DR. YURI RAYDUGIN is an experienced risk management professional who has developed and implemented successful risk systems for several major energy companies, including Royal Dutch Shell, SNC-Lavalin, and TransCanada Pipelines. He provides risk management coaching and training as well as risk assessment services and has published white papers and articles for a number of journals, including the International Journal of Risk and Contingency Management.

Foreword xv

Preface xix

Acknowledgments xxix

Part I: Fundamental Uncertainty of a Project Outcome

Chapter 1: Nature of Project Uncertainties 3

Phases of Project Development and Project Objectives 4

Quest for Predictability of Project Outcome 5

Sources and Types of Deviations from Project Objectives 7

Key Objects of Risk (or Uncertainty) Management: Do We Really Know What We Try to Manage? 15

Uncertainty Exposure Changers 24

Conclusion 26

Notes 26

Chapter 2: Main Components of a Risk Management System 29

Risk Management Plan 30

Organizational Framework 32

Risk Management Process 39

Risk Management Tools 52

Conclusion 59

Notes 60

Chapter 3: Adequacy of Methods to Assess Project Uncertainties 61

Review of Deterministic Qualitative (Scoring) Methods 62

Review of Deterministic Quantitative Methods 68

Review of Probabilistic Qualitative Methods 76

Review of Probabilistic Quantitative Methods 80

Conclusion 87

Notes 88

Part II: Deterministic Methods

Chapter 4: Uncertainty Identification 91

When Risk Management Becomes Boring 92

Three Dimensions of Risk Management and Uncertainty Identification 93

Risk Identification Workshops 95

Sources of Uncertainties and Risk Breakdown Structure 98

Bowtie Diagrams for Uncertainty Identification 101

Three-Part Uncertainty Naming 107

Role of Bias in Uncertainty Identification 110

Room for Unknown Unknowns 113

Conclusion 118

Notes 118

Chapter 5: Risk Assessment and Addressing 119

Developing a Risk Assessment Matrix 120

Using a Risk Assessment Matrix for Assessment As-Is 129

Five Addressing Strategies 136

Assessment after Addressing 141

Project Execution through Risk Addressing (PETRA) 145

Role of Bias in Uncertainty Assessment 147

Conclusion 149

Notes 150

Chapter 6: Response Implementation and Monitoring 151

Merging Risk Management with Team Work Plans 152

Monitor and Appraise 153

When Uncertainties Should Be Closed 154

When Should Residual Uncertainties Be Accepted? 155

Conclusion 155

Note 156

Chapter 7: Risk Management Governance and Organizational Context 157

Risk Management Deliverables for Decision Gates 158

Ownership of Uncertainties and Addressing Actions 160

Management of Supercritical Risks 162

Risk Reviews and Reporting 164

Bias and Organizational Context 168

Conclusion 175

Notes 175

Chapter 8: Risk Management Tools 177

Three Dimensions of Risk Management and Structure of the Uncertainty Repository 178

Risk Database Software Packages 181

Detailed Design of a Risk Register Template in MS Excel 184

Commercial Tools for Probabilistic Risk Analyses 185

Conclusion 191

Notes 192

Chapter 9: Risk-Based Selection of Engineering Design Options 193

Criteria for Engineering Design Option Selection 194

Scoring Risk Method for Engineering Design Option Selection 195

Decision Tree for Engineering Design Option Selection (Controlled Options) 199

Conclusion 202

Note 202

Chapter 10: Addressing Uncertainties through Procurement 203

Sources of Procurement Risks 204

Quantitative Bid Evaluation 207

Package Risk Management Post-Award 209

Conclusion 209

Notes 210

Chapter 11: Cost Escalation Modeling 211

Overview of the Cost Escalation Approach 211

Example of Cost Escalation Modeling 219

Selecting the Right Time to Purchase 223

Conclusion 224

Notes 224

Part III: Probabilistic Monte Carlo Methods

Chapter 12: Applications of Monte Carlo Methods in Project Risk Management 227

Features, Value, and Power of Monte Carlo Methods 228

Integration of Deterministic and Probabilistic Assessment Methods 230

Uncertainty Objects Influencing Outcome of Probabilistic Analyses 231

Origin and Nature of Uncertainties 233

Role of Correlations in Cost and Schedule Risk Analyses 240

Project Cost Reserve 242

Project Schedule Reserve 244

Anatomy of Input Distributions 246

Probabilistic Branching 250

Merge Bias as an Additional Reason Why Projects Are Often Late 251

Integrated Cost and Schedule Risk Analysis 253

Including Unknown-Unknown Allowance in Probabilistic Models 256

Conclusion 259

Notes 260

Chapter 13: Preparations for Probabilistic Analysis 261

Typical Workflows of Probabilistic Cost and Schedule Analyses 262

Planning Monte Carlo Analysis 264

Baselines and Development of Proxies 267

Why Using Proxies is the Right Method 271

Mapping of Uncertain Events 272

Building and Running Monte Carlo Models 277

Conclusion 277

Notes 278

Chapter 14: Using Outputs of Monte Carlo Analyses in Decision Making 279

Anatomy of Output Distributions 280

Overall Project Uncertainty and Confidence Levels of Baselines 283

Project Reserve Criteria 287

Uncertainty of Cost Outcome and Classes of Base Estimates 291

Cost Reserve Drawdown 296

Sensitivity Analysis 298

Using What-if Scenarios for Advanced Sensitivity Analysis 304

Are We Ready for Construction, Logistics, or Turnaround Windows? 305

Validating Results and Closing Probabilistic Analysis 306

Conclusion 308

Notes 308

Part IV: Risk Management Case Study: Project Curiosity

Chapter 15: Putting Together the Project Curiosity Case Study 311

Scope of the Case Study 312

Project Curiosity Baselines 313

Project Risk Management System Adopted by Project Curiosity 319

Overview of Project Uncertainty Exposure of Project Curiosity 326

Templates for Probabilistic Cost and Schedule Analyses 330

Building and Running Project Probabilistic Cost and Schedule Models 331

Three What-If Scenarios 333

Conclusion 334

Notes 335

Chapter 16: Decision Making 337

Key Points of the Probabilistic Analysis Report 338

Decision Gate Review Board Findings and Recommendations 350

Conclusion 352

Note 353

About the Author 355

Index 357