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Practical Schedule Risk Analysis, contains a complete treatment of schedule risk analysis from basic to advanced concepts. Benefits of the process, relationship to traditional critical path method scheduling and to project management, the role of the corporate culture in ensuring success and the criticality of collecting risk data are emphasized fo
List of contents
Chapter 1 Why Schedule Risk Analysis? Looking Beyond the Critical Path Method; Chapter 2 Uncertainty in Activity Durations: Using Probability Distributions; Chapter 3 Uncertainty Along a Schedule Path: Using Monte Carlo Simulation; Chapter 4 A Good Project Schedule is Needed: Critical Path Method Scheduling 101; Chapter 5 Collecting Risk Data: Exploring Methods and Problems; Chapter 6 Where Parallel Paths Merge: Introductionducing the 'Merge Bias' and Risk Criticality; Chapter 7 Probabilistic Branching: Analyzing Discrete Risk Events; Chapter 8 Using Risks to Drive the Analysis and Prioritize Risks: Introductionducing the 'Risk Driver' Method; Chapter 9 Schedule Contingency Plans: Using Conditional Branching; Chapter 10 When Activity Durations Move Together: Incorporating Correlation; Chapter 11 Risk Management in the Organization: Identifying the Mature Risk Management Culture;
About the author
David T. Hulett is internationally recognised as a leading expert in risk management, leader in risk management activities of the Project Management Institute (project manager of the Risk Management chapter of the PMBOK�⮠Guide, 2000 and 3rd editions), deputy PM of the Practice Standard for Project Risk Management (ongoing) and frequent conference speaker. He has a consulting and training practice with clients in many industries around the world.
Summary
Practical Schedule Risk Analysis, contains a complete treatment of schedule risk analysis from basic to advanced concepts. Benefits of the process, relationship to traditional critical path method scheduling and to project management, the role of the corporate culture in ensuring success and the criticality of collecting risk data are emphasized fo
