.g., by zone, level, type). Another unique feature is the tool’s task status-based 4D construction progress simulation (Figure 7.3b), where each object is color-coded based on its status (here shown in diferent shades of gray, while the actual color-coding is green [ready], orange [can be made ready], and red [cannot be made ready]). The object coloring is particularly helpful to mitigate risks within the planning process as resulting disruptions and efects on future planning are visualized. More information about the tool can be found in Heigermoser et al. (2019).

Implementation of the Lean-BIM Duality This section provides strategic recommendations to management and leaders in the industry on how to implement the duality. Integrating Lean and BIM requires a comprehensive understanding of both concepts and how they can be utilized in diferent projects and situations. The implementation needs to occur from two diferent perspectives: 1 2

the organizational structure and processes on projects.

Covering both is essential for a successful implementation of Lean Construction 4.0. Management needs to ensure that the strategy is implemented across all projects in equal measure and that project learnings are reported back to the organization to continuously learn and transform the delivery of future projects.

From the Perspective of Organizational Structure Embed the Lean-BIM Duality as Part of Corporate Strategy BIM technology and the culture of Lean defne the character of an organization from within, as they are an indispensable part of corporate strategy. Both transform key business operations by holistically changing and optimizing work processes and afect parts that go beyond 105

Daniel Heigermoser and Borja García de Soto

Figure 7.4

Strategic recommendations to implement the Lean-BIM duality

their border by infuencing the entire supply chain network. Hence, companies need to establish management practices to govern this major transformation and base strategy on the business needs of the organization. It is essential to defne objectives and key results (OKRs) as a goal-setting system while keeping the threads in mind. One way of monitoring OKRs is applying Lean and BIM capability maturity models that help companies track their transformation. Based on the formulation of the transformation strategy, management should select and prioritize technologies, tools, processes, and methods for adaption. Although there are case studies describing the implementation and project processes of the Lean-BIM duality, there is little background on general frameworks for organizations illustrating how to implement the duality from a holistic perspective on organizational and project levels. This allows providing a framework for best practices for its implementation. A high-level top-down Lean and BIM strategy and implementation plan should include the strategic recommendations illustrated in Figure 7.4. When defning the strategy, management needs to consider their existing BIM and Lean capability maturity. The number of companies implementing BIM along the entire value chain of projects is rapidly growing, and some are becoming profcient in adopting BIM (Papadonikolaki et al., 2016). For those companies, it is essential to also employ Lean and focus on changing employees’ mindsets and transforming process workfows.

Appoint a Corporate Lean-BIM Director and Leadership Team Owing to its major impact on a corporation’s operational processes, the transformation process – particularly the implementation of BIM – is a continuous complex undertaking that requires clear distribution of tasks and responsibilities and holistically improves its operations. Therefore, corporations should appoint a Lean-BIM Director at the management level and be directly responsible to the CTO and COO. The Lean-BIM Director should be 106

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knowledgeable in Lean and BIM and experienced in transformational initiatives at the corporate and project level. As the Lean-BIM Director is not involved in implementing Lean and BIM in daily operations, it is sufcient for him/her to understand the fundamentals and theory behind both methods. His/her area of expertise should be to understand the value proposition, the business opportunity, the risks, get buy-in from management for fnancial aspects, and ensure that the initiative is implemented throughout the organization (Dave et al., 2013; Matt et al., 2015).

Take First Steps in Integrating Lean and BIM by Setting Up a Pilot Project During the initial implementation phase, people might be overwhelmed by the technology and new workfow processes. Hence, organizations need to fnd ways and procedures for formulating, implementing, assessing, and adapting the strategic implementation plan and the adaption roadmap along the way. The best way to test the developed Lean-BIM process workfow and selected software tools is to set up a pilot project. The pilot project should be of a smaller size, and the project environment should be conducive to meet the defned OKRs and frst implementation milestones (Smith & Tardif, 2009). Dave et al. (2013) defned three critical factors that enable a successful early-stage Lean-BIM implementation: • • •

supporting collaboration across stakeholders, ensuring information availability, and taking a lifecycle approach.

At this stage, the organization should critically review its internal Lean and BIM capability maturity (see section below). If the capability maturity is low, the organization should invest in consultants to support the implementation in the frst projects. Investing in external support providing in-depth expertise promises an accelerated start with a guaranteed return on investment in the long run. For organizations, pilot projects are a low-risk strategy as fnancial investments and required manpower resources are low, and the project duration is manageable. The Lean-BIM Director’s responsibility is to appoint a Lean-BIM Project Lead for each project implementing the strategy. The Lean-BIM Project Lead is responsible for the successful implementation on each project, should meet the defned adaption roadmap milestones and OKRs, and is directly responsible to the Lean-BIM Director. The LeanBIM Director is also responsible for the continuous learning and professional development of the leadership team and project leaders. The majority of learning should be within the organization by ensuring knowledge capture and sharing between teams, thereby spreading lessons learned and new technological, managerial, and operational knowledge across the organization. This focus on learning enables the continuous improvement and optimization of the organization’s processes prior to expanding the implementation from the pilot project towards multiple projects (Dave et al., 2013).

From the Perspective of Project Structure and Processes During project kick-of, the project management team and client should immediately start discussions about the value of Lean-BIM and how to best integrate project information available through BIM efectively into the processes. At this early stage, new roles and responsibilities that come with integrating Lean and BIM should be determined, and processes on managing collaboration between stakeholders should be established. Defning clear roles 107

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and distributing responsibility among team members will maximize the value that both bring to the project and already requires a high level of collaboration; however, it is a critical step for further success. Based on the outcome of these early high-level decisions, a BIM Execution Plan and Conditions of Satisfaction need to be developed, clearly defning the responsibilities of team members, illustrating the information fow between stakeholders, defning criteria and project objectives, and aligning the team’s focus.

Lean-BIM Project Leadership Team and Last Planners Primarily two new roles should be introduced when implementing Lean-BIM on a project. First, the Lean-BIM Master, responsible for client liaison/relations, long-term planning, and fulfllment of project milestones; aligning the model with customer requirements; and ensuring that all participants understand the theory, practices, and process. Second, depending on the project size, one or more Lean-BIM Product Owners are responsible for process and planning compliance during the make-ready short-term planning and ensure that the LPS methodology is precisely implemented in conjunction with BIM to prevent partial implementations. Both roles could also be defned as Lean-BIM project integrators for long-term planning and short-term planning and are unique professional roles that require in-depth knowledge in both areas, Lean and BIM, to leverage maximum benefts (Sacks et al., 2010). Depending on the project size, both roles could also be executed by the same person. To put Lean into practice, the role of the Last Planner, commonly established in the LPS, is required. Last Planners are experienced team members directly responsible for work execution with extensive knowledge about optimally performing the planned work in their feld. While the personas named Last Planners during construction are typically known as foremen or supervisors, Last Planners during design are represented by architects, structural engineers, and MEP specialists (Ballard & Tommelein, 2016). The number of Last Planners is not limited to a certain number; on the contrary, everybody who can improve the reliability of planning and efciency of production can play its role.

Integrated Lean-BIM Framework of Process Workfow Putting Lean-BIM into practice on a project level requires a defned process workfow. The framework developed in Figure 7.5 proposes a best practice process framework based on Ballard et al. (2002) on integrating the LPS. It has been modifed by adding BIM as a digital integration layer. It is split into an “as needed” long-term planning frequency and a “weekly” short-term planning frequency and shows the main stakeholders required for each planning phase. Initially, as mentioned earlier, the BIM Execution Plan and the Conditions of Satisfaction should be defned collectively with the project’s leadership team, the client, and the main project stakeholders. Then, the framework illustrates the interaction between Lean and BIM from a long-term, low planning detail to short-term, high planning detail to lessons learned and as-built models. Toledo et al. (2016) also defned a similar framework.

Weekly Work Planning and Start-of-the-Day Meetings To put the proposed Lean-BIM process framework into practice, projects should use weekly work planning meetings for short-term make-ready lookahead planning, commitment planning, and learning by analyzing work done. BIM models should be used as an indispensable part of the planning meetings to serve as an information input and visualization instrument 108

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Figure 7.5 Lean-BIM framework based on Ballard’s LPS process (adapted from Ballard et al., 2002)

that illustrates project details to project participants, which they would otherwise not be able to grasp (Toledo et al., 2016). Participation of all teams involved in the execution of tasks, as well as their pro-active collaboration and exchange of information, is critical to eliminate constraints and clashes, promote teamwork, and prevent last-minute project changes and rework. Weekly learning meetings will also support planning reliability by collectively analyzing the planned vs. executed work plans to embrace learning opportunities for future planning. In addition, participants involved in the current’s week execution should utilize 15 minutes long start-of-the-day meetings to review daily planning, share information between Last Planners, and eliminate last conficts. These meetings can be performed without 109

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the use of BIM, as participants should have a clear understanding of their work. Salem et al.’s (2006) study on the efectiveness of start-of-the-day meetings showed that 67% of participants assessed the meetings as value-adding. Poudel et al. (2019) identifed a similarity between the LPS’ daily meetings and the daily Scrum meetings during sprints. Both are used to review tasks that have been executed since the previous meeting and tasks planned to be done until the next daily meeting. To translate the decisions into action, Kanban cards with daily targets for each team should be distributed in a digital form or, alternatively, analog.

Assessing the Organization’s Lean-BIM Capability Maturity Transforming an organization towards Lean Construction 4.0 by implementing an integrated Lean-BIM facility lifecycle approach requires eforts and change across the organization. Examples of BIM implementation guides can be found at Sacks et al. (2018), Smith and Tardif (2009), and Hardin and McCool (2015). However, besides promoting and implementing change, organizations should establish metrics and benchmarks to assess the integrated Lean-BIM performance across diferent capability stages, categories, and granularity levels. Without defned metrics and maturity assessment tools in place, organizations and project teams are not able to consistently track development and identify areas for improvement. In addition, by tracking the capability maturity, organizations are better able to identify areas where the greatest successes have already been achieved and areas that require further training, development, and fnancial investment (Succar, 2010).

BIM Capability Stages and Maturity Levels To holistically assess the Lean-BIM maturity, organizations need to consider three environmental levels: the macro-level to assess the performance of the industry; the meso-level to evaluate the performance of organizations and business units; and the micro-level to assess the performance of projects and individuals. When implementing BIM, organizations need to go through three capability stages, namely object-based modeling, model-based collaboration, and network-based integration (Figure 7.6a). Each requires a specifc set of abilities to excel in deploying the implementation of the diferent capability stages. Within each stage, BIM maturity levels (Figure 7.6b) assess the degree of excellence and consistency of implementing BIM technology and pave the way towards a high-level BIM maturity within a BIM capability stage (Succar, 2010).

Figure 7.6

(a) BIM capability stages and (b) BIM maturity levels (adapted from Succar, 2010, based on SEI’s capability maturity model [SEI, 2008])

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Depending on the assessment objective, frequency, and available resources, a higher- or a lower-granularity level assessment model should be selected. At an early stage of implementation, easy-to-use tools are recommended. However, the more advanced an organization is in the implementation process, the more critical the organizational and people aspects become. Low-detail and self-administered tools should be complemented with high-detail level and specialist-led tools (Succar, 2010).

BIM Capability Maturity Assessment Models Initially developed BIM maturity measurement tools, such as the National BIM Standard Capability Maturity Model (NBIMS CMM) (National BIM Standard Project Committee, 2007), provide an easy-to-use and quick technological assessment but are criticized for their subjectivity, inadequate reliability, and emphasis limited on technology without considering organizational and people aspects. Thus, more holistic, quantifable, and practical tools were developed to also measure maturity on organizational and project levels. The BIM QuickScan has been widely used in practice to assess BIM performance on the organizational level and includes both quantitative and qualitative criteria (Sebastian & Berlo, 2010). Another BIM maturity assessment tool focusing on the organizational level is the BIM maturity matrix (MM) by Succar (2010). In contrast, the Virtual Design and Construction (VDC) scorecard, developed by Stanford University, assesses the BIM maturity at the project level (Kam et al., 2013).

Integrated Lean-BIM Capability Maturity Assessment Model The University of Salford adapted an easy-to-use integrated Lean-BIM capability maturity model based on the NBIMS CMM (Mollasalehi et al., 2018). However, it can only be used for quick guidance, and organizations should revert to measurement tools that assess BIM and Lean maturity individually. Thus, for more detailed assessments, e.g., the Lean Enterprise Self-Assessment Tool (LESAT) by Nightingale and Mize (2002) and the BIM assessment models introduced above should be used. Both results can be integrated into a Lean-BIM capability maturity score (Figure 7.7). Within the matrix, diferent capability stages and environment levels (e.g., organization, business units, projects) can be tracked and used to support organizations to achieve a higher Lean-BIM maturity.

Figure 7.7

Proposed integrated Lean-BIM capability maturity model

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Barriers and Challenges to Implement the Lean-BIM Duality into an Organization The application of BIM has been around for approximately two decades (Sacks et al., 2018); however, the return on investments in Lean Construction 4.0 has been less than satisfactory for a long time, and only a few years ago, a turning point of increasingly integrating digital solutions into the core construction process could be observed. While organizations are fully aware of the need to transform and digitize operations, the main factor for the slow-moving implementation is that the core organizational issues, people, processes, and technology, are usually not addressed with the required balance (Figure 7.8). Contrary to the widespread assumption that technological aspects mainly drive the implementation, it is a highly people- and process-oriented process and should be in a ratio of 40-40-20 (people-processtechnology), as suggested by Shelbourn et al. (2007).

People Challenges Implementing the Lean-BIM duality requires change at mainly two levels, the behavioral and mental levels. Implementing change at those levels is considered difcult. Thus, LeanBIM leaders are challenged to make things happen by training cultural change, changing people’s mindset, and giving people confdence that new ways of working can be superior to traditional ones. Extensive and continuous training in the new working culture and philosophy should be a central part of the change initiative as the majority of Last Planners have most likely never heard about Lean Construction, and the majority of construction managers, subcontractors, and suppliers have never applied Lean techniques. Lean-BIM Directors and leaders on the organizational level are responsible for introducing internal training opportunities to guide employees and external parties through the critical stages of implementation. Training should particularly address the following aspects: • • •

worker’s lack of self-criticism that implementing operational and cultural change improves processes and operations promote decentralized decision-making, especially for people who equate it to a loss in their power (Howell & Ballard, 1998) countermeasures that support the courage to cause principle-driven action and stand against attempts to dilute the Lean-BIM duality.

Figure 7.8 Lean-BIM implementation challenges – people-, process-, and technology-oriented (adapted from Shelbourn et al., 2007)

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People challenges mainly occur at the lower-level management and operational level. A particular challenge is understanding the diferent levels of expertise of the various team members and persuading or training people within their area of expertise. While training a core team of curious and ready-to-change employees is not challenging, getting people to understand and apply, as well as keeping them motivated to continuously challenge and improve their performance, is the core challenge. The focus should be to develop a partnering culture of mutual trust and open communication and collectively allocate responsibility (Toledo et al., 2016). Working in such a high-trust environment also implies sharing information openly and tolerating mistakes as a means of learning. This new way of working might be extremely confictive with traditional ways of working. Also, diferent team members from the Lean-BIM project teams should have the opportunity to lead during a project and challenge themselves in new ways to ensure that they stay committed and reinforce the Lean-BIM culture.

Process Challenges The implementation of Lean-BIM impacts many organizational and project processes and should be done strategically in a top-down business change program (Dave et al., 2013), which requires buy-in from top-level management. It is crucial to understand the pain points of stakeholders (a.k.a. customer profles). Traditional work processes and structures can only be changed if resistance is eliminated by removing issues that cause pain to customers and highlighting the outcomes that one can expect. Mapping the value of the Lean-BIM duality by creating a value proposition canvas for each customer profle helps to understand how to create value for each customer group. It describes the most important pains and gains that customer profles are facing and proposes pain relievers to alleviate customer pains and gain creators to beneft your customers. In doing so, one will realize that diferent customer profles show diferent resistance to changing their traditional ways of working. Understanding the customers’ needs is essential to facilitate the Lean-BIM implementation and lower the resistance to change processes. Finally, it facilitates employees in all hierarchy levels to support the implementation in their required feld. In summary: • •

•

Top-level management is responsible for shaping the collective ambitions and providing resources (i.e., investments that require high-level time/cost commitment) Mid-level management should support the ambitions and is responsible for implementing processes, allocating resources, and providing direction to the teams (e.g., by building, training, and retaining them) Execution level has been trained to behave in a certain way and in their specifc area of expertise for decades. They mainly engage in exploitation-related processes, so they fnd themselves trapped in a lower performance equilibrium. They should be trained to invest more time and resources in exploration-related processes to drive their project’s digital transformation.

Technological Challenges The evolution of the AEC industry towards Construction 4.0 and smart buildings and infrastructure adds a new layer of complexity to the delivery of projects and maintenance of assets and will be pushed further in the years to come. To drive technological innovation, organizations need to recognize the convergence of digital technologies with the asset-heavy 113

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construction of projects and face the challenge of implementing and tailoring technology respective to the needs of their organization. However, while BIM can only provide the technological toolset for Construction 4.0, it does not defne the required processes and cultural environment that needs to be developed. Therefore, it is essential to provide adequate training for less tech-savvy employees to ensure the required BIM end-user capability from a technological perspective. This implies highly task-specifc training combined with in-depth, hands-on learning, which comes at much higher costs and time efort than standardized, theoretical training but keeps employees engaged and motivated. More tech-savvy employees and recent graduates with good technological skills should be allowed to take on more responsibility within this feld, act as leaders, and support less technically experienced employees, thereby challenging particularly more senior employees to act. A beneft should also be drawn from their limited practical experience to obtain an unbiased point of view towards long-standing processes and how to change them. Despite those challenges, immediate results should be demanded, albeit with caution. Demanding the wrong or overambitious results will harm or even destroy the transformation, as this will contribute to losing people’s motivation and trust. Immediate results that could be demanded are illustrated in Table 7.1. As soon as momentum is created, the scope could increase to continue improving the Lean-BIM maturity. However, it is worth mentioning that with any transformation, the frst few projects usually sufer a loss before considerable cost savings and a return on investment can be realized in the long term. This fact once again highlights the importance of a strategic top-down implementation, as well as strong leadership and buy-in from senior management, as project directors might be more focused on improving the short-term fnancial position of their project and might avoid putting an even higher fnancial burden on their project by leading the transformation.

Conclusion and Outlook In the last years, the AEC industry slowly became aware of the opportunities the digital revolution ofers and other industries have already experienced. Today, organizations and entrepreneurs heavily invest in digital tools and solutions that will drastically change how production is managed and construction projects are delivered. A study conducted by PwC (2019) showed that although most organizations are working with BIM intensively, only 15% of construction companies developed a ready-made, mature BIM strategy, and approximately 50% of companies currently do not have a roadmap to follow. The lack of strategic planning at the senior management level will create difculties when reaching the full potential that BIM and other digital user applications and physical technology solutions ofer. Also, as most organizations do not approach the transformation strategically with clear OKRs, implementing the Lean-BIM duality and capitalizing on the synergies of applying both simultaneously will be a demanding undertaking for many. Today, most organizations perceive BIM as technically challenging and time-consuming. Hence, to initiate or accelerate a company’s Lean-BIM integration, immediate action should focus on analyzing the status quo by identifying its organizational, processual, and technological profciency and gaps. Organizations (or projects) willing to develop and implement a pathway towards Lean Construction 4.0 should follow the strategic recommendations presented, support collaboration across all stakeholders from senior management to blue-collar workers and create a value proposition for each stakeholder group that addresses the challenges of people and processes and use technology as an enabler. To that end, this chapter supports leaders and managers in the AEC industry by guiding them through their digital transformation. 114

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PART 3

Simulation Modeling and Virtual Lean Construction

8 SIMULATION AND MODELING FACETS IN LEAN CONSTRUCTION Mani Poshdar, Mohammed Adel Abdelmegid, Vicente A. González, Michael O’Sullivan, and Luis Fernando Alarcón Introduction Computer simulation refers to experimentation on a computer with a simplifed imitation of an operations system as it progresses through time for gaining a better understanding and/or improving the system (Robinson, 2014). The use of computer simulation enables an evidence-based decision-support method that provides a low-cost environment free from contractual and operational pressures. Thus, it ofers an efective resolution for increasing safety, productivity, and quality issues in the architecture-engineering-construction (AEC) industry. As chapter 1 discussed, Lean Construction 4.0 should embrace changes under the triad of process (production philosophy)-people (culture)-technology to enable an efective uptake of I4.0. This chapter presents three lean process objectives and how computer simulation as a technological advancement can support them while supporting the cultural side of the changes. First, it reviews the potential contributions of computer simulation in creating a production-driven management approach in construction production planning and control. This review investigates the use of simulation to support a shift from the contract-minded management style to the production-driven style. Second, the application of computer simulation modeling to support bufer management is discussed. Bufer management seeks to balance the theoretical demands and the practical realities. Our discussions address the use of computer simulation technology for establishing the intended balance. Third, we discuss the application of computer simulation in creating a Green-Lean framework. We explain how computer simulation can be implemented to support simultaneous management of production and environmental waste. Despite their likely synergy, these two areas had been treated as separated subjects until recently. Finally, the synergies between the triad composed of people, processes, and computer simulation as technology are illustrated by providing examples of their relationships in supporting the Toyota Production System (TPS) principles and minimizing or eliminating production waste.

DOI: 10.1201/9781003150930-11

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Computer Simulation for Production Planning and Control Lean-Based Planning and Control One of the main motivations for Lean Construction is to overcome the limitations of the traditional contract-minded management style by introducing a more integrated production management-based approach that focuses on stabilizing workfow by matching capacity with the planned workload (Ballard et al., 2007). Therefore, there has been an increasing interest in enhancing production planning and control systems in Lean Construction research. For instance, it was one of the most popular tracks in the latest Annual Conference of the International Group for Lean Construction (IGLC) (Alarcón & Gonzalez, 2021). Several research initiatives have been reported in the feld of construction production planning and control. Dallasega, Marengo, and Revolti (2020) identifed the strengths and shortcomings of diferent production planning and control approaches in construction, summarizing their characteristics in three main categories: • • •

Activity-based approaches, including the Critical Path Method (CPM), Earned Value Analysis (EVA), and the Last Planner® System (LPS). Location-based approaches, including the line of balance (LoB) method, the fowline method, and the location-based management system (LBMS). Object-based approach, i.e., building information modeling (BIM).

This section will focus on the LPS as the main approach that incorporated Lean principles to improve production planning and control of construction systems (Ballard, 2000). The LPS follows the concept of staging the construction planning process by starting with a high-level strategic plan then providing more details as the tasks are getting closer to production. Therefore, the LPS consists of four main stages: Master Planning, Phase Scheduling, Lookahead Planning, and Weekly Work Planning. Note that the LPS implementation scope has been varying between diferent countries and organizations as reported by (Daniel et al., 2017). They also found that the concepts related to the LPS have been applied in construction under other names such as ‘collaborative planning’, ‘collaborative programming’ and ‘collaborative project execution’. In the remainder of this section, we discuss how computer simulation can be used to support the LPS implementation through an integrated approach that aligns the diferent stages of the LPS with the development process of a computer simulation model.

Simulation for Lean-Based Planning and Control Computer simulation is well-suited to support production planning and control as it enables the examination and evaluation of diferent scenarios digitally before decisions are made. Computer simulation has been utilized to support research on the LPS development from diferent perspectives. For example, Hamzeh et al. (2015) utilized Discrete Event Simulation (DES) models to mimic the Lookahead planning stage in the LPS. DES models systems over time, with state variables changing instantly at separate points in time (Law, 2014). DES simulation enabled improvements in the task anticipation process, resulting in the schedule performance of a project being improved. Mota et al. (2010) developed a system dynamics (SD) simulation model to observe the behavior of diferent performance indicators during the LPS implementation, which proved the ability of simulation to capture the relationships between diferent production variables to support evidence-based decision-making during 120

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LPS implementation. Although computer simulation has been proved useful to support the LPS implementation, there is limited research on how computer simulation can be integrated with the LPS, to capture relevant production planning and control information and data to build a computer simulation model. To overcome this, an integrated framework that links the LPS with computer simulation through the conceptual modeling stage of simulation studies is discussed in the next section. A case study is then presented to illustrate the application of the framework.

Integrating Computer Simulation with the LPS Abdelmegid et al. (2021) introduced an integrative framework that combines the LPS, and an early stage of the computer simulation development lifecycle called ‘conceptual modeling’, to support decision-making during the LPS implementation. Conceptual modeling helps to drive integration as it represents a ‘linking pin’ between computer simulation and engineering activities (Van der Zee, 2012). Conceptual modeling is an iterative process to develop a software-independent description of the computer model to defne its objectives, inputs, outputs, content, assumptions, and simplifcations (Robinson, 2008). Computer simulation modelers spend a considerable cognitive efort to abstract an operating system to be implemented in a computer simulation. Capturing this cognitive efort in a documented approach understandable for general stakeholders, yet sophisticated enough for technical simulation modelers, is the main objective of the conceptual modeling phase of simulation studies (Nance, 1994). In the context of Lean Construction, conceptual modeling for simulation can support Lean principles from two perspectives. First, Lean Construction was initially motivated to provide an improved conceptualization of construction production systems by moving from the conventional view of construction as a transformation process to a more holistic view that considers fow and value (Koskela, 2000). This diferent view of construction production systems formed the production theoretical foundation of Lean Construction, which proved to be a complex concept for most construction users (Bashir et al., 2015). Therefore, the eforts to improve the practices of conceptual modeling in construction simulation studies can support the production theoretical foundation of Lean Construction by providing a clearer understanding of construction production systems. Second, using a well-defned conceptual model in construction simulation studies can minimize time, reduce unnecessary data collection, and improve the quality of the simulation model (Abdelmegid et al., 2020). Thus, conceptual modeling can be considered a ‘Leaner’ approach when conducting construction simulation studies. Figure 8.1 illustrates the integrated LPS/Simulation framework by Abdelmegid et al. (2021). The LPS side of the framework follows the planning and control structure proposed by Ballard and Tommelein (2021), which represents the LPS as a series of actions: ‘Go/ No Go decision?’ SHOULD-CAN-WILL-DID. The integrated framework is a closed loop system that utilizes the LPS information and data to feed into a simulation model. The outcomes of the model can be fed back to the LPS to provide insight and support decisionmaking. Please note that this fgure represents a high-level description of the framework to allow fexibility as the LPS implementation varies depending on the nature and size of the project. Moreover, the process of building a computer simulation model is non-linear with multiple routes and iterations to reach a satisfactory level of validity. As can be seen in Figure 8.1, the early planning steps of the LPS provide useful input to the conceptual modeling stage of a simulation study. For instance, the execution planning 121

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Figure 8.1

Integrating LPS and computer simulation

and master scheduling can assist the initiation process of the simulation study and guide the problem defnition. The handofs between activities defned in the phase schedule are essential to understand the inputs and outputs of the simulation model as well as the system structure. The detailed operations design in the lookahead plan provide a clear understanding of the system behavior and controls. The conceptual model can then be implemented to a computer model that is used to experiment with the system to examine diferent scenarios. The lower-level planning and control aspects of the LPS represented in the weekly work planning and learning steps can have a two-way relationship with the computer model as they can provide data and information to fne tune the model while making use of the advanced analytical capability of the model to test the reliability of the weekly work plans and make sense of the performance metrics to support implementing countermeasures.

A Case Study on the Integration of Computer Simulation and Lean Production Planning and Control The integrated LPS/Simulation framework was implemented in a case study to demonstrate its applicability. A construction project to expand and renovate a stadium in Chile was selected as the LPS was comprehensively employed in this project to plan and control construction operations. The project included diferent construction operations such as earthmoving, reinforced concrete foundation and framing, steel structure erection, and roading. These diferent operations were executed in a risky environment due to the existence of the old stadium’s structures and the uncertainties in the delivery of due to an unstable supply chain and scarcity of labor. These aspects posed a real challenge to the implementation of the LPS, which provided a suitable problem to harness the advanced capabilities of computer simulation, to support decision-making in such a complex and risky environment. 122

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The simulation model was built based on the integrated LPS/Simulation framework at its two stages, conceptual model then computer model. The conceptual model was developed by following the seven steps illustrated in Figure 8.1. LPS documentation was the main source of data and information. Based on the problem defnition, three main objectives were identifed for the simulation study with the aim to minimize disruption to construction operations: • • •

Defne a suitable number of workers for each crew. Optimize storage space for the concrete formwork. Design an optimum supply chain strategy to secure reinforcing steel.

It is important to indicate that one essential part of the conceptual model development is making assumptions and simplifcations to support abstracting the system to a reasonably practical level that allows experimenting with the system within an acceptable level of validity. The LPS information may be considered when making assumptions and simplifcations about the system. However, the proposed framework does not substitute the need for a modeler’s creativity to make assumptions and simplifcations depending on the scope and constraints of the model. As can be seen in Figure 8.2 (a), the construction site was represented

Figure 8.2 (a) Simplifed representation of construction site of the case study; (b) A visual representation of the computer model used for the case study

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as a series of sectors for site operations with two supporting areas that act as the departing points for crews (labor) and materials to initiate their work. This abstraction was based on the information from the LPS’s phase schedule stage such as handofs between trades and project logistics. Detailed descriptions of the outputs of the conceptual modeling stage of this case study are available in Abdelmegid et al. (2021). Figure 8.2 (b) shows a screenshot of the computer model of the case study, which was developed by implementing the conceptual model in the SIMIO platform ( Joines & Roberts, 2015). This computer model was used to test decisions related to each of the three objectives listed above. The outputs of the model were used to assess diferent combinations of crews to maintain a balance between the project duration and crew utilization. Original operations design included one crew for each of the three main foundation operations: excavation, formwork, and installing the steel reinforcement. This original design led to an estimated duration of 140 days. Reductions to project duration can be achieved by increasing the number of crews; however, this increase can lead to waste due to inefcient resource utilization. Therefore, based on the results of the simulation study, a decision was made that two crews per operation was a reasonable number to achieve a reduction of 25 days in project duration without highly impacting resource utilization. Adding more resources showed the potential to only save one extra day but leading to major time waste.

Simulating and Modeling Bufer Management Bufer Management in Lean Ballard and Howell (1997) suggest three components to implement Lean Thinking in the planning and control of construction projects: 1 2 3

Shield production from variation and uncertainty in the fow of directives and resources (reduction of infow variability). Stabilize workfow by reducing the fow variation. Improve performance in downstream activities.

A common method to achieve the frst two goals is the use of bufers to decouple activities. Thus, the progress can be safeguarded despite variations in timing, sequence, and quality of resources. Bufer management is still not a widespread concept across the AEC industry. Even more, the existing bufering strategies are informal from a planning and control standpoint, and they do not provide robust approaches to determine size and allocation of bufers in projects (González, Alarcón, & Molenaar, 2009; González, Alarcón, & Yiu, 2013; Poshdar, González, Raftery, Orozco, & Cabrera-Guerrero, 2018).

Simulation Optimization of WIP Bufers In multi-stage production systems, the work in process (WIP) storage can be utilized as a buffer between activities. Therefore, WIP bufers are understood as a type of inventory bufers (Hopp & Spearman, 2011). In construction, ‘the diference between cumulative progress of two consecutive and dependent activities or processes, which characterizes work units ahead of a crew that will perform work’ represent WIP (González et al., 2009). In repetitive projects such as multi-story buildings, multi-housing projects, and roading projects, WIP is more apparent as activities are performed in discrete repeated units (González et al., 2013). 124

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There is a principle of interdependence and variability in repetitive projects that makes downstream processes particularly vulnerable to the impact of variability from upstream processes (Tommelein, 1998). A rational use of WIP to shield downstream processes from the variability impact makes them behave as suitable ‘bufers’ (González et al., 2009). This section describes a robust simulation optimization (SO) framework to model the optimum size of WIP bufers in the context of repetitive projects using DES and evolutionary strategy (ES) algorithms (González et al., 2013). When DES is combined with an ES, i.e., a metaheuristic algorithm that belongs to a subset of evolutionary computation that mimic natural evolution principles to carry out parameter optimization (Tekin & Sabuncuoglu, 2004), a SO process takes place (Carson & Maria, 1997). In SO, The simulator represents a function ϕ(x1,…, x n) for some input parameter vectors x = (x1,…, x n). The optimization goal is to fnd min x∈W E[ϕ(x)] or max x∈W E[ϕ(x)], where the response E[ϕ(x)] is the expectation of ϕ(x) and W is a feasible range for the parameters. (Buchholz & Thummler, 2005) The idea of WIP bufer optimization was initially explored by (Alarcon & Ashley, 1999) focusing on project cost minimization. Then, the specifc SO notion for WIP bufers was introduced by González et al. (2006) paying attention to project schedule (time) minimization. According to González et al. (2009), there exists a ‘balance problem’ or tension between the goal to decrease the impacts from variability using WIP as bufers and the Lean ideal to minimize inventory such as WIP. González et al. (2009) developed a SO framework that addresses the ‘balance problem’ efectively by providing a multi-objective structure to optimize WIP bufer sizes in repetitive projects. The SO framework enables the optimum WIP bufer size to be identifed by minimizing either project cost or schedule and maximizing project productivity. The multi-objective analytic model (MAM), developed by González et al. (2009) as a mathematical metamodel resulting from the SO process, proposes a tradeof between project goals to optimize WIP bufer size using Pareto Front concepts (Feng et al., 1997). See more details of the conceptual rationale and development of the SO framework and MAM in Figure 8.3.

Multi-objective Probabilistic Bufer Allocation Bufers can often be represented by extra time added to the project duration. This section discusses the development of a multi-objective probabilistic bufer allocation method (MPBAL). The method embodied the power of mathematical modeling (Vectorial Algebra) to represent the success criteria associated with projects, which, in turn, enable the defnition of optimization goals for bufer management (Poshdar et al., 2018). The succes criteria in a project defne a set of principles to achieve the desired outcomes (Chan & Chan, 2004). These criteria are classifed into two broad categories: (1) Deterministic Criteria such as the designated project lifecycle (make-span), cost, net present value (Demeulemeester & Herroelen, 2006). They do not consider any randomness; (2) Stochastic Criteria such as the Timely Project Completion Probability (the probability of having the project completed on-time or earlier), and the Schedule Stability (the magnitude of the diference between the planned schedule and the actual scenario). Randomness is one of their key constituents. A high-quality schedule should meet a combination of deterministic and stochastic criteria (Demeulemeester & Herroelen, 2006; Lamas & Demeulemeester, 2016; Van et al., 2006). These criteria must be quantifed, which, in turn, enables a mathematical optimization process. 125

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Figure 8.3 Simulation optimization framework to model WIP bufers in repetitive projects

Quantifcation of the Project Objectives The rest of this section will discuss a case study to illustrate an optimization proof of concept, where the used combination of criteria is shown in Figure 8.4. The schedule of project activities can be presented as an Activity on Arc (AoA) network. Each activity on the network is associated with three random variables: First, the time performance of the individual activity (presented by a Probability Density Function [PDF]]; 126

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Figure 8.4 The combination of success criteria used in the study

second, the start time of the activity determined by the completion time of its predecessor(s) on the network, and third, the completion time of the activity calculated from the two previous variables. An approximate combinatorial analytic method proposed by Poshdar (2015) combines PDFs along a chain of activities. So that, a set of the secondary nodes can be added to the AoA network to represent the cumulative probabilistic index (CPI) calculated for the start time (CPII) and the completion time (CPIC) of each activity (Figure 8.5 (a)). The CPI presents the PDF accumulated from the frst node of the chain of activities to the point of calculation. Therefore, the expected completion time for each activity can be calculated by adding the completion time of its predecessors to the duration of the activity, extended by the size of time bufers. The completion time (CTi) of the last activity determines the expected completion time of the entire project. The total cost of the project comprises the direct and indirect costs per day added to the fxed cost such as the cost of materials. As the duration of activities and the project completion are determined by criterion 1, the total cost can be readily quantifed. The plan reliability is calculated from the CPIc of the latest activity on the network. To calculate the schedule stability, MPBAL uses the average of the weighted sum of Pr(CTi)/ Pr(CTimax). As demonstrated in fgure 8.5 (b), a worst-case scenario can be defned that presents the earliest activity duration with a cumulative probability of 100%. The average weighted sum of Pr(CTi)/Pr(CTimax) over the full chain of activities gives the schedule stability.

Optimizing the Bufer Allocation Scenario A mathematical goal-seeking formulation (Yu, 2013) determined the optimality level provided by adding units of Time bufers to the planned activity schedule. It was designed to iteratively extend the duration of the project activities evaluating the impact on the four criteria of adding one unit of time in each iteration. MPBAL translated the problem to a vector space and expressed optimality as a distance function to assess the optimality of each possible solution. It presented the ideal point in this vector space by Y* = (f 1*, f 2*, f 3*, f4*), where f k* possessed the best value for each quantifed objective (success criteria) among all the potential scenarios. Accordingly, Y* presented the ideal scenario. Similarly, a Ywst={(f 1)wst, (f 2)wst, (f 3)wst, 127

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Figure 8.5

(a) Probability and time expressions as developed by MPBAL; (b) The elements of the cumulative probability graph

(f4)wst, } was defned, which contained the worst-case scenario for each objective. Table 8.1 gives the extreme values that could be associated with each objective. The optimality of the solutions was expressed by using vector algebra (Boyd & Vandenberghe, 2018). Table 8.1 The extreme values of each objective The objective number (k)

The ideal value ( f k*)

The worst-case value ( f k)wst

The calculated value for the unbufered case

CTpmax

The calculated value for the unbufered case

The calculated f 2 associated with CT pmax

100%

The calculated value for the unbufered case

1.0

The calculated value for the unbufered case

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° Y −Y * ˙ [r ( y )]i = ˝ ˇ ˝˛ Ywst − Y* p ˇˆ i

(8.1)

where: [r(y)]i referred to the defned ratio when one unit of Time bufer is allocated to the ith activity of the network; i denoted the ordinal precedence number of the activities on the project network; Y was the set of the objectives quantifed for the case under examination; Y* represented the ideal value; Ywst represented the worst-case values, and ||.||p denoted the p-norm. The expression in (8.1) presented a function that assigned positive lengths to each vector. The bufer placement scenario that would result in the smallest [r(y)]i would represent the best mathematical solution among the existing options to allocate the additional unit of Time bufer to the project network (Equation 8.2).

{

r ( y ) = min [ r ( y )]i

}

i =1

(8.2)

This quantifcation and formulation process enabled the creation of a computer-based simulation framework that could fnd the optimal bufer allocation following a multi-staged process (see more details in (Poshdar et al., 2018)). This approach supports decision-making by involving a complete set of results and project scenarios. Thus, the decision maker can investigate the interaction between the bufer size and the gains in each of the four project success criteria. It underpins not only the information about what could happen in the project but also the insights into achieving the optimum results. MPBAL avoids repeated random sampling, which is prone to numerical errors originated by computer round-of, iteration, and statistical sampling. Furthermore, the standard methods often address a singular fxed solution, which does not ft into the multi-objective nature of real-life decision making in projects as MPBAL does (Poshdar et al., 2016; Yu, 2013).

Green-Lean Simulation in Construction From a Lean Construction perspective, waste consists of anything that consumes time and cost such as processes and resources, but creates no value (Koskela, 1992). The fow and value views of production are embedded in the Lean concept. Thus, a streamlined process fow means that value is efectively delivered to the end-customer by decreasing waste. In contrast, inefectiveness in the fow of work may result in production waste (Ohno & Bodek, 2019). However, Lean Construction typically accounts only for a type of waste that has production and economic efects on design and construction, but it ignores its environmental impact (Arroyo & Gonzalez, 2016). Belayutham et al. (2016) argue that a waste commonly ignored in Lean Construction is environmental waste. EPA (2007) defnes environmental waste as ‘the excessive use of resources that results in afuence released into the air, water or land that may endanger people and the environment’. From a Lean Construction standpoint, environmental waste does not add value, but only increases cost by the excessive use of resources (so it aligns to the general production waste concept from Lean) (Belayutham et al., 2016). It has been found that production waste may generate environmental waste, as there is a known causal relationship between production and environmental impacts (Golzarpoor 129

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et al., 2017). However, it is challenging to link Lean and the environment as environmental waste has not been typically the focus of improvement within traditional Lean Management (Belayutham et al., 2016). However, Sustainable Construction, which represents the use of sustainable development principles in construction, has the reduction of environmental waste as its core (Tan et al., 2011). By linking Sustainable Construction and Lean Construction principles, a new set of Green-Lean principles are enabled allowing the simultaneous management of production and environmental waste (Belayutham et al., 2016; Golzarpoor et al., 2017). In this regard, Golzarpoor et al. (2017) developed a DES-based framework to model production and environmental waste in projects, based on Green-Lean principles, that has two main modules: 1

An SO module that uses ES algorithms (González et al., 2009) to optimize the performance of project operations against multiple production and environmental goals in tandem. An Input/output (I/O) module to assess environmental loads based on Life Cycle Inventory (LCI), which involves creating an inventory of input (i.e., water, energy, and raw materials) and output (i.e., emissions to air, and physical waste to land and water) fows for a production system (ISO14040, 2006).

A hypothetical project composed primarily by earthmoving and foundations operations was assessed. Three work sites were considered. Loading soil, pouring concrete, parking trucks, transporting soil from excavation zones to dumping zones, and transporting concrete from a batching plant were the production operations modeled. Loaders, trucks, and mixers were the main resources considered. Minimization of cost, time, fuel consumption, and carbon emission through the optimization of resources is accomplished with the Green-Lean DESbased framework. Lean improvements focusing on production aspects such as decreasing cycle time and batch size, minimization of transportation and motion, appropriate use of machinery capacity were tested. The main fnding indicates that enhancing production performance via Lean interventions seems to not only decrease production waste, improving project time and cost; but also, to decrease environmental waste, i.e., minimize use of fuel and generation of carbon emissions. These fndings suggest areas of tradeof where sometimes a production waste improvement does not necessarily imply an environmental waste improvement, and vice versa. The Green-Lean DES-based framework showed great potential to deal efectively with these production and environmental tradeofs (Golzarpoor et al., 2017).

Synergies between Computer Simulation and Lean This section illustrates the synergies between people, process, and computer simulation as a technological advancement triad by providing examples. Table 8.2 shows the synergistic relationship between simulation and modeling, and Lean principles as defned by the TPS in Liker and Meier (2006). In addition, Table 8.3 shows how these digital technologies can minimize or eliminate production waste as defned by Ohno and Bodek (2019). The integration between conceptual modeling and the LPS can support some of the TPS principles from diferent perspectives. For example, by utilizing the LPS data and information, conceptual modeling enables better abstractions of construction production systems to be created, which can help decision-making even before the computer simulation is completely developed (Principles 7 and 13). In addition, computer simulation is well-oriented 130

Simulation and Modeling Facets in Lean Construction Table 8.2 Simulation and modeling approaches relationship to TPS principles (adapted from Liker and Meier, 2006) Conceptual modeling and LPS

Bufer SO

MPBAL

Green-Lean simulation

1 Philosophy as the foundation

–



2 Creation of an ongoing process fow



3 Use of pull systems



4 Leveling out workload



5 Culture of stopping to fx problems

–

6 Standardizing tasks and processes



–

7 Use of visual control



–

8 Use of only reliable technology



9 Growing leaders

–

11 Respecting the extended – network of partners/ suppliers

–

12 Going and seeing for yourself

–



13 Making decisions gradually



–



14 Becoming a learning organization



–



TPS principle

10 Development of outstanding people and teams



Table 8.3 Simulation and modeling approaches versus a waste category minimization/elimination (adapted from Ohno and Bodek, 2019) Waste category

Conceptual modeling and LPS

Bufer SO

MPBAL

Green-Lean simulation

1- Transportation



–



2- Motion



–



3- Waiting



4- Inventory



5- Unnecessary processing

–



–



6- Overproduction

–



7- Defectives

–

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for dynamic production systems where people and processes are interacting in complex ways (Principle 8). The integrated framework can also support the reduction of diferent types of construction wastes. As mentioned in the case study, a computer simulation model can assist to digitally test diferent decisions made during the LPS implementation to assess their infuence on project objectives. For example, diferent site layouts can be examined to minimize travel time and excessive motion of workers and machines. In addition, waiting times can be captured for diferent entities within the system to investigate their relationship with the design of operations in the LPS. Finally, the simulation model for the case study was used to minimize inventory by testing diferent strategies to supply construction materials (e.g., steel and formwork) to site under diferent uncertain conditions. The Bufer SO framework aligns, for instance, to the creation of an ongoing process fow (Principle 2) by minimizing cycle time of transformation processes as one of the project goals via the optimization of WIP bufer sizes. It also supports leveling out workload (Principle 4) by minimizing the inventory of WIP between processes, so an uninterrupted fow is continuously attempted. In terms of waste minimization, the Bufer SO framework can enable the reduction of waiting times when cycle times are minimized, for example. By optimizing the level of WIP between processes, inventories are minimized to a practically acceptable level for projects as stated in the ‘balance problem’. MPBAL supports the creation of an ongoing process fow (Principle 2) by linking processes and people. It builds the links at the data collection and entry phases by utilizing a network visualization for the fow patterns to become visible. Furthermore, the trade-of between diferent objectives under each potential bufer allocation scenario is presented by a graphical illustration. Therefore, the users can identify the impact of each scenario on the fow patterns and the potential amount of waste produced in the form of idle/waiting time. MPBAL generates a comprehensive set of potential project development scenarios that assist the decision-makers to gain a deep understanding of the relevant circumstances. It imitates the performance relationships between activities so that the decision-makers can test and observe the situation as it is (Principle 12). The iterative approach to allocating bufers to the activity unit by unit promotes a gradually undertaken decision-making process (Principle 13). The Green-Lean Simulation framework supports, for example, the philosophy as a foundation (Principle 1) as it allows an explicit consideration of both Lean and Sustainability philosophies, which implies that immediate improvement in production performance metrics (including those fnancially driven as cost and time) is less important than a holistic view on both production and environmental goals in the long run. This framework is also aligned with the becoming a learning organization principle (Principle 14) because an AEC organization must shift its classical cost-time bottom-line mindset toward one based on production and environmental aspects in tandem. This requires a permanent and coherent process of organization learning and change. In terms of waste minimization, the modeling and SO structure focused on operations of the framework to enable the explicit minimization of motion, transport, and waiting. Attention to machinery capacity of the framework also enables decreasing overproduction and unnecessary processing as the necessary outputs and processing are the main outcomes from the multi-objective optimization of several project goals at once. This also results in reductions of energy consumption (i.e., fuel) from machinery and environmental emissions (i.e., carbon emissions).

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Furthermore, computer simulation can support the implementation of Lean Behavior in several aspects, among them: 1

Lean Project Delivery (LPD) promotes the concurrent design of product and processes (Forbes & Ahmed, 2010). Computer simulation is an excellent tool for designing Lean Processes concurrent with facility design. LPD promotes the optimization of the whole not the parts (Mesa, Molenaar, & Alarcón, 2019). The computer simulation of a supply chain can help identify those global optimization opportunities. Computer simulation visualization can help develop and communicate the construction processes to an extended audience in the concurrent design process: designers, contractors, specialty contractors, owners (Abdelmegid, González, et al., 2021). Continuous improvement can be implemented through learning cycles if we adopt computer simulation as a standard process design tool (Abdelmegid, O’Sullivan, González, Walker, & Poshdar, 2021).

Conclusion This chapter discussed the contributions of computer simulation in enabling Lean Construction 4.0. It presented diferent settings where computer simulation as a technological advancement could support processes such as control and planning, bufer management and Green-lean while supporting the people’s side in some cases in the implementations. LPS presents a well-known production planning and control method for lean construction, which also provides a unique opportunity for computer simulation modelers. Typically, the modelers spend a considerable cognitive efort to abstract an operating system. LPS follows the concept of staging the construction planning process by starting with a high-level strategic plan and providing more details as the tasks are getting closer to production. This structured approach facilitates capturing the modelers’ eforts in a documented approach understandable for all stakeholders. Simultaneously, the eforts to improve the practices of simulation modeling can support the expansion of the theoretical foundation of Lean Construction. It also minimizes time, reduces unnecessary data collection, and improves the quality of the simulation model, which fts into the aims of lean philosophy. Bufers decouple activities in a system to help control the fow variations and stabilize the work environment. A dichotomy exists between the lean ideal where bufers are associated with unwanted waste and real-life applications that necessitate the bufers presence in the systems. Bufer management seeks to address this dichotomy. Computer simulation technology supports a bufer optimization process to take place, which enables an efective management of bufers and a suitable approach to balance the ‘Lean’ and ‘practical’ use of bufers. A strong causal relationship associates production waste with environmental waste. Therefore, enhancing production performance by Lean interventions should result in a decreased level of environmental waste. However, existing studies show areas of tradeof exist where a production waste improvement does not necessarily enhance environmental waste control, and vice versa. Computer simulation enables a new set of Green-Lean principles for simultaneous and efective management of production and environmental waste.

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The review of the computer simulation and modeling approaches in this chapter suggests the potential of this technology to support 10 out of the 14 Lean principles as listed by the TPS. ‘Creation of an ongoing process fow’, ‘Use of pull systems’, ‘Leveling our workload’, and ‘Use of only reliable technology’ seem to be the Lean principles more frequently supported by the simulation and modeling approaches reviewed in this chapter. The provided examples suggest clear and strong synergies between simulation and modeling and Lean Construction. Further research is required to identify additional synergies that will keep supporting and developing the practical and theoretical foundations of Lean Construction 4.0.

References Abdelmegid, M. A., González, V. A., O’Sullivan, M., Walker, C. G., Poshdar, M., & Ying, F. (2020). The roles of conceptual modelling in improving construction simulation studies: A comprehensive review. Advanced Engineering Informatics, 46, 101175. https://doi.org/https://doi.org/10.1016/j. aei.2020.101175 Abdelmegid, M. A., González, V. A., O’Sullivan, M., Walker, C. G., Poshdar, M., & Alarcón, L. F. (2021). Exploring the links between simulation modelling and construction production planning and control: A case study on the last planner system. Production Planning & Control, 1–18. https://doi. org/10.1080/09537287.2021.1934588 Abdelmegid, M. A., O’Sullivan, M., González, V. A., Walker, C. G., & Poshdar, M. (2021). A case study on the use of a conceptual modeling framework for construction simulation. Simulation, 98(5), 00375497211056087. https://doi.org/10.1177/00375497211056087 Alarcon, L. F., & Ashley, D. B. (1999). Playing games: Evaluating the impact of lean production strategies on project cost and schedule. Proceedings of the 7th Annual Conference of the International Group for Lean Construction, Berkeley, California, USA. Alarcón, L. F., & Gonzalez, V. A. (2021). Lean Construction in Crisis Times: Responding to the Post-Pandemic AEC Industry Challenges. International Group for Lean Construction. Arroyo, P., & Gonzalez, V. (2016). Rethinking waste defnition to account for environmental and social impacts. 24th Annual Conference of the International Group for Lean Construction, Boston, Massachusetts, USA. Ballard, G., & Howell, G. (1997). Implementing lean construction: Improving downstream performance. In L. Alarcon (Ed.), Lean construction (pp. 111—125). A.A. Balkema, ISBN9054106484. https:// books.google.co.nz/books?hl=en&lr=&id=cvHjf W_UsvsC&oi=fnd&pg=PA115&ots=X7_-hBVLNv&sig=yarYCfSuXYWr3cgC_c2Wsdhvsbg#v=onepage&q&f=false Ballard, G., Kim, Y. W., Jang, J.-W., & Liu, M. (2007). Road map for Lean implementation at the project level. The Construction Industry Institute, (11), 234. Ballard, G., & Tommelein, I. (2021). 2020 Current Process Benchmark for the Last Planner (R) System of Project Planning and Control. https://doi.org/10.34942/P2F593 Ballard, H. G. (2000). The Last Planner System of Production Control [Ph.D diss., The University of Birmingham]. https://etheses.bham.ac.uk/id/eprint/4789/ Bashir, A. M., Suresh, S., Oloke, D. A., Proverbs, D. G., & Gameson, R. (2015). Overcoming the challenges facing lean construction practice in the UK contracting organizations. International Journal of Architecture, Engineering and Construction, 4(1), 10–18. https://doi.org/10.7492/IJAEC.2015.002 Belayutham, S., Gonzalez, V. A., & Yiu, T. W. (2016). A cleaner production-pollution prevention based framework for construction site induced water pollution. Journal of Cleaner Production, 135, 1363–1378. https://doi.org/10.1016/j.jclepro.2016.07.003 Boyd, S., & Vandenberghe, L. (2018). Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press. Buchholz, P., & Thummler, A. (2005). Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization. Proceedings of the Winter Simulation Conference, Orlando, FL, USA. Carson, Y., & Maria, A. (1997). Simulation Optimization: Methods and Applications. Winter Simulation Conference Proceedings, Atlanta, GA, USA. Chan, A. P., & Chan, A. P. (2004). Key performance indicators for measuring construction success. Benchmarking: An International Journal. https://doi.org/10.1108/14635770410532624

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Simulation and Modeling Facets in Lean Construction Dallasega, P., Marengo, E., & Revolti, A. (2020). Strengths and shortcomings of methodologies for production planning and control of construction projects: A systematic literature review and future perspectives. Production Planning & Control, 32(4), 1–26. https://doi.org/10.1080/09537287.2020.1 725170 Daniel, E. I., Pasquire, C., Dickens, G., & Ballard, H. G. (2017). The relationship between the last planner® system and collaborative planning practice in UK construction [Article]. Engineering, Construction and Architectural Management, 24(3), 407–425. https://doi.org/10.1108/ECAM-07-2015-0109 Demeulemeester, E. L., & Herroelen, W. S. (2006). Project Scheduling: A Research Handbook (Vol. 49). Springer Science & Business Media. EPA. (2007). The Lean and Environment Toolkit. https://www.epa.gov/sites/default/fles/2013-10/documents/leanenvirotoolkit.pdf Feng, C.-W., Liu, L., & Burns, S. A. (1997). Using genetic algorithms to solve construction timecost trade-of problems. Journal of Computing in Civil Engineering, 11(3), 184–189. https://doi. org/10.1061/(ASCE)0887-3801(1997)11:3(184) Forbes, L. H., & Ahmed, S. M. (2010). Modern Construction: Lean Project Delivery and Integrated Practices. CRC Press. Golzarpoor, H., González, V., Shahbazpour, M., & O’Sullivan, M. (2017). An input-output simulation model for assessing production and environmental waste in construction. Journal of Cleaner Production, 143, 1094–1104. https://doi.org/10.1016/j.jclepro.2016.12.010 González, V., Alarcón, L. F., & Gazmuri, P. (2006). Design of work in process bufers in repetitive building projects: A case study. 14th Annual Conference of International Group For Lean Construction, Proceedings IGLC–14, Santiago, Chile, González, V., Alarcón, L. F., & Molenaar, K. (2009). Multiobjective design of work-in-process bufer for scheduling repetitive building projects. Automation in Construction, 18(2), 95–108. https://doi. org/10.1016/j.autcon.2008.05.005 González, V., Alarcón, L. F., & Yiu, T. W. (2013). Integrated methodology to design and manage work-in-process bufers in repetitive building projects. Journal of the Operational Research Society, 64(8), 1182–1193. https://doi.org/10.1057/jors.2012.163 Hamzeh, F. R., Saab, I., Tommelein, I. D., & Ballard, G. (2015). Understanding the role of “tasks anticipated” in lookahead planning through simulation. Automation in Construction, 49, Part A, 18–26. https://doi.org/10.1016/j.autcon.2014.09.005 Hopp, W. J., & Spearman, M. L. (2011). Factory Physics. Waveland Press. Joines, J. A., & Roberts, S. D. (2015). Simulation Modeling with SIMIO: A Workbook. Simio LLC Pittsburgh. Koskela, L. (1992). Application of the New Production Philosophy to Construction. https://purl.stanford.edu/ kh328xt3298 Koskela, L. (2000). An Exploration Towards a Production Theory and its Application to Construction. Technical Research Centre of Finland, Espoo, Finland. Lamas, P., & Demeulemeester, E. (2016). A purely proactive scheduling procedure for the resource-constrained project scheduling problem with stochastic activity durations. Journal of Scheduling, 19(4), 409–428. https://doi.org/http://dx.doi.org/10.1007/s10951-015-0423-3 Law, A. M. (2014). Simulation modeling and analysis. Boston, USA: McGraw-Hill Education. Liker, J. K., & Meier, D. (2006). Toyota Way Fieldbook. McGraw-Hill Education. Mesa, H. A., Molenaar, K. R., & Alarcón, L. F. (2019). Comparative analysis between integrated project delivery and lean project delivery. International Journal of Project Management, 37(3), 395–409. Mota, B. P., Viana, D. D., & Isatto, E. L. (2010). Simulating the last planner with systems dynamic. 18th Annual Conference of the International Group for Lean Construction, Haifa, Israel. Nance, R. E. (1994). The conical methodology and the evolution of simulation model development [Article]. Annals of Operations Research, 53(1), 1–45. https://doi.org/10.1007/BF02136825 Ohno, T., & Bodek, N. (2019). Toyota Production System: Beyond Large-Scale Production. Productivity Press. Poshdar, M. (2015). An Advanced Framework to Manage Uncertainty and Bufers in Construction The University of Auckland. The University of Auckland. Poshdar, M., González, V., Raftery, G., Orozco, F., Romeo, J., & Forcael, E. (2016). A probabilistic-based method to determine optimum size of project bufer in construction schedules. Journal of Construction Engineering and Management, 142(10), 04016046. https://doi.org/10.1061/(ASCE) CO.1943-7862.0001158

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Mani Poshdar et al. Poshdar, M., González, V. A., Raftery, G. M., Orozco, F., & Cabrera-Guerrero, G. G. (2018). A multi-objective probabilistic-based method to determine optimum allocation of time bufer in construction schedules. Automation in Construction, 92, 46–58. Robinson, S. (2008). Conceptual modelling for simulation Part II: A framework for conceptual modelling. Journal of the Operational Research Society, 59(3), 291–304. https://doi.org/http://dx.doi. org/10.1057/palgrave.jors.2602369 Robinson, S. (2014). Simulation: The Practice of Model Development and Use (2nd edition.). Palgrave Macmillan. https://link.gale.com/apps/doc/A11498668/AONE?u=learn&sid=AONE&xid=55789f21 Tan, Y., Shen, L., & Yao, H. (2011). Sustainable construction practice and contractors’ competitiveness: A preliminary study. Habitat International, 35(2), 225–230. https://doi.org/10.1016/j. habitatint.2010.09.008 Tekin, E., & Sabuncuoglu, I. (2004). Simulation optimization: A comprehensive review on theory and applications. IIE Transactions, 36(11), 1067–1081. https://doi.org/10.1080/07408170490500654 Tommelein, I. D. (1998). Pull-driven scheduling for pipe-spool installation: Simulation of lean construction technique. Journal of Construction Engineering and Management, 124(4), 279–288. https:// doi.org/10.1061/(ASCE)0733-9364(1998)124:4(279) Van de Vonder, S., Demeulemeester, E., Leus, R., & Herroelen, W. (2006). Proactive-reactive project scheduling trade-ofs and procedures. In J. Jozefowska & J. Weglarz (Eds.), Perspectives in Modern Project Scheduling (pp. 25–51). Springer US. https://doi.org/http://dx.doi. org/10.1007/978-0-387-33768-5_2 Van der Zee, D. J. (2012). An integrated conceptual modeling framework for simulation — Linking simulation modeling to the systems engineering process. Proceedings of the Winter Simulation Conference (WSC) Berlin, Germany. Yu, P.-L. (2013). Multiple-Criteria Decision Making: Concepts, Techniques, and Extensions (Vol. 30). Springer Science & Business Media. https://doi.org/http://dx.doi.org/10.1007/978-1-4684-8395-6

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9 MODELLING CONSTRUCTION PRODUCTION ENVIRONMENTS AS COMPLEX ADAPTIVE SYSTEMS Ali Lahouti and Tariq S. Abdelhamid Introduction With the advancement in data collection systems, data mining, information technology (IT), and network technologies achieving higher speeds and more reliable services, the manufacturing sector has been catapulted into the digital age. Waiting on the sidelines is not an option with organizations charting out advanced manufacturing strategies such as Industry 4.0 (Negri et al., 2017). Given that each strategy is a path or a roadmap to a destination, Industry 4.0 is a path to the destination of smart manufacturing. In the early 1980s, intelligent manufacturing was introduced to leverage artifcial intelligence (AI) in manufacturing environments. Naturally, with AI evolving into AI 2.0, the focus in manufacturing has shifted from knowledge-based intelligent manufacturing to data-driven and knowledge-enabled smart manufacturing. In fact, the term ‘intelligent manufacturing’ is now replaced by smart manufacturing to emphasize the creation and use of data (Zhong et al., 2017). Smart manufacturing is enabled through smart technologies such as cloud computing (CC), Internet of Things (IoT), big data analytics (BDA), deep learning (DL), machine learning (ML), cyber-physical systems (CPS), and digital twins (DTs). We will briefy detail CPS and DTs for two main purposes: (1) projecting where Lean Construction 4.0 will continue evolving towards digital/smart technology; and (2) establishing how the chapter’s subject matter is connected to digital/smart technology. Both CPS and DTs are preferred means of cyber-physical integration approaches. The two provide the ability to take existing manufacturing systems and business models to a new level because of the integration capabilities between the virtual and physical environments. The idea in CPS is to bring the dynamic physical work and cyber world together through communication, computing, and control (3Cs) such that real-time sensing, information feedback, dynamic control, and other services are provided (Hu et al., 2012; Liu et al., 2017). In CPS, as shown in Figure 9.1, the physical entities in the system execute tasks and collect data, and the virtual part conducts the analyses and allows for making decisions. Almost in parallel, the concept of DTs was proposed to achieve cyber-physical integration. The diference is that the emphasis in a DT is on creating high-fdelity virtual models of physical objects in a virtual space in order to simulate the objects’ behaviours in the real world and provide feedback to the decision maker (Grieves, 2014). The major break-through DOI: 10.1201/9781003150930-12

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Figure 9.1 Conceptual framework for CPS/digital twins (adapted from Lu et al., 2020)

DTs provide are in the product development life-cycle – we can consider the next evolution of BIM for construction environments. In summary, CPS can be thought of as more suited for creating better processes and DTs are for producing better products. The CPS and DTs ofer wide opportunities to advance Lean Construction 4.0 challenges related to production system design, production systems control, product development and design, and the integration with the supply chain for of-site fabrication solutions to enable Lean assembly. Notwithstanding these potentials, we would like to take this discussion in a diferent direction, one that is often overlooked. The focus in this chapter is on the role of digital/smart technology in facilitating the understanding of crew planning and management of production operations. Specifcally, crew responses to feld problems, crew actions to varying site conditions from drawings, and crew choreography are emphasized. The key role technology plays in this line of exploration is allowing experimentation with multiple scenarios without subjecting any crewmember to the possibility of injury as well as learning in a fail-safe environment. In addition, digital and smart technology allows the representation of the construction environments as complex adaptive systems. The guiding maxim is that technology is always serving people and not the other way around.

Construction Operations Construction work crews often deal with ambiguities at the work face about ‘what-to’ do while completing their assigned activities using directives. A directive refers to ‘the information or instructions required for the construction crew’ to start a task – e.g., submittals, construction specifcations, shop drawings; and request-for-information (Abdelhamid, 2011). Prior research suggested that insufcient (i.e., variation in) ‘information’ and implicit ‘instruction’ adversely impact productivity performance of a construction project. For example, Howell and Ballard (1997), Chua et al. (1999), Dai et al. (2009), and Formoso et al. (2011) argued that errors in shop drawings, inadequacy of construction specifcations, or inadequate information about project design, plans, and procedures created waste, resulted in rework, and ultimately hindered project productivity performance. Kaming et al. (1997), Koskela (2004), Lahouti and Abdelhamid (2012), and Arashpour et al. (2013) discussed that missing 138

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and/or unclear instruction brought about rework; that work redone was a waste and resulted in a longer execution time and cost. Starting a task with incomplete resources is technically starting a task with whatever is available and to improvise and to proceed with the means at hand. According to Ciborra (1999), improvisation is a natural human instinct and plays an ‘important role’ when an element in a process falls short; it is a greatly contextual and absolutely situational action, which cannot be planned before it actually happens. As cited by Cunha et al. (1999), improvisation is the ability to efectively adapt to the unforeseen; it is efciently planning with resources to match the unexpected situation; and it is ‘making do with materials at hand’ (p. 307). Koskela (2004) referred to this improvisation as a ‘making-do’; a situation in which a construction task starts or continues with either unavailable or non-optimal and non-standard inputs. Cunha (2005) also defned improvisation as using whatever is readily available to make the best out of a situation when there is a problem. Hamzeh et al. (2012) defned improvisation as the ability to come up with a resolution for an unexpected issue and only utilizing resources that are available. Figure 9.2a conceptualizes the acts of improvisation and making-do in the sequence of the construction work as a step intended to overcome disruptions and determine next work. Formoso et al. (2011) introduced ‘lack of information’ among the main causes of improvisation and making-do in a construction project operation. Desai and Abdelhamid (2012) discussed that incomplete understanding of construction project(s) and its scope(s) in entirety would lead to arise of an unexpected situation(s) out of the blue; and argued that construction work crew would take on decision-making responsibilities based on their ‘experience and knowledge’. Hamzeh et al. (2012) asserted that when an activity could not start due to its unavailable prerequisite resources such as perfect information and a complete directive, the construction work crew would improvise and would utilize available information in order to execute that activity. The authors further argued that even employing the Last Planner ® System (LPS) as a Lean Construction tool might not eliminate improvisation, or interpretation, or making-do because focus of the LPS® would be on availability of ‘inputs’ and ‘prerequisites’, it could not anticipate unexpected events – thus improvisation and making-do would help construction work crew to manage the situation.

Figure 9.2 Conceptual reference framework for improvisation and cue interpretation: (a) Improvisation; (b) Cue interpretation

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Pikas et al. (2012) stated that when a planned construction activity was not sound with accord to the LPS®, it technically meant that the construction work crew was in need for more information. Therefore, the work crew evaluated its surroundings, gathered readily available information, and improvised and did make-do in order to fulfl its objective. Menches and Chen (2013), and Menches and Chen (2014) introduced ‘lack of information and direction’ as an unexpected event, which would disrupt construction project work fow; and as a trigger for improvisation and in-situ decision-making in a construction project operation. The researchers argued that ‘disruption’ and ‘improvisational decision-making’ were naturally built in any construction project because unexpectedness was an element of a construction project and this led the work crew to improvise to resume execution of the plan. Moore (2013) discussed that imperfect condition and/or insufcient information at points of installation in a construction job site, a work crew would improvise, and would make-do as a ‘secondary’ plan of work. Ben-Alon and Sacks (2017) developed a tool that is based in agent-based modelling, with BIM used to provide a realistic building environment, to represent the production issues encountered by MEP crews. The tool allows testing of diferent workfow related scenarios such as labour allocations, labour movement, and information and supply availabilities. The mismatch between provided directives and existing situations is certainly one source of ambiguity forcing a work crew to improvise. We argue in this chapter, and our work in general, that work crews are also forced due to this ambiguity to take cues and interpret physical hints present at the work face surroundings to make a decision on initiating appropriate tasks. We further posit that dynamics of a construction work crew, as a collection of two or more skilled trade workers who are continuously engaged in direct (i.e., face-to-face) interactions to purposively perform towards a common objective, infuence its interpretation of work face cues and physical hints. Taking cues and interpretation of physical hints difers from the act of improvisation: Improvisation in construction operation is to make the best out of incomplete, limited, available resources and to come up with a solution in the spur of the moment; cue interpretation as conceptually illustrated in Figure 9.2b is, by contrast, to take existing physical conditions or partially completed/developed prior assignments at the work face, and use those to make sense of incomplete piece(s) of information. Cues are physical hints; they are existing or developing elements of a construction job site – which can potentially misguide the work crew under specifc circumstances; and they are NOT always an accurate step towards generating solutions or completing missing information. In other words, cues (physical hints) appear to ofer complementary information to the work crew members yet have the potential to mislead them. As Lahouti and Abdelhamid (2012), and Lahouti (2013) argued in order to restore the continuity of operations, the construction work crew directs its eforts to deploy the most likely resolution by taking advantage of existing or developing cues; and by interpreting the most relevant physical hints at the work face surroundings to compensate for the missing information and instruction.

Construction Work Crew Performance as a Group in Presence of a Cue The impact of incomplete instruction and imperfect information has on production performance on a construction job site – not only due to the frequency of such occurrences, but also for the economic burden caused by rework – is not well understood. The specifc case investigated here pertains to incomplete instruction and/or imperfect information as resolved by work crews using the interpretation of cues and physical hints on a construction job site. 140

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Figure 9.3

Input-process-output conceptual framework for cue interpretation in a construction work crew: (a) Schematic process map for cue interpretation; (b) Input-process-output (I-P-O) conceptual framework

We set out to understand how interactions between work crew members may infuence the interpretations, and to investigate whether a relationship exists between interactions of work crew members and interpretation of a cue when the work crew receives an incomplete instruction and imperfect information. As Figure 9.3a depicts, the output of interaction(s) between work crew members is interpretation of the cue(s). A decision, which is based on cue-interpretation, will then defne what will be executed on the job site. The conceptual Input-Process-Output (I-P-O) framework illustrated in Figure 9.3b was used to systematically present how a construction work crew functions as a group: when at least two individuals form a group to deliver an output (Adopted from literature by (McGrath, 1964, p. 70; Hackman & Morris, 1975, p. 50; McGrath, 1984, p. 13). In this conceptual model, a construction work crew may be represented as a linear, causal chain where: • •

•

Input – factors feed into a construction work crew, and set the stage for members’ collective work; Process – technically what takes place in a construction work crew while members utilize resources and collectively perform an activity/a task to deliver a meaningful output; it is the relationship between Input and Output; and Output – that is a tangible result of a construction work crew’s activities, dependent of course on the Input and Process constructs. 141

Ali Lahouti and Tariq S. Abdelhamid Table 9.1 Population characteristics of interviewee construction crews Sample size Skilled trade

Owner self-performing

Specialty, sub-contractor

Electrician Rough carpenter Trim carpenter Tin knocker

2 5 7

47 33 43 39

Population size

14 (8%)

162 (92%)

An Average of 17 Years Experience (Commercial Building to Light Industrial)

A construction work crew is a number of individuals trained in a specifc trade discipline who work together on a construction job site for a specifc period of time on a technical task that requires using equipment, tools, and/or technology. Four types of input factors in Figure 9.3b likely trigger members of a construction work crew to interact with one another and render a decision (more explanation available in Lahouti (2017)). To understand the interaction a construction work crew goes through during decision-making in an ambiguous situation, and also to learn about factors which infuence such interaction in the presence of cues and physical hints, a population of 176 construction skilled trade workers were qualitatively interviewed. Interviewees were selected based on availability, and their willingness for participation. The characteristics of interviewee population are reported in Table 9.1. The interviews were conducted one-on-one at construction job sites, and each lasted for an average of 30 minutes. Each interviewee was asked to refer to the following scenario that he or she might have encountered throughout her or his career as a construction skilled trade worker: •

When you arrived on a job site, you realized that the situation did not match the instructions (e.g., construction drawing; verbal request; work-order) originally received; and you needed to acquire more information or informative instruction to complete your activity/task. In the meantime, there was a readily-available physical cue at the work face which might have – rightly or wrongly – flled in for the missing pieces of information.

Autonomous Problem-Solving Two discussion topics followed the initial question. In the frst follow-up discussion, each interviewee was asked to describe her or his most-likely, immediate action in such a situation considering that he or she would often work in a crew. Notes were taken from discussions and responses each skilled trade worker provided. From the content analysis of interview responses to the follow-up questions, 183 ‘catchphrases’ were extracted, which were then clustered into three (3) mutually exclusive action categories presented in Figure 9.4a. Further analysis of interview contents revealed that construction work crew members referred to ‘Autonomous Problem-Solving’ – that is, he or she would try to resolve the issue in crew and/or between crews – 127 times (70% of the catch-phrases) – as summarized in Figure 9.4b (detailed results can be found in Lahouti (2017)). 142

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Figure 9.4

Analyses of work crews most-likely immediate actions in an ambiguous situation: (a)Work crews most-likely immediate actions in an ambiguous situation; (b) Frequency analyses of work crews immediate actions

The interview content summarized in Figure 9.4 supported the need in this investigation– that is, when a construction work crew faces an ambiguous situation at their work face, the work crew tends to rely on readily available hints in its surroundings in order to make a decision about proceeding. It should be noted that the right-hand fgure in Figure 9.4 refects the make-up of the 69% (so, the 69% = 58% ‘Discuss with other crew member’ + 9% ‘Review drawing, work-order, etc.’ + 2% ‘other’). On that basis, and also fndings presented by Lahouti and Abdelhamid (2012), and Lahouti (2017), a conceptual framework for such a process was developed as illustrated in Figure 9.5a. This autonomous problem-solving framework illustrates a construction work crew arriving at a job site to fnds its workface diferent from provided work-directives, and the construction documents (e.g., shop drawings and work orders) refect a diferent the work face condition than what they expected. As the interview 143

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fndings revealed, 70% of the actions that the work crew immediately takes on to complete its assigned work focus on fnding the missing information, interpreting present ambiguity, and going through decision-making by relying on surroundings cues and hints which are readily available. When the decision leads to incorrect and/or undesired outputs (i.e., a waste), the work crew must undo its work, and to fnd additional information from other sources, and then correctly re-complete its work. The dotted rectangle (large ‘black’ colour in Figure 9.5a) identifes the cue-interpretation loop. Figure 9.5b conceptually presents how the missing information about activities/tasks forces a work crew into an interpret routine, which, in turn, demands interactions between crew members. The quality of these interactions is a function of the characteristics of each member– the input factor of concern in this research. As a result of these interactions, physical hints at the work face are interpreted and accordingly a decision will be rendered to complete the activity/ task.

Figure 9.5

Conceptual framework for construction work crews autonomous problem-solving and heuristic of cue interpretation: (a) Work crews autonomous problem-solving; (b) Heuristic of cue interpretation

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Construction Work Crew Interaction and Cue Interpretation Construction Work Crews Interactions The second follow-up discussion focused on factors which were infuential to interactions between work crew members. Each interviewee was asked to discuss the factors (e.g., individual and environmental) in her or his opinion which were the most important in the process of making a decision as a member of the work crew. From the content analysis of these unstructured and open discussions, 2,070 catch-phrases were extracted, and clustered into 14 descriptive factors. It should be noted that these factors were described in such a fashion to inductively represent catch-phrases, and to describe implied contents of interviews. Figure9.6 reports these infuential factors. As Figure 9.6 indicates, the factor Knowledge and Skills of a work crew member was the most frequently mentioned by interviewees. By stating ‘Knowledge and Skills’ the construction work crew members were referring to the degree of experience, diversity in expertise and specialties, capabilities, level of craftsmanship, and types of trainings. These were the most impactful factors on their within- and between- crews interactions and processes of rendering a decision on ‘what-to’ do. This conclusion is consistent with Hamzeh et al. (2012) claim that knowledge, skills, and abilities of a construction work crew member infuence the improvisation outcome. The literature on Small Groups and their Dynamics also recognizes a relationship between knowledge, skills, and abilities of group members and the overall performance of group and its outcome (Bennis & Biederman, 1997; Forsyth, 2014; Jones, 1974). The infuence of a strong leadership voice in teams is not overlooked and is refected in how one crew member will convince the rest to proceed in one direction or another. This is respectively captured in both Knowledge and Skills and Confdence and Trust. Confdence and Trust was the second infuencing factor to which construction work crew members referred the most frequently. The interviewees recognized this factor from two diferent aspects of: (a) Morality – whether their crew member could be trusted as a human

Figure 9.6 Frequency analysis of infuencing factors on work crew members interactions

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being; and (b) Professionalism – whether their crew member made a suggestion to only complete her or his assignment or he or she ofered a well-thought, value-adding decision. The third most-frequently cited infuential factor by interviewees was Open-mindedness. Moral and Personality of the construction work crew members was the fourth most-frequently mentioned factor.

Agent-Based Modelling and Simulation To evaluate whether an association exists between the aforementioned infuential factors (Figure 9.6) and construction work crew performance in presence of cues and physical hints, a simulation model was developed to conceptually demonstrate interactions between members of a construction work crew when a crew was to interpret cues, and physical hints at the work face. The simulation model used an Agent-Based Modelling and Simulation (ABMS) approach. An ABMS is constructed of two key components: a set of agents and an environment. Agents are heterogeneous entities which share a common environment, and autonomously interact with one another and also their underlying environment in order to represent a real-world system. Each agent owns unique characteristics, is bounded by a set of rules that govern its decision-making, and follows a specifc set of actions each time it autonomously makes a decision (Bonabeau, 2002; Du, 2012; Ligmann-Zielinska, 2010; Sawhney, et al., 2003). For the problem under consideration, an abstraction is frst needed as reported in Figure 9.7a. Essentially, a dark grey circle identifes agents and represents a construction work crew of n members. This work crew was expected to reach the only dark grey square of agent environment to symbolize completion of a construction activity – each counted with variable cycle. Completing an activity required conducting a collection of tasks – which were simulated by relocating from one white square to another. A white square (i.e., tasks) gets replaced with a black square to abstractly demonstrate imperfect and incomplete instructions. Black squares as obstacles were considered to be incomplete instructions because each would only instruct agents to NOT relocate onto them with NO further information about ‘what-to’ do. Combinations of black and white squares created cross-paths to symbolize presence of cues and physical hints. In other words, a cue was represented by availability of more than one white square onto which agents could relocate. The work crew in this agent-based model had to interact with cues and physical hints, interpret them, to decide which activity should be conducted. The shortest path between each white square (or rather a neighbour) and the only red square (i.e., destination) were introduced to symbolize the most efcient set of tasks to complete a construction activity. Figure 9.7b graphically presents the aforementioned essential components of the ABMS in NetLogo environment which is capable of exploring correlations between behaviours of individual entities and patterns that emerges from interactions between those entities (Wilensky, 1999). Members of the simulated work crew, in the meantime, were in possession of three behavioural factors which were identifed as the most infuential from the analyses of the conducted interviews. These were (i) Knowledge and Skills; (ii) Confdence and Trust; and (iii) Open-mindedness. Aggregating these infuential factors in the ABMS framework was a challenging task. The process involved presenting these factors in terms of probability of correctly identifying a Von-Neumann neighbour (i.e., an approach in cellular automata in which each cell is adjacent to four neighbours of north, east, south, and west) which would lead the shortest path to the destination neighbour. The confdences and trust were 146

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Figure 9.7 Presentation of developed ABMS: (a) Essential components; (b) Graphical presentation

incorporated using probability of occurrence by defning weights to the rendered decision based on skilfulness of a crew member. The incorporation of these characteristics as well as how an agent traverses the simulation environment requires technical treatment that are not pertinent to the focus of this chapter. Detailed explanations of this both conceptually and computationally are available in Lahouti (2017). In summary, in this ABMS environment presented in Figure 9.7b, alternative decisions would be generated by the factor skilfulness while power of decision-making would be controlled by the factors confdence and open-mindedness, respectively.

Exploratory Analyses One cycle of simulation was completed when the agents reached the destination. This agentbased model was simulated for a total of 199,948 cycles. Simulation cycles were independent from one another – i.e., each was an attempt by agents to reach the destination passing through 32 non-diagonal neighbours with NO learning from the previous experiment(s). It must be noted that understanding the evolution of learning was not the scope of exploration; and each independent cycle of simulations was to represent a diferent construction work crew attempting to conduct its work. In each cycle, achieving the shortest path was the aim– which took place through interactions between the members of work crew. The number 32 represented the most efcient (i.e., shortest) performance for the layout under study (case of perfect information). 147

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Each cycle symbolically represents the change in activity duration when the construction work crew was forced to take cues and physical hints and interpret them in order to determine ‘what-to’ do. In this ABMS, agents follow their moral principles and neither intentionally took wrong cues nor interpreted them inaccurately to beneft from a lengthy activity or mislead their colleagues as a form of horseplay. In each simulation cycle, three dependent output variables were measured: • • •

Number of neighbours visited Number of neighbours re-visited Number of neighbours dead-end

The measures of these outputs summarized in Figure 9.8 reveal two major points: •

•

First – The population size of agents contributed to the variation of each output’s category. For example, the mean number of neighbours visited and dead ends reached were the least (i.e., ≈ 57 and ≈ 0, respectively) for the group of fve agents, while those for a group of two agents were the most (i.e., ≈ 65 and ≈ 0, respectively). In a diferent fashion, groups of any agent number re-visited roughly the same number of neighbours on average. These measures may imply that a group of agents – in comparison to one individual – infuences the outputs of this model. Second – Values in each output category were more clustered around smaller values as the population of agents grew larger. For instance, the mean number of neighbours visited decreased by (roughly) 20% from ≈ 72 to ≈ 57 for one and fve agents, respectively. The number of neighbours that fve agents visited were 57% more clustered around the mean than those of one agent (i.e., standard deviations of ≈ 111 and ≈ 49 for one and fve agents, respectively).

Figure 9.8 Variation in performance with respect to crew size

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Construction Work Crew Size To understand whether changes in outputs would be observed at large with respect to a group size greater that one agent and to make interpretation straightforward, probability density functions (PDFs) were constructed for outputs of each population size. The PDF plot enables an observer to compare magnitudes of probability – i.e., the area under each distribution curve corresponding to a range of data. Figure 9.10a, the probability density of number of neighbours visited for diferent group sizes, demonstrates a pattern in which the likelihood that an operation time would fall within a particular range varies as group size changes. This change may be interpreted in favour of the optimal number of neighbours visited that there is a greater likelihood that this number is optimal in a group of fewer agents. It can also be interpreted in favour of group size – that non-optimal number of neighbours visited would be less likely to occur in groups of more agents in comparison with those of fewer – in case of this investigation from fve to one; although the variations of outputs were not as signifcant in magnitudes between groups of two and fve agents as was between those of one and fve (or even one and two). The vertical dotted line in Figure 9.10a shows the value of optimum number of neighbours visited established at the 32 steps in the maze (i.e., Figure 9.7b).

Construction Work Crew Skilfulness In order to explore whether a correlation existed between variation in outputs and skilfulness level of each agent, frequency analysis for number of neighbours visited was conducted. Figure 9.9 represents population of simulation cycles in which 32 neighbours were visited in colour ‘black’; and population of those in which more-than 72 neighbours were visited in colour ‘light grey’. It can be observed: • • •

Quadrant A accommodated majority of simulation cycles in which 32 neighbours were visited; Quadrant C clustered with a great portion simulation cycles in which more-72 neighbours were visited; and Quadrants B and D were populated by a combination of simulation cycles – those with optimal number of neighbours visited, and those with non-optimal.

In order to better understand the infuence of diferent levels of skilfulness (refected in Figure 9.9) on the likelihood that the number of neighbours visited would deviate from its optimum, PDF curves were constructed. Illustrated in Figure 9.10b, the numbers of neighbours visited were presented in terms of independent input factor skilfulness. In compliance with the analysis of reference value, factor skilfulness was divided into four levels – quadrants A, B, C, and D. Each curve represents a quadrant; and the area under each curve defnes the likelihood that a range of particular values would populate that quadrant. The range [32, 40] was the case in more than 52% of simulation cycles. This likelihood was the least for skilfulness values of quadrant C. In contrast, for the range [40, 72] was the number of neighbours visited also in (roughly) one-third of simulation cycles, skilfulness values of quadrant C provided the greatest likelihood in comparison with those of quadrants A (i.e., the least), B, and D. 149

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Figure 9.9 Skilfulness level for two agents with respect to simulation cycles outputs

Figure 9.10

Variation in number of neighbours visited: (a) With respect to group size; (b) With respect to skilfulness level for two agents.

Dotted vertical lines in the ‘black’ colour defne the boundaries of these ranges. It should be noted since density distributions for dependent outputs of quadrants B and D are similar, only population of outputs corresponding with quadrants B was considered for further analyses. A rational explanation for clusters of values in quadrants A and C of Figure 9.9 and also their greater likelihoods in Figure 9.10b would be the likelihood that n decisions – two, in this case – at a given location were similar. Moreover, at any location the likelihood of dissimilar decisions was greater for skilfulness values in quadrants B and D. The latter explanation is based on the fact that those values were drawn from diferent sections of skilfulness spectrum – value of skilfulness for one agent was greater than 0.50 while that of the other agent was lesser than 0.50. This unlikelihood for similarity of decisions, unarguably translates to greater likelihood of interaction between agents in the process of determining the interim destination. 150

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It can be argued, in other words, that greater volumes of interactions between agents took place in simulation cycles which populated the quadrants B and D; and consequently a trigger for greater involvement of the factors ‘confdence’ and ‘open-mindedness’. Thus, further analyses focused on those populations (i.e., curves in colour ‘light green’ and ‘dark green’). It was, so far, illustrated that interactions between n agents – two in this case – would be enabled by diferent levels of their skilfulness. Lahouti (2017) also showed the interactions would be intensifed based on agents’ levels of confdence and open-mindedness; and that intensity of these interactions in turn would infuence output variables of this ABMS. In order to explore how infuential each of these three factors were – or rather to investigate how sensitive the outputs of ABMS were to these three factors – or even how much each of these three factors contributed in the variation of ABMS outputs, a Sensitivity Analysis was conducted. The details of the sensitivity analysis are outlined in Lahouti (2017). The analysis used the SimLab software (2017) with variations in outputs of the 199,948 ABMS cycles with a decomposition relative to the corresponding input factors. Based on the sensitivity indices summarized in Figure 9.11, it can be concluded that input factors skilfulness, confdence, open-mindedness, and crew independently caused 45.95% of outputs variations in this AMBS – with the most signifcant infuence of 28.68% by factor skilfulness; and that interactions between all four input factors contributed to more than a half of variations in the outputs – i.e., 54.05%. The fact that values for frst-order indices summed less than 100% demonstrate the complexity of this model and an additive correlation between impacts of these input factors on variations of model outputs. Additional sensitivity analysis was conducted for Output Rework and Call for Assistance. More variations in ‘Output Rework’ were caused by interactions between input factors in which factor skilfulness led the intensity followed by the factors open-mindedness, confdence, and crew-type. Similarly, ‘Call for Assistance’ was a result of interactions between input factors than their independent infuences.

Figure 9.11

Independent and overall contribution of input factors to outputs variations: Number of neighbours visited

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Discussion Construction Operations It was explored whether being a group contributed to variations of ABMS outputs. Analysis results demonstrated that the levels of uncertainty in the model outputs would vary for work crews of diferent size (i.e., number of members). This variation is an implication of some processes or rather interactions between members of a crew, and it does not imply that a construction work crew of one member may be extra efcient, more productive, and/or likely successful than that of two or more members in accurately interpreting the next steps based on surroundings cues and physical hints. As illustrated in Figure 9.8, a crew of a larger size would experience a better performance in variation of operation time due to greater volume of interactions between crew members in decision-making and cue-interpretation. However, it does not necessarily improve the performance mean. The crew size does facilitate the intensity of interactions between crew members and, consequently, contributes to predictability and reliability of workfow. Nevertheless, interaction is not enough to eliminate variations in performance and/or workfow. Additional analysis also showed that, with respect to level of skilfulness, a homogeneous group made similar decisions; and that heterogeneity of skilfulness values propagated greater uncertainties in distributions of outputs. This implies that a construction work crew of comparable (i.e., homogeneous) skilfulness levels may complete identical tasks more efciently in comparison with a heterogeneously skilled crew. Homogeneity of crew members in skilfulness is argued to result in similar decisions which in turn decreases not only the intensity of interactions but also their impacts. That is, crew members take similar cues and/or interpret them similarly to determine the ’what-to’ do’s; which eliminates great intensities of interactions to introduce the fnal decision. On the other hand, it may be inferred from these analytical results that heterogeneity of skilfulness enables and encourages work crew members to interact for rendering the fnal decision as each member takes a diferent cue and/or makes a diferent interpretation of that – which, in turn, becomes more infuential. It can be further inferred that the quality of interactions output depends on confdence and open-mindedness levels of each construction work crew member: The more the work crew members are heterogeneous in confdence and/or open-mindedness, the greater infuences their interactions impose. Sensitivity analyses of ABMS outputs revealed that skilfulness of crew members may be introduced as a factor, which will need more attention since it strongly impacts the amount of rework and waste, operation time, and efciency in resources utilization, and also frequency of call-for-assistance, respectively. As previously mentioned, one side of knowledge and skills is about directives (information and instruction), lack of which invites cues and surroundings physical hints in determination of ‘what-to’ do. Therefore, this is aligned with the problem at hand since the more complete and more explicit is a directive, the more effciently it is executed; the less amount of waste is generated; and the fewer occasions when supervisory assistance is required. It is of value to note that how-to do represents the technical aspect of knowledge and skilfulness – which greatly infuences the amount of rework and waste, and frequency of call-for-assistance. These, in turn, will impact operation time due to lack of knowledge on how to complete a task. Additional discussion regarding open-mindedness and confdence in ability, and reliability of a crew member can be found in Lahouti (2017). 152

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Te Last Planner System® Fundamentally, this research ofers a sound explanation to better understand ‘why’ the LPS® of Project Planning and Production Control is instrumental in maintaining a reliable, and predictable workfow for a construction operation. LPS® (Ballard, 2000) suggests that a construction task be shaped based on four quality criteria: Defnition; Soundness; Sequence; and Size (defned in Ballard (2000)). The criterion ‘Soundness’ outlines that a task requires a set of resources to start and to produce a physical outcome at the right time. External conditions, directives and instructions, materials, personnel, etc. are among such resources. The soundness criterion is of particular relevance to this research for two reasons. First, it ensures that directives an activity needs are present, explicit, and complete. Therefore, ‘what-to’ do is clearly known and there will be minimal likelihood – if any – of cue interpretations. Second, it channels, directs, and focuses the interactions between crew members towards an advantageous output – i.e., making an activity ready for execution. Either it is in the make-ready stage or towards the weekly work planning, two phases of LPS®, the soundness criterion minimizes ambiguity, and serves as a channel to efectively employ the inevitable interactions between crew members so implicitness is uncovered. In other words, it minimizes – if not eliminate – the iterative nature of cue interpretation (illustrated in Figure 9.5a), generation of waste, and inefciency of operation. The consideration of Lean Construction principles embodied in the LPS® against the fndings of the ABMS presented in this chapter reveal that there are sound and fundamental underlying team dynamics that the LPS® process is addressing to make workfow reliable and predictable. These dynamics relate to Skilfulness, Confdence, and Open-Mindedness. It is important to note that efects of these factors will be more meaningful and sensible when considered in combination with one another. This is what the LPS® enables through its systemic and disciplined process. The reality, however, is that a work crew is what it is! That is, with the fair assumption that members of a construction work crew are trained in a specifc discipline, and are knowledgeable about how-to do their assigned activities, it is hard to argue that traits they bring to the crew will neither change nor will they be altered. And as mentioned previously, exploratory and sensitivity analyses concluded that heterogeneity of such traits drives their interactions. Thus, it is still appropriate to argue that those interactions may be directed towards positive infuences, which is proven through the successes of the LPS® (Ballard & Tommelein 2021)

CPS and DT Work With the fndings presented thus far, and those with further exploration and relaxation of assumptions and the natural progression of the work described here, a solid foundation will be available for developing a CPS/DT decision-making system that assists workers in real time. That is what we envision, and it is not far-fetched by any means. We envision that a crew will have special helmet mounted system that transmits to a CPS/DT system. In essence, the helmet mounted system will transmit a picture (perhaps video at some stage) of the workface and site environment in general. The CPS will reconstruct the environment using the DT capabilities, and the ABMS prototype presented here will be engaged to go through the decision alternative generating process. The decisions will be suggested to the crew and an interaction will take place, enabling the crew to run through what-if scenarios real time with data rich input. 153

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We have only discussed the decision to proceed with work based on lacking information or varying reality on the site and the resorting of the crew to look for cues. In addition to assisting in decision regarding this matter, as modelled through this chapter, there are other issues that the helmet mounted CPS/DT system can assist crews in as well. For example, safety and code violations, as well as workmanship issues can all be integrated into the real-time decision-making assistance system.

Conclusion The problem addressed in this chapter focused on the interpretation of cues and physical hints by a construction work crew in an ambiguous situation and where explicit directives were absent. It was also posited that interactions between members of a construction work crew would impact interpretation of cues and would thus impose infuences on work crew performances. Reference frameworks were developed to conceptually present: (i) phenomenon of cue interpretation in construction workface; and (ii) a work crew operation with a process of interpreting a cue and physical hint. These frameworks were translated into an agent-based modelling and simulation where 199,948 cycles of experiments were conducted, and a series of three outputs were determined to represent: (i) activity operation time; (ii) amount of rework; and (iii) frequency of call for assistance. Exploratory statistical analyses and sensitivity analyses (SA) were conducted. In addition to the main problem of cue interpretation during construction operations, this chapter also ofered a computational validation of the quality-tasks concept, and constraint screening utilized in the LPS®. Its ability to prevent planning failures is undisputed, given its mounting empirical evidence of the past quarter-century. The conclusion is that ‘it works’. Why it work has still not been adequately answered. This work has illustrated a principled explanation that leads to a stronger understanding for why the LPS® works in maintaining reliable and predictable workfow on construction sites. In the absence of the routines and practices of the LPS®, the crew is not shielded from the damaging efects of having to interpret cues and physical feature of workplaces as proven in this research. The cost is manifested in the delays caused by waiting for directive clarifcations, and possibility of rework. Finally, the key role that technology advances played in this chapter, and therefore in the opportunities to advance Lean Construction 4.0, is represented in the facilitation of experimentation with multiple scenarios without subjecting any crew member to the possibility of injury as well as learning in a fail-safe environment. The ABMS allowed the representation of the construction environments as a complex adaptive system, and future research will explore more fdelity with both CPS and DTs as preferred means of cyber-physical integration approaches. It is envisioned that a CPS can allow real-time guidance of crews in operations through 3Cs, enabled through utilization of the concept of DTs for creating high-fdelity virtual models of physical environments that crews encounter.

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10 SOCIAL NETWORK ANALYSIS TO SUPPORT IMPLEMENTATION AND UNDERSTANDING OF LEAN CONSTRUCTION Rodrigo F. Herrera and Luis Fernando Alarcón Introduction Lean Construction is a management philosophy based on the principles of the Toyota Production System (Fakhimi et al., 2016). The basic conceptual innovative notion of understanding construction as production was introduced by Koskela (1992). The successful implementation of Lean Construction is dependent on several factors; it is not enough to use a collection of methods or techniques; the organization’s culture must also be transformed (Bhasin & Burcher, 2006). While there is no universally accepted concept of Lean culture, the following characteristics can be identifed: Lean can improve the project team’s ability to communicate with the customer systematically and openly; Lean is focused on the systematic elimination of waste, with all workers responsible for identifying non-value-adding activities; the emphasis in Lean is on empowerment and coaching; Lean highlights continuous improvement (Hu et al., 2016). Collaboration, teamwork, connectivity, cooperation, and dedication are all examples of culture in the context of Lean Construction (Schöttle et al., 2014). Some characteristics of an organization that applies Lean Construction are collaboration, alignment of interest and objectives, gain and pain sharing, trust, teamwork, open communication, confict resolution, continuous improvement, team integration and cohesion, commitment management, fat organization, team experience, decentralization, formalization of communication, matrix strength, and others (Herrera et al.2021; Schöttle et al., 2014). The architecture, engineering, and construction (AEC) sector is organized into diferent disciplines at various stages of the project lifecycle (Dainty et al., 2006). The high fragmentation and complexity of AEC industry projects require high levels of collaboration, which cannot be addressed solely by technology but must be understood through the social phenomena that arise in project teams (Phelps, 2012). The social factors of a project team can be approached by Lean culture; thus, in order to gain a deeper understanding of the dynamics that occur in a project team, variables such as teamwork, knowledge fows, collaboration, commitment management, trust, and organizational learning, among others, must be studied (Herrera et al., 2020). The evaluation of social variables is a difcult process. Teamwork is a method of approaching this topic using various instruments available in the literature to assess it (Herrera DOI: 10.1201/9781003150930-13

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et al., 2020). Communication, coordination, collaboration, and respect are all factors that most instruments utilize to measure teamwork (Valentine et al., 2015). These instruments are mostly surveys that assess teamwork from the organization’s overall perspective, making it difcult to analyze individual or sub-group performance (Paris et al., 2017). Social network analysis (SNA) is a technique for evaluating various types of interactions from both a person and a group perspective simultaneously, and it has been used to study the fow of information in the AEC sector (Alarcon et al., 2013). The SNA enables an organization’s culture to reach a previously untapped dimension of information and knowledge fow (Lee et al., 2018). It allows analyzing the strength of team project members’ relationships, critical to organizational success, creativity, efciency, and worker satisfaction in a Lean-driven construction company (Flores et al., 2014). This chapter reviews the application of SNA to identify and understand the organization’s social network through an analysis of interactions to make visible its potential weaknesses and strengths. The chapter begins with a review of the basics of SNA and graph theory. Later, we present a general exploration of SNA in the AEC sector in recent years. Then, we reviewed several applications in various projects and company organizations: we introduce SNA as a diagnosis tool; we illustrate the infuence of selected Lean Construction management practices on project and company organizations. For instance, the Last Planner System (LPS) implementation on planning and commitment networks and the relationship between network metrics and project performance. At the design ofce level, we discuss the application of SNA to explore the infuence of the implementation of building information modeling (BIM)/Lean practices in design teams and the implications for Lean implementation, studying the people-process-technology triad as the core of Lean Construction 4.0. In the fnal section, we discuss how SNA can be used to understand the performance of project teams and the challenges of the applications of this tool to understand the implementation of Lean Construction 4.0 in AEC organizations.

SNA Fundamentals Organizational relationships are represented as a network of nodes or actors connected by carefully specifed connections is the goal of this method (networks) (Pryke, 2012). A sociogram and mathematical metrics such as density, length, and diameter can be used to graphically characterize each network (Marin & Wellman, 2011). A social network is composed of one or more sets of units called “nodes”, “actors”, or “vertices”, as well as links or social linkages between them. The nodes are usually people, e.g., specialists, team members, or stakeholders. Relationships may apply to confict and information fow, coordination, control, trust, or afect. In most social network research, attribute data identifes the nodes, relationships, or both (O’Malley & Marsden, 2008). A pair of two nodes is known as a dyad, and a triad of three nodes is known as a triad (Pryke, 2012). An egocentric network is composed of an actor, the other actors in its immediate neighborhood, and their relationships; a star is made up of an actor and all incidental relationships (Segarra et al., 2017). There are two ways to visualize networks: as matrices or as graphs. Networks are often represented in the AEC industry using graphs or sociograms, in which people (nodes) are vertices, and non-null relationships are lines (Marin & Wellman, 2011). A directed or undirected relationship may exist between two actors. When two individuals have a duty to interact bidirectionally, undirected links occur; directed links, on the other hand, can be unidirectional or bidirectional, implying that the interaction fows from Person A to Person 158

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B (Hoppe & Reinelt, 2010). Arcs and edges are undirected and directed linkages, respectively; arrows at the end(s) of arcs indicate their directionality. Although most sociograms are binary, value-weighted graphs can be made by displaying non-null tie values along arcs/ edges or permitting thinner and thicker lines to represent line value (Freeman, 2004). Social network graphics have been assimilated as spatial defnitions of its members’ actions. The components that make up the networks defne topological regions that mark knowledge and interaction barriers within the community and subjectively defne the direction that team members should take (Scott, 2013). As a result, the topology of networks and their components can be used to analyze the patterns of interaction between individuals and the entire network or their immediate social context (Lewin, 2013). When assessing friendship or work relationships within social networks, SNA metrics come in useful. The degree of cohesiveness at the individual level is measured by the average degree, diameter, density, number of components, and average clustering coefcient. The average degree, diameter, density, number of components, and average clustering coefcient all represent the degree of cohesion at the network level (global metrics) (Abraham et al., 2009) (Table 10.1). Table 10.1 SNA – networks metrics (Adapted from Table 2 in Kereri & Harper, 2019) Metric Network density

Description

A measure of how well a network is a connection to indicate a number of interactions. Clustering A real number between 0 coefcient and 1 to defne whether there is clustering or not. Average path Any sequence of nonlength repeating nodes that connects the two nodes (i,j). Where n is the number of nodes of the networks Component Represents the network connectivity degree Degree It is used to measure centrality a node’s infuence of popularity as a distribution of relationships. Closeness A measure of the speed by centrality which information can reach other nodes from a given starting node. Calculated by measuring the average of all shortest paths from a node “a” to all other nodes “ j” in the network. Where n is the number of nodes of the networks

Formula actualconnections potentialconnections numberofclosedtriplets numerofalltriplets ( openandclosed )

1 n ( n − 1)

˜shortestdistancebetweeniandj i˛ j

# subsetofnodesinterconnectedbyedges / relationalties.

# linksintooroutofanode

1 n( − 1)

159

˜shortestdistancebetweenaandj a˛ j

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Table 10.1 shows some typical global metrics to analyze the sociograms generated with the SNA. Figure 10.1 displays four sociograms in a six-person network as an example (nodes). The global metrics graphically and statistically describe the diferent interactions (links) among the members of each sociogram. Figure 10.1a exhibits a network focused on node F, the sole member of the network who can communicate with the others. A degree of association of 1.6 persons supports this tendency, suggesting that each node connects with an average of 1.6 additional nodes out of a total of fve possible connections. Furthermore, the density of this sociogram is 0.33 (5/15), which is lower than that of Figure 10.1b, where the real and possible relationships are equal (15/15). Figures 10.1a and 10.1b have diferent clustering coefcients; in the frst, no triads are formed between the nodes, whereas in the second, all feasible triads are formed; consequently, the clustering coefcient in Figure 10.1a is 0 and that in Figure 10.1b is 1.

Figure 10.1

Examples of sociograms and their metrics (Adapted from Castillo et al., 2021)

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Nonetheless, both sociograms have exactly one modularity class, meaning that there are no sub-communities throughout the overall network. Only three potential connections are presented in Figure 10.1c, and it is the network with the lowest density and average degree of all the sociograms displayed. The node colors in Figure 10.1c similarly reveal three distinct sub-communities (number of modularity classes equal to three). Furthermore, because this network lacks a triad, the clustering coefcient is 0. The sub-communities do not have to be completely isolated to have more than one modularity class. Because of a substantially superior interaction between one community of nodes and the other, two modularity classes, or two sub-communities (black and white nodes), may be shown in Figure 10.1d. Figure 10.1d also shows triads between the nodes FBC, AEF, and ADE, indicating a clustering coefcient that is higher than the sociograms in Figures10.1a and 10.c but lower than Figure 10.1b.

SNA in the AEC SECTOR Recently, a study was conducted on SNA in the construction sector (Kereri & Harper, 2019). To better understand network creation and its application in the development of construction teams, the study explores three social network formation models, emphasizing the contrast between random, small worlds and truncated scale-free networks. Due to clusters generated by contractual, professional, task, or economic links, construction project teams emerge as small-world networks. On the other hand, social networks are used to understand how team members interact because they account for social links that are not formal (Kereri& Harper, 2019). A review of the concept “SNA” and “Construction” in the subject area of “Engineering” in the Scopus database reveals 252 journal and conference articles in English, published from 1998 to 2021. Chinowsky et al. (2008) conducted one of the frst studies exploring the application of SNA in construction (cited 227 times according to Scopus). Figure 10.2 shows the number of articles per year found in this search, where a steady increase of articles can be seen in the past ten years. The drop in the number of articles in 2021 cannot be analyzed since this search is updated until May 22, 2021. By using the 252 articles, we made a co-occurrence map with the keywords indicated by the authors. The co-occurrence map was performed in the free software VOS viewer,

Figure 10.2 Number of articles per year – Scopus query “SNA” and “Construction”

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Figure 10.3

Author keywords co-occurrence map

considered keywords with an occurrence in three or more documents. Figure 10.3 shows the author keyword co-occurrence map, where the association strength method was used, the keywords are represented in the nodes, and the size of the node is related to the keywords with a higher occurrence. Figure 10.3 shows that construction is a transversal theme that includes global industry studies, construction projects, and construction project management. The most recently studied topics are aligned with BIM, social media, and project performance, which, in turn, are strongly linked to project management and project organization. Additionally, it is important to reinforce those ever-present keywords in SNA studies associated with interaction types, i.e., information fows, collaboration, coordination, communication, trust, and knowledge management. The following features are important to consider when conducting an SNA: type of organization, interaction dimension, and metrics. Table 10.2 shows each of these features based on previous research in AEC organizations that utilized SNA. Interaction and information fow are the most analyzed networks in the AEC industry (Table 10.2). The metrics that are most assessed are those that are related to the organization (diameter, density, average path length), not those that are related to people (degree, centrality, betweenness). Furthermore, assessments are conducted on companies (Alarcón et al., 2013; Flores et al., 2014; Segarra etal., 2017) or large-scale projects (Hickethier et al., 2013; Priven & Sacks, 2013; Schröpfer et al., 2017), with a large number of participants (50 people or more). Moreover, except for Al Hattab and Hamzeh (2015), these studies do not specify whether the network interconnections are directed or undirected. 162

SNA to Support Implementation of Lean Construction Table 10.2 SNA experiences in the AEC industry (Adapted from Herrera et al., 2020) Source

Type of organization

Type of interactions

Metrics

(Alarcón et al., 2013)

Mining companies

(Flores et al., 2014)

Construction Companies

(Al Hattab & Hamzeh, 2015)

Design teams

Interaction; information fow; problem-solving; planning; trust Interaction; information fow; problem-solving; planning; innovation; trust Interaction; information fow

(Segarra et al., 2017) (Schröpfer et al., 2017) (R.F. Herrera etal., 2018) (Castillo, Alarcón, & Pellicer, 2018)

Architecture ofces

Mean degree; diameter; density; average path length Mean degree; diameter; density; average path length Density; average path length; modularity; clustering; centrality Mean degree; density; average path length Density; degree; betweenness Density

Construction complex projects Designs team (complex projects) Construction companies

Interaction; information fow; innovation Knowledge transfer

Interaction; information fow; planning; learning; trust Personal confdence; innovation Diameter; density; development; interaction, average path length; and relevant information exchange; average degree planning; and problem-solving

SNA as a Diagnosis Method SNA can be utilized as a powerful diagnostics method to reveal a previously hidden fow of valuable information (Alarcon et al., 2013). This method aims to model a team’s information fow using the SNA framework, providing leverage for improving fow and enhancing Lean implementation. Table 10.3 describes the six steps to apply the diagnosis method. The diagnosis approach described could be supplemented with inferential statistics analysis (ISA) to analyze the existing situation further and round tables with key members of the project team to translate and comprehend SNA and ISA results into the settings of their organizations (Flores et al., 2014). Step 5 of the diagnostic approach, determining the context, is carried out in a round table discussion (RTD). This frst RTD’s goal is to provide context for quantitative analysis based on sociograms and SNA indicators and foster collaboration between the consultant team and higher management to improve data interpretation. Then, the ISA links the SNA results to additional individual characteristics of each person, such as age or seniority. Finally, a second RTD is used to combine SNA and ISA from an organizational psychology perspective and provide pragmatic interpretation feedback to upper management to implement organizational reforms (Flores et al., 2014). Step 6, related to the defnition of an action plan, proposed three types of interventions (Anklam, 2009): structural/organizational, knowledge-network development, and individual/leadership. Wearable electronic badges that can automatically track the amount of face-to-face engagement, conversational time, physical proximity to other people, and physical activity levels are among other ways for analyzing individual and collective patterns of behavior in 163

Rodrigo F. Herrera and Luis Fernando Alarcón Table 10.3 SNA as a diagnosis method (Adapted from Alarcon et al., 2013) Step

Description

1 Defne model settings 2 Collect data

The evaluators defne who the participants will be, what the hypothesis to be explored or tested will be, and what types of interaction must be evaluated. The evaluators obtain data about the interactions in the project team. A survey is a typical technique to collect data, and it can be complemented through email exchange, document approvals, or revisions. The evaluators can use computers tools to create the sociograms and calculate the SNA metrics. There is a number of software available to do this task (open sources and paid) The evaluators examine the sociograms and SNA metrics to look for gaps, or junctures, between individuals and groups. The data will act as an indicator and guide for the questions to be performed on the analysis. Through interviews, the evaluators can understand the context that is behind the data and the diagnosis. The focus in an SNA diagnosis is the dialogue that ensues from their examination and the insight and action that emerge from the dialogue The evaluators check if the initial hypotheses are reassured. The inventions can be structural/organizational, knowledge-network development, or individual/ leadership

3 Process data

4 Examine data

5 Discover the context

6 Defne an action plan

work teams (Olguín et al., 2009). In addition, there are many approaches for mapping the social networks of projects using the BIM methodology. Zhang and Ashuri (2018) suggested a method for mining massive amounts of design logs created throughout the BIM design process to uncover social networks in BIM-based collaborative design processes. These authors presented a three-step procedure: •

•

Data extraction: to gather BIM Revit log data from a number of designers across a range of projects. A Revit log data is a set of design event logs that document all of the designers’ modeling operations when running commands and are saved as journals in the program fles directory under the Revit product version folder (Zhang & Ashuri, 2018). Social network modeling: to make a weighted sociogram that displays the interrelationships between actors (Pryke, 2012). Three major components must be determined from design event logs in order to establish a social network: performed determination, relation determination, and weight determination (Zhang & Ashuri, 2018). Social network analysis: Based on a networked structure made up of nodes and weighted edges that connect them, SNA provides an efective and visual approach for measuring network structure confguration. The resulting sociogram is subjected to SNA to explore network structure confgurations at three levels: macro-level (the entire network), meso-level (subsets of nodes), and micro-level (individual nodes) (Zhang & Ashuri, 2018).

Thus, there are diferent techniques to capture information from work teams, from the use of wearable electronic badges for face-to-face interaction, the use of information from common data environments and collaborative project management platforms to evaluate information fow, and the use of surveys to assess other levels of interaction, such as trust, learning, and collaboration. 164

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Impact of BIM Uses and Lean Management Practices on Organizations Many studies have advocated BIM as a viable solution to the problems of communication, collaboration and coordination of project teams (Baiden et al., 2006); Lean Construction has also been used to the design process (Ko & Chung, 2014). Furthermore, by emphasizing teamwork and information integration and sharing, Lean principles and BIM features might help to improve design productivity (Knotten et al., 2017). Design mistakes can be treated in a way that lowers their frequency and spread, which is one of the theoretical benefts of BIM and Lean. Furthermore, BIM and Lean methodologies enable more efcient information fow and generate a more cohesive social network with enhanced cooperation and connectedness both within and between teams, resulting in a more cohesive social network (Al Hattab & Hamzeh, 2015). The combined implementation of Lean Construction and BIM successfully addressed the people-process-technology triad (Fakhimi et al., 2016), which is the core of Lean Construction 4.0 (Hamzeh et al., 2021). One way to validate the above, from the “people” perspective, is through empirical assessment of project team interactions, using SNA. Herrera et al. (2021) compared two case studies using high-rise building construction projects in Chile to provide quantifable empirical evidence of the diferences between the various types of design team interactions. The researchers participating in this study employed SNA to examine the interactions among the design teams for these projects since it allows for the extraction of qualitative and quantitative data from many types of interactions, as well as the use of sociograms and graph theory metrics to describe the design team’s behavior (Pryke, 2012). Project A was picked because it used traditional, informal design management without the BIM’s methodological and technology assistance, whereas project B was picked because it used the BIM methodology. Both projects had on the design teams a project manager (PM), client representative (CR), geotechnical engineer (GE), architect (A), structural designer (SD), plumbing specialist (P), electrical specialist (E), irrigation designer (ID), gas specialist (G), and landscape designer (LD. A BIM manager (BM) and a construction business representative (CO) were also included in Project B, for a total of 10 and 12 design team members in projects A and B, respectively. The BIM-Lean management evaluation revealed that one of the projects had a low level of Lean practice adoption and no use of BIM throughout its design and planning phases (project A). Lean methods were widely used during the design and planning phases of the second project, and BIM was widely used (project B). The following BIM-Lean management methods were primarily used in Project B: designers and representatives from customers and construction companies should be involved as early as possible and in a methodical manner; during weekly sessions, plan in a methodical, systematic, and collaborative manner; problem-solving and decision-making that is collaborative and constantly monitored; design reviews and development in a BIM common data environment, as well as specialties coordination with designers working in a single federated model The survey responses from all project design teams A and B (10 and 12 members, respectively) were used to conduct an interaction analysis; hence, the degree of the node metrics is proportionate to the total number of team members. In both projects, the global interaction networks show connected teams (Figure 10.4), indicating that no person or team is isolated from the others. However, this network demonstrates that project A’s project manager was a bottleneck, i.e., four project team members would be separated if the project manager was absent or did not interact (namely, 40 percent of the design team). Furthermore, because 165

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Figure 10.4 Global interaction network and degree: Project A and project B (Adapted from Herrera et al., 2021)

Figure 10.5

Collaboration network and degree: Project A and project B (Adapted from Herrera etal., 2021).

project B was a rounded network with uniform interactions across design team members, extra bridges were available to hold the team together if any team member failed to engage in any contact. The collaboration network is analogous to a design team’s planning and problem-solving network (Figure 10.5). This network reveals that 40% of project A’s crew was disconnected, which means that this isolated group did not participate in any collaborative or workplanning spaces. On the other hand, the network illustrates that project B’s team was connected; in other words, all the project’s stakeholders collaborated with the team’s members. Project A did early plan in a single, centralized step, but Project B did it progressively and systematically. Project B has a trust network indicator of 76.74%, while project A has a trust network indication of 69.05% (number of trust linkages/number of role-knowledge links). Project B has twice as many role-knowledge links as project A; hence, its trust level is more than double that of project A. In other words, this team’s eforts to get to know the work team, organize and standardize information fows through a BIM shared data environment, and collaborate with the team in a formal work setting (weekly meetings) resulted in increased trust among the team members. In a design team, building trust among team members is necessary for learning from others (Herrera et al., 2020). Also, Lean methods can help team members learn more quickly (Hu et al., 2016). 166

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The increased interaction between team members resulted in a greater understanding of each team member’s responsibilities; without bottlenecks and indispensable individuals, a denser, more homogenous, and more efective fow of important information; all team members are involved in the planning and collaboration; among specialists in a trusting and learning context; during the design phase, as well as better commitment management.

SNA Metrics and Project Performance Lee et al. (2018) identifed 38 SNA metrics and ideas in nine complex-project-management knowledge categories after conducting a systematic qualitative review based on 65 peerreviewed publications. The majority of SNA research was used to improve communications management. Coordination, information, negotiation, and knowledge exchanges that build trust are represented by fows in communication networks. A network with a low-density value focuses on individuals rather than network collaboration. Besides, in other forms of resource networks, network density was utilized to determine the connection. The client, referral, funding, authority, supplier, and internal market networks of construction frms were examined (Badi et al., 2017). There were few studies on the application of SNA to schedule management. Only two studies were undertaken to examine trade interactions to determine which trades should be employed in a CPM schedule. Degree centrality and eigenvector centrality were both chosen as relevant indicators for detecting key trades. In addition, the use of SNA in quality management has been linked to an increase in project deliveries. Aljassmi, Han, and Davis (2014) used in-degree centrality to determine the extent to which a cause originated from another cause and directly linked to the originating cause to investigate the interrelationships between defect causes in a complex engineering system. Additionally, SNA studies of health, safety, security, and the environment (HSSE) management involve examining the communication patterns of safety teams and an accident network. To understand the connection of low- and high-performing teams in resolving safety concerns, network density was used to safety communication and training networks (Alsamadani et al., 2013). Particularly at the design phase, Al Hattab and Hamzeh (2015) describe a new approach to managing design errors that focuses on team structures, interaction dynamics, and error dissemination. This study aimed to understand the process of design error emergence better and compare traditional versus BIM/Lean-based systems for design error management using SNA and simulation. Theoretical fndings suggest that combining BIM and Lean practices reorganizes design teams’ structures and communication, allowing them to detect errors early, reduce their recurrence, and limit their spread. The results of applying SNA to model the structures of each project design network, and based on the theoretical assumptions, show that integrating BIM and Lean principles favors information exchange and creates a more coherent social network with more collaboration and connections inside and across teams. In addition, under each hypothetical structural type, agent-based modeling was used to model the dissemination of design errors. The results of the various difusion scenarios, based on the theoretical setup, show that a BIM/Lean network is more successful in reducing and limiting errors. Continuous and real-time communication, collision detection, automated code checking, design charrettes, and continuous learning are some of the defense mechanisms that could assist reduce errors. In addition, during the design phase, Herrera et al. (2018) evaluated Lean management practices, performance indicators, and SNA metrics in airport projects. This study aimed to examine the performance of the temporary organizations that occur during the development 167

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of airport project designs. This temporary organization’s specialists must evaluate Lean management techniques, performance, and interaction to reach the project goals. These behaviors directly impact the contact between diferent professions in the organization, as evidenced by a low level of work information fow and problem planning and resolution. Since members of the temporary organization may not be aware of the roles played by other team members, kick-of meetings are necessary to kick-start the anticipated interaction between the various specialists. Because this causes unscheduled labor that pervades each project’s percentage of plan completed, the low level of interaction signifcantly afects project performance, particularly in terms of rework (average 20%) and design quality issues (average three per week). Even more, Zhang and Ashuri, (2018) examined the relationship between the characteristics of the social network for the design phase (assessed through BIM log mining) and the production performance of designers. The authors of this study found that all node centrality indicators are signifcantly and positively associated to designers’ production performance (represented by the number of commands executed during the studied time). In the construction phase, Castillo et al. (2018) analyzed the relationships among SNA properties, LPS management practices, and performance (cost deviation, schedule deviation, safety indexes, planning efectiveness, quality, productivity). Data from nine building projects from two Chilean construction companies were studied to understand these relationships better. The degree of LPS practice adoption, social network measurements, and key performance indicators were all subject to correlation analysis (key performance indicators, KPIs). These three variables were shown to have strong positive correlations. It was also discovered that a high degree of LPS practice application is frequently correlated with higher project performance, though not always with improved network metrics. The fndings reveal project performance correlations with the organization and LPS practices, which should help managers make better decisions about organizational and management strategies to increase project success. Because ideal measurements have not yet been defned, more research into the infuence of social network features on project performance is required. Lean Construction 4.0 is associated with multiple tools and methods that can be used in combination to achieve project objectives. This requires safe, collaborative, inclusive, and transparent working environments (Hamzeh et al., 2021). Therefore, there is a need to evaluate these work environments, and SNA is a technique that would allow this objective to be met independently or in combination with other tools.

Conclusion The members of a team must have a high level of interaction for the team to be successful. To assess the interaction, there are diferent methods (electronic badges, BIM logs, emails, surveys) to understand the interactions using SNA to evaluate the interaction from a multidimensional point of view. Each interaction aspect is represented as a network, which can be analyzed separately; however, it is also necessary to examine two or more networks. The following are some of the practical applications of using SNA to study the relationships of multiple teams: (a) gaining a better understanding of the team’s interactions from multiple angles; (b) correcting the interaction to make it more efcient and less reliant on a single individual; (c) identifying the factors that contribute to a team’s common misunderstanding; and (d) taking steps in this direction, such as learning about role expectations. These advantages can help project teams better understand project requirements, minimize waste, and improve the value of their design and construction processes. Therefore, SNA is a key tool for assessing the work environments and interactions required when implementing Lean 168

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Construction 4.0. Besides, SNA should be employed as a tool for continual improvement rather than a punitive mechanism by both the assessment team and the evaluated team. Diferent studies highlight that the use of Lean practices and tools complemented with technologies aligned with virtual design and construction and BIM methodology allow an increase of interactions in work teams, strengthening elements such as providing timely information to the right people, increased transparency of information, greater collaboration and horizontal interaction, increased learning among team members, greater understanding, and fulfllment of commitments among team members. This increase in interaction from diferent points of view can be shown using SNA, to have empirical evidence of this improvement in terms of organizational performance. Moreover, some studies show that SNA metrics and sociograms have a relationship with the performance of projects in the diferent stages of their life cycle, i.e., including their planning, design, and construction. SNA allows evaluating diferent characteristics of the Lean Construction 4.0 culture in an organization. For instance, SNA allows assessing the level of collaboration among diferent project team stakeholders. It also allows to measure the information fow in a project, the networks of planning and resolution of problems, and analyze the open communication and the degree of formalization of communication. In addition, some studies present the assessment of trust among team members using SNA and measuring personal trust and confdence in fulflling commitments. By using sociograms, it is possible to visualize the relationship among functional departments or areas and the other stakeholders of the project; and sociograms allow visualization of the degree of centralization of the information; for example, a star network implies a higher level of centralization than a round network. Finally, recent studies are increasingly studying the assessment of commitment management using SNA. It is possible to visualize the four steps of the commitment cycle: request, negotiation, declaration of compliance, and feedback.

Acknowledgments The authors acknowledge fnancial support from FONDECYT (1181648) and GEPUC from Pontifcia Universidad Católica de Chile.

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SNA to Support Implementation of Lean Construction Knotten, V., Lædre, O., & Hansen, G. K. (2017). Building Design Management–Key Success Factors. Architectural Engineering and Design Management, 13(6), 479–493. https://doi.org/10.1080/17452007. 2017.1345718 Ko, C., & Chung, N. (2014). Lean Design Process. Journal of Construction Engineering & Management, 140(6), 1–11. https://doi.org/10.1061/(ASCE)CO.1943-7862 Koskela, L. (1992). “Application of the New Production Philosophy to Construction”. Technical Report # 72, Center for Integrated Facility Engineering, Department of Civil Engineering, Stanford University, CA. https://leanconstruction.org/uploads/wp/media/docs/Koskela-TR72.pdf Lee, C., Chong, H., Liao, P., & Wang, X. (2018). Critical Review of Social Network Analysis Applications in Complex Project Management. Journal of Management in Engineering, 34(2), 1–15. https:// doi.org/10.1061/(ASCE)ME.1943-5479.0000579. Lewin, K. (2013). Principles of Topological Psychology. Munshi Press. Marin, A., & Wellman, B. (2011). Social Network Analysis: An Introduction. In John Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 11–25). O’Malley, A. J., & Marsden, P. V. (2008). The Analysis of Social Networks. Health Services and Outcomes Research Methodology, 8(4), 222–269. https://doi.org/10.1007/s10742-008-0041-z Olguín, D., Waber, B. N., Kim, T., Mohan, A., Ara, K., & Pentland, A. (2009). Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational behavior. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39(1), 43–55. https://doi. org/10.1109/TSMCB.2008.2006638 Paris, C. R., Salas, E., Cannon-bowers, J. A., Paris, C. R., Salas, E., & Cannon-bowers, J. A. (2017). Teamwork in Multi-Person Systems : A Review and Analysis. 0139(May). https://doi.org/10.1080/ 00140130050084879 Phelps, A. F. (2012). Behavioral Factors Infuencing Lean Information Flow in Complex Projects. 20th Annual Conference of the International Group for Lean Construction 2012, IGLC 2012. Priven, V., & Sacks, R. (2013). Social Network Development in Last Planner System Implementations. 21st Annual Conference of the International Group for Lean Construction 2013, IGLC 2013, 474–485. Pryke, S. (2012). Social Network Analysis in Construction. Wiley-Blackwell. Schöttle, A., Haghsheno, S., & Gehbauer, F. (2014). Defning Cooperation and Collaboration in the Context of Lean Construction. In 22nd Annual Conference of the International Group for Lean Construction, 49(0), 1269–1280. Schröpfer, V. L. M., Tah, J., & Kurul, E. (2017). Mapping the Knowledge Flow in Sustainable Construction Project Teams using Social Network Analysis. Engineering, Construction and Architectural Management, 24(2), 229–259. Scott, Jhon. (2013). Social Network Analysis (3rd ed.). SAGE Publications Inc. Segarra, L., Herrera, R. F., Alarcón, L. F., & Pellicer, E. (2017). Knowledge Management and Information Flow through Social Networks Analysis in Chilean Architecture Firms. 25th Annual Conference of the International Group for Lean Construction, IGLC 2017, 413–420. https://doi. org/10.24928/2017/0244 Valentine, M., Nembhard, I., & Edmondson, A. (2015). Measuring Teamwork in Health Care Settings: A Review of Survey Instruments. Medical Care, 53(4), 16–30. https://doi.org/10.1097/ MLR.0b013e31827feef6 Zhang, L., & Ashuri, B. (2018). BIM Log Mining: Discovering Social Networks. Automation in Construction, 91(3), 31–43. https://doi.org/10.1016/j.autcon.2018.03.009

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11 EXPLORING THE SOCIOTECHNICAL NATURE OF LEANBASED PRODUCTION PLANNING AND CONTROL USING IMMERSIVE VIRTUAL REALITY Canlong Liu, Vicente A. González, Ignacio Pavez, and Roy C. Davies Introduction It is well-known that the architecture-engineering-construction (AEC) industry is a complex, uncertain, and dynamic business, where productivity has been lagging behind other sectors over decades (Barbosa et al., 2017). This outcome is not surprising, as managerial decisions associated with projects in this industry are based mainly on intuition, experience, and informal heuristics (McCray et al., 2002). In addition, the AEC industry has been characterized by not being very proactive in coping with change and innovation. In fact, a recent McKinsey report placed the AEC sector as the second lowest industry to adopt information technologies during the industry 3.0 transition (Agarwal et al., 2016), being reluctant to an extensively uptake of smart and digital technologies (SDT) (Farmer, 2016). This situation demands that project management in the AEC sector looks closely into the roles of production management theory, people, and SDT, since their synergy ofers opportunities to manage projects more efectively and enable a more resilient approach for the industry to embrace change and innovation (Hamzeh et al., 2021). The socio-technical systems (STS) refer to systems that involve a complex interplay between tools, people, and the environmental factors of an organizational system. On the one hand, the AEC projects are temporary organizations composed of subsystems, resources, information, production processes, and people that interact to each other and are open to the environment. Therefore, the AEC projects organizations can be viewed essentially as STS. On the other hand, Lean Thinking has reshaped manufacturing frms fostering value creation and people-centered organizations. Namely, it has provided the technical means (i.e., tools and processes) to improve organizations and frms’ performance (Womack & Jones, 2013), but also some social mechanisms that empower and motivate individuals and teams to reach outstanding outcomes (Liker & Meier, 2006). Thus, Lean Thinking and its adoption to the AEC industry, to say, Lean Construction, can be viewed as an STS that is well suited to deal with the socio-technical nature of projects. 172

DOI: 10.1201/9781003150930-14

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The Last Planner© System (LPS) has been often considered one of the pillars of Lean Construction and its benefts have been widely reported (Daniel et al., 2015). LPS is a Leanbased production planning and control system for AEC projects, which focuses on increasing planning reliability and predictability by decreasing workfow variability (Ballard, 2000). LPS has a planning and control framework that ofers an intermediate layer of adjustment and support (Lookahead planning and weekly planning), feedback and learning, and reliable commitment-building (Ballard, 2000), which essentially shifts the ‘push’ view from traditional project management towards a ‘pull’ view. Even though the extensive popularity of the LPS among Lean Construction proponents, a deep and holistic implementation of its principles and key tools is still a challenge in many AEC organizations nowadays (Perez & Ghosh, 2018). The LPS intrinsically deals with the dual and symbiotic socio-technical nature of AEC projects (Liu et al., 2020). Technically speaking, the LPS brings a robust Lean-based production planning and control methodology to the AEC projects environment. Even more, it engenders positive impacts on the social mechanisms infuencing project teams and organizations (González et al., 2015). The challenges in the implementation of the LPS seem to originate in the limited understanding from AEC organizations about its socio-technical nature (Perez & Ghosh, 2018), which can lead to a slow adoption and dissemination of the LPS in the industry (Fuemana et al., 2013). Unfortunately, the lack of knowledge on LPS’s socio-technical nature has not been efectively addressed in current research (González etal., 2015; Pasquire & Ebbs, 2017), preventing a more widespread uptake within the AEC industry (Ebbs et al., 2017; Hamzeh, 2011; Liu et al., 2020). We argue that new methodological avenues to improve the understanding of the LPS’s social and technical complexities should be explored, in order to minimize or mitigate implementation barriers. One key challenge is to assess the LPS implementation from a socio-technical perspective, observing how its technical framework, i.e., its methods and tools, infuences current production planning and control processes in a project, and in turn, how this afects teams and the organization at social and behavioral levels. A popular methodological approach to observe, test, and validate diferent aspects of specifc management interventions during construction is the case study method (Yin, 2017). However, it also brings difculties to isolate the specifc impacts from management interventions due to the compounded efect of other dynamics occurring in a project at the same time (e.g., occupational health and safety programs, adoption of new construction technology, other ongoing improvement initiatives), and the undesired and expected effect of observations over project members (Gao et al., 2021). This indicates a need to understand the complex relationships of social and technical factors that cannot be explained in case studies, and to efectively analyze both social and technical dimensions comprehensively. In this regard, we propose the use of SDT such as immersive virtual reality (IVR) to fll this gap. Recent research suggests that IVR allows researchers to manipulate variables in hypothetical contexts and analyze their specifc responses within controlled experimental environments, which is difcult to realize in real-life settings (Duca, 2019; Pan & Hamilton, 2018). Therefore, IVR provides robust methodological means to study project management problems in the AEC industry. IVR primarily represents computer-generated virtual environments with high visual impact, where users have a feeling of being physically present and immersed, providing high levels of engagement and perception (Feng et al., 2018). Thus, IVR has an ability to

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‘inducing targeted behavior in an organism by using artifcial sensory stimulation, while the organism has little or no awareness of the interference’ (Lavalle, 2017). According to this defnition, once participants are immersed into a virtual environment, they can feel they are physically inside of it, even though such environment is artifcially simulated. This virtual environment may become highly realistic, making it difcult for individuals to diferentiate between the virtual and the real worlds (Lavalle, 2017). Therefore, IVR enable individuals to behave as close as possible to reality (Feng et al., 2018). But it also enables experimental environments where hypotheses can be investigated and tested by setting specifc interventions (independent variables) and assessing specifc outputs (dependent variables), isolating experimental (management) interventions from other unwanted efects (Gao et al., 2021). In this chapter, we explore the socio-technical intricacies of the LPS by illustrating a potential application using the power of IVR. IVR is a toolbox of SDT in Industry 4.0 (I4.0) (Satoglu et al., 2018). We argue that the connection between an I.40-driven SDT and a Lean-based planning tool within the context of the AEC industry, under the umbrella of the STS theory, helps to deepen the discussion around Lean Construction 4.0. The following sections discuss the current implementation barriers and the socio-technical nature of the LPS, followed by a section that introduces the benefts of using IVR to analyze the LPS socio-technical nature. Then, we describe in-detail the technical development of an IVRbased experimental environment to explore the LPS socio-technical nature. Finally, the limitations and future avenues of the study are addressed in the conclusion section.

Background Barriers to Implementation of LPS The limited penetration of the LPS in some AEC organizations could be originated from the nature of Lean adoption in a traditional project setting. Many organizations that adopt LPS may be only interested in the application of tools and methods rather than in the Lean transformation. This may lead to a poor committment with the cultural change required to efectively adopt the LPS. Shifting the traditional organizational culture of projects towards a Lean one may imply that the project organization itself will require a higher level of social integration, decentralized decision-making, and enhanced communication (González et al., 2015), with the associated efort to make that shift. However, practitioners might fnd it difcult to embrace a Lean transformation due to the complex nature of the AEC industry and an organizational culture that is reluctant to assimilate the levels of innovation and change required by Lean (Poshdar et al., 2019). In that regard, the LPS faces barriers grounded on the unresolved AEC industry challenges. Liu et al. (2020) explored in detail this matter and summarized the key barriers in the LPS implementation, namely: (1) resistance to change; (2) lack of cooperation; and (3) lack of understanding of LPS principles and training methods. Even more, they were able to sort these barriers into social-driven (1 and 2) and production-driven (3), being perceived as closely related to the STS concept. Some researchers argue that the social and technical dimensions related to these three barriers have been poorly managed in the past, hindering a more efective dissemination and uptake of the LPS social dynamics, practices, and knowledge in the AEC industry (González et al, 2015; Hamzeh 2011; Pasquire & Ebbs, 2017). There is an appeal that social aspects should be shaped appropriately to harmonically match its technical components and vice versa (Ebbs et al., 2017; Liu et al., 2020). 174

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Socio-technical Nature of LPS From a system perspective, construction organizations are composed of a collection of subsystems that operate in an interrelated fashion. These subsystems include technical subsystems (e.g., software, facilities, tools, and machines), social subsystems (individuals, groups, and their interactions), managerial subsystems (e.g., safety management, risk management, and facility management), goals and values subsystems (e.g., societal values and commercial goals), and structure subsystems (e.g., patterns of authority, communication, and workfow) (Kast & Rosenzweig, 2017). In construction projects (perceived as social systems), diferent stakeholders communicate, share knowledge, and argue with each other; and as technical systems, tools and procedures are provided to guide them to perform diferent project-specifc tasks (Jin et al., 2021). The LPS understood as a socio-technical process has signifcantly changed the way project management is carried out, specially production planning and control, and how it deals with both the social and technical dimensions of projects (González et al., 2015; Saurin & Rooke, 2020). Thus, it is deemed appropriate to use the STS theory to fnd ways to efectively mitigate the implementation barriers of Lean-based approaches (Marsilio & Pisarra, 2021) like the LPS. Figure 11.1 presents a socio-technical framework to illustrate the interactions between the social and technical dimensions of a construction project when implementing LPS. This framework provides a system-wide perspective to understand the LPS socio-technical nature. The components of the socio-technical framework have been identifed from previous studies, in order to understand the contextual factors related to the LPS’s implementation methodology (Murguia, 2019), production principles (Ballard, 2000), social mechanisms (Ghosh et al., 2019; González et al., 2015; Pavez & González, 2012; Priven & Sacks, 2015), and outcomes (Fernandez-Solis et al., 2013). Table 11.1 provides additional information on the socio-technical dimensions and outcomes of the framework. Figure 11.1 also shows the interaction among social and technical dimensions and how they may be infuenced by some external conditions (e.g., culture, demographics, resources), and the linkages between LPS socio-technical processes, outcomes, and the feedback loop that takes place.

Figure 11.1

Socio-technical framework of LPS

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Canlong Liu et al. Table 11.1 Social and technical dimensions of LPS Dimensions

Categories

Variables

Description

Communication

Verbal interactions both for task and relationships The action of making promises to complete specifc tasks The willingness to be vulnerable to another party based on the proven reliability of an individual The load imposed on individual’s cognitive system resulting from performing a particular activity The degree of independence and freedom provided to employees in a specifc job when defning tasks The one’s perception about the capability to respond to challenges and problems The difculty, clarity, and consensus of goals

Commitment Cognitive

Trust

Cognitive load

Autonomy

Self-efcacy

Goal setting

The LPS technical dimension represents its general and well-known methodological structure, and planning and control components, which is split into commitment management (i.e., coordination of requests and promises), standardized control (i.e., bottom-up breakdown of tasks into steps), empowerment (i.e., grassroots involvement in distributed decision-making), visualization (i.e., quality improvement of information fows), continuous improvement (i.e., identifcation of corrective actions on root causes of problems), and physical layout (i.e., enabling co-location) (Hamzeh, 2011; Majava et al., 2019). See more details in Table 11.1. The LPS social dimension is represented by using the afect, behavior, and cognition (ABC) framework (Ostrom, 1969), as it deems to be appropriate as reported by Van Dun and Wilderom (2012). They explored team dynamics within Lean implementations, and conceptualized team dynamics as the interaction of their afective, behavioral, and cognitive characteristics, which interact with technical enablers and external conditions to generate team outcomes within an organization. In that regard, afective characteristics account for motivational tendencies, and emotional responses (Robert, 1970). Behavioral characteristics are represented by social behaviors striving toward goals, dealing with task demands and conficts, coordinating eforts, and adapting to unexpected situations (Van Dun & Wilderom, 2012). Cognitive characteristics relate to thoughts and beliefs to guide task-related behaviors among team members (Kozlowski & Ilgen, 2006). See more details in Table 11.1. A four-dimension structure is proposed based on the study of Badano (2016) and Fernandez-Solis et al. (2013) for the outcomes of the framework as shown in Figure 11.1: Stakeholder’s LPS Knowledge and experience, Attitude (i.e., acceptance of LPS implementation), Relationship (i.e., interpersonal familiarity) and Technical performance (i.e., time, cost, quality). These outcomes can potentially shape the socio-technical processes and dimensions underpinning a successful implementation of the LPS (Castillo et al., 2018; González 177

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et al., 2015; Pavez & González, 2012; Priven & Sacks, 2015). For example, Weekly Work Plan’s (WWP) meetings assist to determine executable works by managing planning commitments, facilitating direct and bottom-up communication between the ‘Last Planners’ (i.e., project personnel attending the WWP meetings) on an equal basis. These communication patterns could generate higher interpersonal trust and less stress among ‘Last Planners’. In addition, the successful implementation of the LPS, as an outcome of these socio-technical processes, gradually increases ‘Last Planners’ knowledge on the LPS itself, which could, in turn, smoothen the LPS technical implementation as their self-efcacy or perceived ability to respond to challenges and problems is improved. Although the socio-technical nature of the LPS has attracted scholars and practitioners’ attention alike, traditional research approaches in construction engineering and management such as case studies have not been able to efectively reveal the socio-technical nature of the LPS. When using case studies, it is challenging to isolate the precise impact of an intended managerial intervention (independent variable) from the combined impact of other events occurring at the same time in a project (e.g., safety initiatives, human resource management). Thus, the targeted efect (dependent variable) on project members is afected by undesired and difcult-to-assess impacts from unaccounted factors (Gao et al., 2021). Therefore, it is necessary to explore an efective experimental methodology to understand well the LPS socio-technical nature and its impacts on a project organization.

IVR as a Socio-technical Experimentation Tool Jayaram et al. (1997) defned virtual reality (VR) as ‘a synthetic or virtual environment which gives a person a sense of reality’, enabling users to interact with the virtual world. VR technology has received increasingly attention from academia and industry due to its increased afordability, fexibility, and functionality (LaValle, 2017). VR technologies are divided into immersive VR (IVR) and non-immersive VR (NIVR). IVR technologies such as head-mounted displays (HMD) or Cave Automatic Virtual Environment (CAVE) systems enable user to be fully immerse within a computer-generated environment, where the gap between the real and the virtual vanishes; while NIVR such as desktop-based allow users to still recognize the gap between real and virtual environments through the use of screens or conventional workstations (Radianti et al., 2020). In particular, IVR technology brings benefts to a wide range of applications in diferent domains such as entertainment, healthcare, education, tourism, and behavior or psychological research (LaValle, 2017). In the AEC industry, IVR has been adopted in of-site production training (Goulding et al., 2012), construction safety training (Li et al., 2018) project planning and control (Dallasega et al., 2020), and on-site layout optimization (Muhammad et al., 2020). IVR can also allow stakeholders to better collaborate with each other, enable a clear understanding of comprehensive designs (Alizadehsalehi et al., 2019); provide training to foremen in production planning decisions (Sacks et al., 2015); support collaborative decision-making (Du et al., 2018); and identify design conficts (Romano et al., 2019). On the other hand, LaValle (2017) argue that IVR technologies represent the next generation of ‘Virtual Laboratory’ making exceedingly difcult to people to distinguish between the real and the virtual, which enables what is so-called Ecological Validity (behaviors observed are more or less consistent in the virtual world and the real world). This feature provides many opportunities of conducting behavioral research using IVR (Feng et al., 2020; Gao et al., 2021). In that respect, multi-user IVR has recently attracted the attention of researchers due to its advantages to simulate realistically social interactions, for instance, 178

Socio-Technical Nature of Lean-Based Production Planning Table 11.2 Suitability of adopting IVR to studying the socio-technical nature of LPS Dimension

IVR features

Rationale

Experimental environment

Controlled environment Reproducibility

Researcher can edit the virtual environment and manipulate social or technical variables. Researcher can replicate the experiment with the same conditions for verifcation. It supports behavioral observation and assessment of psychological responses. It provides a sense of presence in the virtual environment, which makes participants believe that the virtual environment they are in is the real world. It simulates visual or environmental interactions, so participants can have an experience as close to the real world as possible. Social interaction with other people is enabled. Integration of IVR technology with biometric sensors and instruments is allowed. Low cost in conducting research.

Ecological validity

Investigate social behavior Immersion

Realism

Accessibility

Interaction Compatibility Cost-efective and portable Connectivity

It supports multi-user environments.

where a person can interact with a virtual agent (person) or another real person. Therefore, multi-users IVR environments are already gaining popularity in social behavioral studies and in psychological experiments such as inducing social cognition and social stress (Bernard et al., 2018). In order to study the LPS socio-technical nature, it is essential to elicit and monitor people’s behavior, and cognition in a controlled experimental environment, and in a way that can be analyzed and measured systematically. We argue that IVR technology is a powerful tool for human behavior research and can efectively support the study of the LPS socio-technical nature; as it has advanced technological features that enable the implementation of controlled experimental environments, with moderate to high levels of ecological validity through accessible interfaces (Pan & Hamilton, 2018). Table 11.2 explores the suitability of IVR to study the LPS socio-technical nature: 1

Experimental Environment. The ability to edit 3D visual scenes and measure responses to intended interventions enables the researcher to test hypotheses in a repeated and scalable manner, which is unfeasible with the case study method (Brookes et al., 2019). For example, it is possible to manipulate an LPS technical variable with full control and measure a participant’s behavioral and social responses, allowing repeatability of the research experience. Ecological Validity. IVR features on immersion, realism, and interaction can enable truly realistic LPS implementation scenarios, which can prompt a natural behavioral response from participants, enhancing the ecological validity of the research outcomes (Feng et al. 2018). Accessibility. New and emergent IVR head-mounted displays are cost-efective, portable (e.g., Oculus Quest 2 or HTC Cosmos), and highly compatible with data collection devices such as biometric sensors and instruments (e.g., iMotions biometric sensors 179

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platform, www.imotions.com) wearable by participants during experiments. These features enable the possibility of conducting large-scale behavior and psychological experiments to study the socio-technical processes that can take when implementing the LPS.

An Immersive Virtual Reality Prototype to Investigate the Socio-Technical Nature of the LPS In the next sections, we provide a detailed description of an IVR prototype to study the LPS socio-technical nature. To do so, we establish the conceptual and technical foundations to set IVR-based experimental environments and describe our vision to undertake further experimental research using the framework proposed in this chapter.

Social-technical Variables in the LPS In this section, we aim to illustrate how an IVR prototype can assist to explore the socio-technical nature of the LPS. The IVR prototype allows to specifcally study the interplay between the social variables and the technical aspects (e.g., production principles and tools) associated with the LPS and its implementation. This also shows the potential of IVR technology as a research and experimental tool to study Lean-driven socio-technical phenomena in AEC projects. We selected six technical areas of the LPS based on Ballard’s research (Ballard, 2000), as their impact on social aspects was assessed efectively, namely constraints analysis, workfow control, meeting and planning, decision-making, PPC, and Reasons for Non-completion (RNC) (See Table 11.3). In terms of social variables associated with the LPS implementation, eight were selected from Figure 11.1 because they are suitable to be measured and analyzed, such as mental stress, buy-in, learning, communication, commitment, trust, cognitive load, self-efcacy, and goal setting. These social variables have been found to be the most signifcantly afected by the LPS implementation as mentioned in previous research (Castillo et al., 2018; Ghosh et al., 2019; González etal.,2015). Table 11.3 presents the integrated IVR-LPS framework to explore socio-technical nature of LPS. The proposed framework links production planning and control technical dimensions (based on LPS planning and control structure, see Figure 11.1) and the previously mentioned social variables to articulate the IVR design environment, i.e., the scenarios considered, which are the places where a full narrative unit occurs. In the traditional scenario, the production planning and control technical dimensions consider top-down workfow control, centralized decision-making, as well as PPC measure. In turn, the LPS-based scenario considers constraint analysis, bottom-up workfow control, Lookahead planning and WWP, distributed decision-making, and measure and discussion of PPC and RNC. It has been assumed that the LPS technical features act as independent variables that can be modifed and controlled in the experimental setting implemented in IVR. The social variables associated with these technical variables are assessed in these two scenarios as dependent variables. In order to quantitatively assess the impact on the social variables, a number of conceptual and measurement approaches are adopted. For instance, we are interested to understand how people communicate with one another across all technical dimensions. The communication can be conceptualized by: (1) Using social network attributes (e.g., density, centrality, frequency, channels) based on Social Network Analysis (SNA), which is a method based on network theory for examining the relationship and 180

Socio-Technical Nature of Lean-Based Production Planning Table 11.3 The integrated IVR-LPS framework IVR scenario modeling

Production planning and control technical dimension (independent variables)

Traditional scenario

LPS-based scenario

Related social variables (dependent variables)

Constraint analysis

Not applied

Applied

Workfow control

Top-downa

Bottom-upb

Meeting & planning

Traditional planningc

Decision-making

Centralizede

WWP and lookahead planning meetingd Distributed f

PPC

Measurement only Not applied

Measurement and discussion Applied

Communication, mental stress, self-efcacy, cognitive load Communication, buy-in, mental stress, self-efcacy, cognitive load Communication, commitment, goal settings, mental stress, selfefcacy. cognitive load, trust Communication, mental stress, self-efcacy, cognitive load Communication, mental stress, self-efcacy, cognitive load Learning, self-efcacy, communication, mental stress, cognitive load

RNC

a b c d

Manager is responsible for workfow control. Subcontractors, suppliers, and in general, Last Planners are involved in workfow control. Manager is a main communicator and plan maker. WWP and Lookahead planning processes are incorporated and the manager has a facilitator role in meetings. e Decision-making authority is concentrated in the manager. f Each stakeholder (attendees to planning meetings) is empowered to make decisions.

communication structure presenting individuals as nodes and their communication fows as ties that link the nodes (Alarcon et al., 2013); and (2) assessing the communication purpose and frequency of commitment-related behaviors (e.g., information sharing, requests, negotiations, evaluations, declaration of satisfaction) by using the Language Action Perspective (LAP), which highlights how communications and language coordinate activities and commitments within organizations (Flores, 2013). In addition, we are interested in commitment efectiveness that can be measured through the PPC values calculated by individuals, where the agreed or planned commitment is compared to the actually executed commitment. The buy-in behaviors can be assessed in the workfow control by observing and determining the number of proactive actions regarding the involvement of individuals in decision-making and problem-solving. While learning behaviors can be assessed by observing and determining the number of actions leading to information sharing during workfow control, and the actions of knowledge sharing during RNC evaluation sessions, which encourages a refective analysis of the root causes of problems resulting in further improvements of the plans. Some psychological variables such as self-efcacy, mental stress, and cognitive load are considered in all technical domains. These variables can be assessed by analyzing cognitive processing efort, emotional arousal, and other behavioral responses using biometrics data and self-reporting via questionnaires. In terms of meeting and planning, we pay attention to interpersonal trust, which can be conceptualized as the participant’s perception of the average trust level in the group. 181

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IVR Prototype Overall Framework In order to simulate realistically the social interactions that take place when implementing the LPS, a multiplayer (multiuser) environment was considered as a suitable IVR solution. The IVR prototype was developed using C# programming language in Unity3D. Unity3D is a popular game engine for developing desktop, VR video games, and applications (www.unity. com). It is compatible with most of the currently available VR HMD such as Oculus Quest and HTC Vive. Figure 11.2 presents the architecture for a multiplayer IVR prototype to run the traditional and LPS-based scenarios mentioned previously, containing three clients, a host, and a cloud server to be used. We adopted a state-of-the-art standalone wireless IVR headset: Oculus Quest 2, which enables a great degree of freedom and matched motion, i.e., correspondence of users’ motions in the virtual and real worlds (LaValle, 2017). The controllers being used were also wireless without additional tracking devices, enabling participants to use them anywhere. All headsets are connected to a cloud server with Wi-Fi. PUN 2 (Photon, www.photonengine. com) is selected as an online hosting platform, providing a multiplayer solution to transfer and synchronize data (e.g., behavior and voice) around clients. In addition, a facilitator will join the network as a host by using a desktop computer with an Intel Core i9–10980HK processor, NVIDIA GeForce RTX 3080 graphic card, 32 GB of RAM, and 64-bit Windows 10 operating system. By running the Unity3D desktop editor, the host will have master control over the game (i.e., start the game, pause the game, end the game, and access data).

Figure 11.2

Multiplayer IVR architecture

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General Outline of the IVR Environment The multi-user IVR environment is designed for sessions that include three participants, maximum. They must complete as much work as they can in a pavement construction project (containing three diferent pavements or subtasks), within a three-week period (simulated as 20 minutes per week in the IVR environment). The group of three participants is required to follow traditional (non-Lean essentially) and LPS-based approaches to complete the same tasks. There are four diferent types of bricks provided to fnish the pavement project. Participants in the group are assigned to three diferent roles: subcontractor, manager, and supplier. Generally, the subcontractor is responsible for completing the on-site work or tasks. The supplier should produce specifc bricks and deliver them to subcontractors for paving. The manager is responsible to monitor the workfow and make the plans. Except for providing a tutorial regarding interactions IVR interactions (e.g., move, and grab bricks), and an introduction of the way to build pavements with bricks, the IVR environment and facilitator did not provide specifc prompts, hints, or feedback to participants regarding the efective way to deal with tasks. The IVR environment allows researchers for observation of the behaviors and measure of the psychological state of participants when simulating the traditional and LPS-based execution of the pavement construction project. Three scenarios and sub-scenes have been designed in the IVR environment, where the storyline that participants go through is built upon: (1) A Login Room scenario allows participants to insert a username and choose a role (see Figure 11.3a). (2) A Traditional Construction scenario with several sub-scenes: Instruction, Construction, Production & Shipping, and Meeting & Planning sub-scene (see Figure 11.3b). The Instruction sub-scene contains an instruction board to present the game rules and process (see Figure 11.3c). The Planning sub-scene has a whiteboard and pens to make the plan (see Figure 11.3d). The Production & Shipping sub-scene has a workstation to produce bricks and pallets to ship bricks (see Figure 11.3e). The Construction Site sub-scene

Figure 11.3

The scenarios and sub-scenes of IVR environment: (a) Login Room scenario; (b) A Traditional Construction scenario with several sub-scenes; (c) The Instruction sub-scene. (d) The Planning sub-scene; (e) The Production and Shipping sub-scene; (f ) The Construction Site sub-scene

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has roadbeds where the subcontractor can lay bricks and grab shipped bricks from wooden pallets (see Figure 11.3f ). (3) An LPS Construction scenario is another scenario where LPS principles and tools have been incorporated in the project’s production planning and control process. It contains similar sub-scenes and layout as the Traditional Construction scenario but has diferent rules and processes (the traditional Meeting & Planning sub-scene is replaced by a Lean-based Lookahead Planning Meeting and WWP Meeting sub-scene).

IVR Storyline The storyline is represented by a set of scenarios, scenes, or milestones, in which participants can make decisions and take actions accordingly to push the development of a plot (story). From a higher-level perspective, the storyline in the IVR scenario follows the steps below (more details in illustrations of Figure 11.4). The whole IVR simulation has both the traditional and the LPS-based construction scenarios, which contain four main milestones: Instructions, Meeting & Planning, Working, and Recap (see Figure 11.4i). In the beginning, participants go through the traditional scenario. Three participants and a facilitator meet for briefng and introduction (see Figure 11.4a). In the Meeting and Planning stage, the manager is responsible for compiling the plan (deciding on bricks production schedule, brick delivery schedule, and construction schedule) (see Figure 11.4b). Once the manager makes the plan, other participants have to follow the plan to carry out the paving tasks. The supplier produces diferent types of bricks and delivers

Figure 11.4

The storyline of the IVR scenario: (a) During the introduction stage, in the Meeting and Planning sub-scene, facilitator introduce pavement specifcations and objectives; (b) During the Meeting and Planning stage, in the Meeting and Planning sub-scene, participants make the plan, analysis RNC, and PPC; (c) During Working stage, in the Production & Shipping sub-scene, supplier produce and deliver bricks; (d) During Working stage, in the Construction Site sub-scene, subcontractor pave the bricks; (e) During Working stage, in the Construction Site sub-scene manager monitor the working and update the progress; (f ) During Recap stage, in the Meeting & Planning sub-scene, participants meet together to review weekly work; (g) IVR storyline fowchart.

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them in batches (see Figure 11.4c). The subcontractor executes the paving tasks themselves (see Figure 11.4d). The manager can communicate with others and guide them to perform tasks and update the progress (see Figure 11.4e). There are fve-minute meetings at regular intervals every 15 minutes, which means that the manager, subcontractor, and supplier should stop their work for recap (see Figure 11.4f ), discussing previous week outcomes regarding traditional performance metrics such as cost, time, and quality. These processes will repeat three times (three weeks) to fnish the project using the traditional approach. After that, participants will go through the LPS scenario. The storyline is similar to the traditional scenario but has diferent planning and control rules and tasks (see diferences in Table 11.3).

Envisaged Application Table 11.4 illustrates the diferent social variables that are observed, measured, and assessed within the IVR scenarios and sub-scenes. We consider the traditional group as control group and the LPS-based group as the experimental one. The selected social variables are measured and analyzed in the diferent IVR scenarios. For example, to explore how commitment management interplays with social variables such as communication, mental stress, and commitment, we assess the dynamics that emerge within the organization (participants involved) when implementing commitment management (e.g., WWP meeting) in the IVR simulation. The envisaged applications are explained as follows. (1) For the assessment of mental stress, we can collect raw biometrical data from Electrodermal activity (GSR) and Heart Rate Variability (HRV) sensors when participants conduct diferent tasks during the Meeting and Planning stage. It allows to collect individuals’ data measuring skin resistance and heart rate variability as they are correlated with individual’s stress level (Seshadri et al., 2019). The mental stress level can also be measured in a self-reported survey after the IVR simulation. We envisage that a higher stress level could be measured in the LPS scenario initially, but then it will fall down to a lower level, while the stress level will linearly increase during the entire traditional scenario (Conti et al., 2006). (2) For the assessment of communication, we capture participants’ communication behavior from video recording; thus, their communication frequency, time and targeted individuals can be obtained and analyzed by SNA. We can also envisage that there will be a higher communication density and longer communication time in the LPS scenario compared to the traditional scenario (Castillo et al., 2018; Priven & Sacks, 2015). Meanwhile, the communication purpose can also be coded based on the Interaction Process Analysis (IPA) coding scheme by replaying video recordings (Ghosh et al., 2019), which could help to identify the frequency of information sharing and its variations in the LPS scenario. (3) For the assessment of commitments, commitment-related behavior can be coded based on the IPA scheme and LAP metrics (Salazar et al., 2020). These methods could assist to assess the percentage of negotiations and its diference in the two scenarios. The commitment efectiveness can be measured by using in-game performance data such as PPC values that are logged by participants manually in the Meeting and Planning stage. We can observe the variations in the two scenarios as well. In the LPS scenario, participants may have more instances to negotiate and obtain higher PPC values in comparison with the Traditional one. Therefore, these results could reveal that the commitment management of LPS might reduce participants’ mental stress, drive transparent communication, and improve commitment behavior and quality (Conti et al., 2006; Ghosh et al., 2019). This IVR-based experimental framework can assist to improve the understanding of the LPS socio-technical nature, thus designing and testing some practical interventions to improve further project planning and control efectiveness. For example, if we observe 185

Canlong Liu et al. Table 11.4 The measurement of social variables in the IVR experimental environment

Domain

Social variables

IVR scene

Afective

Mental stress

All scenes

Behavioral

Buy-in

Learning

Communication

Commitment

Tools and methods HRV, GSR, self-reported questionnaire

Lookahead planning meeting and WWP meeting (LPS meeting); traditional meeting; construction, production & shipping WWP meeting and traditional meeting

All scenes

Video recording and behavior coding

Video recording, SNA

WWP meeting and traditional meeting, construction, production & shipping

Measurement Emotional arousal (measure): - Heart beat frequency (HRV) - Skin conductivity (GSR) - Five-point Likert Scale (self-reported questionnaire) Observed behavior (measure): - Frequency of individuals taking over other’s tasks. - Frequency of individuals’ involvement in decision-making Observed behavior (measure): - Frequency of coded learning behaviors (e.g., knowledge transfer and implementation of changes) Observed behavior (measure): - Frequency, time, targeted individuals, and purpose of coded communication behaviors Observed behavior(measure):

Video recording, - Frequency and percentage behavior of coded commitmentcoding, and related behaviors (e.g., LAP; in-game request for requirements, data logging negotiation, evaluations, declaration of satisfaction) In-game data (measure):

Cognitive

Trust

All scenes

Self-reported questionnaire

- Logged PPC value. Trustworthiness (measure of trust): - Five-point Likert Scale consists of three dimensions namely ability, benevolence, and integrity (e.g., team member is very capable of performing job)

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Domain

Social variables

IVR scene

Tools and methods

Cognitive load

All scenes

EEG

Self-efcacy

Goal setting

All scenes

Self-reported questionnaire

Lookahead Self-reported planning meeting questionnaire and WWP meeting, traditional meeting

Measurement Electrical activity over human’s forehead areas (measure): - The intensity of brain waves (e.g., fundamental frequency of a time series of data) Observed behavior (measure): - Five-point Likert Scale (e.g., individual’s perceived capability to respond to challenges and problems) Observed behavior (measure): - Five-point Likert Scale (e.g., the clarity and difculty levels of the project tasks)

some groups having high cognitive loads and low learning levels throughout meetings and during construction (both in the Traditional and LPS scenario), this may lead to lower project performance and weaker commitment (measured via PPC levels and frequencies of commitment-related behaviors), and a low acceptance of LPS. In order to modify positively this pattern of behavior and performance, practical interventions such as additional tutorial sessions (e.g., introduction of LPS knowledge) and learning incentives scheme (e.g., rewards) to reduce cognitive load and encourage learning can be tested in the IVR experimental environment. If the interventions work positively in IVR, they can then be utilized in real cases having reasonable expectation of the potential impacts.

Conclusion The purpose of this chapter was to investigate new methodological avenues to better understand the socio-technical nature of LPS, in order to minimize arising implementation barriers. To do so, this chapter identifed the LPS social and technical dimensions, and proposed to understand the LPS from an STS perspective. Then, the chapter explored the suitability, and benefts of using SDT such as IVR in studying the socio-technical nature of LPS. In this regard, this chapter illustrates the development of an IVR prototype, and a hypothetical construction project application to study the LPS socio-technical nature. The potential benefts were also explored for shed light of future research. Overall, this chapter strengthens the theoretical understanding of the LPS socio-technical nature, showing that social and technical dimensions in projects interact with each other when implementing the LPS. The outcomes generated from this interaction can, in turn, reshape potentially the social and technical nature of the LPS during implementation, which aligns with the STS theory. Thus, the theoretical contribution lies in advancing Lean project 187

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management by providing a process model and framework (containing a set of variables and potential synergies) that enables new experimental avenues to study this topic. From a practical standpoint, this chapter explained the connection between an I 4.0-driven technology as IVR and a Lean-based planning tool as the LPS within the context of the AEC industry. The proposed IVR environment has potential to enable a research platform to observe LPS implementation challenges and pin down socio-technical interactions, which helps to deepen the discussion around the LPS socio-technical nature. In addition, purposetraining platforms that consider IVR along with gamifcation can be a natural extension of the IVR platform proposed in this chapter, in order to improve the uptake of the LPS in the AEC industry. Therefore, some questions can be established for future research: (1) What behavioural and psychological patterns can be observed in experiment settings mimicking LPS-based and traditional management of projects? (2) What is the appropriate balance between the social and technical aspects of the LPS implementation that can signifcantly improve its uptake? (3) What practical interventions that considered balanced socio-technical synergies can be designed to improve further Lean-based project planning and control efectiveness? (4) What other implementation and cultural and social challenges can be observed when using the LPS? (5) Can the LPS be more efectively used if the proposed IVR is used for realistic, immersive teaching and training? However, there are several limitations envisaged when using IVR technology. (1) Some participants may sufer from headache, sickness, and dizziness when they are virtually moving with HDM (attributed to limited display and computational performance). (2) The IVR environment is a simplifed LPS implementation. There are only a few technical and social dimensions simulated in the IVR environment that mimic the LPS use in a real-life context. While this IVR framework is yet theoretical, it sets the stage for experimental testing of the socio-technical nature not only of the LPS but also other Lean-based tools and methods. Further research should consider testing the IVR experimental framework in more realistic settings and use cases, and improve the interaction and complexity in the virtual environments.

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Digital Production Planning, Control and Monitoring in Lean Construction

12 METVIZ LPS Metric Visualization, Monitoring, and Analysis System for Project Control Lynn Shehab, Ali Ezzeddine, Gunnar Lucko, and Farook Hamzeh Introduction The essential building blocks of the Fourth Industrial Revolution, known as Industry 4.0, are cyber-physical systems or “networking of the material world” (Klinc and Turk, 2019). Industry 4.0 has reshufed the basic methods and concepts of control, organization, and development of products and processes through its various technologies (Muñoz-La Rivera et al., 2021). It pertains to diferent changes in manufacturing systems that are often ITdriven (Lasi et al., 2014), and connects embedded smart production processes and system production technologies (Craveiro et al., 2019). The counterpart of Industry 4.0 in the Architecture, Engineering, and Construction (AEC) sector is Construction 4.0, which may be adopted as the future direction for the construction industry. It primarily promotes digitization and automation of the construction industry (García de Soto et al., 2019), in addition to exploiting the potentials brought by the large amounts of data in digital form and the massive digitization of material and information processes (Klinc & Turk, 2019). Within Construction 4.0, various techniques are employed to foster its implementation including but not limited to building information modeling (BIM), augmented reality (AR), mobile applications, artifcial intelligence (AI), human-computer interaction (HCI), and mobile computing (Muñoz-La Rivera et al., 2021). Automated construction methods have been proven to improve production efciency and accuracy, and Construction 4.0 technologies in general have the potential to prolong lifecycles and preclude construction wastes such as unnecessary work (Schönbeck et al., 2020). However, in the context of construction projects, the full potential and value of automation, digital processes, and fully industrialized construction are still unexplored (ibid.). Commonly known for having continuous improvement as one of its main drivers, Lean Construction is a beftting enabler of Construction 4.0. The fusion of Lean practices with Industry 4.0 may be referred to as “Lean Construction 4.0”, where the synergies between smart and digital technologies with production management theory must be taken into account (Hamzeh et al., 2021). Such fusion not only provides the AEC industry with a leading strategy of production management through Lean but also ensures the progressive evolvement of Lean Construction. Proactivity is one of the main principles promoted by Lean Construction as it allows planners to be one step ahead of the unknowns and to steer DOI: 10.1201/9781003150930-16

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the project while looking forward (Koskela et al., 2002). As one of the main pillars of Lean Construction, the Last Planner System (LPS) for production planning and control enables project planners to collaboratively produce a reliable workfow while proactively monitoring their performance using developed LPS metrics (Ballard, 2000). Various tools, metrics, and techniques in Lean Construction have been developed to aid projects teams in the planning and control process of construction projects. A previous study by Abou-Ibrahim et al. (2019) investigated the efect of the planner’s planning method on the project’s cost and time. The study found that planners with more information regarding the make ready process of tasks are able to achieve more resource-balanced weekly work plans. However, several researchers have highlighted some shortcomings of current planning and control methods at the disposal of construction managers (Su et al., 2018), such as difculty in using and updating some methods in practice, little use for site management, lack of progress measurement, lack of consideration of the criticality of tasks, difculty in measuring construction progress in realtime, and the manipulation resulting from using some metrics separately as project status indicators (Dallasega et al., 2021). Therefore, and in the absence of a Lean Construction 4.0 tool that aids project teams in planning, monitoring, and controlling their plans, this chapter presents a digital system named MetViz. It also showcases how available digital tools such as Microsoft Lists, Power BI, and PowerApps could be used to develop a custom-made system for the LPS of Lean Construction called MetViz. This system provides planners with the ability to track the progress of the make ready process of tasks, automatically calculate LPS metrics, and visualize all required information on a digital dashboard. Microsoft Lists and PowerApps are used for data entry. Microsoft Lists allows users to enter the data in a user-friendly web-based interface, while PowerApps allows users to enter the data using a user-friendly mobile application. The data is automatically connected to Power BI, which is used to visualize the required information through an interactive digital dashboard.

Te Last Planner® System and LPS Metrics The “Last Planner” refers to the last individual or team responsible for production unit control or the completion of assignments at the operational level (Ballard & Howell, 1994). Accordingly, the LPS is an operating system for production planning and control, and was developed to improve the planning reliability, enhance construction productivity, and ensure a smooth workfow (Heigermoser et al., 2019). LPS regards planning and controlling as a social process that focuses on reliable commitments, collaborative planning, and continuous learning and improvement (Olivieri et al., 2019). It includes various tools that enable its proper implementation such as reverse phase planning, fve whys, daily huddle meetings, stickies on the wall, and constraints analysis (Fernandez-Solis et al., 2013). LPS has been implemented in various projects and diferent environments (Alsehaimi et al., 2014) with promising implementation results including increased workfow reliability, improved supply chain integration, better project participants communication, enhanced work practice quality, reduced project delivery and production time, and increased learning among project teams (Fernandez-Solis et al., 2013). LPS encompasses four tiers of planning processes with diferent chronological spans: Master Scheduling where project milestones are set, Phase Scheduling where reverse phase scheduling and collaborative planning are performed, Lookahead Planning where constraints are identifed and removed, and Weekly Work Plan (WWP) where weekly work assignments are made, reliable promising is practiced, and metrics are measured (Hamzeh et al., 2008). 196

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Several LPS metrics have been proposed by various researchers to help identifying underlying planning or execution issues in project control. Among the developed LPS metrics is Percent Plan Complete (PPC), which is the ratio of the number of tasks that were completed at the end of the WWP compared to those that were promised or committed (Lagos & Alarcón, 2021). Other common LPS metrics are Tasks Anticipated (TA) and Tasks madeready (TMR). TA is the ratio of the number of tasks anticipated in the WWP compared to the total number of tasks in the WWP, while TMR is the ratio of the number of tasks that were completed to those that were listed in the lookahead plan. Percent Required Completed/Ongoing (PRCO) is the ratio of the number of critical tasks that were done or are going to be done by their planned completion dates divided by the total number of tasks in the WWP, and Required Level (RL) is calculated by dividing the number of critical tasks in the WWP by the total number of tasks. Completed Uncommitted (CU) is the ratio of the number of tasks completed from the backlog to the total completed tasks. Additionally, Percent Complete New (PCN) is the ratio of the number of new tasks that are done by the total newly emerged tasks. Capacity to Load Ratio (CLR) measures the ability of the teams in efciently using their resources (Rizk et al., 2017) and is calculated by dividing the number of executed tasks by the number of tasks in the WWP.

Visual Management and Dashboards ‘Use visual control so no problems are hidden’ is one management principle set by Toyota that stresses on the importance of using visual systems to monitor and control a process (Liker, 2004). Visual management is considered a strong enabler of proper control as it provides planners and managers with the ability to identify critical warnings before going through raw data. In the context of construction management, this could be seen by the use of digital dashboards that provide clear metrics and warnings about performance, deviations, and problems (Song et al., 2005). Ezzeddine et al. (2021) deployed a construction control room including a digital dashboard to monitor project progress. The study found that the dashboard was helpful in continuously providing valuable insights on diferent project metrics. While developing a 4D-based system, Magill et al. (2020) found that the use of digital dashboards presents valuable benefts for project teams, especially those working on construction sites. This is supported by a study by Hamzeh et al. (2020) where a dashboard for advanced LPS metrics was developed. The study highlighted the efectiveness of using a dashboard to provide clear and understandable visualizations of the required metrics. Moreover, another study showed that the use of digital dashboards provides a smooth fow of information between the connected teams, thus increasing the teams’ productivity (Pedó et al., 2020). Jansson et al. (2016) also noted that dashboards could create smoother workfows between project teams due to their efectiveness in rapidly visualizing essential data. Furthermore, Viana et al. (2014) showed that visual tools such as dashboards allow teams to better understand information during the decision making process. While visual management and dashboards have proved their efectiveness in project monitoring and control, several points should be carefully taken into consideration while developing these systems. An important task ahead of developing a dashboard is to create a standardized monitoring process to ensure the consistency of the fed information and data (Laine et al., 2014). Moreover, due to the huge amounts of data fowing into the system, it is important to identify the required and most important data to be visualized to keep the visual tools simple and clear (Murata, 2018). Pauwels et al. (2009) proposed a systemic way 197

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for developing a dashboard. First, the users of the dashboard should identify the key metrics and visuals that they require to see on the dashboard. Then, the frequency of the input data should be identifed to develop the dashboard input system according to this frequency. After these two steps, the relationship between the visualized metrics should also be visualized so that teams could directly analyze the metrics for faster decision making. Finally, dashboards should have the ability to predict future metrics to provide a proactive approach for project control (Pauwels et al., 2009).

Te MetViz System Triggered by the need for a system that allows projects teams to monitor and visualize project vitals within a “Lean Construction 4.0” environment, the MetViz system was developed. The MetViz system serves the need for a comprehensive system that allows inserting task data in terms of readiness and completion based on diferent task statuses. It also facilitates visualizing LPS metrics and tracing tasks in terms of numbers and paths through an interactive dashboard and mobile application. The basic concepts upon which the MetViz system is developed are discussed in the following sections.

Task-Tracing Concept Hamzeh et al. (2008) frst introduced the concept of comparing phases, processes, operations, and steps of the lookahead planning to breaking down materials by size into boulders, rocks, pebbles, and dust. Hamzeh et al. (2015) further converted this comparison into a simulation model to understand plan failures and the make-ready process and to investigate capacity planning in the lookahead process (Abou-Ibrahim et al., 2019). The model includes several lookahead plans of consecutive weeks to show how tasks move from one week to another. Originally, phases that are equated to boulders are broken into processes (rocks) as gross constraints were identifed and removed. These processes are identifed on WK3, or three weeks before execution. In WK2, or two weeks before execution, the tasks are identifed as either Ready (R) if they are not constrained, or Not Ready (NR) if they are constrained. In WK1, some of the NR tasks in WK2 are made Ready and join the group of R tasks. Other tasks remain NR, but some have the potential to become ready and are named Not Ready but Can Be Made Ready (NR-CMR), while others have no chance of becoming ready in the concerned week (NR-CNMR) and are moved to the plan for upcoming weeks. The current Weekly Work Plan (WWP) thus consists of Ready tasks (R) and not ready tasks that can be made ready (NR-CMR). New tasks (N) that were not previously may also suddenly emerge (Abou-Ibrahim et al., 2019). The constraints of the aforementioned task types are double-checked. Some of the ready tasks (R) are found to be indeed ready and called Ready Ready (RR) and become Done once they are executed. The remaining previously Ready tasks (R) are found to be constrained (NR-R) and cannot be executed, thus become Not Done. Similarly, some of the NR-CMR tasks are actually made ready by removing their constraints and become Done once they are executed. The remaining NR-CNMR fail to become ready and join the Not Ready tasks. Finally, some New tasks (N) are made ready (R-N) and executed to become Done, while the others are not ready (NR-N) and join the Not Done tasks. The tasks that are Not Done now include those that were not ready after previously being thought to be ready (NR-R), those that were originally not ready and were indeed not 198

MetViz: Project Control Visualization Table 12.1 Metric abbreviations and metric names Metric abbreviation

Metric name

R RR-R NR-R NR NR-CMR NR-CNMR N R-N NR-N

Ready Ready-Ready Not Ready, was thought to be Ready Not Ready Not Ready, Can be Made Ready Not Ready, Cannot be Made Ready New Ready-New Not Ready-New

able to be made ready (NR-CNMR), and new tasks that were not able to be made ready (NR-N). These Not Done tasks join the upcoming weeks plan and undergo the exact same procedure again, and so forth. The diferent metric names along with their abbreviations are summarized in Table 12.1.

Task-Tracing Paths To be able to track the status of the diferent tasks from the lookahead planning phase until the end of the WWP, seven diferent paths are identifed that each task may take to reach its fnal destination which is either successfully executed or not. The seven identifed paths are shown in Figure 12.1.

Figure 12.1 Tasks paths network

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• • • • • • •

Path 1: Total Tasks ◊ Ready ◊ Ready Ready ◊ Done. Path 2: Total Tasks ◊ Ready ◊ Not Ready Ready ◊ Not Done. Path 3: Total Tasks ◊ Not Ready ◊ Can Be Made Ready ◊ Done. Path 4: Total Tasks ◊ Not Ready ◊ Can Be Made Ready ◊ Not Done. Path 5: Total Tasks ◊ Not Ready ◊ Cannot Be Made Ready ◊ Not Done. Path 6: New Tasks ◊ Done Path 7: New Tasks ◊ Not Done

System Development Data Entry To develop a well-integrated system, it is important to standardize the format of the gathered data. This would reduce potential errors and the efort of information entry as teams are able to enter the data in a systematic and easy way (Laine et al. 2014). In the case of MetViz, all tasks should have the same required information and the information should be predefned and not user-created. Microsoft Lists is used to create the database for the data entry. Lists allows users to create a data entry sheet similar to that of Microsoft Excel with the main diference being that Lists is web-based and has a more user-friendly interface. Figure 12.2 shows its interface. The user must insert 8 pieces of information for each task starting with the task’s name. As this tool spans from the lookahead phase till the end of the WWP, the user will fll Status 1, Status 2, and Status 3 as they progress towards the end of the WWP. The choices for each status are predefned as per the previously discussed task tracing paths diagram. Status 1 is the task’s status at the beginning of the lookahead phase which could be either R for ready or NR for not ready. Status 2 is flled around one week ahead of the WWP. Status 2 updates that of one where it indicated whether the task constraints have been removed and whether the task has been made ready for execution of not. Finally, at the end of the WWP, the users will fll Status 3 by indicating whether the task has been successfully executed or not by choosing the Done or Not Done options. Moreover, the user is also able to indicate whether the task is critical or not by choosing either of the available options. To give planners a better understanding of the constraints, users are able to type and describe the constraint which they are trying to resolve for each task. The fnal inputs are the Date Planned and Date Completed. The Date Planned indicates the start of the WWP, while the Date Completed is inserted if the task is Done during this WWP. Any relevant attachments that might be needed for additional details may be added to the entry as well. Although Lists provides an easy web-based data entry solution, often construction personnel need to insert live data while on site. Hence, Lists might not be the best solution for those on site as it might not be practical to carry a laptop or a tablet around or wait to go back to the site ofce. Even in case tablets were available, accessible, and agreeable, the proliferation of smartphones is an opportunity that has become an expectation. For this reason, another data entry method is provided through the development of a mobile application which could be accessed through smart phones, tablets, and a laptop, if needed. The mobile application is developed using Microsoft PowerApps that enables users to rapidly develop and deploy custom built applications without the need for any coding experience. Users can enter the same date into the application as into Lists. MetViz has a straightforward and easy to navigate user interface. Figure 12.3a shows that the users can view and search for the tasks that are inserted. Users can also click on any of these tasks to view the data inserted for each 200

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Figure 12.2 Lists user interface

in Figure 12.3b. As for adding a new task, the user clicks on the add button indicated by the plus sign on the upper right of the screen and add all the needed input in Figure 12.3c. With the digital transformation that the AEC industry is experiencing, the developed data entry sheet through Microsoft Lists and mobile application through Microsoft PowerApps are two solutions that provide a systematic way of adding, standardizing, and communicating data between the planners and the construction site.

Digital Dashboard After providing an easy way to collect planning data, it is also crucial to be able to rapidly visualize this data to better understand and analyze it. Microsoft Power BI ofers a solution by providing a platform to develop digital dashboards which can be automatically updated with each new data entry or modifcation. The purpose of this dashboard is to visualize the progress of the make ready process by showing the fow of tasks from each stage to the other (i.e., from Not Ready to Can be Made Ready to Done), and to automatically calculate the visualized LPS metrics without the need for any manual intervention. At the top part of the dashboard shown in Figure 12.4, advanced LPS metrics such as the TA, TMR, PPC, RCR, 201

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Figure 12.3

PowerApps mobile application user interface. (a) MetViz mobile application interface, (b)Task details, and (c) Adding a new item

Figure 12.4 MetViz dashboard

PRCO, PCN, and RL are visualized using gauges. All these metrics are automatically calculated and updated based on the provided input from Lists or PowerApps. On the left side, a line chart plots the TMR and PPC metrics over the span of the entire project so that planners are always able to identify sharp changes in project performance. Below the line chart, Tasks by Status circular visuals are placed to give planners a rapid indication on the number of tasks in each status, where the bigger the circle, the larger the number of tasks. For example, users 202

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Figure 12.5 Visualizing the paths and metrics of not ready tasks

can directly notice how many new tasks are found, and they can visually compare it to other types such as the Ready Ready tasks. Users can also switch between the desired status. If users require more information on the fow of each task, they can use the Task Status Tracker on the right side of the dashboard. This visual is a digital replica of the task tracing fowchart previously explained. The users are able to see how many and what tasks fowed from being Ready to Ready Ready to Done in an easy and visual manner. Moreover, it can directly be noticed that the tasks that were Not Ready, but Can Be Made Ready (NR-CMR), have all been successfully executed on time, as shown in Figure 12.5. Figure 12.5 also shows how the entire dashboard visuals are automatically altered to represent metrics and values of only the tasks that were classifed as Not Ready previously by clicking on the NR symbol. For more information regarding planned dates and type of constraints on each task, the Task Summary table on the corner right side of the dashboard provides such information. While Figures 12.4 and 12.5 are in grayscale, each gauge representing metrics and each circle representing statuses in the dashboard are given a unique color to help diferentiate between the diferent metrics and statuses. As showcased, this dashboard, along with the linked Lists and mobile application, provides a system that can be used to digitize certain planning and progress monitoring aspects of the LPS in rapid and easy way through its ease of access and user-friendly interface.

System Verifcation To verify the functionality of the proposed MetViz system, TMR and PPC data from 114 weeks obtained from a large construction project in the United States were utilized. Data on TMR and PPC values for 114 weeks were recorded on an Excel sheet that is directly connected to Power BI. Fourteen sample tasks with diferent original and fnal statuses are also used for testing. Accordingly, whenever a new entry is inserted into the sheet, it is automatically updated and visualized on the MetViz dashboard. Regarding the 14 tasks, they 203

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are inserted either directly through the platform or through the mobile application. In either case, all inserted tasks are automatically visualized on the dashboard along with the updated LPS metrics and task paths as shown in Figures 12.4 and 12.5.

Discussion and Impact on Industry Inexpensive custom-built software tools like MetViz have the potential to change the way that the AEC industry approaches its planning and control functions. However, such potential positive impact is tempered by two counteracting interests of construction frms. On the one hand, they are by their nature tradition-bounded and tend to be hesitant to adopt technological innovations (Muñoz-La Rivera et al., 2021). They tend to rely on the use of very few scheduling software products, whose use has become prescribed by owners in contractual requirements (Hatipkarasulu, 2020). Another reason for relying on of-the-shelf commercial tools may be liability avoidance (Albeaino & Gheisari, 2021). In this regard, companies have indeed become reliant on a very small selection of software with narrow capabilities – notably lacking incorporating Lean Construction – to the point of being constrained by it, perhaps imperceptibly so. This may hinder or prevent them from easily trying and applying new methods that may improve their processes. Yet on the other hand, increasing acceptance and adoption of Lean Construction principles by companies is itself opening a door toward the broader difusion of Industry 4.0. Construction frms are willing to adopt new technologies when they perceive tangible benefts or see competitors use them (Hamzeh et al., 2021). Several commercial solutions that support Lean production planning in construction projects have been introduced. However, the fow of tasks along the diferent statuses throughout the project has not been the focal point of researchers or software developers in the AEC industry. The introduced solutions do not ofer the option to track or monitor specifc weekly data, status data, or metrics of a specifc type of tasks by fltering them out, as shown in Figure 12.5. Therefore, and in the absence of commercial tools that are explicitly designed to support this aspect of task tracking in Lean Construction and its implementation within the aforementioned standard software, the tracking of paths and the metrics that the MetViz dashboard provides fll a major gap in the toolbox of project managers. Beyond having provided a customizable tool, MetViz, this chapter also establishes a template for how further extensions or new tools can be rapidly created to address specifc user needs. The transparency of the underlying model makes it fully auditable; it is in efect a “white box” model, which is an important concern to ensure veracity of software-generated outputs as the basis for decision-making. While ultimately each project that is built is unique within a certain set of specifc circumstances, real location, per one permitted design, and under the contractual constraints by the owner, this does not necessarily mean that it requires creating a custom-built tracking system. The defnitions of the path and metrics in MetViz are generic and should apply to most projects without the need for modifcations. That said, the tool is fully extensible and customizable if a decision-maker wishes to add more high-level or fne-grained views to it.

Notes on Human-MetViz System Interactions Lean Construction 4.0 must maintain the people-processes-technology triad at its core by embracing the changes propelled by Industry 4.0 without disregarding the human touch (Hamzeh et al., 2021). It is therefore essential to discuss the interactions among humans and 204

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the MetViz system by highlighting the importance of the human mind in the monitoring, controlling, and predicting process, in addition to how MetViz can facilitate the users’ role in mentioned process. MetViz’s key features include a data entry platform and a visualization, monitoring, and prediction dashboard. The human judgment and input are indispensable for the data entry platform, as only humans are capable of detecting hidden constraints and their potential cascading efects. Such constraints and corresponding efects determine the status of each task and can infuence the criticality as well. Accordingly, MetViz provides planners with a platform for describing, categorizing, and labeling tasks, without dispensing their vital input. The dashboard serves multiple purposes. Initially, it shows the fow of all tasks from one stage to another as they progress along the weeks. Such visualization may be scrutinized by users who are curious about the general fux or a specifc task fow. The dashboard also automatically calculates and displays updated LPS metrics in real time based on the provided input. Some LPS values are shown as plots to facilitate tracking their changing values throughout the weeks. Analyzing the changes in such plots requires human judgment that can investigate questionable variations or deviations such as sharp drops or sudden jumps. There is a profusion of software developed to serve the scheduling process without the need for human intervention. While the scheduling process may be fully automated, the planning process is, and most probably will always be, in indispensable need for human judgment and involvement. Planners do not only rely on calculations and facts but also employ their logical evaluation and extended predictions based on previous experience or projected perception. One might argue that automating the planners’ tasks such as calculating LPS metrics could reduce the planners’ understanding of these metrics. While it is true that with MetViz planners are no longer manually calculating LPS metrics, their analysis and interpretation of metrics requires profound understanding of each metric’s value indication, root cause, and possible future trajectory. This manifests the impossibility of discarding the planners’ contribution in LPS analysis process. In applications, software, or interface technologies in general, User Experience (UX) refers to the users’ experience pertaining to the reactions, behaviors, and perceptions in terms of emotions and thoughts during the utilization of the product ( Joo, 2017). User Interface (UI) refers to products in which users can interact with the system through techniques or commands (Kristiadi et al., 2017). UX/UI aims at facilitating the usability of applications or software through providing pleasant graphics, clear navigation, and a user-friendly interface and overall experience. MetViz acknowledges the criticality of the mentioned factors on the human-machine interaction by providing an intelligible dashboard presenting the diferent data and information that need to be analyzed, including the metrics, statuses, and plots, in an orderly fashion. It also ensures efcient usability by exploiting the options provided by Microsoft PowerApps whereby clicking on one element, i.e., a metric, triggers automatic fltering of the screen plots and data to display the selected element’s data exclusively.

Conclusions and Future Work MetViz is an LPS Metric Visualization system that includes a data entry platform and a visualization dashboard. It provides an automated tracking system to monitor and control productivity represented by LPS metrics, in addition to a mobile application that allows site personnel to insert updated site data. The developed dashboard and tracking system provide planners with better decision capabilities by allowing them to visualize productivity, 205

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monitor the production fow in the WWP, and predict future performance based on previous data. MetViz may act as a decision support system where planners can better analyze the project’s performance. The data entry platform provides a clear and easy way to insert data such as the name, status, and criticality of each task, in addition to its constraint description, if any. Each task planned and completed dates are also inserted through the platform. The visualization dashboard includes a user-friendly and attention-grabbing interface. It is interactive as specifc statuses or task fows may be visualized separately for clarity and intelligibility. The system was verifed through case study data from a large construction project in the United States to prove its practicality and efcacy. As Lean Construction 4.0 must maintain the peopleprocesses-technology triad at its core by embracing the changes propelled by Industry 4.0 without disregarding the human touch, it is asserted that human intervention and judgment remain key in the MetViz system. Future work could connect MetViz with scheduling software, or even better, create an add-on for commercial tools that enhance them with the new capabilities. It could also be integrated with resource management and cost-loading, both of which already exist as options within scheduling software), which could open avenues to defning additional meaningful metrics. Studies on the impacts of the proposed system could also be conducted to analyze how its implementation can better enhance project performance in terms of time, cost, quality, productivity, and Lean practices implementation. Finally, further enhancements to UX/ UI features in MetViz may be made to better refne its usability and to boost its functionality. This can ensure better quality when MetViz is implemented in a real project and onsite.

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13 PRODUCTIVITY FUNCTION Mathematical Foundation for Production Management in Construction Ricardo Antunes, Vicente A. González, Michael O’Sullivan, Omar Rojas, and Kenneth Walsh Introduction The frst application of one of the frst management studies, i.e. motion study, was in construction (Gilbreth, 1909). From early times of motion studies and scientifc management (Taylor, 1911) manufacturing has been advancing understanding and managing its production at much higher speed than contruction. Most important in the context of this book was the creation and development of Lean Thinking that inspired Lean Construction. This chapter presents further development of Lean Thinking: a mathematical explanation of production in construction. Such work has been conducted by Taiichi Ohno (Toyota Production System (Ohno, 1988), Jay Forrester (Industrial Dynamics) (Forrester, 1961), and Wallace Hopp and Mark Spearman (Factory Physics) (Hopp & Spearman, 2001) among others in manufacturing. The Toyota Production System, or Lean Manufacturing, should be familiar to the reader by now. Industrial dynamics (Forrester, 1961) is the study of the information-feedback characteristics of industrial activity to show how organizational structure, amplifcation (in policies), and time delays (in decisions and actions) interact to infuence the enterprise’s success. One of the goals is to: “Construct a mathematical model of the decision policies, information sources, and interactions of the system components” (Forrester, 1961). The mathematical modeling approach derivates from feedback control systems. Factory Physics (Hopp& Spearman, 2001), for instance, provides a set of equations that apply to manufacturing production queuing systems. This chapter presents the development of a modeling approach and the resulting equations that unite the aforementioned work, applied to production in construction. Despite the title of this chapter, equations will not be found here. Not because they do not exist, but because we believe that, at least for now, it is more important to understand what these equations mean than how they are mathematically formulated. You are welcome to delve into the mathematics and the development of such equations in the referenced literature (Antunes, 2017; Antunes et al., 2015, 2016, 2018; Antunes & Gonzalez, 2015). Next, you will fnd several concepts of production in manufacturing and later their applicability to project management within the context of the construction industry. The development of reliable models, equations, and theory relies heavily on the data collected. In traditional AEC project management, that seems challenging as the data collection process to update project schedules and activities is already problematic. That is why Lean DOI: 10.1201/9781003150930-17

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Construction 4.0 is vital to further developing the Productivity Function and vice-versa. The multiple ways that new technologies enhance the efectiveness for data-collection from a construction transformation standpoint will progressively supply more quantity and better quality of data for the further development of the production theory presented in this chapter. As the theory develops, the AEC industry will be able to adopt more tangibles Lean principles.

Production in Manufacturing In this section, you will be presented with a few concepts and Laws (tautologies) of production in manufacturing from Factory Physics (Hopp & Spearman, 2001). Despite these being presented as manufacturing, you will probably relate them to what you already know about project management in the AEC industry.

Troughput Throughput is the number of production units sold per unit of time (Hopp & Spearman, 2001). However, throughput in production systems has a slightly diferent meaning. Throughput is the number of units produced per unit of time, the production system throughput rate, or the output frequency. The throughput value is presented as the average value of the production system throughput rate. Throughput does not generate revenue; it creates an inventory. It is utilized to measure the performance of an individual production process and/or routings in a network of transformation processes.

Capacity Capacity is the maximum number of units that a production system releases per unit of time (Hopp & Spearman, 2001). Capacity is equivalent to the maximum throughput. Maintaining a production system at capacity is complex. Since most production systems are unstable at capacity, work is released at unsteady rates. That disturbs the fow and process throughput, thus producing irregular intermediate inventories between these processes.

Work-in-Process Work-in-process (WIP) consists of the intermediate inventories between transformation processes. WIP excludes the inventories at the extremes of the production chain, i.e., the frst raw material inventory and the fnished goods inventory (Hopp & Spearman, 2001).

Cycle Time The cycle time is the time spent to produce a good, i.e., to complete a production cycle. Cycle time measures the time a product or a service takes to be completed (Hopp & Spearman, 2001). Alternatively, cycle time is also utilized to measure the performance of an individual transformation process or routing in a network of transformation processes. The cycle time of the frst completed product or service is also known as lead time (but that is the only time they are the same, more details are provided in the next sections). The cycle time should be considerably shorter than the lead time. This observation holds because, at the time the frst product is released, the following product should be queued at the last transformation 210

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process. Consequently, the time necessary for releasing the subsequent product is equivalent to the processing time to fnish the next product. As such, the unit of measurement of cycle is time per unit.

Lead Time Lead time is the time assigned for production between the start and end of the transformation process chain (Hopp & Spearman, 2001), as the cycle time (more or less). However, lead time is measurement in time units, such as hours, minutes, and seconds. During normal operations, the cycle time is less than or equal to the lead time.

Utilization Utilization is the ratio of the actual output to the full potential output of a transformation process (not to be confused with capacity) expressed as a percentage. The actual output and the full potential may be expressed in currency units, unit amount of production, or time, whichever provides better management information (Kumar & Suresh, 2009). The diference between actual and potential output (measured by the utilization ratio) can be used to display potential problems in the process, such as machine failure, job waiting, or lack of parts. Few processes operate at capacity due to stability issues. As a result, utilization is also rarely close to 100%. If utilization is high, the process is operating under capacity. Conversely, low utilizations indicate an excess of capacity (Hopp & Spearman, 2001).

Law: Little’s law Named after John D. C. Little, Little’s law relates three lower-level variables of management in a queuing system. A queuing system consists of a fow of discrete items arriving at a constant pace to a stable system that services and releases these items for further processing. For a system following a First-In, First-Out (FIFO) sequencing, Little’s law states that, under invariant conditions (steady-state), the average number of items in a queuing system equals the average rate at which items arrive multiplied by the average time that an item spends in the system (Little & Graves, 2008). Over the years, the original Little’s law equation evolved to a more generic form comprising operations management (Hopp & Spearman, 2001). WIP is equivalent to the expected number of units in the system. The average output of a production process per unit time (throughput) is the arrival rate during the period, and the cycle time is the average waiting time in the system per arrival during the period. Thus, Little’s law can also be written as: WIP is equal to cycle time multiplied by the throughput. Little’s law, which is based on the average behavior of variables over a very long period, is likely to produce an imprecise approximation of project-driven production. However, to describe most relations of production in manufacturing, the approximation described is sufciently accurate.

Bottleneck Rate In a production line, the line’s bottleneck rate is given by the throughput of the process with the highest long-term utilization, i.e., the lowest efective rate (Hopp & Spearman, 2001). In general terms, the bottleneck rate points out the process that is working closest to its capacity. Accordingly, the bottleneck process constrains the throughput of the production line. 211

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Law: Best-Case Performance The best performance of a production line refers to the minimum interval to produce a good; it means a minimum cycle time or the production line’s maximum throughput (Hopp & Spearman, 2001). The best-case requires a minimum work-in-progress, ideally zero. Zero inventories are unrealistic. That would mean that goods are being produced instantaneously and that there are no inventories. Also, there is no straightforward solution because Little’s law involves three variables. Nevertheless, the best-case performance establishes a region where the line is at its highest production level. As a result, once one variable is set, the remaining variables can be manipulated to optimize the production system. In addition to the best-case, Little’s law produces two other cases: the worst-case and the practical worst-case.

Law: Worst-Case Performance In the worst-case scenario, the production line operates at the maximum cycle time and minimum throughput (Hopp & Spearman, 2001). In a production system operating at its worst-case performance, the next transformation process is always idle, and the process lead time is either equal to or less than the previous process’ lead time. The number of items arriving at a process is greater than the number departing. As a result, the items pile up in the queue at the next process entrance. Nevertheless, both best- and worst-case performance are boundaries. In practice, the performance of a production line does not behave at either of these limits. The practical restriction is the average time at a workstation, which includes the time taken for other jobs in other workstations and the job being performed in the station itself.

Law: Variability The impact of process time variability (fuctuations on the output rate) in manufacturing systems is straightforward; increasing variability always degrades the performance of a production system (Hopp & Spearman, 2001). Due to the damages that variability can cause in a production system, several strategies aim to protect the system from variability as shown in the sequel.

Law: Variability Bufering The most common strategy for protection against variability is the use of bufers as a bumper or cushion. The bufering approach considers an in-excess use of at least one of the variables that can be consumed without harming the system’s performance. Variability in a production system will be bufered by some combination of inventory, capacity, and time (Hopp & Spearman, 2001). In circumstances where bufers are inefective, variability may propagate through the transformation process impacting the production fow. Thus, laws concerning the production fow, material fow, capacity, utilization, and variability propagation must be stated.

Law: Conservation of Material The frst law regarding production fow is the conservation of material in and out of the transformation processes (Hopp & Spearman, 2001). Law: Conservation of Material states that in a stable system, over the long run, the rate out of a system will equal the rate in, less 212

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any yield loss, plus any parts produced within the system. It means that in a steady-state system, the fow of material is constant, consuming the necessary and only the necessary material to produce the goods. It includes the ordinary transformation rate and loss of material.

Law: Capacity The concept of stability in manufacturing systems requires that the input rate in transformation processes must be less than capacity. The reason again being variability. If the input rate equals capacity, any variation in the transformation processes may degrade the process performance and incur very long waiting times. The diference between the input rate and capacity creates a bufer that should grant the system stability by absorbing any variability. In steady-state, all plants will release work at an average rate that is strictly less than the average capacity (Hopp & Spearman, 2001).

Manufacturing Teory Does Not Apply Directly to Construction Manufacturing is either a continuous or a repetitive process in which machinery and human resources are specialized and qualifed. Production fow and material routes are established. Thus, most manufacturing processes can be automated. The scenario is diferent in construction. While capacity is known and measured in manufacturing, it is difcult to measure it accurately in construction. Increasing production output in construction often means adding more human resources. That often causes a decrease in productivity due to a lack of space and tools for example. These conditions place production in construction in an variant state. Thus, while the concepts of manufacturing production still apply to construction, the equations do not. That is because production processes in construction are not stable for long enough for these equations to provide accurate outcomes. For processes that are not in steadystate, a diferent approach must be used. Thus, the new approach has to ofer explanations for systems at invariant and variant systems, pointing out algebraic relationships. This new approach sees these systems as one that embraces diferent dynamic states. That is used to analyze and prescribe management actions undertaken to improve processes, as explained later.

Productivity Function and Production Teory for Construction Mathematical models have enabled a comprehensive understanding of production mechanisms that support practices to improve productivity in manufacturing. The system approach or system analysis was the problem-solving methodology of choice. The frst step of this methodology is to adopt a “system view.” In this view, the problem is observed as a system established by a set of subsystems that interact with each other. An input is applied to a process to produce an output. These three elements constitute an input/output, which we will refer to simply as the system from now on. Inputs are, for instance, materials, tools, equipment, labor, management, time, and weather conditions (Blanchard & Fabrycky, 2011). The outputs are (usually) the product of the processes, for example, absolute quantities such as squared meters of plastered wall, meters drilled, or any other relative measure of progress. The process is the transformation procedure, or operation that when applied to the input will create the output. A system can be composed of single or multiple inputs applied to a process that outputs a single or multiple outputs. Regardless of the system components in terms of how many inputs and outputs, or what the input(s) and output(s) are, and what the process is; there are a few restrictions to a system: there is no output on lack of input; and there is no output without a process. 213

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Production Process System Representation Most projects follow a cycle similar to the plan-do-check-act cycle, originally developed for manufacturing operations. Plan-do-check-act applies to continuous process improvement (Rumane & Badiru, 2013) and consists of a four-stage infnite loop (Antunes, 2017). First, the team establishes goals and develops the strategies to achieve them, creating a plan. Second, the plan is implemented. The team carries out the actions addressing key points, according to the plan. Third, the team measures the outcomes of their actions comparing the results to the goals. Fourth, where the current process performance matches the goal, the team institutionalizes the new process’s performance, thus setting a benchmark, as well as the actions performed to achieve the goal, thus creating standard procedures. In the case where the actions are not efective, the team must return to the frst cycle stage. The plando-check-act cycle restarts to implement further improvements. That results in a system that is being constantly fed back by the current output state. If the current output state is not the one desired, the input will change to achieve the output goal. The process improvement itself will alter the process as such that the system will have increased the output when using a constant input. In terms of systems (Antunes, 2017; Antunes & Gonzalez, 2015), the plan is the desired production throutput. Check is a comparison between the plan and the current output. The result is the measured deviation or error. Based on this deviation, actions must be implemented. For example, a plan establishes that an output of 50 m 2 of plasterboards should be installed (process) with an hour to complete the job on time. Two workers are initially assigned to the job (input). If the workers are incapable of reaching the throughput of 50 m 2/h, that might require corrective action, for instance, increase the number of workers to increase the output. On the other hand, if the workers produce a higher output than the plan, the deviation will work the other way: decrease the number of workers to reduce the output, thus matching the plan.

Construction System The interconnectivity between project stages is explicit if subsequent phases rely on the accomplishment and performance from previous ones. An activity or stage may impair or favor a subsequent action depending on the level of correlation and dependence (Antunes et al., 2016). The interdependence of activities forms a conduit to the propagation of unforeseen events. Uncertainty is a potential risk. Projects carry risks throughout their entire life cycle that may impact the project execution whenever not suitably treated resulting in throughput variability and other project (Antunes & Gonzalez, 2015). Understanding how risk transforms into variability, especially how variability afects networked activities, motivates an opportunity to develop methods to avoid and mitigate (flter) the propagation of risk (noise). Control theory may ofer a proxy theory to explain the efects of variability in construction projects by extending the elements of the dynamic systems.

Transient, Steady-State and Unsteady-State Two parts compose a system response in the time domain, transient, and steady or unsteadystate. Figure 13.1 ilustrates those concepts. The transient is the immediate system reaction of an input change from a rest state (Ogata, 2010). If the system is stable, the response will tend to a constant value, as time goes to infnity (Mandal, 2006). When the output reaches 214

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Figure 13.1 Step response

this value, the response is then at steady-state. The time that the system response takes from the moment the input changes to the steady-state (Ogata, 2010) is the settling time, the duration of the transient state. On the other hand, if the response never reaches a fnal value or oscillates surpassing the thresholds (arbitrary boundaries around the fnal value), the system is then unstable, i.e., at an unsteady-state. Consequently, the system outputs at unsteady-state vary with time even when induced by an invariable input. Consider a small group of traders pouring concrete for ramp (already framed) on a car park. Once the necessary material, labor, and permissions are in place, the work begins. That moment is the step. The inputs (material, labor, and permissions) are on so the production can start to generate the output (let’s say square meters of concrete). At the very beginnig, the throughput is small because there is not enough concrete dumped that can be pulled out. Later the driver has to repossition the truck so the workers can fnish the area obstructed by the truck. Again the throughput might lower. And fnally the workers can work freely and reach a steady throughput and fnisih the job due to the availability of concrete and space. As the reader can imagine, in order to reach the conditions that enable a constant throughtput in construction work is quite challenging. That is why we said the construction processes rarely reach steady-state.

Productivity Function and Transfer Function The transfer function of a system is a transformation from an input function into an output function, capable of describing an output (or multiple outputs) by an input (or multiple inputs) change. In other words, the output function in time is equal to the transfer function in time convoluted with the input function in time, which are represented by ordinary diferential equations (Mandal, 2006). In the frequency domain, the convolution operation transforms into an algebraic multiplication, which is simpler to manipulate and reversible. The application of the transfer function modeling to the repetitive construction process is one of the elements that constitute Productivity Function. It also includes applying concepts from dynamical systems, control systems, and system identifcation to explain, model and manage project and construction endeavors. Work activities in construction have diferent dynamics. Now, let’s consider two road works activities: pothole covering and painting. Starting with the latter, painting, such as painting a mid-lane, has a small transient, can reach a steady-state throughput easily, and last for a long time. This activity is likely to be performed by a truck driven at a constant speed 215

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Figure 13.2

Step response of well drilling (A), formwork setting (B), and plastering (C)

that sprays the paint on the tarmac—so let’s take a second to imagine the outcome if the truck abruptly accelerates and breaks often. Thus, the throughput is constant once the truck reaches the cruise speed. The truck can keep going until it runs out of fuel or paint or the driver needs a break. It is more likely that they will run out of tarmac to paint before one of those situations happen. Pothole repair is another story. The hot asphalt must be shoved into the potholes; it requires strength. If the work has to cover a long distance, it also requires endurance. In this case, the work is likely to be conducted in short bursts so the workers can have some breaks. Even though the work is executed in short periods, it is expected that the workers’ performance will decrease as the work hours accumulate on the day. So, we want to fnish the work as soon as possible for this work. We overshoot throughput. That means that the throughput is higher than the throughput at steady-state. Despite not being sustainable for an extended period, the overshoot helps to compensate for the rest periods. Again, construction processes have diferent dynamics. The productivity function can extract this information. Figure 13.2 (adapted from Antunes et al., 2017) shows three examples of that. Once again, the horizontal axis is time and the vertical axis a the output. Figure13.2A shows a drilling process where the throughput builds up the steady-state. Figure 13.2B is the model of carpenters manually setting the formwork for slabs. This process’ dynamics is similar to the pothole repair. It starts at high throughput and decreases over time. Figure 13.2C introduces more complex process dynamics. This plastering activity where the workers install a wall throughput starts near the steady-state levels but plummets immediately rising to steadystate later. That indicates that the process might have required some readjustment.

Production Forecast Forecasting in construction is often inadequate and one of the weakest project control functions (Construction Industry Institute, 2012). The numerical estimation approach of Productivity Function can be incorporated into project management software or used as a stand-alone tool to forecast, access, and simulate critical processes that require in-depth project controls (Antunes, 2017). Simply by replacing the traditional steady-state model with the Productivity Function, more accurate results should be obtained (Antunes et al., 2017). Because the Productivity Function provides calculations to the steady-state, such as the fnal value, while also explaining the transient behavior of the production system (Antunes et al., 2018). Furthermore, dynamics simulation, which relies on the mathematical models defned by ordinary diferential equations, e.g. Productivity Function, has a signifcant role in the supply chain (Higuchi & Troutt, 2004) and production in manufacturing (Forrester, 1997). The application of dynamics simulation in construction is rare, specifcally due to the lack of mathematical models to describe the production in construction. This is one gap that may be flled by the Productivity Function. 216

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Variability Analysis The greater the coefcient of variation (ratio of the standard deviation to the mean), the lower the mean output. In a process chain, the output of a process is the input of another (conservation of material). When variations of the output of the process afect the input, or behavior of the following process, this is called fow variability. How much the output variation of the process afects the following process depends on the coefcient of variation of the arrival rate of the process and the utilization. Variations on the input of a process close to utilization will propagate variations. Conversely, variations to the input of a nearly idle process are bufered by the elastic capacity of the second process. Variations always occur in project-driven processes due to their fnite nature. The stages of a process changeover (Antunes et al., 2018) are: •

Setup: the process is being prepared to run. ◦ There is no input. ◦ There is no output. ◦ Production is idle.

•

Startup: the process begins to run ◦ The input (resources and material) is applied to the production process ◦ Work-in-process builds up ◦ Here, the throughput changes fast. Throughput can be lower than capacity or over it (production rush or overshoot) for shorter periods. ◦ Also here is where the lead time is measured, and calculated (Antunes et al., 2018), as the frst artifact of production is released.

•

Steady-state: the production is at equilibrium ◦ Throughput is constant. Production artifacts are released at uniform pace. ◦ Throughput is at capacity. The production rate is at a sustainable high to last long runs. ◦ Cycle time is constant and at its minimum

•

Cleanup is the termination of the process while fnalizing the work-in-progress. ◦ Input levels decrease. ◦ Work-in-progress is consumed. ◦ Throughput decreases to zero. ◦ Production is terminated and the process ceases to exist. ◦ Some cleaning and packing activities may exist after production ends.

The diference between the throughput of these stages and throughput at capacity is production loss. Although it is possible to get throughput overcapacity for short periods, overcapacity production often includes issues such as product quality, overburdened workers, and safety. Based on the knowledge of dynamic systems, the lowest level of variation happens when the system is at steady-state (Ogata, 2010). Thus, reducing processes’ setup, startup, and cleaning times increase productivity (Antunes et al., 2016)

Calculating Cycle Time The accumulated throughput over time results in units of a service or product produced over time. At steady-state, where the throughput is constant, the cycle time is given by the inverse 217

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of throughput. In other words, cycle time is equal to one divided by throughput. Moving throughput to the left side of this equation, we have cycle time multiplied by throughput equals one. One means a single artifact produced by the process. On a graph with time as an independent variable and throughput as a dependent variable, such as Figure 13.1, a unitary area under the throughput curve means one artifact produced. As throughput at steady-state should determine the capacity of the system, the cycle time (best) at steady-state is the shortest production time of the system while stable (Antunes et al., 2018), i.e., Law: best-case performance. By contrast to the steady-state, the throughput of the production system constantly varies while the system is in a transient state. The unitary area under the throughput curve can be calculated by a limited integral of throughput from zero to cycle time will provide the cycle time of the frst artifact produced (Antunes et al., 2018). Furthermore, as the throughput decreases, the cycle time increases. Hence, the production system’s maximum cycle time (worst), i.e., Law: worst-case performance, is found at start-up. Considering that the production system will increase its throughput over, the cycle time (worst) is the time taken to produce the frst output from a stationary state. Consequently, if the process increases its throughput, the longer it will take to reach the steady-state, the smaller the area under the curve; hence, the smaller its average throughput. The average throughput can be calculated by the average function value. In other words, for processes with equal capacity, the longer the transient time, the longer the average cycle time. Also, for processes with equal transient time, the greater the capacity, the smaller the average cycle.

Calculating Capacity The production capacity of project activities relies on intuition rather than engineering (McCray et al., 2002). Establishing the capacity of a system is paramount to benchmarking and assessing performance productivity levels (Abdel-Razek et al., 2007; Olomolaiye et al., 1998; Zhao & Dungan, 2014). The Productivity Function can also calculate the cycle time on stagnant conditions. The output at steady-state of a system represented by a Productivity Function in the frequency domain can be calculated using the fnal value theorem. The fnal value theorem uses the limit of the Productivity Function in the frequency domain when the frequency tends to zero, thus no changes (Antunes et al., 2018). That is equal to the limit of the Productivity Function in the time domain when time tends to infnity. If you remember, time is the inverse of frequency :o)

Applicability Forecast and Accuracy By considering the transient state, the Productivity Function produces models that are more accurate in describing the process dynamics than the steady-state approaches (Antunes et al., 2017). Furthermore, the Productivity Function is always equally or more accurate than any steady-state approach. How do you manage activities with diferent dynamics, such as the road works example? Do you set an average based on the frst hours of the pothole repair (which we overshoot throughput) and expect the workers to perform like that for hundreds of hours? Maybe you include the time that they are resting as well. But how about the decline in performance throughout the day?

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Production Plan, Monitoring, and Control The Productivity Function provides a mathematical foundation to develop algebraic expressions for the calculations of cycle times (average, best- and worst-cases), throughput at capacity (Antunes et al., 2018), and the infuence of the transient state time in the production variability (Antunes et al., 2016). The Productivity Function has been applied in feedback loop control, yielding a controlling approach (Productivity Function Predictive Control, Predictive Control for short) that can achieve high performances even when processes operate close to capacity (Antunes et al., 2018). Moreover, this performance enhancement is higher when Predictive Control is applied to processes in a parade of trades (Tommelein, 1998). A beneft of Predictive Control is its focus on minimizing the variances of output to a setpoint or plan. This adaptive version estimates a Productivity Function cyclically within a period (Antunes & Poshdar, 2018); thus, the control relies on a model that is accurate to the current time frame. Therefore, if the production system evolves (which is the goal of continuous improvement) and that makes the model obsolete, adaptive Predictive Control relearn the process and estimate a new model automatically.

Decision-Making Support The Productivity Function provides benefts other than forecasting and automatic allocation of resources for production control. Understanding processes’ dynamics supports control initiatives that increase productivity by maximizing average throughput while reducing variation (Antunes, 2017). Maximizing average throughput means: •

Eliminate setup: ◦ Although it is infeasible to fully eliminate setup times, actions should be taken to that efect because there is no output during the setup stage.

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On startup: ◦ Achieve steady-state as fast as possible. ◦ Reducing the time the process stays in the startup stage will decrease the efect of the variation, during this stage, on the overall process mean output. ◦ overshoot throughput during startup (when possible considering potential issues): This means to sustain an output rate overcapacity to compensate for the time that throughput was below capacity.

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Eliminate cleanup: ◦ The less work to be fnalized, the faster the work fnishes.

Challenges Although the Productivity Function can describe a variety of systems (including multivariable systems), a structure that can embrace nonlinear and/or time-variant systems is required; and respectively, the introduction of linear time-varying space state models which can also describe nonlinear systems (Antunes, 2017). Productivity Function is fully developed to nonlinear systems. However, the nonlinear modelling approach accuracy has yet to be tested. The modeling approach (black box) increases complexity on nonlinear and time-variant systems requiring more powerful computational resources. Fortunately, 219

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nowadays, computers are powerful and present in everyone’s pocket. Thus, what is needed is the development of software tools to support project managers, such as a framework for an information integration system for construction using the Productivity Function and its Production theory. The modeling and production theory is what we described in this chapter. We omitted the Mathematics, but that can be found in the references. Data collection is the central aspect that Lean Construction 4.0 supports Productivity Function. The case shown in Figure 13.2A is a process where the actual data was automatically recorded in real-time. Similarly, that could be implemented without further complexity on the road painting example. A camera would flm the surface painted while the global positioning system (GPS) records the coordinates and measures acceleration. On these processes, the conditions are more or less regular. For the repair of the potholes, the complexity is higher. The camera has to calculate the area of each pothole and evaluate if it was completed or not. It also could determine the actual efort on that task. It is possible to count how many workers are on the pothole area and classify how many and how long they have been working from the footage (worker with crossed arms equals not covering a pothole, for instance). Although this implementation requires more computer processing, it can be done. However, AEC has an infnity of production processes with much higher variable conditions. On the verge of Lean Construction 4.0 and its data-driven approach, a production theory is still required to make sense of all the information produced by smart devices. The monitoring and collection of data are aspects at the interface of Lean Construction 4.0 and the Productivity Function. In one-way, Lean Construction 4.0 fosters the use of digital technologies such as computer vision, Internet of Things, LiDAR (laser scanners), and other tools to collect data in real-time to feed the Productivity Function. Although the technology is available, its combined application to the management of construction processes is not. Jointly, these technologies can provide valuable information about a construction site (e.g., layout and location of equipment), the workers, and the work. By using the collected data, the Productivity Function can generate production models, forecasts, and other relevant production parameters for project management and continuous improvement of project activities. These models can be calculated simply by incorporating productivity goals and actual throughput or more comprehensive production considerations such as calories spent per worker, weather conditions, completed work measures, or almost anything else. The models built are, foremost, mathematical equations that describe Lean practices aiming at continuous improvement, process stability, and reduced changeovers on the AEC project-driven environment. The notion of the Productivity Function working in real-time can be a valuable tool for project managers, providing project status data and predictive analysis supported by Lean thinking.

Conclusion This chapter presented the Productivity Function as a mathematical foundation to production in construction and theoretical production parameters such as capacity and cycle time, as well as the infuence of transient time on productivity. The measurement of productivity and production performance in construction will establish the baseline using historical evidence rather than the usual labor/time approach. The understating of production performance serves productivity development and standardization. More accurate historical information can be generalized to diferent projects enabling comparison and continuous improvement methodologies from project to project. As such, companies will quantitatively assess and qualify the performance of contractors in previous projects. 220

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The Productivity Function supports two tenets (Antunes & Poshdar, 2018): Genchi Genbutsu and observer efect. Genchi Genbutsu, a Toyota Production System principle, means “go to the source and get the facts to make the right decision.” For this approach, the Productivity Function data-driven approach can provide a signifcant beneft: prompt access to information. This beneft can be signifcantly extended with automated data acquisition such as site cameras, wearable, sensors, and drones, providing near-real-time information. By observing an event, the observer may alter the event and consequently modify the observation (observer efect). In this case, the Productivity Function (observer), while monitoring, forecasting, and controlling, aims to modify the production process to avoid project deviations before they occur. The observer efect is familiar in human sciences, where people alter their behavior when aware of being observed (Supervisory Control). As such, the awareness monitoring may modify the production system and its model. Thus, production is constantly monitored, and the information is used to improve (control) process productivity. Altogether, the Productivity Function further develops production theory while promoting Supervisory Control and Data acquisition and supporting Lean principles on the AEC industry path towards Lean Construction 4.0 and beyond.

References Abdel-Razek, R. H., Abd Elshakour M, H., & Abdel-Hamid, M. (2007). Labor Productivity: Benchmarking and Variability in Egyptian Projects. International Journal of Project Management, 25(2), 189– 197. https://doi.org/10.1016/j.ijproman.2006.06.001 Antunes, R. (2017). Dynamics of project-driven systems: A production model for repetitive processes in construction [The University of Auckland]. https://researchspace.auckland.ac.nz/bitstream/handle/2292/35634/ whole.pdf ?sequence=2&isAllowed=y&TSPD_101_R0=90263bae35d7710e2b61be5b06b924d8y0600000000000000000528b8cbffff 000000000000000000000000000061411d8000e27fe9880850ab25feab20007b6ac3c191576de39e46 Antunes, R., & Gonzalez, V. (2015). A Production Model for Construction: A Theoretical Framework. Buildings, 5(1), 209–228. https://doi.org/10.3390/buildings5010209 Antunes, R., González, V. A., Walsh, K., & Rojas, O. (2017). Dynamics of Project-Driven Production Systems in Construction: Productivity Function. Journal of Computing in Civil Engineering, 31(5), 17. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000703 Antunes, R., González, V. A., Walsh, K., Rojas, O., O’Sullivan, M., & Odeh, I. (2018). Benchmarking Project-Driven Production in Construction Using Productivity Function: Capacity and Cycle Time. Journal of Construction Engineering and Management, 144(3), 04017118. https://doi.org/10.1061/ (ASCE)CO.1943-7862.0001438 Antunes, R., González, V., & Walsh, K. (2015). Identifcation of repetitive processes at steady- and unsteady-state: Transfer function. 23rd Annual Conference of the International Group for Lean Construction, 793–802. https://doi.org/10.13140/RG.2.1.4193.7364 Antunes, R., González, V., & Walsh, K. (2016). Quicker reaction, lower variability: The efect of transient time in fow variability of project-driven production. 24th Annual Conference of the International Group for Lean Construction, sect.1 pp. 73–82. http://iglc.net/papers/Details/1332 Antunes, R., & Poshdar, M. (2018). Envision of an integrated information system for project-driven production in construction. 26th Annual Conference of the International Group for Lean Construction, 134–143. https://doi.org/10.24928/2018/0511 Blanchard, B. S., & Fabrycky, W. J. (2011). Systems engineering and analysis (W. J. (Wolter J.. Fabrycky (Ed.); 5th ed.). Prentice Hall. Construction Industry Institute. (2012). RR244-11 Global project controls and management systems (No.RR244-11). https://store.construction-institute.org/detail.aspx?id=RR244_11_E Forrester, J. W. (1997). Industrial Dynamics. Journal of the Operational Research Society, 48(10), 1037– 1041. https://doi.org/10.1057/palgrave.jors.2600946 Forrester, Jay W. (1961). Industrial dynamics (1st ed.). The M.I.T. Press. Gilbreth, F. B. (1909). Bricklaying system. The Western Newspaper Union.

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14 DIGITAL TWINS TO ENABLE FLEXIBILITY IN OFF-SITE CONSTRUCTION Beda Barkokebas, Fatima Alsakka, Farook Hamzeh, and Mohamed Al-Hussein Introduction An elevated level of competition coupled with rising market demand is driving the increasing adoption of of-site construction (OSC) methods in the construction industry (Zakaria et al., 2018). However, the need for fexibility in responding to changing market demand (Gbadamosi et al., 2020) or accommodating design changes based on customer preferences (Goulding et al., 2015) is making many OSC practitioners resistant to innovation in their practices. Indeed, OSC practitioners show reluctance to increase automation in their processes due to the potential loss of fexibility to scale labor resource allocation accordingly in response to cyclical changes in market demand (Darlow et al., 2021). As such, the manufacturing nature of operations in OSC might not be fully leveraged to avoid compromising this manpower-related fexibility. In this regard, the growing need for fexibility in production has been one of the principal drivers of the fourth industrial revolution (Lasi et al., 2014). “Industry 4.0”, as it has been called, can be defned as a set of concepts and technologies used to monitor, digitize, analyze, and provide solutions to improve manufacturing environments (Pascual et al., 2019). Through the deployment of ad-hoc systems that analyze real-time data, Industry 4.0 (I4.0) provides fexible and integrated systems to meet clients’ custom demands while supporting optimized decision-making in the process (Kagermann et al., 2013). These technologies enable more robust management that is based on real-time data and, hence, dynamic decision-making. Using I4.0 technologies, decision makers are able to adjust processes in real time based on the actual status of operations in order to support a fexible production. In turn, the efectiveness of this real-time management can be improved through the application of the digital twin (DT) concept, which is gaining momentum within the context of I4.0. Although the available applications and defnitions of DT reveal some discrepancies in its conceptualization (Kritzinger et al., 2018), many defnitions still align with the original concept introduced by Grieves (2014), who defned DT as a digital representation of a physical product consisting of three elements, the physical product in real space, the virtual product in virtual space, and data and information connections that link them. In other words, a DT is a construct that enables a seamless connection between physical and virtual environments to simulate and improve a product or process in real time. DOI: 10.1201/9781003150930-18

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In the context of OSC, DTs allow for greater fexibility in manufacturing operations; given that DTs enable real-time monitoring and progress tracking, they can be deployed to intervene in the operations, make changes to the shop foor, and, accordingly, update plans and schedules in response to diferent forms of variation (e.g., variations in demand, design changes, resource shortages, and machine breakdowns). Such interventions not only promote fexibility in operations but also bolster the overall incorporation of Lean Thinking into manufacturing operations in a number of respects. For instance, as the diferent types of fow (i.e., labor, materials, information) become visually accessible to the management team in real time with the use of DTs, various forms of process waste (e.g., sitting inventory, excessive movement, waiting) can be identifed and minimized. For another example, the overall efect of any interventions implemented can be simulated using the DT, thereby allowing for optimization of the overall operations rather than merely local optimizations. It should be noted that while the implementation of DTs facilitates Lean practices, the successful development of DTs themselves requires a Lean approach. As argued by Hamzeh et al. (2021), the implementation of I4.0 in the architecture-engineering-construction (AEC) industry, in general, requires consideration of the people–processes–technology triad rather than focusing on technology alone. In line with this, Sacks et al. (2020) propose the use of Lean principles, such as variation and waste minimization, as the guiding production theory underlying the implementation of DTs in the AEC industry. For instance, the “go and see to learn” principle, which consists of visiting the shop foor to thoroughly understand the current state (Liker, 2020), should be the starting point of the development process. In other words, because a deep understanding of the process is a prerequisite for building an accurate digital representation of the real world, there is a reciprocal relationship between Lean and the implementation of DTs. In this context, this chapter presents a framework for DT as a specifc system for production planning and control that enables fexibility in OSC. The various components forming the system, their functionalities, and the connections among them are described. Moreover, two case studies are included as a proof of concept. The frst case study presents an application of a machine learning (ML)-based forecast model that serves as one component of the DT for production planning and control. The model forecasts the durations needed to complete projects and provides insights on production fexibility. The second case study presents an application of the system that demonstrates its capacity to increase the fexibility of OSC operations. Here, fexibility is increased by adjusting the production capacities at diferent workstations based on the current state of operations and work progress. More specifcally, the system is deployed for assigning multi-skilled workers to diferent workstations with the objective of minimizing waiting time across the shop foor. This case study also serves as evidence on how DTs could promote Lean by enabling fexibility.

Overview of Digital Twin Applications in Of-Site Construction DTs have been deployed in various industries for a wide range of applications, including, for example, (1) designing new products; (2) visualizing, monitoring, and updating the real-time status of production processes; (3) optimizing production and products; and (4) predicting the behavior of physical systems (Tao et al., 2019). As research on DTs has entered a stage of rapid growth (Tao et al., 2019), its use has also been recorded in the AEC industry. Caramia et al. (2021) identify the frst publications on DT applications in the AEC industry as having appeared in 2018, typically with a focus on operation and maintenance rather than design and construction. Indeed, the majority of DT applications in AEC documented in the 224

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literature to date have focused on operation and maintenance of infrastructure projects and building systems incorporating technologies such as building information modeling (BIM), laser scanning, and sensors (Lu et al., 2020; Shirowzhan et al., 2020). Yusen et al. (2018) also propose the application of DTs to manage industrial projects throughout their life cycle. Although relatively few studies have been conducted on DT applications in OSC, it is worth noting that existing research in this area has already demonstrated the value of DTs with regard to the four pillars of construction—time, cost, quality and safety—as well as logistics, as summarized in Table 14.1. Table 14.1 DT applications of OSC Application Safety

Quality

CostTime

System description Safety risk management of prefabricated building hoisting

A virtual model of the hoisting process is built and linked to the real process through a self-organizing Wi-Fi network distributed IoT structure, where smart cameras, sensors, and radio-frequency identifcation are used to collect real-time data. Data is modeled, and the results of the hoisting safety risk factors analysis are transmitted to the DT model and visualized using a building information modeling (BIM) model. Geometric quality A DT of the as-built façade is developed and used evaluation of to compare the built façade to the as-designed digital model. Data on the geometric properties prefabricated facades of the built façade is acquired using the laser scanning technology, which helps produce a 3D representation of the façade. The as-built model is compared to the design model to evaluate accuracy, completeness, and correctness and, accordingly, identify any construction errors. The evaluation results are communicated back to the builders who, in turn, take corrective actions if needed. Quality control of Similar to the study conducted by Tran et al. fabricated systems (2021), a DT is developed to represent the geometric status of fabricated structural systems and compare it to the design BIM model. 3D laser scanning is also used to capture as-fabricated geometric data of the structures. Performance Since there are diferent types of data used in assessment of time digital twins (DTs) in of-site construction (OSC) and cost (i.e., data on production processes, workstations, resources, building elements) and collected from various sources (e.g., BIM models, technical documents, and sensors), the study is focused on developing ontologies for representing and formalizing such data. The study demonstrates the application of the developed ontologies for retrieving information such as that pertinent to the cost of products and activities’ durations.

(Liu et al., 2021)

(Tran et al., 2021)

(Rausch etal., 2021)

(Ayinla etal., 2021)

(Continued)

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System description Logistics monitoring and simulation

Unity (i.e., a game engine that creates interactive 3D models in real-time) is used to create a virtual model of the building modules during the transportation stage based on BIM data and real-time data collected through sensors. The virtual model is used to track and monitor the location of the modules in real-time. The model is connected to an application that fnds the optimal delivery routes based on the current location and the geometry of the modules considering logisticrelated risks. If any logistic risks (e.g., accidents, constraining bridge height, construction works) are identifed, alternative routes are found and communicated to mitigate them.

(Lee & Lee, 2021)

Methodology The DT for production planning and control presented in this study was developed by following the design science research approach proposed by Ofermann et al. (2009). The problem of low fexibility in the OSC industry is well-known among researchers and industry practitioners. A literature review was frst conducted to ascertain the nature and extent of the issue of low fexibility in the OSC industry and to confrm the lack of a viable solution rooted in I4.0. A DT for production planning and control was then designed and developed with input from the literature as well as from experts at an OSC company based in Alberta, Canada. The experts helped to confrm the validity of the system, which was then implemented in two case studies. As mentioned above, the frst case study represents the application of one component of the system as a proof of concept, while the second case study represents an application of the system to increase fexibility through the use of the multi-skilling concept.

A Digital Twin for Production Planning and Control The proposed DT for production planning and control is presented in Figure 14.1. As depicted in the fgure, data is produced, collected, stored, and seamlessly exchanged across the various components of the DT. The various constituent components of the proposed system, as well as the software and hardware that support its deployment, are briefy described in the following subsections.

Hardware The hardware components that support the deployment of the DT for production planning and control are as follows: •

Sensors: Sensors are utilized to identify in real time the location of a given panel or module on the shop foor at a given time. This data is used to compute the actual manufacturing durations at each workstation, as well as to track the actual progress of each project in order to update the production schedule in near-real time. The type of 226

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Figure 14.1

•

Proposed digital twin applied to of-site construction

tracking system (e.g., RFID, barcodes, live-stream images) best suited for the DT will depend on the given company’s facilities and practices. For example, the use of livestream images is not practical in the case of volumetric modular construction, as it is challenging to capture the obstructed views of the 3D module. In this respect, sound selection of a suitable tracking system is vital to the success of the DT implementation. Communication devices: Given the low level of automation in most OSC facilities, any prospective process improvement measures identifed by the DT for production planning and control need to be communicated to the shop foor so that they can be implemented accordingly. The communication devices used in the proposed system for communicating improvement measures to the shop foor and updating the production schedule include cell phones and interactive screens (i.e., touch boards and tablets).

Design and Production Module This module’s function is to provide a production list of projects to be manufactured during a specifc planning period, as well as a list of the features of each project. Project “features” in this context refers to detailed project information such as wall length and surface area, number of doors, and number of windows. Given the value of using BIM in OSC projects in terms of (1) seamlessly generate project features (Liu & Issa, 2012), (2) support automated drafting (Alwisy et al., 2019), (3) bring about signifcant time reductions during the design stage (Barkokebas et al., 2021), and (4) easily interoperate with other databases, BIM is considered a valuable tool in the design phase of OSC. Hence, BIM is deployed to generate the project features that are to be fed into the system. As for the production list, it identifes, and specifes the completion deadline for, each project to be manufactured during the given planning period. This list, combined with the corresponding project features, is fed into an ML-based forecast model and a special-purpose improvement entity (as needed), as explained in the subsequent subsections. 227

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Machine Learning-Based Forecast Model The controlled environment in OSC allows for a high level of standardization in construction manufacturing operations in comparison to traditional construction. This aspect also allows for the task times at diferent workstations—and, accordingly, the manufacturing duration of a given project—to be estimated. Such estimates can be obtained using ML techniques leveraging real-time data. Indeed, this approach has already been used in OSC in planning- and scheduling-related applications, such as optimizing the fabrication sequence of wall panels and measuring progress by comparing actual to planned models (Altaf et al., 2018; Arashpour et al., 2020). In the data-rich environment that OSC provides, regression ML algorithms are the cornerstone of DT to forecast and schedule future projects. In the present study, they are applied to develop forecast models in the proposed system to perform two main functions: (1) to estimate project durations—which are used as the input to the special-purpose improvement entity (SPIE) module, as described in the following subsection—in near-real time; and (2) to generate insights on production fexibility. For instance, tree-based and support vector regression models are often used in such settings due to the favorable trade-of between performance and accuracy, as well as their ease of explanation (Cadavid et al., 2019). In the proposed system, various algorithms are tested using data collected at the case production facility, and the algorithm that performs best in terms of its accuracy in predicting future durations is implemented. In this regard, it has been noted that the integration of structured project-related information extracted from BIM models with actual manufacturing durations obtained via sensor monitoring presents a valuable opportunity to gain insights on production and forecast performance (Zhang et al., 2016). The forecast model in the present study has four types of input data: (1) project features extracted from BIM models; (2) actual manufacturing durations of projects, computed based on sensor data; (3) expert knowledge of the manufacturing team (e.g., which tasks are performed at a given workstation, daily production output); and (4) production-related constraints (e.g., workstation capacities, buffer times), which are essential to building an accurate model. Input is also solicited from the manufacturing team, both in the initial development of the ML-based forecast model and any updates that may be required due to changing conditions on the shop foor. The ML model is continuously tuned based on actual data collected from recent projects to improve the accuracy of its predictions and production insights. It is also adjusted in response to any errors resulting from improvement interventions suggested by the SPIE module and implemented on the shop foor (as explained in the subsequent section). An application of this ML-based forecast model is presented in “Case Studies” section.

Special-Purpose Improvement Entity The SPIE module performs two main functions. First, it updates the production status (i.e., shop foor current capacity, productivity, bottlenecks) based on real-time data collected from the shop foor. Second, it identifes the optimal improvement interventions to increase fexibility at the shop foor. The type of intervention to be tested must be specifed before its development. Examples of interventions include adjusting the sequence of upcoming projects scheduled for manufacturing and, accordingly, re-assigning multi-skilled workers to diferent workstations from those to which they had originally been assigned for a given work shift. Moreover, a set of criteria must be specifed in order to determine the intervention that maximizes production fexibility while satisfying the performance metrics that the 228

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company deems most critical to its operations. These criteria must be clearly communicated through all information systems and communication devices to ensure the improvement interventions are aligned with them. For instance, if one of the criteria is to target forms of process waste (i.e., waiting, overproduction, rework), then the optimal intervention will be one that maximizes fexibility to accommodate variability while maintaining a low level of waste in the operations. The SPIE module includes a model that simulates diferent improvement intervention scenarios based on the defned criteria and continuous data inputs from sensors and ongoing projects. The module must be constantly maintained and updated to refect any changes in manufacturing processes based on the knowledge and expertise of the company’s personnel.

Central Database The central database serves as the hub of the DT for production planning and control system, specifcally for the storage and exchange of project information, production information, and real-time data collected from the shop foor. It receives and stores data including (1) locations and corresponding timestamps of panels/modules in production, captured using sensors installed across the shop foor, (2) project features extracted from BIM models, (3) the production list, and (4) actual manufacturing durations.

Case Studies Case Study 1: Application of the ML-Based Forecast Model Using BIM and RFID Data This case study presents an application of the ML-based forecast model in the manufacture of wood wall panels at an OSC facility in Alberta, Canada.

Problem Description The case company manufactures wall, roof, and foor panels at its of-site shop foor, and these panels are then transported to the site for assembly. For the purpose of this case study, only the of-site manufacturing phase is studied. The panels are tracked during manufacturing using a radio-frequency identifcation (RFID) system in which antennas are installed at the beginning of each workstation and an RFID tag is afxed to each panel in production. The RFID data contains timestamps indicating the time and date at which a given panel arrives at each workstation across the shop foor. RFID is obtained from 1,044 projects and combined with the corresponding project features extracted from BIM models. In the company’s current practice, project durations are estimated manually (and projects scheduled accordingly) based on the total area of the panels, the daily production capacity, and the scheduler’s expertise. Given this, this case study presents an exploratory analysis of the RFID and BIM data for building the ML-based forecast model.

ML-Based Forecast Model Rationale Figure 14.2 presents the inputs and outputs of the ML-based forecast model for the company under study. 229

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Figure 14.2 Main inputs in developing the ML-based forecast model

The RFID data is cleaned and processed to transform the timestamps into project durations, taking into consideration working hours, weekends, holidays, breaks, etc. A set of criteria is developed, and assumptions are made, based on the nature of the data and input from experts. The following criteria and assumptions are applied to the data transformation process: • •

• • •

No data is considered other than the wall panel manufacturing durations collected. The total project duration is calculated by subtracting the time at which the frst panel is detected at the frst workstation from the time at which the last panel leaves the last workstation. A work shift is ten hours. Bufers are created in the schedule to accommodate unexpected events encountered during production. Types of projects that are produced in low volumes and/or have outlier project features are excluded from the dataset. These are referred to as “atypical projects” in Figure 14.2.

After cleaning and processing the data, data from 873 projects is left to be used in the MLbased model. Due to the small sample size, a twofold cross-validation—using an 80%/20% ratio for training/testing—is applied to ensure the model is not being over-ftted ( James et al., 2021). Case company personnel were consulted extensively concerning the criteria, assumptions, and preliminary outputs. The involvement of experts during the development of the ML model is vital for ensuring efective implementation and accurate results. The model is fne-tuned in consultation with the case company’s personnel until an acceptable level of prediction accuracy is achieved.

Results and Discussion Various regression models are trained and tested in building the ML-based forecast model. Random forest regressor provides the best results in this application, while selecting the most representative project features extracted from the BIM models, due to its built-in feature. Generally, fltering the project features that have the most signifcant impact on project durations helps the OSC design team to make better informed decisions regarding design changes and to assess the impact of accommodating more fexible designs as required by clients. 230

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The developed random forest regression model is found to exhibit an average root mean squared error of 5.36 and a mean absolute percentage error of 29.92% with an average duration of 14.03 hours per project. It should be noted that the authors were not able to fnd a comparable in the literature in terms of a large dataset of task durations from a semiautomated OSC facility based upon which to assess the performance of the proposed model. It should also be noted that the inputs to the proposed model are only refective of project features, so other aspects of production (e.g., waiting and idle times, work stoppages) are not accounted for in the regression. Hence, the error value noted above can be considered acceptable due to the fact that data on other relevant aspects of production (e.g., worker absenteeism, work interruptions) was not available and thus was not incorporated into the model; the available RFID data only contains timestamps at pre-defned checkpoints by which to compute the total durations, but did not identify what might have occurred between the checkpoints. In other words, relying on cycle times processed by the RFID alone does not reveal how long it actually takes to perform a specifc task since there could be waiting or idle time included. The random forest regression model is deployed to predict the durations of 11 projects undertaken during the period, May 1–4, 2018. The predicted durations were used to generate a Gantt chart for the ML-based production schedule. Figure 14.3 plots (1) the ML-based schedule, (2) the schedule that was generated based on the case company’s current scheduling practices, and (3) the as-built schedule updated using RFID data. The percent error corresponding to each was calculated using Equation 14.1.

Figure 14.3

Schedule comparison between current practice, actual and predicted project durations

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˜ =

Estimated schedule duration − Actual schedule duration × 100% Actual schedule duration

(14.1)

Although both the ML-based schedule and the schedule generated using the case company’s current practice predicted an earlier project completion date than the actual completion date, the ML-based schedule has the lower percent error of the two (8.1% compared to 11.98% error). Moreover, the error reduction achieved using the ML-based schedule is signifcant in terms of minimizing delays. Production schedules always include a time bufer to accommodate uncertain events (e.g., machine breakdown, material shortage), and the bufer considered in this study is 10% over the planning period of 40 hours. The ML model takes into consideration this bufer to predict the actual durations with higher accuracy compared to the current scheduling practice. As such, this case study provides evidence that ML-based forecast models can be efective in improving upon and automating current scheduling practices, thereby reducing the reliance on manual inputs and the scheduler’s expertise. That being said, tracking and incorporating into the model more production data (e.g., machine breakdown frequency, resource shortages) will allow for more accurate predictions and production insights. The developed ML-based forecast model was further deployed to generate insights on production fexibility in response to variations in client type. Figure 14.4 plots the total project duration versus wall surface area (the main project feature used to estimate the duration of projects at the case company) according to the type of client (i.e., primary client with whom there is a long-standing relationship versus other clients). As shown in the fgure, project durations are not found to be linearly related to wall surface area, regardless of the type of client. Hence, the data suggests that projects are manufactured at a similar pace regardless of whom the client is. This implies that the shop foor under study has fexibility in accommodating the diferent project features required by diferent clients.

Figure 14.4

Production insights regarding the correlation between project duration, project type and customer

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Case Study 2: Deployment of DT to Increase Labor Flexibility in Near-Real Time This case study demonstrates an application of DT for increasing production fexibility in OSC through the allocation of multi-skilled workers in near-real time.

Problem Description This case study uses data from seven residential projects each consisting of 11 modules produced at a diferent OSC shop foor in Alberta, Canada. The man-hour requirements for each module are estimated using data and a regression model developed by Moghadam (2014) for manual operations in which each worker is fxed at a specifc workstation. In other words, the shop foor is characterized by a low level of automation, and the practice of using multi-skilled workers is not implemented. There are seven workstations on the shop foor, with a pre-determined, fxed number of workers assigned to each, as depicted in Figure 14.5. Two types of skills are required in order to perform tasks at the workstations under study: (1) carpentry skills (highlighted in orange in Figure 14.5) and (2) rough-in skills (highlighted in purple in the same fgure). The production process under study is a push system, meaning that modules are pushed to the downstream workstations as soon as the process at a given station is complete, thereby creating bottlenecks that, in turn, result in signifcant waiting times. The presence of bottlenecks is confrmed by the results obtained by Moghadam (2014), as the man-hour requirements vary considerably between workstations depending on the project’s features, thus creating frequent instances of fxed workers being idle during a given work shift. As such, shop foor in question has a strong need for adjusting the production capacity at the workstation level to enable more fexible manufacturing operations.

Multi-Skilling Rationale The need to reduce production waiting time at diferent workstations based on the modules under fabrication is addressed by re-allocating multi-skilled workers from one workstation to another throughout the given work shift. Three worker allocation approaches are considered: (1) the single-trade (S) approach, in which multi-skilled workers rotate between workstations within their single trade; (2) the multi-trade (M) approach, in which multi-skilled

Figure 14.5

Addressed shop foor

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Beda Barkokebas et al. Table 14.2 Proposed combination of fxed and multi-skilled workers as per each approach Fixed laborers at workstations

Single trade

Multi trade

Combination

Carpentry

MEP

Multi-skilled

Baseline S-1 S-2 S-3 M-1 M-2 M-3 H-1 H-2 H-3

1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2

3 2 1 1 2 1 1 2 1 1

2 2 2 2 2 2 2 2 2 2

4 3 2 1 3 2 1 3 2 1

4 4 4 3 4 4 3 4 4 3

0 1 2 3 0 0 0 1 2 3

0 1 2 3 0 0 0 0 0 0

0 0 0 0 2 4 6 1 2 3

workers are trained in diferent trades and as such can be rotated between diferent workstations and diferent trades; and (3) hybrid (H) approach, in which both single- and multi-trade workers are assigned to workstations. Table 14.2 shows nine combinations of diferent types of workers (i.e., fxed, single-trade, and multi-skilled, or multi-trade multi-skilled) in which some assignments previously occupied by a fxed worker are replaced with single-trade or multi-trade multi-skilled workers. The total number of workers, it should be noted, remains constant (i.e., 18 workers) for all combinations. For each of these combinations, four scenarios are investigated based on the number of times multi-skilled workers are reassigned during a single shift. In this study, multi-skilled workers are assigned one, two, four, or eight times during an eight-hour shift to assess whether a more dynamic reassignment (i.e., more interventions from multi-skilled workers during the shift) afects production signifcantly. A single assignment per shift means that multi-skilled workers are assigned once at the beginning of each shift and work a full shift at the designated workstation. Eight assignments, meanwhile, means that multi-skilled workers are reassigned to new workstations every hour throughout an eight-hour shift according to production needs. The reallocations of multi-skilled workers are performed using the DT proposed in this study as follows: •

• • • • •

Sensors such as RFID and Bluetooth Low Energy are used to collect real-time data such as locations of multi-skilled workers, locations of modules, and cycle times at diferent workstations. Real-time data is used to update the locations of multi-skilled workers, as well as the production status, in the simulation model. The total man-hour requirement for each module is forecast for each workstation based on historical data. On average, fve minutes is considered as the time required for multi-skilled workers to relocate from one workstation to another as required by the DT. Learning model curves are incorporated in the simulation model to emulate the learning efect that multi-skilled workers have while performing their activities. A simulation model of the production process is developed in Simphony.NET. The simulation model is used to test the diferent assignment scenarios and assess their impact 234

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on production fexibility. This, in turn, is used to identify the optimal scenario that balances production and reduces waiting time across workstations. The identifed scenario can then be communicated to the shop foor in near-real time.

Results and Discussion For each of the combinations presented in Table 14.2, four scenarios are simulated based on one, two, four, and eight occurrences of improvement interventions (i.e., re-assignment of multi-skilled workers) being recommended by the DT in the course of a shift. In other words, a total of 36 scenarios are simulated, where each scenario is characterized by the trade approach (i.e., S, M, or H), the combination of fxed versus multi-skilled workers, and how often improvement interventions are recommended by the DT. For each scenario, the simulation model estimates the average processing and waiting times per module, which are plotted in Figure 14.6. As demonstrated in the fgure, all the scenarios that involve the use of DT to increase production fexibility through multi-skilling outperform the baseline scenario, although the processing times do not signifcantly vary among the scenarios. The noticeable improvement in the total duration is due to the signifcant reduction in waiting time between workstations. As shown in Figure 14.6, the results also reveal that scenarios consisting of a larger number of re-assignments result in a lower average total module duration. In fact, the six lowest average production durations correspond to four and eight reassignments per shift despite the slightly higher processing times compared to those under other scenarios. The increased processing times in these scenarios are the result of the time spent by multi-skilled workers on moving between workstations, and the efect of time spent by multi-skilled workers learning the tasks at newly allocated workstations. Waiting time being the major determinant of productivity in this case study, Figure 14.7 demonstrates the efect of the frequency of improvement interventions being recommended by the DT on the average waiting time. As shown in this fgure, a higher frequency of

Figure 14.6

Average cycle time between modules

Note: S-3-1 indicates that a single-trade approach was used under combination three (as per Table 14.2) and multi-skilled workers were re-assigned one time during the shift.

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Figure 14.7 Average production waiting time per module according to combination, labor utilization approach, and number of multi-skilled worker reassignments per shift

interventions per shift signifcantly reduces the average waiting time; the average waiting time is reduced by up to 40% when multi-skilled workers are re-assigned eight times as compared to a single time during a shift. Therefore, this case study demonstrates the efectiveness of using a DT to reduce waiting time through more frequent and dynamic reassignment of multi-skilled workers.

Digital Twins Implications on the Industry I4.0 concepts and technologies in general, and DTs in particular, show great potential as a means of improving fexibility in OSC. The deployment of DTs allows for more fexible data-driven production systems and improved communication between the design and management teams, on one hand, and the workers, on the other hand. The second case study presented in this chapter revealed promising results in relation to using DTs for adjusting production capacity in response to variable processing requirements based on the design of projects. The case studies also demonstrated the efectiveness of combining project-related information with actual production data collected from ongoing projects in real-time as well as from previously performed projects to improve current operations. From a Lean perspective, production fexibility can be improved by using forecast models and the application of multi-skilling to balance production and increase efciency. Indeed, the deployment of DTs in OSC is well-aligned with Lean theory, which places an emphasis on the people–process–technology triad as the foundation of any changes to the production process (Hamzeh et al., 2021). Specifcally, Lean concepts such as “go and see to learn” and extensive consultation with company experts are essential to the development of accurate DTs. It is worth noting that the application of DTs in OSC is also helpful for assessing their prospective deployment in traditional on-site construction based on learning from experiments in OSC, as the controlled environment and the repetitive nature of tasks in OSC is conducive to this. In this way, proposed DT solutions can be fne-tuned in the controlled 236

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OSC setting and then modifed to match the conditions on jobsites, given that similar data structures, data fows, hypotheses, and software could be used. Finally, in future work, the use of DTs in OSC should be expanded from the shop foors to encompass other phases such as logistics and on-site assembly. For instance, data from sensors used to track panels in the shop foor can be used to coordinate on-site installation dates based on information provided in real time from both environments. This information, combined with BIM and laser scanning, is also valuable as a means of proactively detecting and addressing inadequate tolerances in building components on site. This information is also useful in the design process, where designers and consultants require continuous feedback to inform their decision making. A DT can provide a structured repository of previous design solutions and their performance, thus reducing the reliance on an experience-based approach to design.

Conclusions This chapter presented a DT for production planning and control to increase fexibility in OSC, along with two case studies that serve as a proof of concept for the system. The frst case study presented the application of the ML-based forecast model, which is one component of the proposed system. The function of the model is to estimate project durations based on project features, as well as to reveal insights related to production fexibility. The production schedule generated based on the model’s estimated durations performed better (8.1% error) than that generated based on the company’s current scheduling practice (11.98% error), which is mainly based on square footage. Moreover, we and our collaborators at the case company anticipate better performance of the forecast model when more information on the manufacturing process (e.g., machine breakdowns, worker absenteeism, work stoppages) is incorporated. The second case study describes an application of the DT for production planning and control for increasing production fexibility in an OSC shop foor through the allocation of multi-skilled workers. Multi-skilled workers are reassigned during the course of a shift based on the current state of manufacturing and work progress measured using sensor data. The results have revealed a 40% reduction in the average waiting time when improvement interventions are recommended by the DT eight times during an eight-hour shift. This chapter demonstrates the viability of using DTs to address the problem of low fexibility encountered in OSC. Although this chapter presented its application only through the allocation of multi-skilled workers, diferent types of interventions that enable increased production fexibility can be supported with the use of DTs. One example of a similar intervention is the dynamic sequencing of jobs to balance production. These fexibility-enabling interventions will be addressed in future case studies as an extension of the one presented in this chapter.

References Akmam Syed Zakaria, S., Gajendran, T., Rose, T., & Brewer, G. (2018). Contextual, structural and behavioural factors infuencing the adoption of industrialised building systems: a review. Architectural Engineering and Design Management, 14(1–2), 3–26. https://doi.org/10.1080/17452007.2017.1291410 Altaf, M. S., Bouferguene, A., Liu, H., Al-Hussein, M., & Yu, H. (2018). Integrated production planning and control system for a panelized home prefabrication facility using simulation and RFID. Automation in Construction, 85(September 2017), 369–383. https://doi.org/10.1016/j.autcon. 2017.09.009

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Beda Barkokebas et al. Alwisy, A., Hamdan, B., Barkokebas, B., Bouferguene, A., & Al-Hussein, M. (2019). A BIM-based automation of design and drafting for manufacturing of wood panels for modular residential buildings. International Journal of Construction Management, 19(3), 187–205. https://doi.org/10.1080/1562 3599.2017.1411458 Arashpour, M., Heidarpour, A., Akbar Nezhad, A., Hosseinifard, Z., Chileshe, N., & Hosseini, R. (2020). Performance-based control of variability and tolerance in of-site manufacture and assembly: optimization of penalty on poor production quality. Construction Management and Economics, 38(6), 502–514. https://doi.org/10.1080/01446193.2019.1616789 Barkokebas, B., Khalife, S., Al-Hussein, M., & Hamzeh, F. (2021). A BIM-lean framework for digitalisation of premanufacturing phases in ofsite construction. Engineering, Construction and Architectural Management, ahead-of-p(ahead-of-print). https://doi.org/10.1108/ECAM-11-2020-0986 Cadavid, J. P. U., Lamouri, S., Grabot, B., & Fortin, A. (2019). Machine learning in production planning and control: a review of empirical literature. IFAC-PapersOnLine, 52(13), 385–390. https:// doi.org/10.1016/j.ifacol.2019.11.155 Caramia, G., Corallo, A., & Mangialardi, G.(2021). The Digital Twin in the AEC/FM Industry: A Literature Review. Proceedings of the 38th International Conference of CIB W78, 111–122. Darlow, G., Rotimi, J. O. B., & Shahzad, W. M. (2021). Automation in New Zealand’s ofsite construction (OSC): a status update. Built Environment Project and Asset Management, ahead-of-p(aheadof-print). https://doi.org/10.1108/BEPAM-11-2020-0174 Gbadamosi, A. -Q., Oyedele, L., Mahamadu, A. -M., Kusimo, H., Bilal, M., Davila Delgado, J. M., & Muhammed-Yakubu, N. (2020). Big data for design options repository: towards a DFMA approach for ofsite construction. Automation in Construction, 120, 103388. https://doi.org/10.1016/j. autcon.2020.103388 Goulding, J. S., Pour Rahimian, F., Arif, M., & Sharp, M. D. (2015). New ofsite production and business models in construction: priorities for the future research agenda. Architectural Engineering and Design Management, 11(3), 163–184. https://doi.org/10.1080/17452007.2014.891501 Grieves, M. (2014). Digital Twin : Manufacturing Excellence through Virtual Factory Replication - A Whitepaper by Dr. Michael Grieves. White Paper, March, 1–7. Hamzeh, F., González, V. A., Alarcon, L. F., & Khalife, S. (2021). Lean Construction 4.0: Exploring the Challenges of Development in the AEC Industry. Proc. 29th Annual Conference of the International Group for Lean Construction (IGLC), 207–216. https://doi.org/10.24928/2021/0181 James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning with applications in R (2nd ed.). Springe. Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry; Final Report of the Industrie 4.0 Working Group. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: a categorical literature review and classifcation. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi. org/10.1016/j.ifacol.2018.08.474 Lasi, H., Fettke, P., Kemper, H. -G., Feld, T., & Hofmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334-4 Liker, J. K. (2020). The Toyota way, second edition: 14 management principles from the world’s greatest manufacturer. McGraw-Hill Education. Liu, R., & Issa, R. R. A. (2012). Automatically updating maintenance information from a BIM database. Computing in Civil Engineering (2012), 373–380. https://doi.org/10.1061/9780784412343.0047 Lu, Q., Xie, X., Parlikad, A. K., & Schooling, J. M. (2020). Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118, 103277. https://doi.org/10.1016/j.autcon.2020.103277 Moghadam, M. (2014). Lean-mod : an approach to modular construction manufacturing production efciency improvement. University of Alberta. Ofermann, P., Levina, O., Schönherr, M., & Bub, U. (2009). Outline of a Design Science Research Process. Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology, DESRIST ’09. https://doi.org/10.1145/1555619.1555629 Pascual, D. G., Daponte, P., & Kumar, U. (2019). Handbook of Industry 4.0 and SMART Systems. In Handbook of Industry 4.0 and SMART Systems (1st ed., p. 386). CRC Press. https://doi. org/10.1201/9780429455759

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Digital Twins to Enable Flexibility in Off-Site Construction Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin information systems. Data-Centric Engineering, 1(6). https://doi.org/10.1017/dce.2020.16 Shirowzhan, S., Tan, W., & Sepasgozar, S. M. E. (2020). Digital twin and cyberGIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities. ISPRS International Journal of Geo-Information, 9(4), 240. https://doi.org/10.3390/ijgi9040240 Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: state-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186 Yusen, X., Bondaletova, N. F., Kovalev, V. I., & Komrakov, A. V. (2018). Digital Twin Concept in Managing Industrial Capital Construction Projects Life Cycle. 2018 Eleventh International Conference “Management of Large-Scale System Development” (MLSD, 1–3. https://doi.org/10.1109/ MLSD.2018.8551867 Zhang, Y., Fan, G., Lei, Z., Han, S., Raimondi, C., Al-Hussein, M., & Bouferguene, A. (2016, July). Lean-based Diagnosis and Improvement for Ofsite Construction Factory Manufacturing Facilities. https:// doi.org/10.22260/ISARC2016/0131

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15 USE OF THE DIGITAL SITUATION PICTURE TO DECREASE WASTE IN THE DESIGN AND CONSTRUCTION PROCESS Olli Seppänen Introduction The aim of Lean Design management and Lean Construction is to improve fow and consequently decrease waste (Koskela, 1992). Waste in construction is much harder to see than waste in manufacturing, so the reduction of construction waste is a challenging topic. In construction, crews move through a building, while manufacturing work proceeds through various work stations (Sacks, 2016). This situation allows observers of a manufacturing process to easily see any bottlenecks as they occur because they can see a piling-up of inventory before the bottleneck. Process standardisation also allows observers to recognise non-value-adding steps in the process. Construction involves numerous small-scale processes with specialist contractors who move through the building as they complete their work. Although each product has a unique design, all projects consist of standard pieces, such as drywall, mechanical ducts and plumbing. The details of the connections between elements are often customised in projects. These factors all contribute to the construction industry’s productivity problem as well as generating considerable waste. Waste presents several opportunities, however. In many cases, Lean interventions, such as improved logistics (Tetik et al., 2021) or takt planning (Lehtovaara et al., 2021), are benefcial for multiple diferent work types and can increase productivity for everyone. In contrast, productivity-improvement measures that target more efcient transformations often must be individually tailored to individual tasks: for example, by determining better means and methods to install drywall more quickly. Targeting waste thus seems to have broader applicability and could result in productivity leaps across the industry. The challenge, however, is that to increase direct working time, waste must be understood by all stakeholders (including workers), made visible and then measured. Traditionally, researchers have conducted time-and-motion or work-sampling studies to classify worker times into diferent categories (Demirkesen et al., 2020). These methods are timeconsuming and focus only on operations fow, which shows the waste encountered by individual workers. Such methods overlook process fow, which is even more important from a Lean perspective because it defnes whether the process is adding value to the customer. Process fow can also be measured by having a person or camera observing each work location, 240

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but a typical project can have hundreds of locations. The current methods will not be scalable as long as they rely on manual analysis. Waste in design is even more problematic. No known way currently exists to observe a designer and determine if value is being created, because design can only partially be observed, and many of the most value-adding activities happen in the minds of designers or in conversations between stakeholders. Value may be added at any time when a designer is thinking about a problem. The design process is also iterative in nature, and even rejected ideas can often add value. Operations fow in design hence cannot be simply measured by looking at how much time a designer spends in meetings or by tracking the usage of building information modelling (BIM) tools. Observing the process fow in design presents many opportunities, for example, by examining how the level of development/detail (LOD) of elements is progressing in BIM models (Uusitalo et al., 2019). In both design and construction, everyone must have the same picture of what is going on. Traditionally, this ‘situation picture’ (Kärkkäinen et al., 2019) is formed in meetings – for example, design meetings, subcontractor meetings and Owner meetings – during which participants review and coordinate the ongoing tasks and their status. Project participants who implement the Last Planner © System also discuss upcoming tasks and analyse the constraints of those tasks during look-ahead planning (Ballard, 2000; Ballard & Tommelein, 2021). The reports in such meetings are always based on participants’ perceptions, however, which may not be accurate and could be further distorted by human memory. Digital systems have traditionally come into use via human input, which has similar problems. Previous research on recording task statuses in applications has shown that issues with information accuracy often occur (Zhao et al., 2021). In recent years, more objective status data has started to fow from the use of sensors and reality-capture technology. These new technologies allow status to be determined without relying on people’s perceptions, memory and motivation to enter data into systems. Figure 15.1 shows the role of automated and social information distribution in forming the situation picture. The traditional process relying on perceptions and social information distribution is placed on the right. The only way to automatically share information is through human input to systems based on progress or changes in the plans. Sensing opens new opportunities of bypassing the traditional error-prone human pathway on the right and allowing storing direct observations in computer memory. In this chapter, the author will frst discuss design and construction by presenting evidence and measurements of waste in each feld. Currently available and upcoming technology that can help to address waste by providing a shared situation picture of what is going on will then be discussed. Next, the role of the situation picture in continuous improvements will be reviewed. A discussion of ethical considerations then follows, as well as a discussion of how to move from research proof-of-concepts to wide-scale implementation. The chapter ends with a discussion of future research directions and conclusions.

Te Waste and Situation Picture in Design Waste in design may be examined from the point of view of the designer (operations fow) or information (process fow). From the design point of view, researchers have examined which factors cause waste in design. For example, Bonnier et al. (2015) defned numerous waste drivers for design, including task switching, constraints/overburdening, unclear goals and insufcient means of communication. In contrast with the case of the construction phase, however, very few researchers have attempted to evaluate how much of a designer’s time actually adds value and how much is wasted. 241

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Figure 15.1

Automated and social information distribution (simplifed from Kärkkäinen et al., 2019)

Pikas et al. (2020) conducted one of the few such studies to date. The authors observed the design process for eight weeks and categorised events into activities, exchange of information and problem-solving. Of the 88 activities they observed, only 58% were design and engineering activities; others were related to changes, waiting and other activities (Pikas et al., 2020). The authors did not record time spent on activities, however. The method is also cumbersome and could be difcult to automate. One design company in Finland attempted to achieve automation to understand waste by analysing BIM software use (T. Tiilikainen, personal communication, April 28, 2020). Their initial fndings were that the most efcient and experienced design teams actually spent proportionally less time using BIM tools than less experienced teams. Rather than automating the sensing of operations fow in design, a more logical approach thus might be to train designers in the concept of waste and have them self-report waste when they experience it. Researchers have paid more attention to the waste found in design-process fow. For example, Freire and Alarcón (2002) conducted value-stream mapping of a design process and found that design activities accounted for only 16.2% of cycle time (i.e., these activities added value from an information-fow point of view). Most of the time, the drawings were in the ‘inventory’ between diferent stages of the process (Freire & Alarcón, 2002). The phenomenon has also been studied with simulation. Al Hattab and Hamzeh (2018) used Agent-based modelling and found out that non-value adding time was 37% in the design process. Understanding how information fows is critical for design management (Ballard, 2000; Tribelsky & Sacks, 2010, 2011). Tribelsky and Sacks (2010) evaluated information fow by examining transactions in the log fles of centralised project information management systems in 14 projects. They also defned metrics for measuring fow. But because the design 242

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team did not use BIM, key metrics such as batch size and development velocity could not be automatically evaluated but instead required a great deal of manual reviewing of drawings. The authors did fnd, however, that important metrics (e.g. work-in-progress or WIP) from the Lean perspective could be calculated simply by using log fles (Tribelsky & Sacks, 2010). Since these early studies, other researchers have attempted to mine the log fles of model-authoring platforms (Pan & Zhang, 2020; Yarmohammadi et al., 2017) and to achieve a real-time design tracking/monitoring system (Yarmohammadi, 2018; Yarmohammadi & Casto-Lacouture, 2018). So far, these methods are still mostly research-driven and have yet to achieve wider applications in industry. Design companies and design managers have identifed that having a shared situation picture is crucial, however, and have taken steps to implement solutions that allow everyone to know the status of the process in real time. The current digital systems for creating a shared situation picture of design are typically based on designers self-reporting the progress of tasks, often as part of Scrum or Last Planner System implementation (Lappalainen et al., 2021). Many existing software packages enable this functionality, and designers increasingly started to use these tools during the remote-work requirements of the COVID-19 pandemic. The challenges noted in case studies include overly large batch sizes (even 40-hour design tasks), the unreliability of plans (with a percent planned complete [PPC] of 60% or even lower) and numerous undocumented tasks arising unexpectedly (Lappalainen et al., 2021). Even if participants can know exactly which tasks will happen in the next week (i.e., 100% PPC), determining the overall progress of design projects is very difcult. Although these approaches do improve the transparency of the process, they are not adequately connected to actual design deliverables. A better connection to actual deliverables could be achieved with the use of the LOD concept. LOD is a standardised description of defnitions and characteristics of BIM elements during the diferent stages of (and at the diferent levels of ) development. The LOD method was developed to simplify communication processes in the design by articulating the model content and its reliability. Later, LOD came to be used for design-project planning and requirements management (Abou-Ibrahim & Hamzeh, 2016a, 2016b; Svalestuen et al., 2018; Uusitalo et al., 2019). LOD can also be used in process design to pull detailed design deliverables from the procurement and production needs (Uusitalo et al., 2019). New possibilities could be discovered if (a) a design-management process is implemented using LOD, perhaps along the lines proposed by Uusitalo et al. (2019); (b) Scrum/LPS software is used to track tasks related to moving from one LOD to the next; and (c) BIM software packages could be used to track LOD related to each status element. For example, the design tasks found in Scrum/LPS could be tied to locations, systems and LODs, and designers could be asked to update the LOD related to each model element. This sounds simple, but to complicate things, not all the required elements exist before a certain LOD is reached for the system in question (e.g., LOD 300 in the BIMForum specifcation), and systems can advance in LOD in diferent stages (Uusitalo et al., 2019). Before that point, progress may be sensed by examining systems and locations. Tasks in Scrum before LOD 300 typically resulted in additional elements in the model, and progress could be evaluated based on how many elements were being added. After all elements are included for a system (i.e., the system is at LOD 300), the automatic sensing of design status may be changed to track the status of individual elements. The coordination status of elements may be tracked by performing clash detection on the models. Although a small number of clashes do not necessarily mean that the systems are coordinated, the number of clashes between diferent systems is indicative of the progress of the coordination process. Whether the elements are in their fnal position cannot be sensed 243

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automatically, so in this case, the sensing system relies on either task-based system input or updates to the model’s element status. After the fnal position is determined and the systems have been coordinated, more detail is then added for the work to become ready for prefabrication or production, as in LOD 400. This kind of automated sensing system could be a powerful aid for detecting waste in a design process. Not only would we know which tasks designers want to achieve, but we could also see output and compare processes with deliverables. Such a method could enable more standardisation of the process and could potentially be used to teach artifcial intelligence (AI) systems enough about the design process that waste could be measured. In any case, such a system would provide a much better situation picture of where the design process is compared to various targets. Although researchers have taken the frst steps, quite a bit of work remains to be done to achieve a functional system. In addition to the research and development required on the system and software side, the social side should not be neglected. One major social barrier is that designers are not accustomed to planning their work in detail. They are typically aware of the next drawing package or model-delivery deadline, but the actual process of getting to that point tends to be fuid and driven by design meetings where other designers ask questions and discuss progress. Batch sizes are large, and most designers are unwilling to deliver partial deliverables, instead focusing on their part of fnishing a large set of information, such as a drawing set or a complete model for a system. Few designers currently use location-based thinking. Rather, they usually think in terms of systems. This mindset leads to behaviour that can be frustrating to the contractor, although it may make sense to the designer. Despite these social challenges, some projects have been successfully implemented with smaller batch sizes and more structured design processes (Lappalainen et al., 2021). Researchers and practitioners have developed dashboards of design status (Abou-Ibrahim & Hamzeh, 2020). In the near future, a system capable of automatic sensing and measurement of design processes should also be realised by building on previous contributions.

Te Waste and Situation Picture in Construction Waste Measurement in the Construction Phase Wasted efort in the construction process is easier to see than it is in the design process because the work is physical and it can be observed. Such waste is still complex, however, because the workers move through many separate locations. Distinguishing between waste associated with operations fow and that associated with process fow also helps. The methods used to measure each type are diferent. Measuring waste related to operations fow involves tracking individual workers and calculating the share of time they spend on value-adding activities, while measuring waste related to process fow involves tracking individual work locations and investigating the share of time that value-adding work happens in that location. Researchers so far have conducted more empirical work on operations fow. Operations fow is often studied with time and motion studies. The traditional method is to use a stopwatch to record time. More modern implementations include analysing video footage from helmet-mounted camera videos; overhead footage can also be used if the focus is on an individual worker (Demirkesen et al., 2020). The results classify time in diferent ‘buckets’, such as installation work, movement, the hauling of materials, discussions and drawing reviews. Whether actual work or videos are analysed, the process of classifcation is very time-consuming. The results are always interesting, however, and can inform process 244

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improvements. For example, in a master’s thesis study, it was possible to improve a carpenter crew’s productivity by 20% by using simple interventions based on analyses of helmetcamera data and comparisons of quantities produced before and after the intervention (Pasila, 2019). Depending on the exact method used and studied construction phase, the share of value-added time in construction based on time studies can vary from 21% (Pasila, 2019) to 66% (Demirkesen et al., 2020), with studies that evaluate short time periods often showing higher and more variable percentages. Although useful, time and motion studies are cumbersome; researchers have thus devised less cumbersome methods, such as work sampling (e.g., Neve et al., 2022). Process fow can also be measured by observing what happens in a given location. Whenever work locations lack workers, the work is waiting for people, and an opportunity to generate value for the owner is wasted. Measuring process fow is particularly interesting in takt projects, where the basic assumption is that process fow is better (Lehtovaara et al., 2021). Several studies have been conducted related to process fow in takt projects, with the results showing much room for improvement. In a hotel-renovation project, for example, camera analysis of a takt area (one hotel room in this case) revealed that achieving the takt required someone to be present in the work location just 37% of the takt time (Ruohomäki, 2020). Similarly, in an ofce project with larger takt areas and a week takt, work was ongoing in the takt area for only 2%–42% of the takt time, depending on the takt wagon (Salerto, 2019). As shown in the previous examples, for continuous improvement, going inside the ‘black box’ and observing waste directly from an operations or process standpoint is critical. The standard key performance indicators (KPIs) used in project management or even in Lean Construction do not capture waste because they operate on larger entities, such as tasks or locations. For example, achieving 100% PPC is a good target, but the activity inside that assignment can still be wasteful. Similarly, takt production has decreased durations in most reported cases without increasing costs, so clearly some process waste has been reduced. But camera measurements of takt areas still show poor utilisation of work locations, even though the durations in these projects have been reduced by 30%–50%. The construction fow index (Sacks et al., 2017) sufers from the same limitation because it operates with start and fnish dates of processes in locations, and anything that happens within the location remains a black box. Continuous and digitally based waste measurement is required to achieve the next level of improvement. Researchers have proposed several digital, automated approaches for waste detection. Indoor positioning systems (IPS) in particular can be used to gain a rough idea of operations fows. Indoor positioning may be implemented in a variety of ways (Dror et al., 2019) and the standard global positioning system (GPS) can be used outdoors. Resource positioning may be used to calculate KPIs such as uninterrupted presence in work locations (Zhao et al., 2019). The assumption is that workers can only do value-adding work when they stay in a work location for longer than a few minutes without moving to another location. A worker’s uninterrupted presence can be easily and automatically calculated, but such calculations miss all wasted efort that happens inside the work location. The KPI value in reported case studies has ranged from 25% to 45% (Zhao et al., 2019; Seppänen et al., 2019; currently unpublished case studies), which is similar to the share of direct work measured with more cumbersome time and motion studies or work sampling. Uninterrupted presence is mostly related to operations fow. If uninterrupted presence could be calculated for tasks and locations, then it could also be used to detect waste in process fow. Zhao et al. (2021) were able to detect activities’ start and fnish times by using the planned sequence of tasks and patterns of uninterrupted presence in each work location 245

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(Zhao et al., 2021). In takt projects, this approach could be even simpler and would also help to detect re-entrant fow (Brodetskaia et al., 2010), where trade workers return to locations after fnishing their tasks. The challenge for IPS in measuring process fow is that for achieving accurate measurement, every worker of the project would have to carry the tracking device. Otherwise, value-adding work in a location could be missed. In addition to indoor positioning, computer vision may also be used. Having helmet cameras on every worker is not feasible in most countries for privacy reasons, so utilising that footage is not a scalable solution. Categorising time into buckets is also not trivial even for humans in most cases, and implementing computer-vision approaches that allow for detecting value-added work and separate and diferent waste categories would be quite difcult. Researchers have reported some proof-of-concept implementations, but so far they have addressed only individual work types. A scalable solution should work for more than one trade. In contrast, cameras could easily be used in takt areas, where process fow could be automatically monitored. Detection could be based, for example, on machine-readable codes embedded in hard hats or vests. Simple implementations could detect presence only, while more complex implementations could use computer vision to also detect what is going on. If enough cameras are installed, then operations fow could also be evaluated with such a system, as long as individual workers are recognisable from the resulting images.

Te Situation Picture of Production The results from waste studies show that the share of value-adding time is low, and workers spend much of their time walking around the site rather than doing productive work (Neve et al., 2022; Seppänen & Görsch, 2022). Some of this walking is related to obtaining an understanding of what is going on and fnding possible areas in which to work (Ruohomäki, 2020). Workers spend much time looking for or moving materials and tools, both of which are wasteful activities. Some movement is unavoidable, such as moving to the next work location after fnishing work in the previous area, or moving to break areas, but wasteful movement could be decreased signifcantly with better logistics and improved information about what is going on (Pasila, 2019; Seppänen & Görsch, 2022). A real-time situation picture of construction projects would enable both. From the worker point of view, the digital situation picture should answer the following questions: • • • • •

Where should I work? (based on planned and available locations) How should the work be done? (based on installation instructions/work structuring) Where are the required materials? (based on digitally located materials) Where are the required tools? (based on digitally located tools) Is the quality of my previously completed work acceptable? (based on quality data/ machine vision)

Unfortunately, current digital systems are unable to help workers answer these questions although the KanBIM system (Gurevich & Sacks, 2014) appears to be on the right track. Several applications provide partial solutions, but they are mostly tailored for use by management; the workers are typically forgotten, even though they are the only ones who directly provide value to the customer. Replacing walking with digging through various apps to obtain partial answers is not the solution. 246

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New technologies related to the Internet of Things (IoT), computer vision and resource positioning should instead be integrated with design and planning information. The use of AI will be necessary to process the information so that workers can receive the answers they need. Rather than actively looking for information, visual management should be digitalised, and the workers should be automatically exposed to information that they can immediately use to help task execution. Only the frst few steps have been taken to date. Researchers have used computer vision in proof-of-concept applications to detect task progress. The methods they have used are not scalable, however, because most research focuses on individual task types. Image/ video-based approaches require abundant training data. Point cloud-based approaches have wider applicability because they can be used to tell whether an element exists or not and to evaluate which elements in the BIM have been completed. But because tasks such as drywall installation have several intermediate steps (including framing, insulation, and in-wall mechanical, electrical and plumbing [MEP] rough-ins) that cannot easily be detected from point clouds, new approaches are required. Detecting progress is just part of what is required to enhance situational awareness. In addition to knowing which tasks have been completed, several other fows should be detectable. For materials, machine-readable codes or positioning beacons could be used, but doing so would require adopting coding standards, such as GS1 (GS1, n.d.). Important tools could be tracked by positioning or have codes and be detected by cameras. Detecting design information ties back to situation awareness in design. Design deliverables should be linked to the design required by production, and any changes in a design should be fagged. Improved AI is needed to collect all relevant (and only relevant) design information for each worker. In conclusion, much good technology already exists, but these technologies have yet to solve the productivity problem because current solutions are mostly point-based solutions. The solutions also do not solve the productivity problem at the worker level but instead are geared towards management. The feld has too much raw data and not enough automated analysis. Few software providers recognise what types of solutions would best target waste and instead focus more on the benefts for traditional project management. Such benefts are also important, and in the next section, a holistic situation picture in which many diferent stakeholder groups are considered is discussed.

Te holistic situation picture Having a digital situation picture of both design and construction is important for multiple stakeholder groups. In addition to the use of automatic sensing of design and production, data must be shared between parties to provide transparency of operations. While automated sensing tools sense what is happening now and store data about what has happened in the past, the integration of plans stored in various systems is important to coordinate what will happen in the future. This could be used to achieve several important Lean principles. Knowledge of the status of ‘customer’ processes would enable each stakeholder to let the customer pull the work. Continuous measurement of waste based on real-time data would highlight problems in the value stream and help reduce variability and uncertainty. Waste normally occurs between actors so holistic situation picture is necessary to really optimise the whole. The stakeholder groups that should participate (at the minimum) are owners, designers, general contractors (GCs), the most important suppliers (e.g., of engineered-to-order [ETO] products), trade partners and blue-collar workers, all of whom have dual roles as both suppliers and consumers of situation data. 247

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For owners, having a holistic situation picture is important to increase the transparency of the design and construction process and to facilitate trust. Trust is critical for project success because it leads to more innovations and less of a need for bufers. The transparency of information is an important driver for trust (Uusitalo et al., 2019). Having a holistic situation picture will increase the client’s understanding and will help show the importance of decision-making and the impacts of changes at various stages of the process. Owners should provide information about potential changes, which other parties may then use to reschedule their activities to prevent rework while waiting for fnal decisions and change-order requests. Most designers are insufciently connected to construction progress. Their challenge is task prioritisation. By gaining access to a site’s situation picture, designers can check the project’s progress to see which designs are most critical. They can also review progress against designs and note any problems. Designers should provide their planned progress as part of the situation picture so that other parties will know what to expect, and when. Any upcoming changes to existing information should also be noted to prevent on-site rework. In the short run, general contractors are the main users of situation pictures. GCs can use the situation picture to highlight problems about where the management should pay attention and to remove constraints to smooth work. The use of real-time metrics, such as uninterrupted presence and presence indices in locations, allows for the continuous measurement of waste. The GC can use the data to decrease waste in construction projects and continuously improve the production system. Transparency in the design process is also critical for understanding which designs are being worked on and when information will become available for procurement and production. GCs should provide data to other parties related to the planning of future construction tasks. The construction feld has often ignored material suppliers. Researchers have done some work to improve the communication of ETO product suppliers (e.g. concrete elements or steel structures) with sites (Bortolini et al., 2019), but they have neglected bulk material providers. The use case for ETO suppliers is clear. They should know about the status of production, see how their products are being installed and then react to any issues. They should provide detailed production status updates of their products (Lavikka et al., 2020). Other suppliers will also be interested in the performance of their products and the conditions in which their products were installed. By using a shared situation picture, suppliers could develop products that fulfl the owner’s need while also helping to decrease wasted efort during the construction phase. Trade contractors would be another major benefciary of a holistic situation picture. Too often in the current process, workers go to a job site, only to learn that insufcient work is available. Trade contractors must balance their portfolio fow and decide where to allocate their workers. Being able to remotely see the status of work locations and to proactively alert the GC that locations are not ready when the planned start date is approaching would greatly afect many trade contractors’ proftability. Increased transparency would be another beneft. Discussing problems is easier to do when participants have an objective record of the status of each work location each week as well as conditions measured in real time in work locations. For their part, trade contractors should provide information about the availability of workers and proactively check their upcoming work locations to see if the GC has been notifed of all constraints in advance. The greatest beneft of the system would come to blue-collar workers. In the short run, the use of a situation picture would indirectly beneft blue-collar workers by helping other 248

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stakeholders to actively decrease waste. In the longer run, the use of a situation picture would infuence the physical world more directly by automatically checking for constraints, ensuring material deliveries and coordinating trade workers. Workers then would be able to obtain information from the situation picture about the location of (a) their next work location and (b) the materials and tools required for their next tasks. Workers should provide details of any problems they face and volunteer for tracking, because then waste could be analysed on the worker level. In conclusion, all parties who operate in good faith will beneft from the transparency provided by digital situation picture systems, although they all need to share information with the other parties to gain the maximum benefts. Traditional contracts and incentives may prevent the existence of a full situation picture in the short run, however, because various parties may feel it is in their best interest not to share information. The most important new contributors of data are construction workers, but the collection of personal data involves various ethical considerations, as discussed in the next section.

Ethical Considerations Real-time digital situation pictures should be benefcial to all stakeholders and bring a new level of transparency to construction. New technologies are often based on surveillance, however, such as the positioning of resources and the collecting of image data and videos. The frightening prospect of an Orwellian 1984 (Orwell, 1949) scenario looms over the construction feld with continuous tracking, a lack of privacy and the use of such technology against people. Because waste ultimately manifests at the level of designers or workers, most of the more intrusive data-collection approaches are performed at that level. Workers thus should know how their data will be used and how participating in data collection would likely help them. Decreasing wasted efort is a good cause, from which both workers and employers would beneft. As always, transparency of how the information will be used is key. If workers spend most of their time doing non-value-adding tasks, then contractors will be driven to take cost-cutting measures, such as using less skilled and less expensive labour. If waste could be diminished signifcantly, however, then wage increases would be feasible likely. Workers still have legitimate concerns about what data is collected and how the data will be used. Some of their concerns can be addressed with technical solutions. For example, modern image-based technologies allow faces to be blurred, which means that no personal data need to be stored. Knowing the trade of the worker is often sufcient for productivity purposes, meaning that resource-positioning data can be pseudonymised (e.g., ‘Carpenter 1’). No images need to be taken of workers when capturing point clouds or images for progress tracking, so data collection can be organised outside of standard working hours, and any images containing people can be deleted. Concerns related to the use of data require open and transparent discussions. Currently, consenting workers are the only ones to be subjected to data collection. The consent forms that workers sign explicitly indicate how the data will be used and how it will not be used. In the longer run, wider discussions of the scope of data collection will ensue. In some areas, trade unions have a great deal of negotiation power. In such cases, the rules of data collection will be agreed upon between the employer and the various employee unions. In other areas, trade unions lack such infuence, and companies thus will be free to make their own rules. Even in areas without unions, solutions where both the employer and employee win are required, lest legislators be forced to intervene. Unfortunately, any abuse of these systems will 249

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also be detrimental to those companies that use the systems in good faith. The goal of these systems should be to drive continuous improvement and help detect and decrease waste, not to move towards more command and control.

From Research to Large-Scale Implementation Many of the solutions discussed in this chapter are already available as commercial solutions. Several systems that use 360 cameras, drones and point clouds exist, and many already have some rudimentary AI functionality for automated progress analysis. IoT systems for measuring conditions in locations or for indoor positioning are available and are widely used in other industries. Such technologies have also been increasingly implemented on construction sites. Large-scale adoption of these technologies has yet to occur, but they are gaining increasing traction, especially due to the new emphasis on remote management driven by the COVID-19 pandemic. The point solutions available today, however, are insufcient for solving the productivity problem. Emerging solutions are mostly driven by construction technology start-ups funded by increasing venture capital fowing into the area. The start-ups mostly focus on one technology, which is then sold as a cure-all for all problems. Site personnel are fooded with apps that are supposed to help, but they are too numerous and do not interoperate. True interoperability must be obtained between the various systems to really move forward. Data collection should be automated as much as possible, because most people lack the skills or motivation required to properly enter data into systems. For interoperability, software providers should use shared semantics. For example, most applications related to the construction phase are somehow linked to location: images are taken at certain locations, resources are positioned in locations, BIM elements are in certain locations and takt schedules are planned based on locations. But all software handles location in a diferent way. The feld requires standards, and locations should be understood in the same way in most or all solutions. Ontologies are required for this stage. Zheng et al. (2021) have proposed a digital construction ontology to work as a starting point of development. The next step is for start-ups and large vendors to collaborate on and standardise an ontology for Lean workfows and situation pictures. Only then it is possible to achieve real interoperability and open up real possibilities for data fusion and AI. Another barrier for wide-scale implementation is burdensome data collection. IoT applications such as indoor positioning require people to erect and maintain devices. The use of 360 videos requires a human to walk the site with a 360 camera. Today, data is collected by trained personnel. Smaller projects lack the resources or the money for services and seldom use the newest tools. More automation is thus required to achieve large-scale adoption. Finally, the applications of today have too much data and too little automated analysis. Although knowing where each worker is does have benefts, superintendents almost never have the time to conduct manual analyses of productivity based on people’s locations. AI algorithms that can understand the diferent tasks and decisions of diferent user groups are required to provide insights that will be directly applicable to the task at hand. Software developers must understand the needs of diferent user groups and provide automated decision/task support for each user rather than simply displaying mountains of data. While AI technology and computational power have been developed sufciently for implementation, diferent data streams must be combined and analysed together.

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Future Research Directions The feld of automated situation pictures is evolving, so several interesting avenues for future research exist. All areas – technical, social and process-related – have knowledge gaps. Technical research is needed to obtain scalable machine vision and to implement automated data collection without the need for human intervention. The goal should be to have automated sites without the need for human input in data collection or processing. Managers could then focus on ensuring that upcoming tasks have everything the workers require, while the coordination of workers could be handled for the most part automatically, with human intervention necessary only in special cases fagged by AI. This kind of automated site is still a future vision that a great deal of research and development will be required to realise. Research is also needed on the social and organisational side. If workers can be coordinated without traditional management structures, then the roles and responsibilities of diferent stakeholders would certainly be afected. GCs in particular seem to be at risk, because their main role is to coordinate other actors. Platform economies could become possible with automatic coordination. In any case, the new technologies require a more transparent culture, and the technology requires rules. Contract forms must explicitly take new possibilities into account. Research is needed on how best to organise construction sites using situation pictures. The human aspect is also important, since data collection can involve privacy concerns. Coordinating workers with AI also involves intriguing research questions. For example, how likely are people to commit to instructions provided by a machine? Processes will certainly be afected. The current practices of project-based logistics could be replaced by centralised logistics based on situation rooms in which multiple projects are managed. How much power should be given to technical optimisation tools, and how much should be managed using a collaborative process? Which Lean Construction tools require adjustment or can even be completely replaced with technology? What is the role of the technical and social process of planning and controlling? Another exciting research opportunity comes from the sheer amount of data that is collected. It is possible to analyse processes and to observe waste in many projects using situation picture technology. When researchers fnd good ways to analyse data to answer important research questions, their analyses can then become automated and used to help managers make better decisions.

Conclusions The frst steps towards the automatic detection of waste within construction sites have been taken. Such a goal is vital to Lean Construction, because in dynamic environments, directly observing or devising ways to decrease waste are both difcult. The Lean principles of optimising the whole, letting the customer pull and decreasing variability are hard to achieve without such a system in construction. The feld still has a long way to go, however. The requirements seem clear for the construction feld in particular, but a variety of technical, social and process challenges must frst be solved. Technically speaking, most of the parts already exist, but the connection between the parts is missing, and the sheer amount of data makes analysis impracticable for site management and should be automated with the various new AI techniques that are only just starting to emerge. Some of the larger challenges are social and ethical in nature. Much of the required data comes from workers, who will also 251

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beneft the most from waste reduction. Some trade-ofs must be made between privacy and personal data protection and productivity. Today’s digital tools are close to enabling the productivity leap that the previous generation of digital tools and Lean approaches alone have thus far failed to achieve.

References Abou-Ibrahim, H., & Hamzeh, F. (2016a). Enabling Lean Design management: An LOD based framework. Lean Construction Journal, 2016, 12–24. Abou-Ibrahim, H., & Hamzeh, F. (2016b). BIM: A TFV perspective to manage design using the LOD concept. In: Proceedings of the 24th Annual Conference of the International Group for Lean Construction, Boston, MA, USA, pp. 3–12. Abou-Ibrahim, H., & Hamzeh, F. (2020). A visual dashboard to monitor BIM model dynamics. Canadian Journal of Civil Engineering, 47(2), 178–185. Al Hattab, M., & Hamzeh, F. (2018). Simulating the dynamics of social agents and information fows in BIM-based design. Automation in Construction, 92, 1–22. Ballard, G. (2000). Positive vs negative iteration in design. In: Proceedings of the 8th Annual Conference of the International Group for Lean Construction, Brighton, UK, pp. 17–19. Ballard, G., & Tommelein, I. D. (2021). 2020 Current Process Benchmark for the Last Planner® System of Project Planning and Control. University of California, Berkeley, USA. Bonnier, K. E., Kalsaas B. T., & Ose, A. O. (2015). Waste in design and engineering. In: Proceedings of the 23rd Annual Conference of the International Group for Lean Construction, Perth, Australia, July 29–31, pp. 463–472. Bortolini, R., Formoso, C. T., & Viana, D. D. (2019). Site logistics planning and control for engineer-to-order prefabricated building systems using BIM 4D modeling. Automation in Construction, 98, 248–264. Brodetskaia, I., Sacks, R., & Shapira, A. (2010). Implementation of pull control in fnishing works with re-entrant fow. In: Proceedings of the 18th Annual Conference of the International Group for Lean Construction, Haifa, Israel, July 14–16, pp. 274–284. Demirkesen, S., Sadikoglu, E., & Jayamanne, E. (2020). Investigating efectiveness of time studies in Lean Construction projects: Case of Transbay Block 8. Production Planning & Control. Doi: 10.1080/09537287.2020.1859151 Dror, E., Zhao, J., Sacks, R., & Seppänen, O. (2019). Indoor tracking of construction workers using BLE: Mobile beacons and fxed gateways vs fxed beacons and mobile gateways. In Proceedings of the 27th Annual Conference of the International Group for Lean Construction, Dublin, Ireland, July 1–7, pp. 831–842. Freire, J., & Alarcón, L. (2002). Achieving Lean Design process: Improvement methodology. Journal of Construction Engineering and Management, 128, 248–256. GS1. (n. d.). Homepage. www.gs1.org. Accessed May 30, 2021. Gurevich, U., & Sacks, R. (2014). Examination of the efects of a KanBIM production control system on subcontractors’ task selections in interior works. Automation in Construction, 37, 81–87. Kärkkäinen, R., Lavikka, R., Seppänen, O., & Peltokorpi, A. (2019). Situation picture through construction information management. In: 10th Nordic Conference on Construction Economics and Organization, pp. 155–161. Emerald Publishing. Koskela, L. (1992). Application of the New Production Philosophy to Construction. Stanford: Stanford University. CIFE Technical report #72. Lappalainen, E., Uusitalo, P., Seppänen, O. & Peltokorpi, A. (2021). Design process stability: Observations of batch size, throughput time and reliability in design In: Proc. 29th Annual Conference of the International Group for Lean Construction (IGLC). Lima, Peru, 14–16 Jul 2021. pp. 605–612 Lavikka, R., Lahdenperä, P., Kiviniemi, M., & Peltokorpi, A. (2020). Digital situation picture in construction–case of prefabricated structural elements. In. International Conference on Computing in Civil and Building Engineering (pp. 943–958). Springer, Cham. Lehtovaara, J., Seppänen, O., Peltokorpi, A., Kujansuu, P., & Grönvall, M. (2021). How takt production contributes to construction production fow: A theoretical model. Construction Management and Economics, 39(1), 73–95.

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Use of Digital Situation Picture to Decrease Waste Neve, H., Wandahl, S., Lindhard, S., Teizer, J., & Lerche, J. (2022). Learning to see value-adding and non-value-adding work time in renovation production systems. Production Planning & Control, 33(8), 790-802. Orwell, George. (1949). Nineteen Eighty-Four. A novel. London: Secker & Warburg. Pan, Y., & Zhang, L. (2020). BIM log mining: Exploring design productivity characteristics. Automation in Construction, 109, 102997. Pasila, H. -J. (2019). Impact of Lean Intervention on Productivity. Aalto University School of Engineering, Department of Civil Engineering Building Technology, M.Sc. programme. Master’s thesis. Pikas, E., Koskela, L., & Seppänen, O. (2020). Improving building design processes and design management practices: A case study. Sustainability, 12, 911. https://doi.org/10.3390/su12030911 Ruohomäki, A. (2020). Hukan mittaaminen tahtituotannossa [Measurement of Waste in Takt Production]. Aalto University School of Engineering, Department of Civil Engineering Building Technology, M.Sc. programme. Master’s thesis. Sacks, R. (2016). What constitutes good production fow in construction? Construction Management and Economics, 34(9), 641–656. Sacks, R., Seppänen, O., Priven, V., & Savosnick, J. (2017). Construction fow index: A metric of production fow quality in construction. Construction Management and Economics, 35(1–2), 45–63. Salerto, S. (2019). Hukan mittaaminen tahtihankkeessa [Measurement of Waste in a Takt Project]. Aalto University School of Engineering, Department of Civil Engineering Building Technology, M.Sc. programme. Master’s thesis. Seppänen, O. & Görsch, C. (2022). Decreasing waste in Mechanical, Electrical and Plumbing work. In Proceeding of 30th Annual Conference of the International Group for Lean Construction (IGLC). Edmonton, Canada, 27–29 Jul 2022 (pp. 84–94). Seppänen, O., Zhao, J., Badihi, B., Noreikis, M., Xiao, Y., Jäntti, R., Singh, V., & Peltokorpi, A. (2019). Intelligent Construction Site (iCONS) Project Final Report. Available at: https://www.aalto.f/ sites/g/fles/fghsv161/fles/2019-02/icons_fnal_report.pdf. Accessed 14.1.2022. Svalestuen, F., Knotten, V., Lædre, O., & Lohne, J. (2018). Planning the building design process according to level of development. Lean Construction Journal, 16–30. Tetik, M., Peltokorpi, A., Seppänen, O., Leväniemi, M., & Holmström, J. (2021). Kitting logistics solution for improving on-site work performance in construction projects. Journal of Construction Engineering and Management, 147(1), 05020020. Tribelsky, E., & Sacks, R. (2010). Measuring information fow in the detailed design of construction projects. Research in Engineering Design, 21(3), 189–206. Tribelsky, E., & Sacks, R. (2011). An empirical study of information fows in multidisciplinary civil engineering design teams using Lean measures. Architectural Engineering and Design Management, 7(2), 85–101. Uusitalo, P., Seppänen, O., Lappalainen, E., Peltokorpi, A., & Olivieri, H. (2019). Applying level of detail in a BIM-based project: An overall process for Lean Design management. Buildings, 9(5), 109. Yarmohammadi, S. (2018). If these walls could talk: Automated performance measurement for building modeling decisions using data analytics. PhD dissertation. Georgia Institute of Technology, Atlanta, USA. Yarmohammadi, S., & Castro-Lacouture, D. (2018). Automated performance measurement for 3D building modeling decisions. Automation in Construction, 93, 91–111. Yarmohammadi, S., Pourabolghasem, R., & Castro-Lacouture, D. (2017). Mining implicit 3D modeling patterns from unstructured temporal BIM log text data. Automation in Construction, 81, 17–24. Zhao, J., Pikas, E., Seppänen, O., & Peltokorpi, A. (2021). Using real-time indoor resource positioning to track the progress of tasks in construction sites. Frontiers in Built Environment, 7, 59. Zhao, J., Seppänen, O., Peltokorpi, A., Badihi, B., & Olivieri, H. (2019). Real-time resource tracking for analyzing value-adding time in construction. Automation in Construction, 104, 52–65. Zheng, Y., Törmä, S., & Seppänen, O. (2021). A shared ontology suite for digital construction workfow. Automation in Construction, 132, 103930.

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16 UAS APPLICATIONS TO SUPPORT LEAN CONSTRUCTION IMPLEMENTATION Dayana Bastos Costa, Masoud Gheisari, and Luis Fernando Alarcón Introduction In Industry 4.0, construction processes can use unmanned aerial systems (UAS) as a digital tool for monitoring and digitalizing projects. UAS, aircraft without human pilots on board, has seen tremendous improvement over the past few years, and recent technological development has made them accessible in the construction industry. Several applications of UAS in the construction feld include site mapping and surveying, site planning, building inspection, progress monitoring, safety management, material handling, security surveillance, and building maintenance. These UAS applications produce visual documentation, measurable reality, and overlays with design and schedule, facilitating management practices such as quality control, progress checking, safety monitoring, as-built record, operations, and management data integration. Data acquisition and the ultimately generated data through UAS for construction management purposes can promote better transparency, reduce the number of steps, inspection time, and non-value-added activities, and improve the quality of work by providing precise reports and corrections on time. Additionally, the UAS application can increase the workers’ awareness about risks and improve workplace conditions, especially in high-risk processes. Thus, the results provide more excellent visualization and minimization of errors, and improved safety conditions are closely associated with Lean management. This chapter aims to discuss the connection between the features and functionalities of UAS with Lean Construction principles and practices. In addition, two case studies illustrate the implementation of this technology on managerial construction processes and the benefts in terms of Lean management. Finally, this chapter discusses the opportunities and drawbacks of incorporating this technology into the Lean Construction 4.0 context in practice.

UAS Concept and Defnition Unmanned Aerial Systems, also known as unmanned aerial vehicles (UAVs) or drones, are aerial vehicles with various onboard sensors that are remotely piloted (Hassanalian & Abdelkef, 2017). UAS typically comprise a ground control station with a remote pilot in command, guiding, navigating, and controlling an UAV (Figure 16.1). Real-time data transfer 254

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Figure 16.1

Unmanned aerial system components

between the pilot’s control station and the aerial vehicle is common in most UAS. In addition, some UAS use onboard data storage capabilities to enhance data collection and later be used for post-processing purposes (Gheisari et al., 2015). UAS have recently witnessed remarkable software and hardware improvements that have signifcantly increased their use for various applications such as infrastructure and building inspection, trafc monitoring, material transportation and delivery, security surveillance, and search and rescue operations (Albeaino & Gheisari, 2021). As a result, it is envisioned that by 2026, the US UAS commercial market will have more than $30 billion yearly impact (Cohn et al., 2017). UAS adoption in the architecture, engineering, and construction (AEC) domain has also improved over the past few years (Albeaino et al., 2019) for applications such as trafc inspection (Barmpounakis & Geroliminis, 2020), city and regional planning (Banaszek et al., 2017), cultural preservation (Enríquez et al., 2020) and geotechnical and landslide monitoring (Yeh & Chuang, 2020). Construction applications have seen exponential growth, mainly because of UAS’s capability to do the work done by humans but safely and time-efciently and get access to areas that might be unsafe or unreachable by humans (Albeaino et al., 2019). Also, various recent developments in the UAS implementation and deployment, such as regulation updates and clarifcations, increased battery life, low acquisition and maintenance costs, autonomous fight capabilities, enhanced safe navigation features, and various onboard sensors, have helped with making such platforms very popular in construction (Albeaino et al., 2019; Hassanalian & Abdelkef, 2017; Zhou & Gheisari, 2018).

Adopted UAS Technologies in Construction This section discusses the adopted UAS technologies in construction (See Figure 16.2), focusing on specifc vehicle types, fying styles, adopted sensors, and the fight team members in charge of fight operations. 255

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Figure 16.2

Adopted UAS technologies in construction

Three types of UAS have been commonly used in the AEC domain (Albeaino et al., 2019): •

•

Rotary-wings: have hovering and vertical take-of and landing capabilities, and considering the number of propellers, they could be a helicopter or a multi-copter. Rotary-wing aerial vehicles are very popular in vertical construction mainly because of their take-of and landing capability on rough surfaces without having large runways. Fixed-wings: are similar to traditional aircraft and can perform continuous fightdemanding tasks; however, their take-of and landing require runways, and their most common types cannot hover in the air. Such aerial vehicles are also commonly used in construction applications that require fying longer distances or covering more expansive areas (e.g., horizontal construction). Blimps: are lighter-than-air vehicles. They can lift through gas pressure available in the aircraft, allowing signifcantly longer fying time than other types of UAS. However, these aerial vehicles are not commonly used in the construction domain because of their large size, low speed, and susceptibility to the wind.

Based on the above UAS types and their specifc application requirements, there could be three common types of fying styles (Albeaino et al., 2019): •

•

Manual: the pilot is fully controlling the vehicle and its fight mission with no computer autonomy. Flight limitations such as close proximity to structures, lack of navigation or anti-collision sensors, limited fight space, wind, GPS inaccuracy, and fight regulation might lead to using the manual control style. Autonomous: the computer has full control of the UAS and its fight mission. This fying style requires moderate fight expertise from the pilot in command and is commonly used in construction applications because of its ease of use. 256

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•

Semi-Autonomous: both humans and computers control the UAS and its fight mission. This fying style is commonly used when autonomous operations could be interrupted by the pilot in command due to any type of fight limitations.

Depending on the required type of data and specifc application, a wide variety of sensor types (e.g., Radio Frequency Identifcation [RFID] readers, thermal or visual cameras, temperature/humidity/air-quality sensors, motion detectors, LiDARs and laser scanners) can be mounted on the UAS platform. For example, visual cameras can collect regular images and videos of the site for structural inspection or building maintenance; thermal cameras can be used to conduct the thermal assessment of buildings; and laser scanners and LiDARs can be used for damage assessment or infrastructures inspection, and RFID readers can be used to track materials on construction jobsites (Albeaino & Gheisari, 2021). The composition of the team conducting the fight operations can be diferent based on the specifc application or UAS type, but the common fight team members could be (Albeaino & Gheisari, 2021): • • •

Remote Pilot in Command (RPIC) has the primary responsibility for the UAS operation and should be present for all UAS-related fight missions on the jobsites. Visual observer usually assists the remote pilot in command by mainly focusing on any potential collisions in air or ground. Depending on the specifc application and safety considerations, the fight team might include other safety managers, project managers, tower-crane operators, or superintendents.

UAS Application Areas in Construction UAS has been adopted for various types of construction applications such as mapping and surveying (Martinez et al., 2021a), building inspection (Eiris et al., 2020, 2021; GonzálezdeSantos et al., 2020), progress monitoring (Álvares & Costa, 2019), safety monitoring (Martinez et al., 2020, 2021b; Melo et al., 2017), and structures maintenance (Bayomi et al., 2021). This trend is expected to continue, and more UAS will be integrated into the construction domain. Table 16.1 presents various UAS applications within pre-construction, construction, and post-construction phases. UAS assists construction personnel in such applications by providing multiple types of spatiotemporal information about the jobsite to enhance their decision-making and construction procedures. UAS technology can play the role of an assistant robot on the jobsite and help capture the required information safely and in less time. Table 16.1 UAS adoption areas in construction (adapted from Albeaino and Gheisari, 2021) Phase

Application

Defnition

Preconstruction

Site evaluation

Collection of site-related spatiotemporal information for the performance of site feasibility studies Collection of site-related information for project scheduling and activity planning (e.g., site layout, logistics) Using UAS and photogrammetry to survey construction jobsites and assist in earthmoving (e.g., excavations) operations

Site planning Construction

Site mapping and earthwork

(Continued)

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Application

Postconstruction

Defnition

Progress monitoring Collecting visual information to track the progress of diferent construction activities with time Safety monitoring Assisting safety managers in identifying jobsite safety and inspection hazards by providing a comprehensive overview of the jobsite condition using bird’s eye view type of visual information Security surveillance Preventing jobsite trespassing and thieving activities from happening by providing real-time visual feedback Aerial construction Autonomously operate UAS for material or tool pick-up and and material delivery, aerial transportation, and construction handling Site communication Establishing a communication channel with workers onsite using UAS equipped with audio and video transmitters Building inspection Collecting visual or thermal information to maintain and evaluate the buildings (e.g., structural integrity and energy performance). Post-disaster Assessing the conditions of buildings in the post-disaster reconnaissance setting (e.g., earthquake and typhoon) Marketing Relying on UAS’ bird’s eye view visuals to document and promote completed projects and motivate new project owners

Lean Construction Principles and Practices Related to UAS-Based Management Systems Lean Production within the organization identifes and eliminates waste in the value chain (Womack & Jones, 1997). According to Ohno (1988), waste in production is understood as all activities that add cost but not value. The seven types of waste for manufacturing include transport, inventory, motion, waiting, over-processing, overproduction, and defects (Ohno, 1988). Moreover, Lean Production also focuses on reducing process variation (Shah & Ward, 2003), aiming to standardize the processes, improve quality, productivity, and produce according to customer’s expectations (Womack & Jones, 1997). Another important Lean Production goal is providing good work conditions that prevent injuries and strain on workers (Sony, 2018). Thus, Lean Production involves practices and principles that help companies organize and control production. This section presents a list of Lean Production principles and practices applied to the construction industry specifcally to analyze Lean and UAS usage interconnections. Reduce Non-Value-Activities: One of the most used defnitions understands waste in construction as work and work-related activities which do not add value to the customer, such as necessary preparations (indirect work, coordination, etc.) (Diekmann et al., 2004). Possibly advantages regarding removing non-value-added activities related to the application of drones are simplifed steps, the time taken to monitor the process and product, and the right information needed to provide to the customer. Reduce Time: In construction, reduction in cycle times should be focused on several levels of analysis, such as total construction duration, the fow of materials from factory to installation, and task duration (Koskela, 2000). The use of drones in construction can identify improvement opportunities related to logistic management, space-constrained, and 258

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mainly reducing time to monitoring tasks, optimizing decision making due to end-to-end visibility in real-time. Increase Transparency: Process transparency is “the ability of a production process (or its parts) to communicate with people” (Formoso et al., 2002). Transparency can be increased by removing waste, reducing cycle time, using visual signage, displaying process information, appropriate layout, and maintaining visual order (Koskela, 2000). Due to the drone’s capability to capture reality based on images and videos, there is a vast potential to create visual documentation tools and reality models to provide information on time for decision-makers. Simplify Steps: Simplifcation can be understood as reducing the number of components in a product or reducing the number of steps in a material or information fow (Koskela, 1992). The UAS application has the potential to minimize the amount of control information needed. Build Continuous Improvement: Continuous improvement is a systematic form of improvement and goes beyond mere learning as addressed by the learning curve concept (Sacks et al., 2010). Measures contribute to continuous improvement by indicating potential improvement and monitoring progress achieved. The UAS provides measurable reality and, when overlapped with design, can estimate data for quality control, progress verifcation, and safety conditions. Continuous Flow: The fow term has some important intuitive qualities, such as a chain of events (sequence), continuous movement, moving freely, and adding value (Pérez et al., 2014). Flow also refers to materials, information, labor, and equipment movement during the construction process. Applications with UAS can provide continuous visual data helping track the fow and transformation of construction activities. Reduce Variability: The construction industry has diverse areas of variability, such as product, process, and demand. Standardizing activities by implementing standard procedures is often the means to reduce variability in both transformation and fow processes (Koskela, 1992). In addition, UAS-based management can provide documentation assets, such as reality capture and reality models, which might reduce variability in data collected for decision-makers use. Improve Safety Conditions: Safety is a critical managerial task in construction projects. In Lean Construction, poor safety is a form of waste since injuries are costly in human sufering and worker compensation costs, lost time, lost productivity, and higher employee turnover (Nahmens & Ikuma, 2009). The objective of a safety monitoring and control system is to make sure that safety measures are implemented according to safety rules and standards during construction. UAS-based management for safety is a well-studied application, providing crucial information for reducing accidents and improving construction workplaces. Control the Process: Process control is a critical Lean practice. However, in construction, process control is frequently based on individual observations, depending on manual data collected, and relies on textual documentation and subjective interpretations of data (Álvares & Costa, 2019). UAS visual assets ofer opportunities to construct as-built models to identify progress, quality, and safety issues on the site from diferent angles, providing accurate measurements, such as length measurement, area, and volume. Just-In-Time: Just-In-Time is a pull system that responds to actual customer demand, and the customer is the driver of production (Forbes & Ahmed, 2011). UAS applications can provide the correct information at the right place and the right amount. Identify Customer Requirements: Identifying customer requirements means adding value to the product and the process. Product value relates to tangible aspects of a product, 259

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such as material composition, price of construction, fexibility, and process value in the construction environment refers to stages in the building process and interaction between producers such as time and communication, for instance (Forbes & Ahmed, 2011). UAS application is an essential source of communication between project stakeholders by using visual documentation with reality capture and reality models. Update Value Stream Maps: Value stream mapping (VSM) shows material and information fows required to produce outputs, helping users understand the process and identify waste sources (Forbes & Ahmed, 2011). In addition, UAS can access large areas of the construction site, identifying inefciencies regarding logistics.

Connections of UAS and Lean Construction Principles and Practices This section presents connections between UAS specifc applications, functionality, and Lean Construction principles. It is essential to know that some of these connections have been well-studied, and it is possible to identify robust evidence from the literature. On the other hand, some links require more investigations, thus providing opportunities for further research. Table 16.2 presents an overview of the possible connections between UAS and Lean Construction principles and practices, and seven relevant connections will be discussed as follows: • • • • • • •

UAS can increase transparency and reduce non-value added activities and time, UAS can provide information to customer requirements, UAS can support continuous fow by reducing risks and variability of operations, UAS can provide information to improve construction safety conditions, UAS can support site monitoring, visual control, and continuous improvement, UAS can support Just-In-Time operations, UAS can support continuous updating for Value Stream Maps.

UAS Can Increase Transparency and Reduce Non-Value Adding Activities and Time In general, UAS is a powerful tool for transparency and reduction of non-value activities (transport, inspection, processing, and waiting) in managerial tasks. The UAS is a data capture digital tool that collects a tremendous amount of visual data. This UAS characteristic reduces the time, the distance, and the number of people required to perform inspections and monitoring. Visual data technologies, including photos, videos, 3D, and 4D models, can reduce errors by decreasing errors made in routine tasks related to site evaluation, site mapping, progress monitoring, safety monitoring structure, and infrastructure inspection. Also, there is a consensus concerning the time-consuming process for manual-based monitoring and inspection in construction. Thus, the UAS can collect vast amounts of data in a fight, optimizing managerial tasks related to pre-construction, construction, and post-construction applications, such as site evaluation, progress monitoring, and structure and infrastructure inspection. Silveira et al. (2020) developed a post-construction structure inspection with UAS in 167 roofs from ten residential projects of a Brazilian construction company. This UAS application allows increasing transparency by facilitating the identifcation of roof pathologies. The UAS also allows simplifying steps, making faster the inspection of a high number of roofs 260

261

Post-construction Post-disaster reconnaissance Marketing

Construction Site mapping and earthwork Progress monitoring Safety monitoring and inspection Security surveillance Aerial construction and material handling Site communication Structure and infrastructure inspection

x x

X X

x x

X X

x x

X X

x x

Pre-construction Site evaluation Site planning x x

X X

UAS functionality

x x

Reduce nonIdentify Improve Control Build Just- Value stream value adding Reduce Increase Simplify customer Continuous Reduce safety the continuous Inmapping activities of time transparency steps requirements fow variability conditions process improvement Time updating

Lean principles and practices

Table 16.2 UAS functionalities vs. Lean principles and practices

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and proving greater agility in the inspection process, contributing to increasing data quality used in the reports due to high image resolution. Furthermore, the transparency and the reduction of inspection steps contributed to saving time during the inspection, speeding up the preparation of reports, and applying corrective actions (Silveira et al., 2020).

UAS Can Provide Information to Customer Requirement The application of UAS can provide precise information to the construction project, thus satisfying customer needs and creating value for the product. For example, in project evaluation, spatial photographs and measurements using geographic information system (GIS) tools provide information about the dimensions of a site, site routes, and height restrictions. Furthermore, according to Zhou and Gheisari (2018), deploying UAS is economical and saves time and other resources for construction managers, companies, and clients. During construction, the application of UAS for security and surveillance can also fulfll customer requirements. For example, security companies can deploy these vehicles in the hours of darkness to prevent theft and trespass, particularly on large construction sites (Gheisari et al., 2020). UAS can be applied for multiple uses such as structure and infrastructure inspections, marketing, and disaster management during the post-construction stage. For structure and infrastructure inspection, UAS can provide a large amount of data in a short period, allowing the visualization of pathologies in hard-to-reach areas safely and at a low cost when compared to manual visual inspection methods (Silveira et al., 2020). Manual inspections are often neglected due to the signifcant amount of time required for assessment, labor cost, difculty accessing these structures, and safety risks (Conceição et al., 2017). Identifying pathologies and defects such as corrosion and integrity of tiles can prevent severe damage to the structures and infrastructures. Correcting these pathologies on time can improve the life cycle of buildings, bridges, and other structure and infrastructure components. In addition, the UAS application as a marketing tool ofers the clients potential views of the buildings using photos and 3D models. Finally, the UAS for disaster management helps rescuers estimate which buildings and other infrastructure have sustained too much damage to make it safe for rescuers to enter (Gheisari et al., 2020).

UAS Can Support Continuous Flow by Reducing Risks and Variability of Operations UAS can collect data from lower altitudes until various heights and viewpoints at the project and fy-over views above the site due to their small size and maneuverability. The results can be seen in minutes and at a low cost compared to other means to capture aerial images or videos. Aerial images produced by UAS can be taken daily to plan the placement of stored materials, the fow of workers and vehicles in and around the site, and potential issues with installed construction or constructability. UAS can also enhance the construction inspection process by reducing worker exposure to harsh conditions in extreme climates and reducing risks and variability in the production system enabling continuous fow.

UAS Can Provide Information to Improve Construction Safety Conditions UAS applications, including progress monitoring, safety monitoring and inspection, security surveillance, aerial construction and material handling, and site communication, present a 262

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signifcant opportunity for improving safety on construction job sites. Alizadehsalehi et al. (2018) pointed out benefts of UAS for the safety management system, such as: • • • • • • • • • • •

reducing fatal accidents, injuries, and property damage, reduction inspection time, collecting real-time data on safety violations, providing detailed information about unsafe conditions, improving safety performance, reducing the number of safety monitoring staf, improving worker behavior related to safety, providing the location and orientation of temporary resources, storing safety knowledge for future planning or training, using safety-related indicators for decision making, and using UAS for remote interactions with site workers.

Melo and Costa (2019) developed a case study that showed that the UAS could be used to monitor work conditions on construction sites, providing information from diferent angles and perspectives. These authors also mentioned that by visualizing the actual needs of the jobsite, the UAS technology could provide information for the safety planning and control process. Thus, this application can make it possible to visualize the safe work boundaries, especially in high-risk and complex procedures, corroborate new protection measures when necessary, and increase awareness about how the work-as-done (Melo & Costa, 2019). Moreover, the visual assets could be used to improve the workers’ awareness through safety training and testing diferent situational scenarios that workers are normally exposed to (Melo & Costa, 2019). Furthermore, considering structure and infrastructure post-construction and even disaster management, the UAS can be employed to reach an inaccessible location, reduce fatal accidents, injuries, and property damage.

UAS Can Support Site Monitoring, Visual Control, and Continuous Improvement Lean principles such as transparency, control of the process, and continuous improvement are strongly connected to the monitoring and inspection based-UAS applications, such as site evaluation, site mapping, progress monitoring, safety monitoring, structure, and infrastructure inspection. The visual data provided by the UAS associated with performance indicators can provide more visible and understandable information about the monitoring and inspections. Furthermore, visual technologies also make it possible to gather new information about the construction site, the safety and production, and the infrastructure and structure already built (Álvares & Costa, 2019). Therefore, visual performance management is made easier by faster updating accessible data. The shared analysis of the visual data (photos and videos) and models can contribute to better decision-making on complete and updated information about planning redirects, possible reworks, safety, and quality issues, allowing corrective actions to the negative deviations. This can be applied to progress and safety monitoring. Consequently, there are opportunities for continuous improvement for safety and production monitoring by identifying planning failures, progress deviations, quality, and safety requirement nonconformities. 263

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UAS Can Support Just-In-Time Operations The UAS technology is already being used to provide real-time surveillance of construction sites and provide high-defnition video and images. In construction projects where information on progress can be captured from drones, this data can be used to support Just-In-Time operations ( JIT) by providing real-time information to feed the production planning and control system needed for operations. Videos and still images captured by UAS technology can be used for operations analyses using tools such as work sampling and crew balance charts for quick analysis and feedback to production control. By identifying and measuring production fows, batch sizes, work in progress, inventories, resources used, and a broad view of the production process, UAS technology can provide valuable information to support JIT. The principle of Just-in-time can also be applied to aerial construction and material handling. Shortly, with the guidance of algorithms, a feet of UAS should handle material around the construction site, including tall buildings. Therefore, the material can be placed in the right place, at the right quantity, and at the right time.

UAS Can Support Continuous Value Stream Mapping Updating UAS technology can capture the information required to develop a VSM in projects where information about quantities and progress can be captured by UAS and combined with data obtained from other sources to build the VSM. However, continuous updating of VSM information is a potential use that can signifcantly impact the performance of a production system. VSM provides a tool for identifying the current state and building a future state for activities of a product or service from the starting of the specifc process to the customer. Once the future state is designed, the actions for implementation are carried out. They require monitoring and control, which could be facilitated by UAS, providing means to accelerate improvement cycles by providing valuable information and continuous updating. This section presents case studies to exemplify how the UAS application can support Lean Construction practices. The frst case study describes visual documentation (reality capture and reality models) for progress monitoring with 4D BIM and the integration with the production planning and control. The second case study explores how UAS applications can support existing monitoring and control of construction sites.

Case Study 1 – Integrating Visual Progress Monitoring Using UAS and BIM 4D with Production Planning and Control A case study developed by Álvares (2019) and Álvares and Costa (2019) aimed to implement a systematic visual progress monitoring approach integrating into the production planning and control process, supported by 4D BIM and photogrammetric 3D mapping using UAS imagery. The application was implemented for 21 weeks on Project A in Brazil, a residential low-income housing project with a land area of 22,800 m². It consists of a condominium of 20 buildings of fve foors each, with four units per foor: a total of 400 units (Figure 16.3). The construction time was 18 months, and the core constructive process was cast-in-place concrete wall structure. The implementation in Project A occurred associating the visual monitoring with the Last Planner System (Ballard, 2000) hierarchical levels. Five cycles related to the lookahead planning and control were conducted monthly. A 4D BIM model, using Navisworks 264

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Figure 16.3 Project A site orthophoto in March 2018 (adapted from Álvares, 2019)

software, was updated based on the look-ahead planning data for supporting the monthly visual progress analysis and the preparation of the short-term planning. Twenty cycles associated with short-term planning were conducted weekly. To maintain this, automatic UAS fights were performed every week, using the Pix4D app, following a standard grid path defned to cover the construction area. The protocols for safety fights adopted were based on Álvares et al. (2018). The images collected were processed using PhotoScan software, generating photogrammetric point clouds and orthophotos. For the visual progress monitoring of the outside work packages in the monthly cycles, these point clouds (visual representation of as-built progress) were overlapped with the project 3D BIM model and integrated into the 4D BIM (visual representation of as-planned progress) in the Navisworks platform. The progress deviations for outside work were identifed from the visual comparison of the models (point cloud and 4D BIM). The indoor work packages status was determined from direct feld measurements by the project team. The progress deviations of all project work packages were also coded with visual color indicators in the 4D simulation to highlight work behind schedule, ahead of schedule, and on a plan. Based on the production team’s interviews and data collected, it was possible to identify the high impact of the implemented tools and processes for increasing transparency. The management team highlighted better communication and identifcation of progress status and understanding of progress information from visual data technologies and indicators. Concerning collaboration, the high-impact assessment shows a greater integration and communication of the management team in the progress monitoring activity, especially between the production coordination and general management teams in the weekly meetings for planning and control. However, regarding shared analysis of progress status and joint decision making, this will only efectively happen when the management team is more familiar and have more autonomy on this new form of progress monitoring and analysis. The results obtained on this case study allowed the identifcation of benefts and opportunities for improvements to integrating visual progress monitoring using UAS and BIM 265

Dayana Bastos Costa et al. Table 16.3 Case study’s benefts and opportunities for improvement (adapted from Álvares and Costa,2019) Benefts

Opportunities for improvement

•

• • • • •

Better visualization and analysis of construction progress from the use of visual models of progress (4D BIM + Point cloud) with color-code Improvement of the production monitoring and identifcation of planning failures Improvement of compliance with the planned goals New information for the production control from the performance indicators Better identifcation of negative deviations of progress and search for solutions A complete and accurate view of the construction site status (as-built progress) using aerial photographs and photogrammetric products

•

Required greater integration with the project’s management procedures The low familiarity of the project team with the technologies used hampered a better use of the visual models of progress The information on the short-term planning spreadsheet and monthly report of progress should be less detailed and more objective Limitation of the visual progress monitoring only for the external activities, requiring direct feld measurements of the internal activities

4D with the production planning and control (Table 16.3). Most benefts are linked to gaps associated with traditional progress control and can provide vital support to Lean Construction implementation. The limitation indicates opportunities for further investigations and new applications.

Case Study 2 – Analysis and Improvement of Logistics and Productivity using UAS on Construction Sites This was an exploratory study carried out on a 20-story residential building construction site to identify various potential uses of UAS to support construction management eforts to improve logistics and productivity of onsite operations (Chica, 2019). The original hypothesis of this study was that the use of drones to support existing methods of monitoring and control of construction sites would increase the level of efciency in the capture of data on site, the quality of the information available for analysis, thus allowing the identifcation of new opportunities for improvement in construction operations. The general objective of this study was to evaluate the efectiveness of the use of drones as support tools in the monitoring, follow-up, control, and logistics of construction sites, proposing its integration as a complementary tool to existing methodologies in the feld of construction works. Some specifc uses and the results obtained are discussed below: •

Analysis of the logistics of civil works to identify opportunities for improvement. The drones were programmed to produce 2D automatically and 3D models of the building and site using photogrammetry technique, which consists of creating 3D models using 2D photos, by using image processing algorithms to calculate the exact position of objects and relate them to specifc points with X, Y, and Z coordinates to create a point cloud data (PCD). These models were used to optimize access routes and storage areas by the project administration. Information on access roads, plant distribution, location of machinery and equipment, dangerous zones, referential location of infrastructures, and distances were obtained. The reduction of unnecessary movements, transportation, and a more efcient workfow was achieved due to this analysis. 266

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•

Development of work sampling and crew balance charts to measure the productivity of diferent resources with data collected by drones. General work sampling charts and crew balance charts for specifc construction operations were developed from random video observations (Figure 16.4 and Figure 16.5). This made it possible to obtain how much time was spent on non-contributory activities and verify if the supervision was appropriate to cover all the necessary areas. The limitations for the general work sampling were that the work needed to be visible for the camera and the fying time limitations of the drones between exchange of batteries. However, these limitations were overcome by employing careful planning of data capture fights. The data for the crew balance charts were captured without difculties because the operations were visible for the camera and the duration of the operations was within the fying time availability of the drone. As a result, reducing non-value adding activities, better balance of crews, and a general view of activity levels at diferent times can be available for the project administration with this drone’s practical use. Use of drones as support tools in controlling progress and monitoring activities within construction sites. Progress measures were obtained by measuring volumes and areas from the 2D and 3D digital models obtained from the drones, and monitoring activities were performed using specially designed checklists. The fact that this information can be obtained in a short time is particularly important for management, considering that information from traditional methods is usually not on time to prevent waste. Design a methodology for the monitoring and control of operations. Several uses were proposed for the visual data collected with the drones. A methodology that integrates the practices and steps necessary to carry out monitoring and control operations was developed and tested as part of this study, showing the full potential of the drone’s capabilities.

Figure 16.4 3D model and images for productivity sampling (adapted from Chica, 2019)

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Figure 16.5

Sample crew balance chart for steel (adapted from Chica, 2019)

Conclusions This chapter has discussed UAS applications for architecture, engineering, construction, and operation (AECO) as a digital tool to capture the required information safely and in less time for monitoring digitalizing construction projects to support Lean Construction implementation. This chapter provides indications about the connection between Lean and UAS for construction management purposes and shows that UAS can: • • • • • • •

increase transparency and reduce non-value activities and time, provide information to customer requirements, support continuous fow by reducing risks and variability of operations, provide information to improve construction safety conditions, support site monitoring, visual control, and continuous improvement, support Just-In-Time operations, and support continuous Value Stream Mapping updating.

However, many of UAS’s functionalities are not completely exploited in use yet. Therefore, there are opportunities for more investigations to develop new methods to explore the synergies between Lean and UAS functionalities, also polishing the use of this technology in a Lean way. Moreover, there is a need to incorporate visual documentation assets developed into the construction management routines of projects such as quality, production planning, control, safety, and facilities, reducing waste and time, increasing transparency, 268

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and simplifying steps. In this direction, there is huge potential to enhance the visual assets captured with more processing and analysis capabilities, using cloud-based computing and artifcial intelligence, for instance. Despite increasing construction industry awareness of digital transformation needs and advanced UAS applications, construction projects still need to overcome several barriers related to low UAS awareness among construction professionals, increased safety challenges (e.g., physical risks, attentional costs, and psychological impacts), the limitation of use to certain types of projects, as well as challenges associated with the complex nature of construction projects. Hardware and software limitations of UAS should be also signifcantly enhanced for their successful integration into the construction feld. Further developments in regulatory and administrative guidelines and procedures can also facilitate UAS integration in the AECO. Furthermore, the acceleration of the digitization processes in the industry due to the COVID-19 pandemic brings up new opportunities to integrate new and varied digital technologies with UAS. Thus, the Lean philosophy can provide adequate guidance to transform these advances into productive knowledge to ensure the best use of the resources and the best results.

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PART 5

Digital Lean Project Delivery

17 INTEGRATING PROJECT DELIVERY AND INFORMATION TECHNOLOGY Challenges and Opportunities Eder Martínez, Ali Ezzeddine, and Borja García de Soto Introduction Integrated project delivery (IPD) emerged as an alternative to reduce inefciencies and waste associated with the use of traditional project delivery methods (e.g., Design-Bid-Build [DBB]). At its core, IPD integrates people, practices, business structures, and systems into a process seeking to optimize efciency along all project phases. This aims to align the interests of diferent project stakeholders to reach well-defned and mutually agreed project objectives. To successfully implement IPD, project teams must collaborate on all levels. This requires adequate IT systems facilitating communication and inter-organizational information exchange. There is a variety of information technologies and digital tools with the potential of supporting IPD. For instance, building information modeling (BIM) facilitates collaboration among project teams and facilitates the fulfllment of several IPD principles (Manukyan & Papadonikolaki, 2019). However, the potential of IT, particularly in the context of IPD, should be analyzed from a broader perspective, considering how the integration of diferent technologies and digital tools can support efcient project delivery. Using a particular digital tool or IT system may bring efciency into a particular phase of the project lifecycle, but it may generate information bottlenecks down in the process. The objective of this chapter is to compressively analyze the challenges and opportunities of IT to support IPD. For this purpose, we provide an overview of the most commonly used project delivery methods, including IPD. Furthermore, we describe emerging technologies used in the architecture-engineering-construction (AEC) industry and their potential to support IPD. Finally, we discuss the requirements of IT to support the AEC industry transition towards more integrated forms of project delivery.

Project Delivery Methods and IPD This section presents an overview of four project delivery methods: DBB, Design-Build (DB), construction management (CM), and IPD.

DOI: 10.1201/9781003150930-22

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Traditional Project Delivery Methods DBB is one of the most commonly used project delivery methods. DBB emerged due to the increased complexity in building designs, which required high specialization for design and construction teams. As the lack of coordination and communication between the design and construction became wider in the DBB method, the construction management at risk (CMAR) emerged as a response. The CMAR method brings into the scheme a third party playing a coordination role to provide constructability feedback to the design team on behalf of the prime contractors (Konchar, 1997). One of the main advantages of the CMAR is the early involvement of a constructionrelated party that allows the start of construction before design fnalization. It also enhances the design by giving early constructability feedback (Shane & Gransberg, 2010). However, the CMAR may create conficts between the designer and the construction manager due to contradictory interests. For example, on the one hand, the designer focuses on delivering the design meeting the required codes, laws, and technical requirements. On the other hand, the construction manager focuses on developing a design that is constructible, economical, and time-efective (Shane & Gransberg, 2010). The CMAR posed additional costs of project management manpower on the owner and resulted in disputes between the design and construction, making owners Lean toward a complete design and construction package (Konchar, 1997). The DB method also emerged to address the lack of coordination between the project participants from the DBB method (Konchar, 1997). It allows the design and construction teams to better coordinate their work packages (leading to fewer constructability issues and change orders). The cost and duration of the project are also reduced as the design and construction can overlap (Mahdi & Alreshaid, 2005; Pakkala, 2002). Moreover, the contractor can increase project efciency by selecting the optimum construction methods early in the project’s life cycle (Al-Reshaid & Kartam, 2005). However, as the designer and contractor are a single entity, the owner will lack the quality assurance checks between the designer and contractor, leading to quality issues (Al Khalil, 2002). None of the delivery methods mentioned above achieved the required levels of coordination among project teams urging the industry to transition into more integrated forms of project delivery (Walker et al., 2017).

Integrated Project Delivery (IPD) Portrayed as a philosophy (Kahvandi et al., 2019), IPD is a relatively new project delivery method (Fisher et al., 2017). As defned by the American Institute of Architects (AIA, 2007a), IPD is a project delivery approach that incorporates people, systems, business structures, and practices into a single process where the skills and talents of all project participants are exploited in order to reach the best project results, maximize customer and project value while minimizing wastes. (AIA, 2007b) The overarching principles of IPD are mutual trust and respect among all project parties and teams, shared pool of risks and rewards, transparency and communication, early goal defnition and alignment, collaborative innovation, early involvement of all project participants, and using technological advancements as enablers of the process (Kahvandi et al., 2017). From the owner’s perspective, the main advantages of IPD are the improvement of overall project quality, reduction of project delivery time, and enhanced project steering (Manning, 2012). From the contractor’s perspective, IPD help to cut costs, increase planning reliability, and reduce requests for information and change (Kamaruddin et al., 2016). Designers 276

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also beneft from IPD as the owner’s expectations are better defned early on in the project, which helps reducing design iterations (Rached et al., 2014). A study by Hamzeh et al. (2019) showed that the early alignment of all project participants’ interests and goals with the project goals was one of the main factors for efcient collaboration. The disadvantages of IPD are refected by the barriers that stand in the way of its proper implementation. Cultural and behavioral barriers are two of the most prominent obstacles and emerge as a lack of trust, transparency, and collaboration. Kent and Becerik-Gerber (2010) highlighted the lack of trust and greed among the project participants as the main barriers to implementing IPD. Moreover, IPD could backfre with additional fnancial burdens if not correctly implemented or if participants do not sense the promised incentives and risk-sharing expectations (AbouDargham et al., 2019). Despite these barriers, IPD is still considered a promising alternative to traditional project delivery methods. Several studies have focused on overcoming these challenges, especially those related to communication and collaboration through digital and IT technologies.

Information Technology This section highlights the currently adopted information and digital technologies in the AEC industry to enhance project collaboration and communication.

Building Information Modeling BIM has been identifed as the main enabler of IPD as it provides a digital and connected environment for project participants to share information (Sacks et al., 2018; Tahrani et al., 2015). Chang et al. (2017) showed that BIM could make practitioners more aware of potential incentives, which is a strong driver in the acceptance of IPD. The study found that BIM has a strong and positive impact on enhancing communication quality between project participants—one of the main pillars and drivers of IPD (Chang et al., 2017). Elghaish et al. (2019) proposed an IT framework to integrate BIM with IPD. The framework automatically calculated project costs and overheads while providing a fair reward and compensation system based on the calculated cost savings. In an updated prototype, Elghaish and Abrishami (2020) used genetic algorithms and BIM to automatically propose the most optimum constructability method for each activity to better coordinate the design and construction teams. Another study integrated the Last Planner System® with BIM, dividing the project into visual working areas, generating quantity take-ofs, and providing 4D visualization for activities in the Weekly Work Plan (Heigermoser et al., 2019). BIM-based tools are suitable for IPD-based projects as they ofer a collaborative platform for construction planning.

Game Engines Game engines are becoming a strong enabler of the AEC industry’s digital transformation by providing an ideal virtual environment for rapid prototyping and developing digital tools (Ezzeddine & García de Soto, 2021). Tools developed using game engines can support some of the IPD principles, especially those related to collaboration. To better communicate the design, Patz et al. (2016) developed a tool to visualize the project while being able to see the efect of changing design parameters on the lighting of the project in real time. Researchers have explored using game engines to develop walkthrough environments of the project to 277

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better understand the design during early project phases (Van Den Berg et al., 2017). In a similar study, Lin et al. (2018) developed a walkthrough virtual environment for the BIM model where designers and clients found that such tools helped them to better communicate and understand the design and to omit design changes later on in the project (Lin et al., 2018). To better coordinate the design and construction via BIM models, Rahimian et al. (2020) developed a tool that automatically updates the BIM model based on the current asbuilt images of the construction site.

Information and Communication Technology Information and communications technology (ICT) corresponds to a particular application of IT to support relevant aspects of communication, including emails, mobile devices, cloud computing, data storage, and project management tools, among others. In practice, ICT is perhaps one of the most common technologies used in the AEC industry. It is used for administrative purposes, to transfer information among project stakeholders, and through diferent project lifecycle phases. ICT plays a fundamental role in IPD since it supports communication among people and organizations to successfully execute the project (Deep et al., 2021). Several studies have explored the use of ICT to improve communication in projects. Ezzeddine et al. (2021) explored the concept of Obeya rooms, where project team members gather together to make coordinated and informed decisions (Liker, 2004). Wang (2004) developed a web-based system to share project information in an integrated platform. The study suggested that such integrated systems can reduce information sharing time by providing a centralized information hub. Hamzeh et al. (2020) explored digital dashboards to efectively share project information. The digital dashboard allows project participants to clearly share and visualize project information (Song et al., 2005). Using such tools and methods to share information is an enabler of IPD, as IPD-based projects are intense in information sharing (Scott et al., 2013). Another technology known as Blockchain technology can help overcome some of these challenges by providing trusted information and a transaction-sharing digital framework (Shemov et al., 2020; Turk & Klinc, 2017). Elghaish et al. (2019) developed a blockchain and smart contract-based system for fnancial management in IPD projects. The system overcomes some of the barriers of IPD, such as trust and information fow between project participants. In general, emerging IT technologies have signifcant potential to support collaboration in construction project management. Nevertheless, one of the main challenges lies in optimizing the integration of the several digital tools used for diferent purposes along the project lifecycle (Xue et al., 2012)

IPD and IT The use of IT is not strictly mandatory for IPD implementation (AIA, 2007a; Sacks et al., 2018). However, the full potential of IPD would be hardly achievable without proper technology systems that support the intense information transfer and data exchange during the project lifecycle. In fact, AIA (2007b) considers using “appropriate technology” as one of the nine IPD principles. Integrated projects rely on advanced technologies that facilitate information sharing, maximize functionality, and interoperability (AIA, 2007b), especially considering the scale and complexity of modern infrastructure projects. 278

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Challenges It is well-known that the AEC industry lags in terms of innovation and technology implementation. AEC companies invest less than 1% of their revenue on research, development, and IT implementation, less than the average 5% that other industries invest in the same area (Agarwal, 2016). Considering this, it is not surprising that technology limitations (Ghassemi & BecerikGerber, 2011), including lack of IT infrastructure, inadequate information management protocols, and interoperability (Azhar et al., 2014), have already been identifed as relevant barriers to overcome when implementing IPD. IT has also been recognized as a key IPD research topic, particularly considering current advances in online communication and information sharing (Kahvandi et al., 2017).

Fragmentation and Information Systems Interoperability The structure of an industry, including its business models, culture, and operating procedures, infuences the interaction between an organization and the IT ecosystem developed for it (Malone, 1985, Schwarzer et al., 1995). Arguably, the IT ecosystem developed for the AEC industry seems to mirror the fragmentation in the industry. Early research on the use of IT in the AEC industry has already identifed several software lacking proper interoperability. Interoperability can be defned as “the ability of two or more systems or components to exchange information and to use the information that has been changed” (ISO, 2013). Amor and Anumba (1999) pointed out that to ensure data exchange, reusability, and viability of IT systems in the AEC industry, developers should adopt suitable interoperability standards (e.g., by that time, STEP and IFCs). Sun and Aouad (1999) refer to the excessive fragmentation seen in the AEC IT ecosystem as the “island of automation” and highlighted interoperability as a key requisite to fully leverage technology in the AEC industry. Despite the relevance of interoperability, the AEC industry is still developing and using fragmented solutions. Blanco et al. (2018) clustered and mapped out trends in the construction technology ecosystem depicting the large spectrum of diferent technologies explored in the AEC industry. The same authors highlighted that 50% of technology providers researched in the study address or ofer IT solutions to only one or two use cases. That means that most technology providers focus on delivering applications targeted to narrow niches and use cases rather than ofering more holistic solutions. They concluded that integration within this large technology ecosystem remains the biggest challenge to overcome. In order to support IPD and inter-organizational information exchange, an organization requires a proper IT ecosystem capable of receiving, storing, retrieving, and coding information with internal and external stakeholders (Cheng et al., 2001). However, the poor interoperability we see in the construction IT ecosystem afects the ability of project teams to exchange data, resulting in silos of information and naturally counterproductive to IPD. Moreover, many IT applications lack proper interoperability standards integrated into the core organizational systems that support core business processes (e.g., enterprise resource planning [ERP] system, fnance, human resources). Project teams use various IT applications throughout the project lifecycle for cost, schedule, design, production tracking, and simulation tasks. Each produces information and data that must be combined, processed, and communicated. Although the majority of the applications support processes and bring efciency to specifc tasks, the underlying challenge 279

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persists since the subprocesses we aim to support through technology are still disconnected. The real power of IT in the AEC industry will be leveraged when, in addition to easing tasks (e.g., cost, scheduling), the IT tools allow seamless data integration and processing, which helps project teams make decisions based on reliable information. Interoperability is a term that can be applied to any system that is able to work with another system (e.g., legal, organizational, technical interoperability) (Turk, 2020). Thus, we can argue that with the transition to IPD, the industry seeks to optimize the interoperability of the diferent stakeholders and organizations involved in the construction of the built environment. To support this transition, the IT ecosystem should also consider this broader perspective to move from information to knowledge management. Silos of information hinder the generation and preservation of knowledge. Thereby, in the context of Industry 4.0, in addition to understanding how the AEC industry can beneft from using a growing number of cutting-edge technologies, the industry should also ensure technology interoperability to support people and processes.

Requirements for Technology Integration One of the fundamental aspects of technology development and innovation is to have a defned set of requirements. These requirements derive from customer pain points and are used to steer the technology development process, ensuring that the fnal output delivers value to the end customer. When applying this to technology development in the AEC industry, key requirements for IPD, such as technology ecosystem interoperability and integration, are not at the top of the priorities because there are very limited use cases. As per the authors’ knowledge, it is hard to fnd an organization that can prove to be fully integrated (e.g., integrated construction service providers managing projects end to end). The lack of customers with a broader business and strategic view calling for this type of use limits the elicitation of key requirements related to integration and weakens the business case necessary for a more “integrated” technology development approach. The reality is that every stakeholder in the fragmented AEC industry leverages the use of technology to tackle specifc operational challenges. For instance, a typical case of IT implementation in AEC projects involves digitalizing onsite subprocesses (e.g., planning, production tracking, document control). In most cases, the outcomes of these implementations bring efciency to those specifc subprocesses but neglect the relationships and integration among them. IT development is then mainly driven by pressing operational challenges in specifc contexts, and opportunities for a broader usage are not (so far) considered.

Information Technology Validation and Introduction Although there are many emerging technologies, practitioners in the AEC face several diffculties when implementing those solutions in real-life situations. One of the challenges is the lack of validation. Validation is fundamental for introducing new technology (Zelkowitz & Wallace, 1998) and ensures that the intended solution works in a real-life context. Despite the relevance of this, research and development of new technology in the AEC industry lack proper validation (Lucko & Rojas, 2010; Xue et al., 2012). Neglecting this critical step translates into technology solutions that function at the conceptual level but do not necessarily perform well when introduced in a real-life context. In the feld, following the analogy that if the only tool available is a hammer, we see every problem as a nail; we often see IT 280

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solutions looking for problems in which they can be implemented, whereas the logical approach should be the other way around. That is, realize challenges (requirements), prioritize them, and develop a solution to tackle pain points. Beyond the technical considerations, the development of technology should also consider processes, the need of the organization, context, and culture of the AEC industry. Another challenge is the lack of a strategic approach to introduce technology. In many cases, new technology is introduced only because it is exciting to do so. However, this is done with no clear understanding of why and how certain technology can be used (CITB, 2018). It is common to see some AEC frms having several IT applications for the same purpose (e.g., planning or production control). It is unclear how to strategically use these applications across diferent projects and leverage their integration into core business systems. As a result, the investment in new technology does not generate the expected impact, and in many cases, the cost of managing this portfolio of applications exceeds the value delivered to project teams and the organization.

Opportunities Moving to Integration, More Requirements The transition of the AEC industry towards more integrated forms of project delivery will allow organizations and stakeholders to see processes holistically and realize the need for technology solutions required to ft them. As organizations in the AEC industry integrate, the need for IT interoperability will increase, and frms will be able to better articulate their requirements for technology. Moreover, organizations are made up of people who truly form the core of the Lean philosophy. Any digital transformation within the organization should start from the people. This is what the eighth principle of the Toyota way clearly states. Technologies cannot solve a system’s problems without the readiness of the people. Thus, technological enablers of IPD should be developed by the people and with the people in mind. It is already seen as a trend in the IT industry that larger system providers are acquiring smaller companies and startups to ofer a more comprehensive portfolio with better mechanisms for interoperability (Blanco et al., 2018). For instance, Autodesk acquired BuildingConnected and PlanGrid to expand their IT solutions ofering into bidding, risk management, and construction productivity processes (Konrad, 2018). In the past decades, software providers have found consensus in adopting international interoperability standards. The National BIM Standard (NBIMS) and BuildingSMART international (bSI) have been working to unify diferent data models in the industry (Sacks et al., 2018). BuildingSMART has developed an Information Delivery Manual (IMD) to facilitate the building information fow during the project life cycle (BuildingSMART, 2021a). Moreover, many software providers are using IFC (ISO, 2018) standards to enable interfaces with diferent use cases and promote openBIM (BuildingSMART, 2021b, 2021c). Eforts also aim for integration among diferent phases of the project lifecycle. For example, Construction Operations Building Information Exchange (COBie) is an international standard to facilitate facility management information transfer between construction and operations (East, 2007; Lavy & Jawadekar, 2014). There are also several opportunities with cloud computing. Cloud computing technology ofers afordable and scalable access to computing facilities because it avoids acquiring, storing, and maintaining them (Voas & Zhang, 2009). Cloud computing also ofers an 281

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opportunity to improve collaboration in the industry (Almaatouk et al., 2016; Bello et al., 2021), especially considering its potential to be integrated with BIM (Wong et al., 2014). There is a signifcant interest in using cloud computing in the construction area. However, several challenges related to trust, data privacy, governance, and availability remain issues to deal with (García de Soto et al., 2020; Mantha & García de Soto, 2021).

BIM- Buildings as Products The AEC industry usually looks into manufacturing practices as it has fewer players and is less fragmented. In manufacturing, the design, production, warehousing, logistics, and distribution (in some cases, even sales) activities are under only a few players. This facilitates the transfer of knowledge and use of technology among the diferent phases of product development using product lifecycle management (PLM) systems. In manufacturing, detailed product drawings are linked to the ERP (or PLM) system (e.g., Bill of Materials [BOM]), and from this point, the entire value chain is activated. As per the authors’ experience in new product development, it is nearly impossible to move forward in the product development process (project delivery equivalent in the AEC industry) without having a detailed BOM activating all related supply chain activities. Process-wise, the integration in manufacturing allows organizations to defne quality gates along the product lifecycle that ensures the quality of the product and the alignment of diferent stakeholders earlier in the development process (Pawar et al., 1994). Without given established preconditions, it is simply unfeasible to run the business. Conversely, on top of fragmentation, the building facility management in the AEC industry is far from the process quality standards used in manufacturing. In this industry, it is common that incomplete designs go from one phase to the other in the project lifecycle, eventually ending with a hardly constructible and inefcient design at the construction site. BIM and PLM have a signifcant common ground (Sacks et al., 2018), and their synergies ofer several opportunities to derive a technology ecosystem able to support the integration of the diferent activities and stakeholders along the project lifecycle (Aram & Eastman, 2013). Increasing the quality of the building product and bringing a higher level of detail at the front end of the project delivery process will certainly contribute to the efciency of the project lifecycle. Managing buildings like products and having a BIM model as a single source of truth could serve as a baseline to defne automated workfows around the BIM model that supports communication and information exchange among stakeholders involved in the project lifecycle. However, while BIM has been an enabler for many technologies, the scope of digitalization is much wider than BIM. That is why interoperability of the applications around this one source of truth plays a vital role in fully realizing the potential of existing and emerging technologies.

Low Code/Citizen Development The organizational and technical discussions about the adoption and widespread use of IT in the AEC industry should not underemphasize the human factor. IT solutions act as an intermediary between processes, information and people. Thus, computing/IT literacy of people interacting and providing inputs to any IT solutions is key for leveraging technology in the AEC industry (Love et al., 2001; Peansupap & Walker, 2006). In modern times, nearly all people do some sort of computing. The irruption of mobile devices has exposed the population to diferent types of digital communication, infuencing 282

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the level of IT profciency and willingness that people have to interact with new technologies. The natural implication of this to the AEC industry is the insertion of a new generation of people at all levels with a broader understanding of digital solutions. Technological advancements allow people to build their own IT applications without advanced programming or coding skills. Applications, such as Power Apps, Power Automate, or Power BI, ofered as part of the Microsoft 365 package, allow individuals (the “citizen developer”) to drag-and-drop application components, connect diferent sets of data, and generate new customized web or mobile applications (Everhard, 2019). What are the implications for the AEC industry? There is no signifcant literature analyzing how the new “low-code” movement can facilitate the digitalization of the AEC industry. The authors are currently working on two case studies aiming to generate insights for further research. Preliminary observations reveal that (1) the availability of such platforms enables rapid generation of digital tools for very specifc tailored problems with low eforts. (2) Regularly, in a normal technology development, there is a coordinator between the enduser (the one facing an operation issue or client) and the IT expert (with programming skills, owning the code) translating the requirements. With this new setup, the “citizen developer” can play both roles, easing the development of the IT solution. (3) The web or mobile application derived from such an approach is already integrated into the existing organization’s IT systems. This ensures interoperability of the application with the technology ecosystem existing in an organization. Further research is required to analyze the impact of no-code/ low-code platforms in the AEC digital transformation.

Conclusions The transition of the AEC industry towards more integrated forms of project delivery will require IT that facilitates communication and information exchange among stakeholders along the project lifecycle. However, to fully realize the benefts of IT in the context of IPD, several points should be considered. First, the sole implementation of IT will not solve the organizational challenges that the AEC industry faces. The transition towards IPD will trigger organizational and operational changes that will derive additional requirements for the AEC industry IT ecosystem. As the AEC industry integrates, stakeholders in the industry will better articulate the requirements that will improve the interoperability of the fragmented AEC IT ecosystem. IT interoperability is paramount to facilitating AEC industry integration. Second, the AEC industry should Lean toward strategic thinking when materializing investments in IT. On top of using IT to address specifc operational challenges, AEC frms should also look broader to leverage the use of technology considering the whole organization and project lifecycle. The nature of processes, as well as people aspects, should also be considered in the equation. Eventually, IT acts as an intermediary and aims to serve people and processes. Third, from the perspective of scientifc studies, IT research should transition to more empirical-based approaches that allow conceptual work to be tested and validated onsite. Rather than developing as an independent feld, AEC IT research may beneft from a more multidisciplinary approach that considers the organizational aspects and singularities of the AEC industry. This is an interesting topic ofering signifcant opportunities for future research. Organizations and IT are closely connected, and the design of both requires a well understanding of their interrelationships (Schwarzer et al., 1995). However, a deep understanding of the details of those relationships in the AEC is still missing. 283

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BIM and cloud computing have become fundamental platforms that will enable AEC industry integration. Taking a look into manufacturing, this trend has several similarities with PLM. Considering this, it is reasonable to expect that a signifcant part of the AEC IT ecosystem will evolve around BIM. That means connecting and exchanging information with the BIM model via automated workfows facilitating communication and information exchange among stakeholders. BIM is a practical proof of how digital systems can serve as an enabler of IPD. As one of the main pillars of Lean and IPD are information sharing, collaboration, and communication, digital technologies that enable these pillars should be utilized at a higher pace within the AEC industry. This chapter highlighted several technologies that enable these aspects of Lean and IPD. In addition to BIM, game engine-based digital platforms provide virtual worlds where project stakeholders can collaborate while visualizing important project elements. Blockchain technology is strongly aiding the development of reliable information fows within project stakeholders. This blockchain-based fow of information enables the use of IPD through the automation of incentives and reward sharing. Thus, each aspect of IPD is being enabled through digital technologies. Another opportunity is related to the human factor. Today, nearly everybody interacts with technology. This implies that the AEC industry will progressively receive a new generation of people with a broader understanding of digital solutions. Furthermore, the irruption of technologies that allow people to build their own IT applications (Everhard, 2019) will impact technology use in the AEC industry and open opportunities for further research.

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18 BLOCKCHAIN GOVERNANCE FOR INTEGRATED PROJECT DELIVERY 4.0 Daniel M. Hall, Jens Hunhevicz, and Marcella M. M. Bonanomi

Introduction Construction projects are highly complex (Bertelsen, 2003). Traditional project delivery models that use a centralized organizational strategy often struggle to manage this complexity (Levitt, 2011). In response, the Lean Construction community has developed the project delivery model known as Lean Integrated Project Delivery (Lean IPD). Lean IPD uses a new organizational structure for more decentralized decision-making, a new operating system to improve handofs and work commitments, and new commercial terms based on the principles of relational contracting (Mesa et al., 2016, 2019). The Lean IPD model has seen increased adoption across the industry (Hall & Scott, 2019). However, the construction industry is now entering an era of Industry 4.0 and the emergence of new technologies, such as Digital Twins (see Chapter 2 and 14), Internet-of-Things (Chapter 2), artifcial intelligence (see Chapter 5, 8, and 14), and virtual reality (see Chapter 11), ofers new opportunities. To date, these technologies focus on improving the operational system to better manage production fow and eliminate ‘waste’. In this chapter, we suggest that Industry 4.0 also simultaneously provides the opportunity to rethink the organizational structure and commercial terms of project delivery. In other words, how might project delivery models transform in an era of Industry 4.0? We suggest that future project delivery models can take the approach of decentralized organizational structures and relational contracting found in Lean IPD and extend it further. For example, future project delivery models could embrace approaches like hive or swarm behavior that have been found to be highly efective in the management of complex production systems (Helbing et al., 2006). Guided self-organization, increased participant fexibility, and well-coordinated system dynamics can lead to better outcomes for complex systems (Helbing & Lämmer, 2008). In this chapter, we propose that blockchain technologies can act as the technological foundation for this new model of project delivery. Blockchain and other distributed ledger technologies provides a distributed peer-to-peer system for value transactions without requiring a central intermediary. Blockchain is more than cryptocurrency transactions; it has inherent afordances that can also be useful to design new crypto-economic incentive systems. Through smart contracts and tokenization, new forms of micro-economic 288

DOI: 10.1201/9781003150930-23

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coordination can challenge existing assumptions around value and the nature of commercial terms. We call our future vision Integrated Project Delivery 4.0 (IPD 4.0). IPD 4.0 is a project delivery system coordinated through blockchain technologies. While it should be noted that our thinking around IPD 4.0 remains early and underdeveloped, we attempt with this chapter to provide a conceptual and theoretical foundation for how IPD 4.0 might develop. To do so, we frst review the limitations of the organizational structure, operational system, and commercial terms of the traditional project delivery system. We explain the problem of complexity, and how Lean IPD has made the frst steps toward decentralization. Next, we introduce the fundamentals of blockchain technologies and describe how the use of smart contracts and tokens can increase automation, transparency, and alignment towards overall project success. We then describe the key conceptual building blocks of IPD 4.0. This includes our vision for a collective organizational model based upon the crypto commons, an operating system built upon a value-based theory of production, and a commercial system that emphasizes micro-exchanges. We summarize the key diferences between traditional project delivery systems, Lean IPD, and the proposed IPD 4.0. Next, we identify early research eforts and implementations that give tangible examples of how these ideas can be applied. Finally, we conclude with a discussion of the benefts and implications of the proposed IPD 4.0 model and the directions for future research.

Teoretical Underpinning A project delivery system can be summarized by three distinguishing characteristics: the organizational structure, the operational system, and the commercial terms (Thomsen etal., 2009). The organizational structure defnes the roles and relationships between the participants. The operational system describes the timing and sequence of events and practices and techniques of management. The commercial terms defne the legal responsibilities for defning, designing and constructing a project (Mesa et al., 2016, 2019).

Traditional Project Delivery Systems Traditional project delivery systems use a ‘command-and-control’ organizational structure, an activity-based operational system, and a series of transactional contracts (Alarcon et al., 2013). Command-and-control organization assumes that planners can develop detailed plans and performance targets that are feasible to implement and will remain valid for the entire execution of the project (Levitt, 2011). However, the nature of complex construction projects is such that change is inevitable; one or more key assumptions in the plan are likely to become invalid over time. When this occurs, the ‘validity of the baseline plan – even if it was developed by experts with a great deal of execution experience – immediately begins to erode’ (Levitt, 2011). A detailed plan that is constantly changing becomes ‘a virtual ball and chain around the legs of people trying to get the project completed’ (Levitt, 2011). Traditional project delivery systems use an operating system that comes from a transformation view of production. The transformation view of production has dominated construction for a major part of the 20th century (Koskela, 2000). In the transformation view, production is viewed as a transformation of inputs to outputs. Production management requires decomposing the overall transformation into smaller transformations and tasks, and 289

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then carrying out these tasks as efciently as possible (Bertelsen & Koskela, 2002). This is typically achieved using the critical path method (CPM) – regarded as the most important innovation in construction management in the 20th century (Koskela et al., 2014). However, the CPM can struggle to deal with the complexity and ever-changing nature of modern construction projects (Dallasega et al., 2020). Traditional project delivery systems use transactional commercial terms. Transactional contracts emphasize ‘one-of’ exchanges between two parties. In practice, this is achieved through the process of a low-bid tender process. Contracts are written with the assumption of a singular exchange and that can clearly defne the entire scope of work (Henisz et al., 2012). However, there are several challenges to transaction contracting for the delivery of complex projects. At each stage in the project life cycle (e.g., design, construction, and operations), multiple stakeholders can have diferent sub goals (Henisz et al., 2012). For example, designers have little incentive to reduce the life cycle costs of facility management and facility managers have a relatively weak voice during the design phase. Transaction cost economics suggests that the cost of writing general contracts and pursuing third-party intervention (e.g., arbitration, litigation) for contractual disputes can be prohibitive due to the nature of infrequent and highly idiosyncratic transactions (Williamson, 1979). This is especially true for construction projects because they involve shifting counterparties sequenced over multiple phases (Henisz et al., 2012).

Te Problem of Complexity While traditional project delivery methods can be adequate for simple and repetitive projects, the traditional approach struggles to deal with the problem of complexity. Bertelsen frst suggested that construction projects can be understood as complex systems due to the presence of autonomous agents, undefned values, and non-linearity (Bertelsen, 2003; Son et al., 2015). Construction projects are characterized by many mutually interacting parts (Corrado, 2019). Complexity arises when dependencies among the subsystem behaviors become important to the objective or function of the system (Miller & Page, 2009). Complex systems have very diferent characteristics from other systems, such as emergence, nonlinearity, decentralization, and adaptation (Son et al., 2015). System-level characteristics cannot be understood as a simple sum of subsystem behaviors. Instead, the emergent properties of the system are infuenced by the interactions and behaviors that occur between the sub-elements (Bar-Yam, 2004). The governance and management of such complex systems is difcult. Complex systems do not behave linearly. The system does not always do what is desired. The proportional efect of a single change in production or management is difcult to predict as it propagates across the system. Small subsystem interventions might cause a large-scale change in system behavior, while greater intervention eforts might remain useless (Helbing & Lämmer, 2008). In such settings, classic managerial strategies such as the structured hierarchical control used by traditional project delivery are likely to fail. Highly centralized and controlled systems can become unstable in the face of complexity, and skilled, well-informed and well-intentioned system managers can lose control (Helbing, 2013).

Lean Integrated Project Delivery Lean IPD has emerged over the past 20 years as an alternative to traditional forms of contracting, design and supply chain management (Hall & Scott, 2019; Mesa et al., 2019). The 290

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Lean approach has long emphasized decentralization as part of its approach to deal with the challenge of complexity in construction projects (Bertelsen & Koskela, 2004). A full description of Lean IPD goes beyond the scope of this chapter (readers are referred to Mesa et al. (2019) for a more detailed description). Instead, a short summary is provided to describe how Lean IPD departs from traditional project delivery systems with regard to organizational structure, operating system, and commercial terms. Lean IPD uses a decentralized organizational structure composed of interorganizational teams-ofteams (Bygballe et al., 2014). Projects can create an inter-frm project board composed of whom the project frms collectively feel are the most important people (Hall et al., 2018). The organizational structure emphasizes joint project control to encourage collaborative decision-making, team buy-in and shared responsibility for innovation decisions (Hall et al., 2018) and this is reinforced by a co-located, shared workspace (Kokkonen & Vaagaasar, 2018). Lean IPD also uses early involvement of key participants allowing contractors to contribute construction knowledge and experience to design (Bygballe et al., 2014; Papadonikolaki, 2018). Lean IPD uses an operating system based upon principles of Lean Production. Specifcally, Lean IPD emphasizes a fow-based theory of production as opposed to the transformation theory of production (Koskela, 2000; Mesa et al., 2019). Pull techniques govern the fow of materials and information through networks of collaborating trade contractors and specialists. Optimization eforts do not focus on improving productivity, but instead making workfow more reliable and eliminating bottlenecks from the production system. Feedback loops enable learning and rapid system adjustment and decisions about planning are intended to be bottom-up from the so-called Last Planner (Ballard, 2000). Overall, the operating system emphasizes clear handofs and workfow leveling. Lean IPD uses commercial terms based on relational contracts instead of transactional contracts. Relational contracts are long-term agreements based on substantial mutual commitment, extensive cooperation, and trusted communication (Williamson, 1979). Relational contracting is well-suited for construction (Henisz et al., 2012) because highly interdependent but diverse counterparties engage in multiple sequential and complex transactions (Argyres & Liebeskind, 1999). Using this approach, construction managers pursue modifed cost-minimization approach that balances the governance of an individual transaction with that of transaction’s contractual hazards (Henisz et al., 2012). In practice, this is done using multi-party, incentivized contracts such as the Integrated Form of Agreement (IFOA) (Lichtig, 2010). Without a contractual hierarchy, IPD uses ‘pluralistic coordination to align decisions and actions towards an established direction’ (Tillmann et al., 2014). Project clients, contractors, and planners collaborate with one another on equal standing and a shared destiny dependent on the overall success of the project. Put in another way, the Lean IPD model creates a shared fnancial resource pool, shared decision rights, and shared risk and reward for the project outcomes. The project begins to resemble a common-pool resource scenario (Hall & Bonanomi, 2021) with a pooled project budget available to all signatory parties (Thomsen et al., 2009).

Blockchain Technology Historically, transactions of value have been facilitated by trusted private or institutional intermediaries. The recent emergence of blockchain technology removes the need for these intermediaries while still allowing for secure and direct value transactions between actors in a network. 291

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To do this, a copy of transactions (called a ledger) is distributed across many networked computers. The ledger is fully transparent, so everyone can always check and compare different versions for potentially malicious transactions. This is possible because the transactions are stored in a sequential chain of timestamped and cryptographically connected blocks (hence the name blockchain). As soon as new transactions are appended, it is not possible to change past transactions without causing a change in the signature of newer transactions. The encoded consensus rules of the blockchain defne how users agree and add new transactions. These consensus mechanisms are the main innovation of blockchain technology. A well-designed incentive system ensures that it is more proftable to secure the chain rather than to attack it. The most famous consensus mechanism comes from the frst ever blockchain Bitcoin (Nakamoto, 2008) and is called proof-of work (Gervais et al., 2016). For further details, readers are referred to various taxonomies of the distributed ledger technology (DLT) landscape (Ballandies et al., 2021; Tasca & Tessone, 2019). Furthermore, Hunhevicz and Hall (2020) provide information on how these technical characteristics of blockchain enable various use cases in a construction industry context. Since the creation of Bitcoin, many new blockchains have been developed to extend use cases beyond transacting cryptocurrency. The Ethereum blockchain (Buterin, 2014) made it possible for the frst time that Turing-complete code pieces termed ‘smart contracts’ could be executed on a blockchain. Smart contracts allow for the coding of interaction rules with blockchain transactions. This enables transaction workfows and custom containers of value (i.e., tokens). Tokens can then be transferred easily among users of a blockchain. Subsequently, the second big wave of innovation was triggered in the blockchain space predominantly with countless decentralized fnance applications (Schär, 2020). However, the long-term promise of blockchain lies in new economic organization and governance, potentially disrupting or substituting existing forms of coordination (Davidson et al., 2018; Miscione et al., 2019). Blockchain creates ecosystems where the benefts from network efects and shared digital infrastructure do not come at the cost of increased market power and data access by platform operators (Catalini & Gans, 2020). On the one hand, smart contracts can encode coordination rules for digital workfows to coordinate global economic activity of actors in a decentralized way. On the other hand, tokens can incentivize actors within the created economic system towards intended behavior at the individual level. For the coordination of complex construction projects, there is a strong ft between the nature of blockchain and the problems arising from complex systems and misaligned incentives. Construction research has started to investigate blockchain for suited application areas. The most prominent use cases include tracking and securing data in construction processes and the supply chain, as well as improving the fnancial processes with more transparent and automatized payment logic (Hunhevicz & Hall, 2020; Li et al., 2019; Li & Kassem, 2021; Perera et al., 2020; Scott et al., 2021). But while many of these use cases apply blockchain to improve existing construction processes, there is a bigger opportunity to leverage blockchain for novel forms of economic coordination in construction (Hunhevicz, Dounas et al., 2022). Hunhevicz, Dounas et al. (2022) outline these new possibilities of collaboration within and across the built asset life cycle phases by describing the connection of blockchain governance with characteristics of the architecture, engineering, and construction sector. Blockchain-based governance for construction can enable novel decentralized incentive and market structures towards decentralized coordination. Individuals and communities of practice can contribute to value creation in the built environment without formal afliation to 292

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a centralized project organization or frm. There is potential for blockchain as an inherently decentralized technology to embrace the loosely coupled characteristics of the construction industry (Hunhevicz, Dounas et al., 2022).

Towards Integrated Project Delivery 4.0 In this section, we propose how blockchain technologies can act as the foundation for a new project delivery system called Integrated Project Delivery 4.0 (IPD 4.0). Although existing studies have replicated and extended the collective risk/reward sharing mechanisms of integrated project delivery systems (Elghaish et al., 2020; Rahimian et al., 2021), we suggest this can be extended further to a fundamental rethinking of project delivery systems. In the spirit of a radical rethinking for the possible governance, we propose three conceptual principles to act as the foundation of IPD 4.0: a collective organizational structure, a value-based operating system, and micro-contractual commercial terms.

Collective Organization: Te Crypto Commons The proposed IPD 4.0 begins with a novel organizational structure known as a decentralized autonomous organization (DAO). A DAO is a blockchain-powered organization that can run on its own without any central authority (Wang et al., 2019). The management and operational rules of a DAO are solely governed by the rules embedded using smart contracts (Hassan & De Filippi, 2021). Participants in a DAO are responsible to defne governance mechanisms using smart contracts. In this way, the DAO can self-operate, self-govern, and self-evolve (Wang et al., 2019). Basic implementation of a DAO requires that stakeholders organize and develop rules around a treasury, which is then controlled by stakeholders. IPD 4.0 will use this concept for construction project funds. The project begins with an escrow fund controlled by a project DAO. Such escrow funds could come from a lump sum provided by a single owner, or collective funds from a community of interested stakeholders. The rules by which participants can withdraw funds from this escrow would be determined by the governance rules, including blockchain-based tokens to enforce voting rights. In IPD 4.0, the participants in the DAO collaborate and coordinate for the delivery of the fnal project according to the scope, time and budget constraints. Like the shared risk and reward terms of Lean IPD, the DAO governance rules can be encoded to allocate project rewards proportional to the overall success of the project. The project escrow governed by the DAO represents a common-pool fund. The project resources become contractually available for free use by any token holders. The project participants must develop collective governance structures to deal with allocation of these resources throughout the project. Therefore, it will be the job of project participants to self-organize their own efective governance structures to help protect the project escrow from becoming ‘overdrawn’. To do this, governance structures should be based on the principles for governance of common pool resources frst proposed by Nobel-Prize winner Elinor Ostrom (2015). To be specifc, IPD 4.0 teams will need to create principles that: 1 2 3

Defne clear boundaries for project participants. Match rules governing the use of project funds to the local project needs and conditions. Ensure that those participating in the DAO can participate in modifying the rules. 293

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4 5 6 7 8

Make sure that rule-making rights of community members are respected by outside authorities. Develop a system for monitoring the project members behavior. Use graduated sanctions for those who break the rules or do not perform. Provide accessible, low-cost means for dispute resolution. Build responsibility for governing the project in nested tiers, from the lowest levels up to the entire interconnected system (Ostrom, 2015).

Scholars suggest that blockchain is a key enabling technology to scale real-world commons (Bollier, 2015; Fritsch et al., 2021). The Ostrom principles act as guidelines to build such applications of real-world commons by encoding the respective governance rules with smart contracts (Hunhevicz, Brasey et al., 2022; Rozas et al., 2021). This connection was established because blockchains themselves can be described as a digital common pool resource. Actors in the network are motivated to contribute to the system through a bottom-up incentive system grounded in digital tokens that have perceived values to users, enabling peerproduction of blockchains without any centralized coordinator (Red, 2019). Therefore, Ostrom’s design principles act as a theoretical starting point to conceptualize the governance structures of IPD 4.0 on the crypto commons. Following these principles, project participants can program specifc governance structures and practices of the project delivery system on the blockchain (Hunhevicz, Brasey et al., 2022). The adoption of blockchain-based transparent decision-making procedures and decentralized incentive systems for community governance in commons could help avoid the tragedy of the commons (Bollier, 2015), or in this case the tragedy of the project where project participants overdraw resources from the common pool (Hall & Bonanomi, 2021). Blockchain can help create networked project governance to scale project delivery commons, similar to how the stock market enabled corporations to scale (Maples, 2018).

Operating System: A Value-Based Teory of Production IPD 4.0 uses an operating system that emphasizes a value-based theory of production. Koskela (2000) frst argued for the value-based concept of construction production alongside the theories of transformation and fow. Value-based production difers from the traditional transformation theory of production because: • • •

•

Value generation model considers all activities taking place inside the supplier, while transformation considers just the physical production. Value generation considers the customer, while transformation abstracts this away. Value generation inputs are based solely on customer dependent information and outputs are fulfllment of customer needs, while transformation considers all possible inputs, and the output consists of the products or services. Value generation is not a hierarchical model and not all activities are similar (Koskela, 2000).

Because blockchain can enable self-organizing and interconnected supply chains, the IPD 4.0 operating system can be based on guided self-organization to maximize value for each individual task, product or service. Lean-based approaches such as the Last Planner can still be used, with the addition that each activity can be assigned a token representing the perceived value in the current system. So how can a token represent the value of an activity? 294

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Figure 18.1

Revisiting the black box of value generation (adapted from Koskela, 2000)

Koskela (2000) began this in his exploration of the black box of value generation (see Figure 18.1). In Koskela’s conceptualization of production value, the customer formulates measurable or quantifable value of the products or services and passes them to the supplier to deliver this value. For construction activities, Koskela (1992) suggests that there are two types of customers, the subsequent activities and the fnal customer. Therefore, for most construction activities, the supplier is the previous activity in the production system and the customer the next activity. This means that value of an activity is not absolute. Instead, an activity’s value is relative to the needs of future project activities, is time dependent, and ever changing based on the current status of the project. Therefore, participants of subsequent production steps or the fnal customer could assign tokenized value for project activities relative to their perceived value at a given time step. As a simple example, consider a hypothetical trade-of between the delivery of prefabricated concrete columns and the delivery of a pump system, when there is only laydown space for one of the deliveries. How should the project team decide which activity will be postponed? In an IPD 4.0 operating system, each delivery could be ascribed value tokens on the blockchain by afected project participants. In case the delivery of the prefabricated concrete columns would provide a higher value than the pump system, this activity will be done. In the simple example above, such a decision is obvious, but the power comes when the value of each activity is tokenized across the project. A value-based production system will incentivize the production of certain activities based on their relative value to all future activities in the project. Once value is clear, the participants in the system should in theory self-optimize to maximize their own rewards. Instead of the requirement of top-down controlling agents (e.g., managers), self-organizing agents (e.g., workers) can perform tasks with increased fexibility following simple rules. These decentralized interactions will appear closer to collective or swarm intelligence than a well-structured production approach. However, the system can also be designed for guided self-organization keeping project-level objectives in mind while allowing the system to self-optimize around individual agents. The challenge is that setting up these rules towards guided self-organization requires a good understanding of the complex system. Only a slight modifcation of the interaction rules of a complex system can have favorable or undesirable results towards the overall system state (Helbing, 2014). Nevertheless, decentralized and non-hierarchical management approaches using principles of guided self-organization can be successfully implemented if several conditions are fulflled (Helbing, 2014). For example, the use of self-organizing signal 295

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control adapting to local trafc demand performs better than pre-determined trafc light schedules that attempt to enforce prescribed controls (Kesting et al., 2008). Decentralized control of material fows for continuous conveyer systems that optimize local trafc fows performs better at the system level then centralized controlled systems (Gue et al., 2014; Mayer & Furmans, 2010). In general, guided self-organization inspired by biological approaches is seen as very promising in logistics and supply networks to cope with nonlinearity and complexity.

Commercial Terms: Micro-Exchanges The commercial terms of IPD 4.0 will be based on smart contracts that enable microexchanges. From a micro-economic perspective, blockchain enables a fundamental shift in the distribution of rewards within an organization and the structure of that organization’s transaction costs ( Jacobo-Romero & Freitas, 2021). Smart contracts and new forms of reward distribution can therefore promote a bottom-up model of economic organization ( Jacobo-Romero & Freitas, 2021). Project frms no longer will need to sign a single contract intended to cover the entire scope and duration of the project. Instead, commercial terms will be characterized by repeated and frequent micro-exchanges. These micro-exchanges can be transactional or relational in nature. Transactional micro-exchanges will transact funds from the DAO escrow for project tasks, such as design activities, production activities, or information sharing. The value of each transaction has already been tokenized, so the smart contract exchange simply confrms the completion of the activity and transfers the appropriate value. By focusing commercial terms on single exchanges with high asset specifcity, projects can avoid the uncertainty costs priced into the delivery of complex projects. To use an example, let us consider the design of a structural steel system. In traditional project delivery systems, the structural engineering frm would receive a contract to complete the overall system design, to complete detailing for each of the structural steel connections, and to approve the shop drawings of the beams proposed for production. In a micro-exchange environment, a series of smart contracts could be encoded to reward value for the design, detailing, and approval of the structural steel system. First, the contract could reward a systems-level design that ensures optimization of the whole system and not just the parts. Next, the work is parsed into specifc, smaller tasks (e.g., detailing of the connections). A micro-exchange rewards the successful completion of each design and approval activity as they occur. As deadlines near, the relative value of completing certain tasks increases. Therefore, project participants are incentivized to “swarm” their time and attention on the most valuable tasks in order to maximize rewards. When these tasks are confrmed as complete, it triggers automated micro-payment from the DAO escrow. Micro-exchanges can also work on the level of relational contracting. Micro-relational exchanges can make sense if micro-transactional exchange is not possible. For example, if a task involves multiple actors and it is hard to defne clearly the scope of actors and the value of each activity because there is high interdependence and reciprocity. Relational contracts could be formalized into smart contracts, creating shared sub-reward pools for parts of the project. There could be multiple sequential or parallel relational contracts within one construction project. As another example, micro-relational contracts could self-adjust the risk/reward distribution based with token-based peer review mechanisms. For example, reputation tokens 296

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could be issued to stakeholders according to the value contributed and the alignment with the overall perception of value of the community (Pazaitis et al., 2017). The fnal distribution of the reward pool depends on the fnal relative share of reputation tokens of the contractual parties. The Covee protocol is an example that sets up such a peer-review mechanism to determine the fnal rewards of anonymous contributors of a project, but with a score and not reward tokens (Dietsch et al., 2018). In a similar manner, reward tokens could be distributed on a regular basis when intermediate work packages are delivered, adapting continuously the reward structure of the relational contracting parties according to the relative share of reward tokens.

Comparison of Project Delivery Systems and Support of Lean Principles Table 18.1 Comparison of the three project delivery systems Traditional project management Organizational structure Operating system Commercial terms

Command-and-control (Hierarchy) Emphasis on transformation Transactional

Lean IPD

IPD 4.0

Decentralized (Teams-of-teams) Emphasis on fow

Collective(Commons)

Relational

Micro-exchanges (Transactional & relational)

Emphasis on value

Organizational Structure Traditional project delivery organizational structures have overestimated the ability of centralized command and concentrated decision making to control complex projects. As the advanced technologies described in the other chapters of this book bring additional data and information systems to manage, the complexity of project systems will only increase. Complexity science suggests the need for a fundamental redesign of organizational structures that use decentralization and distribution to better deal with these situations. Decentralization is the dispersion of organizational communication while distribution is the dispersion of decision-making (Vergne, 2020). Lean IPD can be considered a decentralized-concentrated organizational form. This organizational form emphasizes problem-solving teams, incentive pay, fexible job design, and information sharing among workers (Mookherjee, 2006). The project sub-teams are tasked with reaching consensus and recommending a course of action, which the project management team can, based on extant knowledge, either accept or reject (Vergne, 2020). However, Lean IPD only includes partial distribution of decision making; decision-making power and autonomy are assigned to sub teams to a certain point (Levitt, 2011), but still require consensus of the project management team or sometimes a senior management team to make fnal decisions (see Figure 18.2). By contrast, the collective structure of IPD 4.0 on the crypto commons takes both a decentralized and distributed organizational form (Vergne, 2020). To be able to make decisions without formally assigning decision-making authority to higher-ranked members, IPD 4.0 must defne a non-hierarchical protocol for its members to reach consensus, which we suggest should be based on the Ostrom principles. In this organizational form, trust is both distributed (i.e., any member can be a decision-maker) and decentralized (i.e., every member 297

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Figure 18.2 Organizational structure of the three project delivery models (from L to R: Traditional, Lean IPD, and IPD 4.0) (adapted from Baran, 1964)

has equal access to data and information) (Vergne, 2020). However, this organizational form comes with challenges. It can be difcult to ban specifc members or censor transactions since no one entity holds the formal authority to do so. Complexity theory suggests distributed networks, what we call the collective commons, ofer an advantage over a concentrated teams-of-teams decision making approach. Distributed networks are more robust. There is redundancy in the network; if someone cannot deliver or decide, the mechanisms are in place so that someone else can immediately help. The project delivery team can be scaled across many individual actors and behave more like self-organizing hive structures than a collaboration of frms. In addition, project contributors could remain anonymous, conduct remote work, or only do small tasks for a short time.

Value-Based Operating System When Koskela (1992) introduced three views of construction production, he argued that traditional project management was overly concerned with the transformation model of production. He conceptualized a new production philosophy for construction, based on two additional theories for production: fow and value. The Lean Construction research community has made great advances in theory and in practice for fow-based production. The Lean Construction techniques that are most widely applied (e.g., Last Planner System) or that are rapidly emerging (e.g., Takt Planning) emphasize fow by reducing variability, reduce cycle times, and increase process transparency. The legacy of the past three decades of Lean Construction research has been an understanding of production fow on the construction site. However, an argument can be made that the current approach overemphasizes the fowbased operating system. Concept and practices for value are present (e.g., Target Value Design) but these are applied in service of production fow. Discussions around value, such as reducing the share of non-value adding activities (Koskela, 1992), are done in order to improve the production fow. Other valuation practices such as Target Value Design are applied 298

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at a high level (e.g., the overall project cost), but little is known about the specifc valuation of production activities. The new operating system of IPD 4.0 returns to Koskela’s assertion that concepts of transformation and fow work in service of value. A value-based production does not exist in isolation. Theories of production based on fow and transformation will still exist but will be re-focused on control of the transformation and the fow for the sake of the customer (Koskela, 2000). However, IPD 4.0 introduces a major shift through value tokens and smart contracts that will incentivize the behavior of the operating system to more closely resemble a guided self-optimizing system (e.g., a swarm or hive) instead of a production line (e.g., train or parade of trades). Because value is only created by fulflling customer requirements, not as an inherent merit of conversion (Koskela, 1992) nor an inherent merit of maintaining steady workfow, the creation and management of value will receive new attention in IPD 4.0. Since blockchain is transparent, all actors can see the valuation of activities, as well as who contributed how much to value generation. This could lead to more open discussions about what is value to a project, how does value change over time, and how can value be maximized.

Commercial Terms Traditional construction procurement using a transactional contract often fails because there is low asset specifcity. At the time of tender, project documentation is often not complete, and it is hard to identify the ‘unknown unknowns’ facing the project team. Relational contracts create long-term agreements built upon mutual commitment, extensive cooperation, and trusted communication. However, from the perspective of market economics, relational contracts are not as efcient as purely transactional contracts with high asset specifcity. The proposed commercial terms of IPD 4.0 imagines a compromise between relational and transactional principles. Using smart contracts, transactions will only occur for specifc activities (e.g., design this structural steel connection in exchange for this fnancial reward). However, not all transactions need to be monetary. The use of reputation tokens (e.g., for conducting a peer review of the structural connection) can help reward and recognize trusted participants and project leaders, potentially determining also the individual share of risk and reward defned in relational contracts.

How Do We Apply Tis? Although the above is highly conceptual and theoretical, it should be noted that several research eforts are already underway that align with our vision for IPD 4.0. For collective organizational structures, Hunhevicz, Brasey et al. (2022) outline opportunities of blockchain governance mechanisms for IPD based on the Ostrom principles. The research identifes 14 potential blockchain mechanisms to support the concept of a crypto commons of project delivery, and 22 specifc ways to apply these to a blockchain-based digital governance of IPDs. Together they can help to create blockchain governance building blocks to manage IPD construction projects in a decentralized way on the ‘crypto commons’. For example, blockchain can be used to defne boundaries within IPDs through access-rights for the users and resources with blockchain addresses and tokens. Since blockchain is inherently transparent, actions can be easily monitored. The system can be designed to incentive trusted behavior in line with the community defned goals. The project participants can develop decentralized proposal and voting platforms to ensure scalable and 299

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inclusive decision making about the project’s rules and values. These rules and values can be then formalized with smart contracts and incentivized through new token-based systems, either representing fnancial rewards or reputation rewards. In addition, blockchain can facilitate the use of graduated sanctions or decentralized jurisdiction systems to collectively resolve conficts fast and locally. There are other eforts to create DAOs in the built environment. For example, Dounas etal. (2020) have prototyped a DAO for decentralized architectural design in consideration of blockchain mechanisms and the design processes. For value-based operating systems, Kifokeris and Koch (2021) developed a proof-ofconcept blockchain application for construction logistics. Elghaish et al. (2020) created an automated fnancial system with a methodology to enhance fnancial transaction management and risk/reward sharing practices in IPD. Yang et al. (2020) developed a blockchain application framework for business processes and information integration among multiple stakeholders for two cases: the design of an external cladding system and the engineering, procurement, and construction management of a large distillation tower. Hunhevicz et al. (2020) prototyped a crypto-economic incentive system for data sets that included a value for providing data and a value for checking that the data is correct. Tezel et al. (2021) have explored project bank accounts for payments, reverse auction-based tendering for bidding, and asset tokenization for project fnancing. The Construction Blockchain Consortium is currently working to create several white papers that consider how value, blockchain, and construction technology can work together (e.g., Campbell-Turner et al., 2020). For commercial terms, there are several emerging examples of smart contracts that transact based on the specifc completion of small pieces of work. Lee et al. (2021) integrated smart contracts with a digital twin of robotic fabrication. The robotic placement of each block, once verifed by the digital twin to be correctly placed, triggers a micro-payment. Hunhevicz et al. (2022) developed a performance-based smart energy contract that took sensor measurements from a digital twin every 15 minutes. If the temperature measurements matched the target performance, incentive payments were made to the contractor and facility manager. Hamledari and Fischer (2021) developed a smart contract-based progress payment system using drones for automated production progress monitoring. However, these eforts are all very early and much more research will be required before IPD 4.0 will be possible.

Implications, Limitations, and Conclusion While there are numerous implications of the IPD 4.0 approach, only three are mentioned here for the sake of brevity. The collective commons, or project delivery as a DAO, has implications for project managers and other decision makers. In the era of Lean IPD, it was argued that a new kind of project manager was needed (Seed, 2014). This ‘project manager 2.0’ was an agile leader who could empower other participants, collaborate across frm boundaries, and make decisions from multiple sources of information (Levitt, 2011). However, in the IPD 4.0 DAO, there is no prescribed hierarchy of decision making. Therefore, future research should identify the skills and competencies required of an IPD 4.0 project manager toinspire collective action for the overall good of the project without relying on formal hierarchy. Lean IPD incentivizes rewards at the frm level. However, the micro-commercial terms of IPD 4.0 are designed to incentivize participants at the individual level. Blockchain can steer the individual human actors in the project delivery system through simple incentives 300

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towards valued contributions. Incentives in a swarm system motivate everyone to maximize their own rewards while also ensuring these rewards align with the overall health of the global outcome (Helbing et al., 2006; Peters et al., 2008). Blockchain-based governance processes can support data-driven bottom-up and collective decision-making through crypto-economic incentive systems. These systems guide individual actors toward behavior that optimizes the overall project (Hunhevicz, Dounas et al., 2022). Here there are major implications regarding professional liability, human resources, incentive design, gig economy, and the long-term future of the frm that require more investigation. Additional research is also needed to understand how to ensure how guided self-optimization retains control on the complete process to optimize the whole system and not just the parts. Finally, blockchain does not discriminate between human and machine participation. Participants only need to own a recognized address on the blockchain. Therefore, each participant in the project could be a machine, another DAO, or a human decision-makers. Therefore, IPD 4.0 ultimately allow for human-machine interaction on equal standing for project coordination. For example, the IPD 4.0 project can open a design competition that accepts submissions from both humans and machine-learning algorithms. Such implications show how the future of artifcial intelligence may be both self-organizing and self-assembling (Risi, 2021). Future research could study how this approach could provide incentives for algorithmic approaches for single functions (e.g., to design a foor plan or automate the creation of a weekly work plan) to be formalized as their own DAO or distributed application and scale across a network of IPD 4.0 projects. To summarize, this chapter is intended as a high-level conceptualization to propose how blockchain technologies can act as the foundation for IPD 4.0. The work is limited in that many of the proposed ideas around IPD 4.0 remain conceptual and therefore somewhat vague. In particular, the ideas for a collective commons organizational structure, a value-based operating system, and micro-exchange commercial terms are meant to provoke discussion. Further research is needed to implement and prototype such systems, in order to refne or overturn the ideas proposed here. The ongoing blockchain research in construction cited above can act as a starting point to build an IPD 4.0 delivery system, and much more research will be needed. Nevertheless, the suggested theoretical and conceptual implications of blockchain on project delivery models can act as a starting point for the future of construction in an era of Industry 4.0.

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19 DECISION MODELS TO SUPPORT THE SELECTION AND IMPLEMENTATION OF LEAN CONSTRUCTION Luis Fernando Alarcón, Keith R. Molenaar, Alfonso Bastías, and Harrison A. Mesa Introduction Lean Construction practices have shown success in terms of overall project performance (Alarcón et al., 2011). When implemented appropriately, project teams and owners can beneft through more collaborative and high performing project teams. However, the implementation of Lean practices can be complex. The fact that Lean practices can be applied in a variety of project delivery methods makes the choice even more complex and uncertain. The use of diferent project delivery systems, contracting strategies, and procurement processes can afect the way that Lean principles correlate to project outcomes. Applied inappropriately, Lean Construction practices will not be efective or could even have detrimental efects. This chapter reviews the application of several modeling approaches that can help project managers apply Lean principles throughout the design and construction process. The chapter begins with a discussion of project delivery systems to set the context for the environment in which Lean practices are applied. Next, it describes a set of key Lean practices that have been proven efective through research and practice. With this context, the chapter categorizes three types of decision support modeling approaches that can help project managers more efectively apply Lean practices. Category 1 focuses on multivariate methods that are based on historical data from past projects and use modeling approaches such as linear regression. Category 2 focuses on simulation models that are based on expert input and use modeling methods such as the virtual design team (VDT). Category 3 focuses on simulation models that are based on a combination of historical project data and expert input and use modeling approaches such as cross impact analysis (CIA) and general performance modeling (GPM). The chapter concludes with the discussion of the application of these decision support approaches, limitations to their usefulness, and future research that will enable even better decisions in the future.

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Project Delivery Strategies and Lean Practices in the Design and Construction Industry Project Delivery Systems A ‘project delivery system defnes the roles and relationships between the participants; the timing and sequence of events and practices and techniques of management; and the contractual responsibilities for defning, designing, and constructing a project’ (Mesa et al., 2016). Various project delivery systems are in use across the design and construction sector (AIA, 2007; ASCE, 2000; Dorsey, 1997; Ireland, 1984; Kenig, 2011). Project managers must apply Lean practices diferently in the diferent project delivery systems. Design-bid-build (DBB), construction management at risk (CMR), and design-build (DB) have historically been the most prevalent decision strategies. Integrated project delivery (IPD) and Lean project delivery (LPD) have evolved and become more commonplace since the early 2000s (El Asmar et al., 2013; Ibrahim et al., 2020; Lichtig, 2006; Mesa et al., 2019). The DBB system separates designer and constructor contracts. The designer contract provides the owner with design documents that can be bid to multiple constructors. The owner then selects the primary constructor through a fxed-price bid to perform the work following the plans and specifcations (Konchar & Sanvido, 1998). Like the DBB delivery system, the CMR delivery system has separate contracts with the designer and the constructor. However, the constructor joins the team before the design is completed and provides preconstruction services. The owner makes the fnal selection of primary members of the project team through qualifcation-based selection. (Konchar & Sanvido, 1998) The DB system provides ‘the owner has one contract with a design-builder, who is a single entity that performs both the design and the construction. Owners can use various procurement and compensation methods with the DB project delivery system’ (Konchar & Sanvido, 1998). The IPD and LPD strategies provide the owner, the designer, and the constructor sign one contract. The owner makes the fnal selection of primary members of the project team through qualifcation-based selection. The basis of reimbursement is a target price in which the owner, the designer, and the constructor collaboratively establish a price for the project and then work together to maximize the value for the owner. (AIA, 2007; El Asmar et al., 2013) A project delivery system is defned by unique characteristics. (Thomsen et al., 2009). The fundamental variants among project delivery systems include the organizational project, contractual relationship, and operational system. The organizational structure establishes how project participants interact with and report to one another. The contractual relationship defnes the contractual responsibilities, risk allocation, compensation method, and the procurement method for selecting participants. The operational system defnes how to manage and execute work over the course of project production (Alarcón et al., 2011; Lichtig, 2005; Mossman et al., 2010; Thomsen et al., 2009).

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Lean Practices Lean practices and behaviors are transversal to any project delivery system. However, they are highlighted or encouraged depending on the selection of the project delivery system because each system has its distinguishing characteristics. For example, in comparison with DBB, IPD/LPD defnes an organization project that encourages early involvement of the constructor and teamwork among the key project participants. Based on their defnition, these characteristics infuence Lean behaviors or can be impacted by Lean practices. The following is a list of Lean principles that apply across diferent delivery methods (Chan etal., 2004; Cheng et al., 2000; Das & Teng, 1998; Meng, 2012; Meng et al., 2011; Mesa et al., 2016, Umstot & Fauchier, 2017; Wood & McDermott, 1999; Xue et al., 2010). • • • • •

• • •

•

• •

Collaboration: refers to an arrangement and behaviors in which two or more parties work together to achieve a defned and common goal. Alignment of interest and objectives: refers to the level of alignment of interest and objectives among the owner, the designer, and the constructor. Gain and pain sharing: defnes the level of sharing of profts or cost savings as well as losses or cost increases among the owner, the designer, and the constructor. Trust: refers to the willingness to rely on the actions of others, to be dependent upon them, and thus be vulnerable to their actions. No-blame culture: refers to the relationship between the owner, the designer, and the general contractor, focusing on identifying and resolving problems instead of judging or allocating blame. Team work: defnes the level of collaboration among the owner, the designer, the general contractor, and subcontractors that allows them better coordination and decision making. Open communication: refers to the open, timely, and appropriate exchange of information, knowledge, and skills among the owner, designer, and constructor. Confict resolution: refers to the use of early warning mechanisms among the owner, the designer, and the general contractor, in order to anticipate and resolve potential problems at the lowest level of management in a timely manner. Continuous improvement: defnes the use of common performance indicators and joint eforts among the owner, the designer, and the general contractor to promote project teamwork by constantly improving and adding value to the project. Team integration: is a concept that has been widely used in alliances as a way to improve collaborative relationships between various organizations. Team cohesion: is dynamic process that is refected in the tendency of a team to stick together and remain united in the pursuit of its goals and objectives despite difculties and set-backs. It is distinguished from group cohesion by the importance of team cohesion within dynamic processes and the pursuit of team goals. Promise-based management: refers to the cultivation and coordination of commitments in a systematic way, but most importantly, the focus on making a good promise. Co-location: defnes the extent to which project participants are located in skillbased functional departments or areas and supervised directly by functional managers or co-located with other skilled specialists in dedicated project teams and with project supervision from a project manager.

The application of these Lean principles across the various delivery methods is complex. Design and construction organizations frequently require managers to make decisions in 308

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situations where they cannot count on precise and complete information, but they need to rigorously justify their choices. Given that the selection and implementation of Lean Construction practices involve a high degree of uncertainty, decision support systems can be useful in determining which Lean principles to apply to a given project and provide insights into how they might infuence project objectives and outcomes.

Decision Models Decision Model Structure and Components The complexity in a decision model was studied by Simon (1960) and Alter (1980). Simon proposed a continuum of decision structure based on the data available and its processing (Figure 19.1) to classify them. He borrowed the term ‘programming’ and ‘nonprogramming’ from computer science to explain the concept of the continuum. This goes from structured to unstructured decision problems. A structured group represents a stable context, recurrent and programmable commonplace, which allows easy access to the information; an unstructured group is a volatile atypical context, unique place, discrete, intuitive, and creative with problematic access to information. A structured problem can be solved easily with, for instance, a structural equation modeling (SEM), but it is not possible for an unstructured problem, where simulation is the best way to provide a reasonable solution. In harmony with this continuum line, Alter (1980) provided a classifcation for decision support systems (DSSs) based on the information available (DDS Type) and its approach (DSS Activity) (Table 19.1). The DSS orientation, Data-Centric and Model-Centric, provides a sense of what is possible and what is required to solve a decision problem. In the Data-Centric orientation, the data is good enough per se, and most of the output comes from a direct evaluation of a set of equations. In the Model-Centric orientation, the information needs to be transformed, treated, analyzed, and modeled before it is incorporated to a decision model, which in most cases is based on a simulation process. A critical aspect of a decision model based on the DSS orientation relies on the nature of the available data. We can fnd objective and subjective information. Objective information can be obtained from diferent sources, but it is very common to get it from the company

Figure 19.1

Continuum of decision structure (adapted from Simon, 1960)

Table 19.1 DSS classifcation (Adapted from Alter, 1980) DSS type

DSS activity

DSS orientation

File drawer systems Data analysis systems Analysis information systems Accounting models Representational models Optimization models Suggestion models

Data retrieval

Data-centric

Data analysis Simulation

Model-centric

Suggestion

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Figure 19.2

Decision model component

structured database. This type of data is related to meaningful and quantifable measurement. There is little to no judgment or minimal user subjectivity. On the other hand, subjective information is directly related to knowledge from experts or tacit knowledge or personal judgment, which may vary from user to user. The level of the knowledge and experience of the users/expert is a factor considered in the determination of input treatment and level of trust (Alter, 1980). A decision model that uses objective information is defned as a data-driven model, and a model that uses mainly subjective information is defned as expert-driven model. The main task of the knowledge engine is to produce reliable results to help the decision-maker in the decision process with a correct and appropriate use of the data. There are several possible engines to be used in a decision model; however, the nature of the data will determine the most appropriate selection of the knowledge engine (Bastias & Molenaar, 2005; Citroen, 2011; Gorry & Morton, 1989; Simon, 1960). The capabilities of analysis vary from system to system. Ideally, a decision-maker will require a sensitivity analysis, capabilities of predictions (outcomes/output), and capabilities to compare alternatives (Figure 19.2).

Decision Model Classifcation The data available and its processing provide a point of departure in the selection of the appropriate decision model. In order to provide an overlook of what is available, this chapter identifes three main categories to be analyzed. •

•

Category 1: Structured decision problem where the objective information is the most trusted and only source available. It is, by defnition, a data-driven model. The information came from projects and the main knowledge engine is based on linear regression models, inferential statistical models, and SEM. The main goal is to apply statistical techniques to allow the identifcation of a linear causal relationships among variables. Category 2: Unstructured decision problem, focuses on making a decision based on expert data-driven input, using simulation tools. This category introduces the VDT system. Category 3: This category represents a hybrid solution based on an unstructured decision problem, but the system allows to combine objective and subjective information when available.

In summary, the fgure 19.3 represents a visual interpretation of the intensity in using the decision model components. The next section describes these models in a greater level of detail and provides examples from the literature. 310

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Figure 19.3 Decision model component mapping to categories

Decision Models that Support Project Delivery and Lean Strategy Decisions Category 1 Model Discussion Introduction Category 1 focuses on making a decision based on data with objective information. These data are typically found from past projects. Researchers and practitioners collect data on structured independent variables from past projects about, for example, project characteristics, management processes, project delivery systems, and Lean practices. The models examine how these independent variables are correlated with dependent variables such as overall project performance, cost variance, schedule variance, and litigation. The projects must be complete for the researchers to explore these correlations. These correlations are then used to infer the future performance of new projects.

Models Based on Structured Data Multiple regression models have proven successful for developing predictive models to support construction engineering and management decisions (Diekmann & Girard, 1995; Molenaar & Songer, 1998). Regression modeling applies to decision support strategies when there is a hypothesis that a change in one variable (X) causes a change in another variable (Y). Covariation is X and Y varying together in a systematic way (i.e., non-chance). The most basic regression technique to analyze covariation is linear regression (see Eq. 19.1). where Y: dependent variable β0 - βK−1: regression coefcients X0 - X K–1: independent variables ε: error term i: individual case index K: number of parameters Various types of multiple regression models have been used to assist project managers in making project delivery decisions. Seminal work in this area was done by Konchar and Sanvido (1998). Konchar developed regression models to explore the hypothesis that unit 311

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costs, construction speed, and delivery speed vary with the application of DBB, CMR, and DB delivery methods. Their model used independent variables such as project size, complexity, team communication, and contract unit cost for predicting project performance. Molenaar and Songer (1998) applied multivariate linear regression modeling to assist public sector owners to select when projects are appropriate for design-build. This regression model applied independent variables about the project, owner, market, and relationship to inform owners on the potential outcomes of budget variance, schedule variance, administrative burden, conformance to expectations, and overall satisfaction. Moon et al. (2011) used logistic regression to explore multifamily housing project delivery. Moon’s regression model applied independent variables about owners’ characteristics, owners’ needs, external environments, and project characteristics to explore the potential outcomes of project duration, project cost, and project intensity. These examples and others have provided insights into how project delivery methods and Lean practices can infuence overall project performance. However, they only explain a portion of the variation between these variables. Researchers have found that multivariate regression analysis techniques, while successful, have limitations. A fundamental assumption of standard regression models is that independent variables are measured without error, and this is frequently not a valid assumption (Molenaar et al., 2000). Take, for example, the variable of team chemistry, which is difcult to measure directly. However, it can be measured through a ‘surrogate’ variable that is made up of other items such as the quality of interactions, and the frequency of interactions. SEM is a method that has been explicitly developed for these types of variables. It is designed to measure latent variables, which are ‘unobservable’ and frequently of interest to researchers and decision-makers. Since Lean practices in construction project delivery variables can be difcult to measure directly, structural equation modeling analysis can be looked at as an extension of regression modeling that addresses errors in variable measurement. Franz et al. (2017) provide an illustrative example of how SEM can be used to provide project managers insights into how project delivery methods and other measurable project characteristics interact with the latent variables of group cohesion and team integration to explore cost, schedule and turnover performance (see Figure 19.4). SEM iteratively re-weigh model parameter estimates. Stated succinctly,

Figure 19.4

Conceptual SEM for exploring efects of group cohesion and team integration on project performance (adapted from Franz et al., 2017)

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Parameter values are estimated from the sample data so as to obtain a solution wherein the variances/covariances reconstructed from the parameter estimates for the specifed model match the corresponding sample values as closely as possible. (Hoyle, 1995) Stated diferently, ftting of structural equation models is done by minimizing the diferences between the sample variance-covariance matrix and the model-implied variance-covariance structure. Iteratively calculating the diference between observed and implied variances and covariances results in a residual matrix, which is sought to be minimized. Franz et al. (2017) found that both team integration and group cohesion infuence various aspects of project performance. The latent variable regarding integrated teams were made up of elements such as design charrettes, goal setting, BIM applications and planning, and colocation during construction. Increases to team integration related to less schedule growth and more project intensity. The group cohesion latent variable was comprised of project variables such as timeliness of communications, commitment to project goals and team chemistry. Improvements in group cohesion related to improved project performance. Other researchers have explored SEM techniques in related areas of project delivery and project management. Chang et al. (2017) use SEM techniques to explore collaboration and communication as it relates to IPD acceptability. Mandujano et al. (2017) use a path model to explore the relationship of Lean management and virtual design and construction implementation strategies. SEM provides improvements to classical multivariate linear regression modeling in terms of helping with our understanding of how Lean practices relate to project delivery methods and infuence project outcomes. The primary reason is that SEM techniques can provide insights into latent variables and many Lean practices are not directly observable. While multivariate regression and SEM techniques are helpful, they are reliant the data collection from past projects. These data are not always available in the form and volume required to construct second comprehensive models. Therefore, researchers and practitioners look to other methods that can incorporate expert data.

Category 2 Model Discussion Introduction Category 2 focuses on making a decision based on expert data-driven, using simulations tools. This category introduces the VDT system. The VDT system is a computational model that simulates information fow in project organizations based on discrete event and agent-based simulations (Levitt, 2012). The VDT uses Monte Carlo techniques to statistically predict the performance of project models (Ramsey & Levitt, 2005). Over the past 30 years, the VDT simulation framework has been created and thoroughly validated (Christiansen, 1992; Concha et al., 2015; ePM, 2002; Ibrahim & Nissen, 2004; Kunz & Fischer, 2012; Kunz et al., 2019; Levitt & Kunz, 2002; Levitt & Nissen, 2003). The VDT system is an appropriate simulation tool for analyzing the design of project organization and work processes, employing a simple language to graphically portray project participants’ interactions and the link between the project organization and work processes (Mesa et al., 2020). Below, we describe how the VDT system can be used as simulation modeling to study Lean practices and behaviors and use expert knowledge to support decisions where project data are unavailable. 313

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How the VDT System Works The VDT system is built upon the basic premise that organizations are fundamentally information-processing structures ( Jin & Levitt, 1996; Kunz et al., 1998). The VDT system represents an organization’s structure as a network of reporting relationships, in which actors send and receive messages via predefned communication lines (e.g., formal lines of authority) via communications tools with limited capacity (e.g., memos, voice mail, and meetings) (Kunz et al., 1998).

VDT Applications to Study Lean Practices and Behaviors LPD system encourages early involvement in the design stage using target value design (TVD). Consequently, this implies a signifcant change in how key project participants are organized and how they work in the project delivery process (Mesa et al., 2020). However, managers do not have precise and complete information to design their project organizations and evaluate the impact of these new changes that LPD addresses using diferent Lean practices or behaviors. The VDT system ofers a consolidated framework for analyzing and simulating the design of the project organization and work process to face this challenge. Mesa et al. (2020) is an interesting example of using this simulation tool to evaluate Lean practices and behaviors. They explored the organizational culture and the work process of an IPD/LPD project in the defnition and preconstruction stages. During the early design stage of this project, key project participants were integrated and involved. This project developed cross-functional teams and co-located them in the same ofce to coordinate project participants’ interdependence, optimize collaboration, and facilitate informal and formal connections. To study how these Lean practices and behaviors impact project performance, they simulated the organizational culture, the project participant’s characteristics, task characteristics and precedence, and types of work of this IPD/LPD project in the VDT system (Table 19.2). Within the organizational culture, the VDT model simulated characteristics such as team experience, centralization, formalization, and matrix strength (co-location). About the project participants, the VDT model simulated roles (e.g., design engineer, project manager) and their organizational attributes such as skills, levels of experience, and task familiarity. The VDT model also captured the task structure as a separate network of tasks. The VDT model simulated task attributes (e.g., priority, complexity, uncertainty, and required skills), relationships (successor or predecessor), interdependencies of communication, and rework loops among tasks, capturing how the work process is organized and performed. According to the participants’ task responsibilities, the VDT simulated direct work, coordination, and waste work. Direct work is the primary production work, such as design work Table 19.2 Organizational culture, project participant’s characteristics, task characteristics and precedence, and types of work that the VDT system simulate Organizational culture Project participants Tasks

Links

Types of work

Team experience Centralization Formalization Matrix strength

Rework Communications Meetings Successor/predecessor

Direct Coordination Rework Decision wait time

Role Experience Skill

Priority Skills Complexity Uncertainty

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for a design team or construction work for a construction team. Coordination work is the information fow among the project participants, and it considers two types of communication: one-to-one information exchange between project participants and group meetings. Wasted work is the work that has to be redone due to corrective iterations and decisions waiting time to report and how to deal with exceptions (ePM, 2005; Mesa et al., 2020). This VDT simulation allowed to understand how much time the IPD/LPD project team spent on design work (value time), coordination work, and waste work. The simulation results showed how organizational culture characteristics (e.g., centralization and co-location) infuenced the project performance. Another example of the VDT’s use is provided by Concha (2014). He simulated Lean practices (e.g., standardization, multifunctionality, and variability reduction) in the VDT system, adapting organizational culture characteristics, such as team experience, centralization, formalization, and matrix strength (co-location) to assess their impact on project performance. He took as reference fve ongoing construction projects to create the VDT models. Then, he collected and validated the application of Lean practices in these projects. The VDT’s results showed the advantages of implementing Lean practices improving project cost and schedule. This study also demonstrated the benefts of using simulations tools to study Lean practices and make decisions based on expert data-driven.

Data Collection Category 2 focuses on making a decision based on expert data-driven, using simulations tools such as VDT. The VDT conceptual model is developed through an iterative process of guided interviews with experienced professionals to gather information about the project organization’s culture, task characteristics and dependencies (delivery process), and the link between the project organization and delivery process for defning participants’ task responsibilities (Mesa et al., 2020). Mesa et al. (2020) propose a series of steps to develop the VDT conceptual model: • • • • • • • • • •

Defne the key project participants (e.g., designer, owner, and constructor). Describe how the key project participants are organized (project organization chart). Describe the types of project teams. Identify the diferent types of project meetings that the project teams have to attend. Describe the frequency and duration of these meetings per week Defne if the project teams use co-location. If this is the case, how did co-location work? Describe the project organization’s level of centralization. Defne the main activities of the project stage (e.g., design and construction) Indicate predecessors, communication, and rework links among the activities. Defne the participants’ responsibilities regarding the main activities.

Category 3 – Expert and Project Data-Driven Introduction The model described in this section is based on the CIA. The CIA is an analytical method developed to study the future behavior of a system by analyzing the interactions between many variables (events) in the system (Gordon & Hayward, 1968). The initial probabilities represent the variables of the CIA method, and the cross-impact matrix shows their 315

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relationship. The initial probability indicates the probability that a variable will occur. The cross-impact matrix shows how the initial probability of a conditional variable is decreased or increased when the conditional variable occurs. The CIA method is an efective tool for describing subjective knowledge, which has been used successfully in many areas, and has been integrated into mathematical models to support project decisions. The following are some examples of how a CIA-based model can support decision-making based on a combination of expert knowledge and expert and project data.

Models Based on Expert Knowledge First, we will discuss situations where project data is difcult to obtain. Organization managers often have to make decisions in situations where they cannot rely on accurate and complete information. However, there is a need to be rigorous to justify a particular decision. With complex and uncertain decisions, such as innovative project delivery strategies, the ability to predict performance impact can bring signifcant benefts to the decision-making process. On the other hand, you need to collect and record the experience that is prevalent within your organization in a way that helps improve the quality of your decision-making process. To address this challenge, the GPM (Alarcón & Ashley, 1996) provides the organization with the potential to build systematic discussions on the factors and characteristics of each situation, giving organizations an important additional capability of strategic decision making, which allows for systematic and rigorous analysis. During the modeling process, assumptions, subjective evaluation of scenarios, and internal and external conditions become explicit. Documentation of this process provides useful historical records that can facilitate continuous updates of strategic scenarios, empirical information, assumptions, and modeling team awareness. This method uses two basic structures. One is a conceptual model that identifes key variables and interactions in the strategic problem being analyzed and estimates their impact on the outcomes of interest to the organization. The second structure is a mathematical model of quantitative analysis (Alarcón & Ashley, 1998). This structure uses a probabilistic model to handle interactions and uncertainties between variables in a conceptual model by combining a simplifed general model with statistical inference. When applied, the model produces a comparative analysis that refects the diference in desired results when various decision options are selected.

GPM Applications to Study Lean Practices and Behaviors This modeling approach was used initially by a Construction Industry Institute (CII) task force to evaluate project management strategies such as team building, incentives, and organizational integration, elements that are key to promoting Lean behaviors and practices (Alarcón and Ashley, 1996). The development of successive software platforms (Alarcon & Bastías, 2000) has extended the use of this modeling approach to evaluate management decisions related to Lean practices and behaviors, for instance: evaluation of owner-contractor relationships, evaluation of company strategies, selection of contractors and suppliers, and evaluation of project delivery strategies. An example of using this methodology to evaluate a Lean project delivery strategy is provided in Mesa et al. (2016). This methodology was used to evaluate interactions between a large number of project delivery system variables and to compare potential performance 316

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Figure 19.5 Conceptual GPM model for Lean project delivery (adapted from Mesa et al., 2016)

between project delivery systems. The inputs to the conceptual and mathematical models were based on expert knowledge and the results were validated by a team of industry experts (Figure 19.5). The model is a simplifed structure of variables and interactions that infuence the decisions you analyze. This structure shows four levels: strategy, driver, process, and performance results (from left to right). Starting from the left side of the model, each layer represents an alternative to each strategy. For example, multiple alternatives to a project organization or project delivery system. Many variables are directly afected by the strategy or decision-making options. These variables are called drivers. Each alternative strategy is evaluated for its potential impact on the driver. The driver then propagates these efects through multiple interactions with the model variables. The model is defned as a set of variables whose efects extend from left to right, and each variable is internally modeled as a set of fve mutually exclusive and collectively exhaustive events. Knowledge of the interactions between the various variables of the model is integrated into the ‘Cross-Impact Matrix’. Elements of this matrix answer the question, ‘How does the performance of row variables afect the performance of column variables?’ This method of determining the impact between variables is a simplifcation of the technique used in ‘cross-impact analysis’. This simplifed form is a technique used in the mathematical models to facilitate the modeling process. This information will later be converted to the numerical format required for the cross-impact format. The model shown in Figure 19.5 was used to capture a conceptual model of Lean/nonLean practices and behaviors present in a project delivery system explicitly modeled by GPM. This type of model examines the impact of risk allocation, procurement methods, compensation methods, and organizational structures present in each project delivery system on project supply chain relationships, taking into account aspects that are not normally included in simulation models. The analysis of this model showed that the most infuential factors in project performance are Lean practices and behaviors related to communication, alignment of interests and goals, teamwork, trust, and gain/pain sharing. We have found that supply chain performance afects the performance of project delivery. 317

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Models Based on Expert Knowledge and Project Data Such a model can also integrate project data if it is available. The Foreign Building Operations (FBO) division of the US State Department used the GPM to analyze the implementation, design, and organizational strategies of embassy modernization and construction program projects (Alarcón & Ashley, 2001). This model was used to predict and assess the impact of specifc project delivery strategies, including Lean practices and actions, on embassy projects developed abroad. This methodology provided an opportunity to build a systematic discussion of the factors and elements of a particular situation, allowing the FBO organization to systematically and thoroughly analyze key aspects of those relationships. To introduce this new analytical approach, the FBO organization selected two specifc projects, Istanbul and Tunis, at various stages of development. Analyzing the frst two embassy projects using this methodology showed great potential to support the formalization of organizational knowledge and support planning and decision making. The strategies were divided into three groups: ‘Project Delivery System’, ‘Project Organization’, and ‘Design Type’. The model includes four project delivery systems with several alternatives considered for the FBO project. The model contains two project organizational strategies considered in the FBO project and their alternatives. The majority of the FBO organization was involved in the modeling process. A group of 75 members of the organization was invited to participate in the modeling process and gained various expertise that needed to be incorporated into the model. Expert knowledge was complemented by project data from previous embassy project FBO records, providing a solid foundation for validating model results. Figure 19.6 shows the expected performance improvement of four diferent performance indicators related to the existing base case. Another example of these types of models is provided by Tran et al. (2016). They provided an example of how a simplifed CIA model similar to GPM would integrate expert knowledge and project risk analysis data to select a project delivery method for a project.

Figure 19.6 Performance improvement for combined strategies (adapted from Alarcón & Ashley, 2001)

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They proposed a hybrid CIA approach to determine the project delivery method for highway projects. It provides a project cost assessment of your project in three basic delivery methods: DBB, DB, and construction manager/general contractor (CMGC). The hybrid CIA approach integrates the results of factor analysis of 31 delivery risk factors evaluated by 137 practitioners to determine interactions between variables in the cross-impact matrix. With these data, researchers were able to reduce the number of decisions required in the CIA model from approximately 3,000 or more to less than 300. The Florida Department of Transportation case project demonstrates that approach.

General Mapping of Lean Behaviors to Model Categories The SEM models presented in this chapter have a rigid structure and are very intensive in data. This situation left the model a challenge to incorporate Lean behaviors and practices. However, this was possible but required a considerable amount of data. The cases reviewed had focus on project delivery methods and project performance. Each case provided an appropriate connection with Lean practices and the data required to build each model. The VDT system allows Lean practices that belong to the organizational culture, such as team integration, information sharing, teamwork, and co-location, to be explored. The VDT system can capture their attributes and simulate their infuence on project performance. Simulation modeling can take advantage of expert knowledge to support decisions about Lean practices and behaviors where project data are unavailable. The GPM allows incorporating most of the Lean practices and behaviors in a direct way. The model presented in this chapter shows a direct efect of gain and pain sharing, trust, no-blame culture, team working, open communication, and confict resolution. The complexity is worked throughout the strategy’s evaluation. This modeling technique lets the decision-maker combine project and expert data in the same context. This approach opens possibilities to apply artifcial intelligence to improve the expert knowledge assessment and reduce the bias from the expert if it is present.

Conclusion In this chapter, we reviewed several methodologies which can support the selection and implementation of Lean Construction practices and project delivery systems which involve the interactions of multiple organizations, practices, and methods. These models provide a necessary bridge between the diferent aspects, which should be present in Lean Construction 4.0 implementation. Process/Philosophy and People/Aspects of Lean Construction can be explored and evaluated using Industry 4.0 technologies such as computer simulation models, machine learning, and predictive models. The integration of these models can provide dynamic decision support in construction projects to enable the implementation of increased automation in construction and the implementation of additional technologies. The modeling methods reviewed provide means for exploring and understanding the impact of Lean practices and specifc characteristics of the management system and the combined impact of multiple factors which are present in construction project management. The novelty of some of the Lean methods and practices makes it difcult to obtain project data for decision-making and for modeling purposes, but this is not an obstacle for developing the virtual models described in this chapter, where we examined models in three categories based on their data requirements, modeling engines, and output. Some models can be developed solely based on expert knowledge or with a combination of expert knowledge and 319

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project data, facilitating the process of virtually prototyping implementation strategies of Lean Construction in order to make more rigorous decisions for the design and selection of project delivery systems. Complex human aspects involved in some project delivery strategies such as trust, team integration and cohesion, collaboration, and many others related to Lean Practices and Behaviors can be incorporated in virtual modeling approaches reviewed in this chapter. Other key aspects of Lean Project Delivery methods, such as alignment of interest and objectives, promise-based management, gain/pain sharing, and continuous improvement, can also be part of the modeling and analysis efort providing an opportunity to better understand the performance mechanisms and the complex interactions required to achieve a successful implementation. Depending on the availability of expert knowledge and project data, the decision-makers have a choice of diferent modeling methods, which can be key players in the future of management decision-making in construction. One important barrier for the use of some of these modeling methods is that the modeling platforms are not readily available for decision-makers and have been mainly used with the support of the academic world. An important challenge for the future application of these modeling approaches is to develop new and more accessible modeling software to support more extended use of these virtual modeling capabilities in construction projects.

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