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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?

  1. Sensitivity analysis
  2. Monte Carlo simulation
  3. Expert judgment
  4. 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.

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