MEGAPROJECT RISK ANALYSIS AND

SIMULATION: A DYNAMIC SYSTEMS

APPROACH

MEGAPROJECT RISKANALYSIS AND SIMULATION:A DYNAMIC SYSTEMSAPPROACH

BY

PRINCE BOATENGKoforidua Technical University, Koforidua, Ghana

ZHEN CHENUniversity of Strathclyde, Glasgow, UK

STEPHEN O. OGUNLANAHeriot-Watt University, Edinburgh, UK

United Kingdom � North America � Japan � India � Malaysia � China

Emerald Publishing Limited

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First edition 2017

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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 11.1. Introduction 11.2. The Problem with Megaprojects 2

1.2.1. Megaproject Risks 21.2.2. Megaproject Risk Assessment 41.2.3. A New Risk Assessment Framework 5

1.3. Purpose and Scope 61.4. Structure of the Book 91.5. Originality and Value 101.6. Summary 12

Chapter 2 The Edinburgh Tram Network (ETN) Project 152.1. Introduction 152.2. Background to the Project 15

2.2.1. The 1871�1956 Era 152.2.2. The New Edinburgh Tram Network 16

2.2.3. Tram Network Construction and CivilEngineering Works 17

2.2.4. Contractual Disputes 282.3. STEEP Risks 28

2.3.1. Social Issues 282.3.2. Technical Issues 292.3.3. Environmental Issues 332.3.4. Political Issues 35

2.4. Summary 40

Chapter 3 Megaproject Risks Assessment Framework 433.1. Introduction 433.2. Risk Analysis Framework 46

3.2.1. The ANP Modelling Route 483.2.2. The SD Modelling Route 55

3.3. Risk Clusters 583.3.1. The Social Risks Cluster 593.3.2. Technical Risk Cluster 613.3.3. Economic Risks 643.3.4. Environmental Risk Cluster 683.3.5. Political Risk Cluster 70

3.4. Summary 74

Chapter 4 Megaproject Risk Quantification 774.1. Introduction 774.2. Analysis of Risk Interdependency 77

4.2.1. Qualitative Approaches 784.2.2. The Quantitative Approach 814.2.3. Descriptive Quantitative Results and Analysis 824.2.4. Standardized Quantitative Results and Analysis 844.2.5. Adjustments of Participants’ Opinions 85

4.3. ANP Model 854.4. Pairwise Comparison 924.5. Risk Interdependency Matrix 93

4.5.1. Project Objectives Cluster 934.5.2. Potential Risks Cluster 954.5.3. Potential Risks Sub-Clusters 95

4.6. Supermatrix Calculation 1104.7. Risk Rating 1194.8. Consistency Test 1194.9. Risk Priority Index (RPI) 1244.10. Summary 128

viii Contents

Chapter 5 Risk Simulation 1315.1. Introduction 1315.2. Project Risk Systems Model 131

5.2.1. Model Causality 1335.2.2. Initial Model Development 1355.2.3. System Boundaries 136

5.3. STEEP Risks System 1415.3.1. Social Risks System 1415.3.2. Technical Risks System 1455.3.3. Economic Risks System 1485.3.4. Environmental Risks System 1545.3.5. Political Risks System 157

5.4. System Verification 1625.5. System Modelling, Testing and Simulation 165

5.5.1. Integrated Stock and Flow STEEP Models 1655.5.2. Model Equation Formulation 1695.5.3. Model Evaluation Tests 1705.5.4. Dynamic Simulation Results and Discussion 184

5.6. System Validation 2165.6.1. Structure Verification 2185.6.2. Parameter Verification 2195.6.3. Boundary Adequacy 2195.6.4. Dimensional Consistency 219

5.7. Summary 221

Chapter 6 Conclusions 2236.1. Introduction 2236.2. Value for Practice 2236.3. Contribution and Impacts 2266.4. Practical Guide 2296.5. Limitations and Further Research 232

6.5.1. Limitations 2326.5.2. Further Research 234

References 235

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

Index 327

Contents ix

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

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

xii Abbreviations

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

Abbreviations xiii

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 riskassessment.. . . . . . . . . . . . . . . . . . . . . . . 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 constructionprojects. . . . . . . . . . . . . . . . . . . . . . . . . 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 ETNproject.. . . . . . . . . . . . . . . . . . . . . . . . . 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

Figure 5.14 Causal loop diagram for environmentalrisks system. . . . . . . . . . . . . . . . . . . . . . . 155

Figure 5.15 Causes tree diagrams for the environmental risksmodel. . . . . . . . . . . . . . . . . . . . . . . . . . 158

Figure 5.16 Uses tree diagrams for the environmentalrisks 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 riskssystem. . . . . . . . . . . . . . . . . . . . . . . . . . 167

Figure 5.22 Integrated stock and flow diagram for the technicalrisks system. . . . . . . . . . . . . . . . . . . . . . . 167

Figure 5.23 Integrated stock and flow diagram for the economicrisk system. . . . . . . . . . . . . . . . . . . . . . . 168

Figure 5.24 Integrated stock and flow diagram for theenvironmental risks system. . . . . . . . . . . . . . . 169

Figure 5.25 Integrated stock and flow diagram for the politicalrisks 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 riskssystem model. . . . . . . . . . . . . . . . . . . . . . 196

Figure 5.29 Dynamic scenario graphs for the social risks systemmodel. . . . . . . . . . . . . . . . . . . . . . . . . . 199

Figure 5.30 Simulation behaviour patterns for stocks in thetechnical risk system model . . . . . . . . . . . . . . 203

Figure 5.31 Baserun and actual scenario simulation patterns foreconomic risks.. . . . . . . . . . . . . . . . . . . . . 206

Figure 5.32 Dynamic patterns for stock entities in theenvironmental risks model.. . . . . . . . . . . . . . . 209

Figure 5.33 Dynamic simulation patterns for stock entities in thepolitical risks model. . . . . . . . . . . . . . . . . . . 212

Chapter 6

Figure 6.1 Proposed framework for dynamic risks assessment inmegaproject. . . . . . . . . . . . . . . . . . . . . . . 225

xvi List of Figures

Appendices

Figure A1 Model validation process. . . . . . . . . . . . . . . . 251

Figure A2 Behaviour reproduction test for the level of STEEPrisks impacts on the system (all variables at baselinelevels). . . . . . . . . . . . . . . . . . . . . . . . . . 259

Figure A3 Behaviour mode sensitivity graphs for social risks andsocial 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 environmentalrisks. . . . . . . . . . . . . . . . . . . . . . . . . . . 276

Figure A7 Behaviour mode sensitivity graphs for political risks. . 276

Figure A8 Dynamic confidence bounds sensitivity graph for socialgrievances. . . . . . . . . . . . . . . . . . . . . . . . 277

Figure A9 Dynamic confidence bounds sensitivity graph fortechnical risks. . . . . . . . . . . . . . . . . . . . . . 277

Figure A10 Dynamic confidence bounds sensitivity graph foreconomic risks.. . . . . . . . . . . . . . . . . . . . . 278

Figure A11 Dynamic confidence bounds sensitivity graph forenvironmental risks. . . . . . . . . . . . . . . . . . . . 278

Figure A12 Dynamic confidence bounds sensitivity graph forpolitical risks. . . . . . . . . . . . . . . . . . . . . . 279

Figure A13 Disaggregation of the dynamic simulation models fortransportation megaprojects. . . . . . . . . . . . . . . 283

List of Figures xvii

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 EIAfor 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 andnatural environments. . . . . . . . . . . . . . . . . . 40

Chapter 3

Table 3.1 Relative importance and data transformation inpairwise 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 inmegaprojects. . . . . . . . . . . . . . . . . . . . . . 62

Table 3.5 A summary of review on technical risks inmegaprojects. . . . . . . . . . . . . . . . . . . . . . 65

Table 3.6 A summary of review on economic risks inmegaprojects. . . . . . . . . . . . . . . . . . . . . . 69

Table 3.7 A summary of review on environmental risks inmegaprojects. . . . . . . . . . . . . . . . . . . . . . 71

Table 3.8 Sources of environmental risks in mega constructionprojects. . . . . . . . . . . . . . . . . . . . . . . . . 71

Table 3.9 A summary of review on political risks inmegaprojects. . . . . . . . . . . . . . . . . . . . . . 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 thequestionnaire survey.. . . . . . . . . . . . . . . . . . 83

Table 4.4 Respondent’s mean scores of importance. . . . . . . . 86

Table 4.5 Matrix for project objectives with respect to decisiongoal. . . . . . . . . . . . . . . . . . . . . . . . . . . 94

Table 4.6 Comparison matrices for PR with respect to cost, timeand quality. . . . . . . . . . . . . . . . . . . . . . . 96

Table 4.7 Pairwise comparison matrix for socialrisk variables. . . . . . . . . . . . . . . . . . . . . . 98

Table 4.8 Pairwise comparison matrix for technical riskvariables.. . . . . . . . . . . . . . . . . . . . . . . . 100

Table 4.9 Pairwise comparison matrix for economic riskvariables.. . . . . . . . . . . . . . . . . . . . . . . . 103

Table 4.10 Pairwise comparison matrix for environmental riskvariables.. . . . . . . . . . . . . . . . . . . . . . . . 106

Table 4.11 Pairwise comparison matrix for politicalrisk 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 forpotential risks cluster. . . . . . . . . . . . . . . . . . 113

Table 4.15 Final mode ANP decision-making priorities for socialrisk sub-cluster. . . . . . . . . . . . . . . . . . . . . 114

Table 4.16 Final mode ANP decision-making priorities fortechnical risk sub-cluster. . . . . . . . . . . . . . . . 115

Table 4.17 Final mode ANP decision-making priorities foreconomic risks sub-cluster. . . . . . . . . . . . . . . . 116

Table 4.18 Final mode ANP decision-making priorities forEnvironmental Risk sub-cluster. . . . . . . . . . . . . 117

Table 4.19 Final mode ANP decision-making priorities forpolitical risk variables. . . . . . . . . . . . . . . . . . 118

Table 4.20 Deriving priorities for risks ratings. . . . . . . . . . . 120

xx List of Tables

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 thepairwise comparison matrices. . . . . . . . . . . . . . 124

Table 4.28 Summary of final ANP decision-making priorityresults 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 systemvariables.. . . . . . . . . . . . . . . . . . . . . . . . 171

Table 5.9 Mathematical equation for the technical risks systemvariables.. . . . . . . . . . . . . . . . . . . . . . . . 173

Table 5.10 Mathematical equation for the economic risks systemvariables.. . . . . . . . . . . . . . . . . . . . . . . . 176

Table 5.11 Mathematical equation for the environmental riskssystem variables. . . . . . . . . . . . . . . . . . . . . 179

Table 5.12 Mathematical equation for the political risks systemvariables.. . . . . . . . . . . . . . . . . . . . . . . . 181

Table 5.13 ANP inputs to the STEEP risk systemmodelling. . . . . . . . . . . . . . . . . . . . . . . . 195

Table 5.14 Summary of the simulation results for the social riskssystem model. . . . . . . . . . . . . . . . . . . . . . 202

Table 5.15 Summary of dynamic simulation results for technicalrisks system model. . . . . . . . . . . . . . . . . . . 205

Table 5.16 Dynamic simulation results for the economic riskssystem model. . . . . . . . . . . . . . . . . . . . . . 208

List of Tables xxi

Table 5.17 Summary of the dynamic simulation results forenvironmental risks system. . . . . . . . . . . . . . . 211

Table 5.18 Dynamic simulation results for the political riskssystem model. . . . . . . . . . . . . . . . . . . . . . 215

Table 5.19 One-way analysis of variance: The extent to whichsteep 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 inmegaprojects. . . . . . . . . . . . . . . . . . . . . . 228

Table 6.2 Practical guide for using SDANP methodology inmegaprojects. . . . . . . . . . . . . . . . . . . . . . 230

Appendices

Table A1 Tests for building confidence in the integrated SDANPmodels.. . . . . . . . . . . . . . . . . . . . . . . . . 253

Table A2 Parameters in the STEEP models. . . . . . . . . . . . 256

Table A3 Parameter distributions of stock and exogenous systementities for STEEP risks models. . . . . . . . . . . . . 261

Table A4 Numerical sensitivity test for the social risksparameters. . . . . . . . . . . . . . . . . . . . . . . 263

Table A5 Numerical sensitivity test for the technical risksparameters. . . . . . . . . . . . . . . . . . . . . . . 265

Table A6 Numerical sensitivity test for the economic risksparameters. . . . . . . . . . . . . . . . . . . . . . . 267

Table A7 Numerical sensitivity test for the environmental risksparameters. . . . . . . . . . . . . . . . . . . . . . . 269

Table A8 Numerical sensitivity test for the political risksparameters. . . . . . . . . . . . . . . . . . . . . . . 271

Table A9 The significance of the dynamics simulation models fortransportation megaprojects in addressing policyproblems. . . . . . . . . . . . . . . . . . . . . . . . 285

Table C1 Respondent’s mean scores of importance for projectobjectives (Po). . . . . . . . . . . . . . . . . . . . . . 291

Table C2 Respondent’s mean scores of importance for potentialrisks (PR1): Social risks. . . . . . . . . . . . . . . . . 297

Table C3 Respondent’s mean scores of importance for potentialrisks (PR2): Technical risks. . . . . . . . . . . . . . . 303

xxii List of Tables

Table C4 Respondent’s mean scores of importance for potentialrisks (PR3): Economic risks. . . . . . . . . . . . . . . 309

Table C5 Respondent’s mean scores of importance for potentialrisks (PR4): Environmental risks. . . . . . . . . . . . 315

Table C6 Respondent’s mean scores of importance for potentialrisks (PR5): Political risks. . . . . . . . . . . . . . . . 321

List of Tables xxiii

List of Exhibits

Chapter 2

Exhibit 2.1 Utility diversions for Edinburgh Trams Networkconstruction . . . . . . . . . . . . . . . . . . . . . . 27

Exhibit 2.2 Road interruption due to tram construction in theEdinburgh city centre . . . . . . . . . . . . . . . . . 32

Exhibit 2.3 Rework and adverse environmental impacts such aswaste . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Exhibit 2.4 The impact of bad weather conditions on productivityand construction delay . . . . . . . . . . . . . . . . . 36

Exhibit 2.5 Delayed construction process caused by bad weatherconditions . . . . . . . . . . . . . . . . . . . . . . . 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 stockcomputation . . . . . . . . . . . . . . . . . . . . . 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 projectobjectives . . . . . . . . . . . . . . . . . . . . . . 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

(2011�2015), 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

• 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.

xxx Acknowledgements

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 analyticaland 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 andthe 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 ofArchitecture 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; andInfrastructure 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; and Waste and Resource Management) and Elsevier(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 researchin the application of system dynamics simulation to construction projects

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 sev-

eral top universities in the world.

xxxii About the Authors

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 asso-

ciated 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 includ-

ing 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 BoatengZhen 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 mega-

project design and delivery. It is through such committed work that we

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 DICVisiting Professor in Complex Project Management,

University of LeedsChair – MEGAPROJECT COST Action

C.E.O. – Projektlernen

xxxvi Foreword

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 pro-

vide predications on time and cost overruns with over 80% accuracy com-

pared to real figures in one megaproject, that is the Edinburgh Tram

Network (ETN) project. This book summarizes their research into mega-

project risk analysis and simulation to inform both academic researchers

and megaproject stakeholders who have interest in qualitative and quanti-

tative 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 mega-

project 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;

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 pro-

vide 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 infor-

mation for academics, researchers and students in the fields of transport,

infrastructure, project management, management science, economic ana-

lysis (cost�benefit 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

2 Megaproject Risk Analysis and Simulation

world was to simplify the marginalization of risks during feasibility studiesby undependably assuming what the World Bank calls the ‘Everything GoesAccording to Plan’ (EGAP) principle. There have been increasing needs fornew ideas and techniques (Davies, MacAulay, DeBarro, & Thurston, 2014;Flyvbjerg et al., 2002; Kwak, Walewski, Sleeper, & Sadatsafavi, 2014) inorder to tackle all risks associated with those significant problems for mak-ing the right decisions (Mentis, 2015) on both business and project towardssuccessful megaprojects.

Evidence gleaned through research worldwide suggests that large andcomplex infrastructure projects such as airport, bridge and highway areusually money pits where funds are simply ‘swallowed up’ without deliver-ing sufficient returns. These problems are as a result of unbalanced subject-ive beliefs and information in assessing risks and uncertainties, and takingcorrective actions to effectively control and manage the identified risks atthe right time (Brookes, 2015; Dimitriou, 2014; Flyvbjerg, 2014; Flyvbjerget al., 2003; Mentis, 2015; Priemus, 2014; Renuka et al., 2014; Spirkova,2014; Van de Graaf & Sovacool, 2014). Flyvbjerg, (2014) further assertsthat the track record of megaprojects under his study was terrible duringdevelopmental phases and reflected many credibility problems especially ontransportation megaprojects. Proost et al. (2014) and Salling and Leleur(2015) emphasised that costs for transportation megaprojects were oftengrossly 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 althoughsome of the cost increases were justified spending indeed. In reality, signifi-cant wastes were caused by design delays, over-optimistic programmingand uncertain authority at the construction and development stages ofmegaprojects.

The construction industry, like many other industries is a free-enterprisesystem, and has sizeable risks built into its structure and project based pro-cesses (Ball, 2014; Fulford & Standing, 2014; Guo, Chang-Richards,Wilkinson, & Li, 2014). From the initiation to the closing stages, construc-tion process especially that for megaprojects is complex and characterizedby a number of uncertainties and interactions (Brookes, 2015) that cannegatively 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 con-ditions (Mentis, 2015), sub-contractor delays (Diab & Nassar, 2012;Eizakshiri, Chan, & Emsley, 2015), community resistance (Jordhus-Lier,2015), political interferences (Kennedy, 2015) and unpredictable site condi-tions (Adam, Josephson, & Lindahl, 2014; Boateng, Chen, & Ogunlana,2012) can compromise the completion of megaproject development on time

Introduction 3