Megaproject Risk Analysis and Simulation: a Dynamic Systems Approach Megaproject Risk Analysis and Simulation: a Dynamic Systems Approach

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MEGAPROJECT RISK ANALYSIS AND SIMULATION: A DYNAMIC SYSTEMS APPROACH MEGAPROJECT RISK ANALYSIS AND SIMULATION: A DYNAMIC SYSTEMS APPROACH

BY PRINCE BOATENG Koforidua Technical University, Koforidua, Ghana ZHEN CHEN University of Strathclyde, Glasgow, UK STEPHEN O. OGUNLANA Heriot-Watt University, Edinburgh, UK

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First edition 2017

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Certificate Number 1985 ISO 14001 To our families Contents

Abbreviations xi

List of Figures xv

List of Tables xix

List of Exhibits xxv

List of Equations xxvii

Acknowledgements xxix

About the Authors xxxi

Preface xxxiii

Foreword xxxv

Chapter 1 Introduction 1 1.1. Introduction 1 1.2. The Problem with Megaprojects 2 1.2.1. Megaproject Risks 2 1.2.2. Megaproject Risk Assessment 4 1.2.3. A New Risk Assessment Framework 5 1.3. Purpose and Scope 6 1.4. Structure of the Book 9 1.5. Originality and Value 10 1.6. Summary 12

Chapter 2 The Edinburgh Tram Network (ETN) Project 15 2.1. Introduction 15 2.2. Background to the Project 15 2.2.1. The 18711956 Era 15 2.2.2. The New Edinburgh Tram Network 16 viii Contents

2.2.3. Tram Network Construction and Civil Engineering Works 17 2.2.4. Contractual Disputes 28 2.3. STEEP Risks 28 2.3.1. Social Issues 28 2.3.2. Technical Issues 29 2.3.3. Environmental Issues 33 2.3.4. Political Issues 35 2.4. Summary 40

Chapter 3 Megaproject Risks Assessment Framework 43 3.1. Introduction 43 3.2. Risk Analysis Framework 46 3.2.1. The ANP Modelling Route 48 3.2.2. The SD Modelling Route 55 3.3. Risk Clusters 58 3.3.1. The Social Risks Cluster 59 3.3.2. Technical Risk Cluster 61 3.3.3. Economic Risks 64 3.3.4. Environmental Risk Cluster 68 3.3.5. Political Risk Cluster 70 3.4. Summary 74

Chapter 4 Megaproject Risk Quantiﬁcation 77 4.1. Introduction 77 4.2. Analysis of Risk Interdependency 77 4.2.1. Qualitative Approaches 78 4.2.2. The Quantitative Approach 81 4.2.3. Descriptive Quantitative Results and Analysis 82 4.2.4. Standardized Quantitative Results and Analysis 84 4.2.5. Adjustments of Participants’ Opinions 85 4.3. ANP Model 85 4.4. Pairwise Comparison 92 4.5. Risk Interdependency Matrix 93 4.5.1. Project Objectives Cluster 93 4.5.2. Potential Risks Cluster 95 4.5.3. Potential Risks Sub-Clusters 95 4.6. Supermatrix Calculation 110 4.7. Risk Rating 119 4.8. Consistency Test 119 4.9. Risk Priority Index (RPI) 124 4.10. Summary 128 Contents ix

Chapter 5 Risk Simulation 131 5.1. Introduction 131 5.2. Project Risk Systems Model 131 5.2.1. Model Causality 133 5.2.2. Initial Model Development 135 5.2.3. System Boundaries 136 5.3. STEEP Risks System 141 5.3.1. Social Risks System 141 5.3.2. Technical Risks System 145 5.3.3. Economic Risks System 148 5.3.4. Environmental Risks System 154 5.3.5. Political Risks System 157 5.4. System Verification 162 5.5. System Modelling, Testing and Simulation 165 5.5.1. Integrated Stock and Flow STEEP Models 165 5.5.2. Model Equation Formulation 169 5.5.3. Model Evaluation Tests 170 5.5.4. Dynamic Simulation Results and Discussion 184 5.6. System Validation 216 5.6.1. Structure Verification 218 5.6.2. Parameter Verification 219 5.6.3. Boundary Adequacy 219 5.6.4. Dimensional Consistency 219 5.7. Summary 221

Chapter 6 Conclusions 223 6.1. Introduction 223 6.2. Value for Practice 223 6.3. Contribution and Impacts 226 6.4. Practical Guide 229 6.5. Limitations and Further Research 232 6.5.1. Limitations 232 6.5.2. Further Research 234

References 235

Appendices 249 Appendix A: Model Validation 250 Appendix B: Structured Interview Questionnaire and Participants 289 Appendix C: Respondent’s Mean Scores of Importance 291

Index 327 Abbreviations

ANP Analytical Network Process AHP Analytic Hierarchical Process BBS Bilﬁnger Berger Civil (UK) and Siemens plc CEC City of Edinburgh Council CDR Cost of Dispute Resolution CDUD Cost of Delay in Utility Diversion CI Consistency Index CLA Cost of Legal Action CLD Causal Loop Diagram COD Cost of Delays COR Cost of Rework CR Consistency Ratio DEG De-Escalation to Grievances Disp. Disputes DOAF Delay of All Forms DOC Delay in Obtaining Consent EC Economic Certainties EcRM Economic Risks Model ETN Edinburgh Tram Network (Project) EG Escalation to Grievances EnC Environmental Certainties EnR Environmental Risks EnRE Environmental Regulation Enforcement EnRM Environmental Risks Model EnU Environmental Uncertainties EP Energy Price EPC Engineering, Procurement and Construction EPCO Escalation to Project Cost Overrun EPTO Escalation to Project Time Overrun xii Abbreviations

ER Economic Risks ERM Environmental Resource Management ErG Error Generation EU Economic Uncertainties FE Foreign Exchange GCP Ground Conditions Problem at a Given Site GFP Government Funding Policy IPV Ideal Priority Value LA Legal Actions LD Liquidated Damages LIR Local Inflation Rate LRC Legislative & Regulation Changes IRPI Ideal Synthesized Risk Priority Indexes IRPV Ideal Risk Priority Index MCDM Multi-Criterion Decision Making MLDMBI Multi-Level Decision-Making Bodies Involvement MP Material Price MPDS Modification to Project Design & Specification MPH Material Price Hike MUDFA Multi-Utilities Framework Agreement NPV Normal Priority Value O&M Operations and Maintenance PA Social Acceptability PC Political Certainties PDP Political Debates on the Project PH Political Harmony PI Political Indecision PIP Political Interferences in the Project PMPS Pressure to Modify Project Scope PoRM Political Risks Model PQD Project Quality Deficiency PR Political Risks Proj.C Project Complexity PS Political Support Abbreviations xiii

Proj.S Project Scope PT Project Termination PU Political Uncertainties RMSI Respondent’s Mean Scores of Importance RPCO Risks of Project Cost Overrun RPI Risk Prioritization Index RPIG Global Risks Priority Index RPIL Local Risk Priority Index RPTO Risks of Project Time Overrun SC Social Certainties SD System dynamics SFM Stock and Flow Model SG Social Grievances SI Social Issues SoRM Social Risks Model SPV Special Purpose Vehicle SR Social Risks SU Social Uncertainties TC Technical Certainties TDUU Time to Divert Underground Utilities TeRM Technical Risks Model TIE Transport Initiatives Edinburgh Ltd. TPAS Threat to Personal & Asset Security TPV Total Priority Value TR Technical Risks TRO Trafﬁc Regulation Order TRPI Total Risk Priority Index TU Technical Uncertainties WCP Worksite Coordination Problems WQS Weighted Quantitative Score WI Wage Inﬂation List of Figures

Chapter 2 Figure 2.1 Stakeholder relationship map for the ETN project. . . 25

Chapter 3 Figure 3.1 The SDANP framework for megaproject risk assessment...... 47 Figure 3.2 ANP network model for risk prioritization...... 50 Figure 3.3 Calculation process for the CR method...... 52 Figure 3.4 The three components of system dynamics models. . . 56 Figure 3.5 A simple stock and ﬂow model...... 57 Figure 3.6 Social risk entry points during mega construction projects...... 61

Chapter 4 Figure 4.1 ANP model structure for STEEP risks prioritization. . 89 Figure 4.2 ANP sub-models for STEEP risks prioritization. . . . 90

Chapter 5 Figure 5.1 Causal loop diagram for STEEP risks on the ETN project...... 132 Figure 5.2 Causes tree diagram for technical uncertainties entity. . 134 Figure 5.3 Uses tree diagram for technical uncertainties entity. . . 134 Figure 5.4 Causality of technical uncertainties...... 134 Figure 5.5 Causal loop diagram for social risks system...... 142 Figure 5.6 Causes tree diagrams for social risks model...... 144 Figure 5.7 Uses tree diagrams for the social risks model...... 146 Figure 5.8 Causal loop diagram for technical risks system. .... 147 Figure 5.9 Causes tree diagrams for the technical risks model. . . 149 Figure 5.10 Uses tree diagrams for the technical risks model. . . . 150 Figure 5.11 Causal loop diagram for economic risks system. .... 152 Figure 5.12 Causes tree diagrams for the economic risks model. . . 153 Figure 5.13 Uses tree diagrams for the economic risks model. . . . 154 xvi List of Figures

Figure 5.14 Causal loop diagram for environmental risks system...... 155 Figure 5.15 Causes tree diagrams for the environmental risks model...... 158 Figure 5.16 Uses tree diagrams for the environmental risks model...... 159 Figure 5.17 Causal loop diagram for political risks system...... 160 Figure 5.18 Causes tree diagrams for the political risks model. . . . 163 Figure 5.19 Uses tree diagrams for the political risks model. .... 164 Figure 5.20 A typical stock and flow model (SFM)...... 165 Figure 5.21 Integrated stock and flow diagram for the social risks system...... 167 Figure 5.22 Integrated stock and flow diagram for the technical risks system...... 167 Figure 5.23 Integrated stock and flow diagram for the economic risk system...... 168 Figure 5.24 Integrated stock and flow diagram for the environmental risks system...... 169 Figure 5.25 Integrated stock and flow diagram for the political risks model...... 170 Figure 5.26 A typical SD equation representation...... 184 Figure 5.27 Evaluation tests for the STEEP risks models...... 185 Figure 5.28 Dynamic risk-free simulation patterns for social risks system model...... 196 Figure 5.29 Dynamic scenario graphs for the social risks system model...... 199 Figure 5.30 Simulation behaviour patterns for stocks in the technical risk system model ...... 203 Figure 5.31 Baserun and actual scenario simulation patterns for economic risks...... 206 Figure 5.32 Dynamic patterns for stock entities in the environmental risks model...... 209 Figure 5.33 Dynamic simulation patterns for stock entities in the political risks model...... 212 Chapter 6 Figure 6.1 Proposed framework for dynamic risks assessment in megaproject...... 225 List of Figures xvii

Appendices Figure A1 Model validation process...... 251 Figure A2 Behaviour reproduction test for the level of STEEP risks impacts on the system (all variables at baseline levels)...... 259 Figure A3 Behaviour mode sensitivity graphs for social risks and social grievances...... 274 Figure A4 Behaviour mode sensitivity graphs for technical risks. . 275 Figure A5 Behaviour mode sensitivity graphs for economic risks.. 275 Figure A6 Behaviour mode sensitivity graphs for environmental risks...... 276 Figure A7 Behaviour mode sensitivity graphs for political risks. . 276 Figure A8 Dynamic confidence bounds sensitivity graph for social grievances...... 277 Figure A9 Dynamic confidence bounds sensitivity graph for technicalrisks...... 277 Figure A10 Dynamic confidence bounds sensitivity graph for economic risks...... 278 Figure A11 Dynamic confidence bounds sensitivity graph for environmentalrisks...... 278 Figure A12 Dynamic confidence bounds sensitivity graph for political risks...... 279 Figure A13 Disaggregation of the dynamic simulation models for transportation megaprojects...... 283 List of Tables

Chapter 2 Table 2.1 Basic information of the ETN project...... 18 Table 2.2 The internal stakeholders of the ETN project...... 19 Table 2.3 The external stakeholders of the ETN project...... 21 Table 2.4 Stakeholder’s attitude and influence on ETN Project. . 23 Table 2.5 Project organization of the ETN project...... 23 Table 2.6 Project environment of the ETN Project...... 24 Table 2.7 Original ETN project board governance structure.. . . 26 Table 2.8 Bridges built to accommodate Edinburgh Tram..... 27 Table 2.9 Disputes and changes in the ETN project...... 29 Table 2.10 Project delivery against key milestones...... 30 Table 2.11 Organizations and groups consulted during the EIA for ETN Line One...... 34 Table 2.12 Specific risks impacting on the project environment. . . 38 Table 2.13 Specific technical risks impacting on the social and natural environments...... 40 Chapter 3 Table 3.1 Relative importance and data transformation in pairwise comparison...... 51 Table 3.2 The average random index...... 54 Table 3.3 Typical stakeholders involved in transport projects. . . 60 Table 3.4 A summary of review on social risks cluster in megaprojects...... 62 Table 3.5 A summary of review on technical risks in megaprojects...... 65 Table 3.6 A summary of review on economic risks in megaprojects...... 69 Table 3.7 A summary of review on environmental risks in megaprojects...... 71 Table 3.8 Sources of environmental risks in mega construction projects...... 71 xx List of Tables

Table 3.9 A summary of review on political risks in megaprojects...... 75 Chapter 4 Table 4.1 Summary of interviewees’ proﬁle and demography. . . 79 Table 4.2 Summary of survey conducted...... 82 Table 4.3 Summary of descriptive results and analysis for the questionnaire survey...... 83 Table 4.4 Respondent’s mean scores of importance...... 86 Table 4.5 Matrix for project objectives with respect to decision goal...... 94 Table 4.6 Comparison matrices for PR with respect to cost, time and quality...... 96 Table 4.7 Pairwise comparison matrix for social risk variables...... 98 Table 4.8 Pairwise comparison matrix for technical risk variables...... 100 Table 4.9 Pairwise comparison matrix for economic risk variables...... 103 Table 4.10 Pairwise comparison matrix for environmental risk variables...... 106 Table 4.11 Pairwise comparison matrix for political risk variables...... 107 Table 4.12 Unweighted super matrix for potential risks...... 111 Table 4.13 Weighted supermatrix for potential risks...... 112 Table 4.14 Final mode ANP decision-making priorities for potential risks cluster...... 113 Table 4.15 Final mode ANP decision-making priorities for social risk sub-cluster...... 114 Table 4.16 Final mode ANP decision-making priorities for technical risk sub-cluster...... 115 Table 4.17 Final mode ANP decision-making priorities for economic risks sub-cluster...... 116 Table 4.18 Final mode ANP decision-making priorities for Environmental Risk sub-cluster...... 117 Table 4.19 Final mode ANP decision-making priorities for political risk variables...... 118 Table 4.20 Deriving priorities for risks ratings...... 120 List of Tables xxi

Table 4.21 Verbal ratings for potential risks...... 120 Table 4.22 Verbal ratings for social risk variables...... 121 Table 4.23 Verbal ratings for technical risk variables...... 121 Table 4.24 Verbal ratings for economic risk variables...... 122 Table 4.25 Verbal ratings for environmental risk variables. .... 123 Table 4.26 Verbal ratings for political risk variables...... 123 Table 4.27 Values of CI, RI, CR and inconsistency for all the pairwise comparison matrices...... 124 Table 4.28 Summary of ﬁnal ANP decision-making priority results for all risks...... 125

Chapter 5 Table 5.1 Technical uncertainties inﬂuence...... 135 Table 5.2 System boundary for social risks system...... 136 Table 5.3 System boundary for technical risks system...... 137 Table 5.4 System boundary for economic risks system...... 138 Table 5.5 System boundary for environmental risks system. . . . 139 Table 5.6 System boundary for political risks system...... 140 Table 5.7 Stock variables for STEEP models...... 166 Table 5.8 Mathematical equation for the social risks system variables...... 171 Table 5.9 Mathematical equation for the technical risks system variables...... 173 Table 5.10 Mathematical equation for the economic risks system variables...... 176 Table 5.11 Mathematical equation for the environmental risks system variables...... 179 Table 5.12 Mathematical equation for the political risks system variables...... 181 Table 5.13 ANP inputs to the STEEP risk system modelling...... 195 Table 5.14 Summary of the simulation results for the social risks system model...... 202 Table 5.15 Summary of dynamic simulation results for technical risks system model...... 205 Table 5.16 Dynamic simulation results for the economic risks system model...... 208 xxii List of Tables

Table 5.17 Summary of the dynamic simulation results for environmental risks system...... 211 Table 5.18 Dynamic simulation results for the political risks system model...... 215 Table 5.19 One-way analysis of variance: The extent to which steep risks impact on project objectives...... 217 Table 5.20 Data validity on the ETN project...... 220

Chapter 6 Table 6.1 SDANP procedure for risks reduction in megaprojects...... 228 Table 6.2 Practical guide for using SDANP methodology in megaprojects...... 230 Appendices Table A1 Tests for building conﬁdence in the integrated SDANP models...... 253 Table A2 Parameters in the STEEP models...... 256 Table A3 Parameter distributions of stock and exogenous system entities for STEEP risks models...... 261 Table A4 Numerical sensitivity test for the social risks parameters...... 263 Table A5 Numerical sensitivity test for the technical risks parameters...... 265 Table A6 Numerical sensitivity test for the economic risks parameters...... 267 Table A7 Numerical sensitivity test for the environmental risks parameters...... 269 Table A8 Numerical sensitivity test for the political risks parameters...... 271 Table A9 The signiﬁcance of the dynamics simulation models for transportation megaprojects in addressing policy problems...... 285 Table C1 Respondent’s mean scores of importance for project objectives (Po)...... 291 Table C2 Respondent’s mean scores of importance for potential risks (PR1): Social risks...... 297 Table C3 Respondent’s mean scores of importance for potential risks (PR2): Technical risks...... 303 List of Tables xxiii

Table C4 Respondent’s mean scores of importance for potential risks (PR3): Economic risks...... 309 Table C5 Respondent’s mean scores of importance for potential risks (PR4): Environmental risks...... 315 Table C6 Respondent’s mean scores of importance for potential risks (PR5): Political risks...... 321 List of Exhibits

Chapter 2 Exhibit 2.1 Utility diversions for Edinburgh Trams Network construction ...... 27 Exhibit 2.2 Road interruption due to tram construction in the Edinburgh city centre ...... 32 Exhibit 2.3 Rework and adverse environmental impacts such as waste...... 35 Exhibit 2.4 The impact of bad weather conditions on productivity and construction delay ...... 36 Exhibit 2.5 Delayed construction process caused by bad weather conditions ...... 36 Exhibit 2.6 Poor well-being conditions on construction site: A worker was having his lunch in a cold rainy day. . . 37 List of Equations

Chapter 3 Equation 3.1 Weighted quantitative score method ...... 49 Equation 3.2 Pairwise comparison matrix computation ...... 52 Equation 3.3 Vector normalization ...... 53 Equation 3.4 Initial eigenvalue computation ...... 53 Equation 3.5 Maximum eigenvalue computation...... 53 Equation 3.6 Computation of value of consistency index ..... 54 Equation 3.7 Consistency ratio computation...... 54 Equation 3.8 Final risk prioritization index ...... 55 Equation 3.9 Mathematical deﬁnition of the integral for stock computation ...... 58 Equation 3.10 Basic stock computation ...... 58

Chapter 4 Equation 4.1 Respondent’s mean scores of importance ...... 85 Equation 4.2 Priority matrix computation for the project objectives ...... 93 Acknowledgements

This book summarizes a dedicated research funded and conducted under the megaproject management research theme at Heriot-Watt University and in the Scott Sutherland School of Architecture and Built Environment at Robert Gordon University in the United Kingdom. The research was also conducted through collaborative research amongst researchers from 24 European countries inside the COST Action TU1003 MEGAPROJECT (20112015), which was funded by the European Cooperation in Science and Technology (COST) and focuses on the effective design and delivery of megaprojects in the European Union. The COST Action on MEGAPROJECT was chaired by Professor Naomi Brookes at the University of Leeds in the United Kingdom. The authors would like to thank all participants for making time and efforts to support the research through interview and questionnaire survey for data collection from the Edinburgh Tram Network (ETN) project. The authors would also like to thank colleagues at the COST Action TU1003 for their advice and comments on the research into the ETN project. Without their supports, this research cannot be completed. The research theme on megaproject management at Heriot-Watt University was set up in 2012 and has been strongly supported by the follow- ing world renowned experts:

• Geoff Baskir, Chair, Aircraft/Airport Compatibility Committee, Transportation Research Board, National Academy of Sciences, USA • Naomi Brookes, Professor of Complex Project Management, University of Leeds, and CEO, Projektlernen, UK • Volker Buscher, Director, Global Digital Business, Arup, UK • John Connaughton, Professor of Sustainable Construction, Head of Construction Management and Engineering, University of Reading, UK • Henry Ergas, Professor of Infrastructure Economics, University of Wollongong, Australia • Stuart Ladds, Head of Property Strategy & Logistics, College of Policing Limited, UK • Heng Li, Chair Professor in Construction Informatics, The Hong Kong Polytechnic University, Hong Kong, China • Edward W. Merrow, Founder and President, Independent Project Analysis, Inc., USA xxx Acknowledgements

• Stanley G. Mitchell, CEO, Key Facilities Management International, Scotland. Chair, ISO TC 267 Facilities Management Committee • David Mosey, Professor of Law and Director, Centre of Construction Law and Dispute Resolution, King’s College London, UK • John Pike, Chairman, Bellrock Property Services, UK • Rodney Turner, Professor of Project Management, SKEMA Business School, France.

The authors would like to thank the entire publishing team at Emerald. Special thanks to colleagues at Emerald Publishing Limited, including Amy Barson, Senior Content Editor; Carole Caines, Books Production Controller; Nicki Dennis, Publisher; Charlotte Hales, Editorial Assistant; Liron Gilenberg, Cover Designer; Philippa Grand, Executive Publisher; Jen McCall, Publisher; and Kousalya Krishnamoorthy, Project Manager at MPS Limited. About the Authors

Prince Boateng, PhD, MASCE, AFHEA, is Lecturer in Building Technology & Quantity Surveying in Koforidua Technical University, Ghana. He is a former Lecturer in Construction and Project Management at Robert Gordon University, Aberdeen, the United Kingdom. He is proﬁ- cient in working with and analysing complex risk data. He uses analytical and system dynamics modelling tools to prioritize and simulate project risks overtime during risks assessment in megaprojects at the construction phase. He has used this expertise in developing innovative risk assessment tool known as SDANP methodology to model and predict project cost and time overruns in many megaprojects in Europe and Africa. Prince’s areas of expertise include risks analysis and modelling with system dynamics and the analytical network process for multi-criteria decision making for the effective megaproject delivery within the European Union and beyond. Zhen Chen is Lecturer in Construction Management in the Department of Architecture at the University of Strathclyde. He is a former Lecturer in Facilities Management and the founder and leader of Megaproject Management research theme at Heriot-Watt University. He serves at technical committees (Facility Management; Project, Programme and Portfolio Management; and Service Life Planning) at British Standards Institution (BSI), and technical committees (Airport Planning and Operations; and Infrastructure Resilience) at the American Society of Civil Engineers (ASCE). He is a member of the management committee of COST Action TU1003 (The Effective Design and Delivery of Megaprojects in the European Union). He also serves at editorial boards for several international journals at ICE (Engineering Sustainability; Infrastructure Asset Management; Management, Procurement and Law;andWaste and Resource Management)andElsevier (International Journal of Project Management). He is the Specialty Chief Editor on Construction Management for Frontiers in Built Environment published by EPFL in Switzerland. He is the Associate Editor for Innovative Infrastructure Solutions at Springer and Frontiers in Built Environment at EPFL. He has engaged in more than 30 research projects, worth over £5 million and has authored over 160 publications in construction engineering and management. Stephen O. Ogunlana, BSc, PhD, is currently the Chair of Construction Project Management at the School of the Built Environment, Heriot-Watt University. Professor Ogunlana has an international reputation for research in the application of system dynamics simulation to construction projects xxxii About the Authors and organizations. He is the author of over 250 scholarly publications in top-tier journals and refereed conferences. He is also the editor of the book Proﬁtable Partnering for Construction Procurement published by Taylor and Francis and Training for Construction Industry Development published by the CIB/AIT and co-editor of Joint Ventures in Construction (Thomas Telford) and Public-Private Partnership in Infrastructure Development — Case Studies from Asia and Europe (Bauhaus Universitat Weimar). His research work has been funded by the Canadian International Development Agency, European Union, Thai National Housing Authority, UNOCAL, Japanese Government, British Council etc. His works on lead- ership were awarded Emerald Literati Award for two consecutive years (2009 and 2010) for the most outstanding paper in the journal Engineering Construction and Architectural Management. Professor Ogunlana is the joint coordinator of CIB W107 Commission on Construction in Developing Economies and a member of the Editorial Board for over 10 internationally refereed academic journals including Engineering Construction and Architectural Management, the International Journal of Financial Management of Property and Construction, International Journal of Energy Sector Management, International Journal of Construction Management, Journal of Engineering Development and Technology, Surveying and the Built Environment, Civil Engineering Dimensions and Akruti Journal of Infrastructure. He has acted as external examiner for several top universities in the world. Preface

This book provides technical details on a dynamic systems approach to megaproject risk analysis and simulation, and it is based on the authors’ long-term research into megaproject management, multi-criteria decision making, and system dynamics. For the first time, the authors have attempted to find a technical solution to tackle overruns on cost and time in megaprojects, and this is based on a comprehensive set of risks associated with social, technical, economic, environmental and political (STEEP) issues in megaproject environment and a dynamic systems approach called SDANP. The approach is an integrated use of tools including analytic network process (ANP) and system dynamics (SD) for risks prioritization and simulation. The new SDANP model is described in this book with a case study on the Edinburgh Tram Network (ETN) project, which was a live case project during the time of the authors’ research into a dynamic systems approach to megaproject risk analysis and simulation. Through this experimental research, the SDANP model has provided interesting results on cost and time overruns with accuracy rates above 80%, respectively, for the ETN project over the time period between 2007 and 2013. The authors expect that this dynamic systems approach to megaproject risk analysis and simulation can be widely tested for the benefits of stakeholders in dealing with cost and time overruns in megaproject development.

Prince Boateng Zhen Chen Stephen O. Ogunlana Foreword

As our journey into the uncertainties of the twenty-first century continues, of one thing we can be sure: megaprojects are viewed as increasingly important in creating solutions to societal problems. Megaprojects will provide the new power plants that will give us with green energy, they will deliver transport systems that work for all without increasing carbon emis- sions, they will provide us with the integrated hospitals and healthcare that we need and they will even delight us with cultural and sporting events! We remain optimistic that the huge complexities of megaprojects in people, capital and technology can be tamed and we can look forward to feeling the benefits of their successful implementations. However, at their heart, megaprojects pose a conundrum. Time after time (and despite their apparent benefits) we do not seem to be able to deliver them on time, to budget and actually producing the output functionality that we need. We only have vague ideas why some succeed and, where they fail, we discover worryingly psychological failings in their planning and design. Given their importance in facing twenty-first century challenges, we desperately need to undertake more research to help us deliver megaprojects more effectively and to insure that the results of that research are available to the widest possible population of stakeholders. It is precisely this gap that Boateng, Chen and Ogunlana have aimed at with the work that they report upon in this book. They take one of the most clearly identified complexities in delivering megaproject, namely risk, and explore new ways of conceptualizing it and dealing with it. They employ a wide range of novel systems dynamics and frameworks to develop an understanding of risk in megaprojects. They provide interesting applica- tions of techniques used elsewhere in simulation to megaprojects. They illustrate their work with an insightful case of the Edinburgh Tram Project, a megaproject which embodies both the huge benefits that megaprojects can bring and the significant issues that inhibit their delivery. Boateng, Chen and Ogunlana are to be congratulated for the zeal with which they have pursued their research objectives and their fervour to share the results of their endeavours with others. This book provides a valuable addition to the work currently being undertaken by academics and practitioners alike in understanding megaproject design and delivery. It is through such committed work that we xxxvi Foreword really will be able to tame megaprojects and insure that they can reliably deliver the outcomes that society so desperately needs.

Professor Naomi Brookes, PhD DIC Visiting Professor in Complex Project Management, University of Leeds Chair – MEGAPROJECT COST Action C.E.O. – Projektlernen Chapter 1

Introduction

1.1. Introduction

Major stakeholders on megaprojects have been facing risks associated with social, technical, economic, environmental and political (STEEP) issues that may lead to significant cost and time overruns compared with initial budget and schedule estimates. Although much attention has been devoted to managing risks in megaproject delivery, results have not always been sat- isfactory in most cases across the world in the past. There have been increasing needs for advanced tools to support better risk assessment so as to inform decision-making in megaproject management. Regarding the complexity of megaprojects, and the great scope for risks and their inter- action in mega construction and development projects, it has been widely accepted that quantitative approaches are necessary supplements in risk analysis process. However, there has been little attempt to apply sophisti- cated methods recommended by industry standards for risk analysis in megaproject practice. Through developing and using a dynamic systems approach over a four-year period, the authors of the book have developed a new tool that can significantly identify major project risk factors and provide predications on time and cost overruns with over 80% accuracy compared to real figures in one megaproject, that is the Edinburgh Tram Network (ETN) project. This book summarizes their research into megaproject risk analysis and simulation to inform both academic researchers and megaproject stakeholders who have interest in qualitative and quantitative risk analysis and simulation for megaprojects across the world. This book covers all aspects of a real case study oriented research into megaproject risk analysis and simulation through a dynamic systems approach. A case study on the ETN project is used as an example of megaproject to develop a general technical framework called SDANP for STEEP risks analysis and simulation on megaprojects. An analytic network process (ANP) is adopted for risk quantification modelling, while a system dynamics (SD) (Brookes, 2015; Dimitriou, 2014; Flyvbjerg, 2014; 2 Megaproject Risk Analysis and Simulation

Flyvbjerg, Bruzelius, & Rothengatter, 2003; Mentis, 2015; Priemus, 2014; Renuka, Umarani, & Kamal, 2014; Spirkova, 2014; Van de Graaf & Sovacool, 2014) for risk simulation over time. Both the ANP and SD provide practical guides for the application of the dynamic systems approach in megaproject research and practice. By providing crucial background information for those who want to understand the dynamics of risks over time and their assessment during the decision-making processes on large transport infrastructure projects (Hickman et al., 2015), this book can prove an important source of information for academics, researchers and students in the fields of transport, infrastructure, project management, management science, economic analysis (costbenefit analysis), public policy, environmental policy and ethics. Practitioners, politicians and policy-makers involved in large transport infrastructure projects can also find this book to be a useful reference on risk analysis and simulation for megaproject management.

1.2. The Problem with Megaprojects

1.2.1. Megaproject Risks

Flyvbjerg, Skamris Holm, and Buhl (2003) found that 258 highway and rail projects ($90 billion worth) in 20 countries did not perform well on budgets as estimated, and about 90% of these projects suffered cost overruns, with the average rail project costing 45% more than what were projected, while it was over 20% in average for highway projects. Based on a continuous research, Flyvbjerg et al. (2003) underscored that cost overrun has not decreased over 70 years in the 20th century and seems to be a global phe- nomenon, which can also be attested on many megaprojects. For example, the Pusan and Muckho harbour project suffered significant cost overruns in the mid-1970s and relied on an extra $75 million loan for it to complete (2003), and the Big Dig project was estimated at a cost of $2.6 billion but was completed at a cost of $14.6 billion, additionally completion was delayed from 2002 to 2005 (2003). These projects have made the learned society and the public acutely aware of the problems of project delay and cost overruns in megaprojects. In addition, these technical problems also indicate clearly that construction cost estimating on major infrastructure projects has not improved in accuracy in the past more than half century, and the magnitude of underestimated project costs has been almost in the same order according to Flyvbjerg, Holm, and Buhl (2002) and Salling and Leleur (2015). It has been identified by Flyvbjerg et al. (2002) that the main possible reason for cost and time overruns in many megaprojects across the Introduction 3 world was to simplify the marginalization of risks during feasibility studies by undependably assuming what the World Bank calls the ‘Everything Goes According to Plan’ (EGAP) principle. There have been increasing needs for new ideas and techniques (Davies, MacAulay, DeBarro, & Thurston, 2014; Flyvbjerg et al., 2002; Kwak, Walewski, Sleeper, & Sadatsafavi, 2014)in order to tackle all risks associated with those significant problems for making the right decisions (Mentis, 2015) on both business and project towards successful megaprojects. Evidence gleaned through research worldwide suggests that large and complex infrastructure projects such as airport, bridge and highway are usually money pits where funds are simply ‘swallowed up’ without delivering sufficient returns. These problems are as a result of unbalanced subject- ive beliefs and information in assessing risks and uncertainties, and taking corrective actions to effectively control and manage the identified risks at the right time (Brookes, 2015; Dimitriou, 2014; Flyvbjerg, 2014; Flyvbjerg et al., 2003; Mentis, 2015; Priemus, 2014; Renuka et al., 2014; Spirkova, 2014; Van de Graaf & Sovacool, 2014). Flyvbjerg, (2014) further asserts that the track record of megaprojects under his study was terrible during developmental phases and reflected many credibility problems especially on transportation megaprojects. Proost et al. (2014) and Salling and Leleur (2015) emphasised that costs for transportation megaprojects were often grossly underestimated while traffic is often overestimated, and the per- ceived failure of the project was subject to a public enquiry, which con- cluded that the planned budget and schedule were hardly realistic although some of the cost increases were justified spending indeed. In reality, significant wastes were caused by design delays, over-optimistic programming and uncertain authority at the construction and development stages of megaprojects. The construction industry, like many other industries is a free-enterprise system, and has sizeable risks built into its structure and project based processes (Ball, 2014; Fulford & Standing, 2014; Guo, Chang-Richards, Wilkinson, & Li, 2014). From the initiation to the closing stages, construction process especially that for megaprojects is complex and characterized by a number of uncertainties and interactions (Brookes, 2015) that can negatively influence the project delivery in many ways (Brookes, 2015; Dimitriou, 2014; Flyvbjerg, 2014; Flyvbjerg et al., 2003; Mentis, 2015; Priemus, 2014; Renuka et al., 2014; Spirkova, 2014; Van de Graaf & Sovacool, 2014). For example, uncertainties about changes in weather conditions (Mentis, 2015), sub-contractor delays (Diab & Nassar, 2012; Eizakshiri, Chan, & Emsley, 2015), community resistance (Jordhus-Lier, 2015), political interferences (Kennedy, 2015) and unpredictable site conditions (Adam, Josephson, & Lindahl, 2014; Boateng, Chen, & Ogunlana, 2012) can compromise the completion of megaproject development on time