Sensitivity Analysis and Simulation Models in Project Risk Management
PMBOK v8 Definition
Sensitivity analysis and simulation models are quantitative risk analysis techniques used to evaluate the effects of uncertainty on project objectives. Sensitivity analysis examines the degree to which individual risks or uncertain elements impact project outcomes (PMBOK v8, p. 197). Simulation models, typically using Monte Carlo analysis, run multiple iterations of a project model to determine the range of possible outcomes and their probabilities (PMBOK v8, pp. 197-198). Both techniques help project teams understand overall risk exposure and support decision-making under uncertainty.
Why It Matters for the Exam
The PMI exam frequently tests your ability to distinguish between sensitivity analysis and simulation models, as well as their appropriate applications in risk analysis. Questions often appear in the "Quantitative Risk Analysis" domain, requiring you to identify which technique is used for specific scenarios—sensitivity analysis for identifying which risks have the greatest impact, and simulation for determining overall project risk exposure and probability distributions.
Key Points to Remember (for the exam)
- Primary Purpose of Sensitivity Analysis: Determines which individual risks or variables have the most influence on project outcomes (PMBOK v8, p. 197)
- Primary Purpose of Simulation Models: Evaluates combined effects of multiple uncertainties to predict overall project outcomes (PMBOK v8, pp. 197-198)
- Key Output of Sensitivity Analysis: Tornado diagram showing relative importance of variables
- Key Output of Simulation Models: Probability distribution (S-curve) showing likelihood of achieving specific targets
- Common Confusion: Sensitivity analysis examines one variable at a time (one-way); simulation examines all variables simultaneously (multi-way)
- Relationship to Decision-Making: Both techniques support decision-making under uncertainty (PMBOK v8, pp. 163, 172, 183)
- Risk Classification Link: These techniques apply to "Known-Unknown" risks (classic risks) where probability and impact can be identified (PMBOK v8, p. 93)
Typical PMI Exam Example
A project manager is evaluating the impact of cost uncertainty on a construction project. The team identifies 12 cost variables with potential variations. To determine which single variable has the greatest potential to affect the total project cost, the project manager should use sensitivity analysis. To determine the probability of completing the project within the approved budget of $5M, the project manager should use simulation models (Monte Carlo analysis).
PMI Exam Traps
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Trap: Confusing sensitivity analysis with simulation models
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Reality: Sensitivity analysis shows "which risk matters most"; simulation shows "what is the overall probability of success"
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Trap: Thinking both techniques produce the same output
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Reality: Sensitivity analysis produces a tornado diagram (variable ranking); simulation produces a probability distribution (outcome range)
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Trap: Applying these techniques to unknown-unknown risks
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Reality: Both techniques require identified risks with estimated probabilities and impacts (known-unknowns), not emergent risks
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Trap: Assuming sensitivity analysis considers variable interactions
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Reality: Sensitivity analysis typically varies one factor at a time; simulation models variable interactions simultaneously
Important PMI Connections
| Related Concept | Relationship Type | Exam Attention Point |
|---|---|---|
| Risk Appetite (p. 94) | Input consideration | Risk appetite defines the threshold for acceptable uncertainty; sensitivity analysis and simulation help quantify whether risk exposure exceeds this threshold |
| Overall Risk (p. 93) | Output of analysis | Simulation models directly calculate overall project risk exposure; sensitivity analysis identifies which risks contribute most to overall risk |
| Decision-Making Under Uncertainty (pp. 163, 172, 183) | Application context | Both techniques provide data for informed decision-making when project outcomes are uncertain |
| Reserve Analysis (p. 192) | Complementary technique | Simulation results inform contingency reserve calculations; sensitivity analysis helps prioritize which risks need reserves |
Quick Review Questions
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A project manager needs to identify which of 15 identified risks has the greatest potential impact on the project schedule. Which quantitative analysis technique should be used?
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During project planning, the team wants to determine the probability of completing the project within 12 months given all identified uncertainties. Which technique would provide this information?
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What is the primary difference between how sensitivity analysis and simulation models handle multiple risk variables?
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A tornado diagram is the output of which quantitative risk analysis technique?
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When stakeholders ask "What is the likelihood we will exceed the budget by 10%?", which technique should the project manager use to answer this question?
PMBOK v8 Reference
Section 2 – Project Management Performance Domains, Risk Domain (pp. 92-93, 197-198) Risk analysis techniques including sensitivity analysis and simulation models are addressed in the context of quantitative risk analysis within the project risk management performance domain.