Sensitivity Analysis
A technique used to identify which specific risks or uncertain factors most strongly influence project results by examining how changes in model inputs are associated with changes in the outputs of a quantitative risk model.
Key Points
- Shows how variation in one input affects the model's output while other inputs are held constant or controlled.
- Often presented with a tornado diagram that ranks inputs by their impact on the outcome.
- Helps prioritize risk responses by highlighting the uncertainties that drive the most outcome variability.
- Requires a quantitative risk model with defined inputs, ranges, and a clear output metric (e.g., cost, duration).
Example
A team models total project cost with uncertain inputs such as labor productivity, material prices, and rework rates. They vary each input across its range to see how total cost changes. The tornado diagram shows labor productivity as the largest driver, so the team focuses on training and buffer strategies to reduce that risk.
PMP Example Question
After completing a quantitative cost risk model, the team wants to determine which uncertain inputs have the greatest effect on the total cost. They create a tornado diagram ranking labor productivity, material price, and rework rate by impact. Which technique are they using?
- Sensitivity analysis
- Monte Carlo simulation
- Expected monetary value analysis
- Decision tree analysis
Correct Answer: A — Sensitivity analysis
Explanation: Sensitivity analysis ranks input variables by their effect on model outputs, commonly shown with a tornado diagram. Monte Carlo produces output distributions, while EMV and decision trees evaluate choices under uncertainty.
