Project Risk Management

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 xvPreface xixAcknowledgments xxixPart I: Fundamental Uncertainty of a Project OutcomeChapter 1: Nature of Project Uncertainties 3Phases of Project Development and Project Objectives 4Quest for Predictability of Project Outcome 5Sources and Types of Deviations from Project Objectives 7Key Objects of Risk (or Uncertainty) Management: Do We Really Know What We Try to Manage? 15Uncertainty Exposure Changers 24Conclusion 26Notes 26Chapter 2: Main Components of a Risk Management System 29Risk Management Plan 30Organizational Framework 32Risk Management Process 39Risk Management Tools 52Conclusion 59Notes 60Chapter 3: Adequacy of Methods to Assess Project Uncertainties 61Review of Deterministic Qualitative (Scoring) Methods 62Review of Deterministic Quantitative Methods 68Review of Probabilistic Qualitative Methods 76Review of Probabilistic Quantitative Methods 80Conclusion 87Notes 88Part II: Deterministic MethodsChapter 4: Uncertainty Identification 91When Risk Management Becomes Boring 92Three Dimensions of Risk Management and Uncertainty Identification 93Risk Identification Workshops 95Sources of Uncertainties and Risk Breakdown Structure 98Bowtie Diagrams for Uncertainty Identification 101Three-Part Uncertainty Naming 107Role of Bias in Uncertainty Identification 110Room for Unknown Unknowns 113Conclusion 118Notes 118Chapter 5: Risk Assessment and Addressing 119Developing a Risk Assessment Matrix 120Using a Risk Assessment Matrix for Assessment As-Is 129Five Addressing Strategies 136Assessment after Addressing 141Project Execution through Risk Addressing (PETRA) 145Role of Bias in Uncertainty Assessment 147Conclusion 149Notes 150Chapter 6: Response Implementation and Monitoring 151Merging Risk Management with Team Work Plans 152Monitor and Appraise 153When Uncertainties Should Be Closed 154When Should Residual Uncertainties Be Accepted? 155Conclusion 155Note 156Chapter 7: Risk Management Governance and Organizational Context 157Risk Management Deliverables for Decision Gates 158Ownership of Uncertainties and Addressing Actions 160Management of Supercritical Risks 162Risk Reviews and Reporting 164Bias and Organizational Context 168Conclusion 175Notes 175Chapter 8: Risk Management Tools 177Three Dimensions of Risk Management and Structure of the Uncertainty Repository 178Risk Database Software Packages 181Detailed Design of a Risk Register Template in MS Excel 184Commercial Tools for Probabilistic Risk Analyses 185Conclusion 191Notes 192Chapter 9: Risk-Based Selection of Engineering Design Options 193Criteria for Engineering Design Option Selection 194Scoring Risk Method for Engineering Design Option Selection 195Decision Tree for Engineering Design Option Selection (Controlled Options) 199Conclusion 202Note 202Chapter 10: Addressing Uncertainties through Procurement 203Sources of Procurement Risks 204Quantitative Bid Evaluation 207Package Risk Management Post-Award 209Conclusion 209Notes 210Chapter 11: Cost Escalation Modeling 211Overview of the Cost Escalation Approach 211Example of Cost Escalation Modeling 219Selecting the Right Time to Purchase 223Conclusion 224Notes 224Part III: Probabilistic Monte Carlo MethodsChapter 12: Applications of Monte Carlo Methods in Project Risk Management 227Features, Value, and Power of Monte Carlo Methods 228Integration of Deterministic and Probabilistic Assessment Methods 230Uncertainty Objects Influencing Outcome of Probabilistic Analyses 231Origin and Nature of Uncertainties 233Role of Correlations in Cost and Schedule Risk Analyses 240Project Cost Reserve 242Project Schedule Reserve 244Anatomy of Input Distributions 246Probabilistic Branching 250Merge Bias as an Additional Reason Why Projects Are Often Late 251Integrated Cost and Schedule Risk Analysis 253Including Unknown-Unknown Allowance in Probabilistic Models 256Conclusion 259Notes 260Chapter 13: Preparations for Probabilistic Analysis 261Typical Workflows of Probabilistic Cost and Schedule Analyses 262Planning Monte Carlo Analysis 264Baselines and Development of Proxies 267Why Using Proxies is the Right Method 271Mapping of Uncertain Events 272Building and Running Monte Carlo Models 277Conclusion 277Notes 278Chapter 14: Using Outputs of Monte Carlo Analyses in Decision Making 279Anatomy of Output Distributions 280Overall Project Uncertainty and Confidence Levels of Baselines 283Project Reserve Criteria 287Uncertainty of Cost Outcome and Classes of Base Estimates 291Cost Reserve Drawdown 296Sensitivity Analysis 298Using What-if Scenarios for Advanced Sensitivity Analysis 304Are We Ready for Construction, Logistics, or Turnaround Windows? 305Validating Results and Closing Probabilistic Analysis 306Conclusion 308Notes 308Part IV: Risk Management Case Study: Project CuriosityChapter 15: Putting Together the Project Curiosity Case Study 311Scope of the Case Study 312Project Curiosity Baselines 313Project Risk Management System Adopted by Project Curiosity 319Overview of Project Uncertainty Exposure of Project Curiosity 326Templates for Probabilistic Cost and Schedule Analyses 330Building and Running Project Probabilistic Cost and Schedule Models 331Three What-If Scenarios 333Conclusion 334Notes 335Chapter 16: Decision Making 337Key Points of the Probabilistic Analysis Report 338Decision Gate Review Board Findings and Recommendations 350Conclusion 352Note 353About the Author 355Index 357