HKSM
Monte Carlo analysis/simulation
A technique that models risk and uncertainty by running many computer-simulated iterations, producing a probability distribution of possible outcomes for a decision or plan.
Key Points
- Uses random sampling of input ranges (e.g., duration, cost) across thousands of iterations.
- Produces probability distributions and percentiles (e.g., P10, P50, P90) for outcomes.
- Supports setting realistic buffers and reserves for schedule and cost.
- Quality depends on accurate input distributions and appropriate correlations between variables.
Example
A project team defines three-point estimates for key activities and assigns distributions to commodity prices. Running a Monte Carlo schedule and cost simulation shows a 60% chance of finishing by June 30 within the approved budget; to reach 80% confidence, the model indicates adding two weeks of buffer and a 7% cost contingency.
PMP Example Question
A project manager wants to estimate the probability of completing the project within budget and by a target date, given uncertain productivity and fluctuating material prices. Which tool should be used?
- Sensitivity analysis
- Monte Carlo simulation
- Expert judgment
- Parametric estimating
Correct Answer: B — Monte Carlo simulation
Explanation: Monte Carlo runs many iterations with variable inputs to generate a probability distribution for schedule and cost outcomes, enabling confidence-based forecasts.
