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Monte Carlo Simulation

A quantitative method that runs a computer model many times, randomly drawing input values from specified probability distributions and chance-based decision paths. The resulting set of simulations produces a distribution that shows the range and likelihood of possible project outcomes.

Key Points

  • Uses random sampling from defined probability distributions for uncertain inputs.
  • Models branching logic and risks to reflect different possible paths.
  • Generates probabilistic results (e.g., P50, P80) for cost, schedule, or scope outcomes.
  • Supports contingency sizing, risk-informed decisions, and communicating uncertainty.

Example

A project team assigns probability distributions to activity durations (for example, triangular estimates). They run 10,000 simulations of the schedule to estimate the chance of finishing by a contractual date. The output shows a completion date curve indicating 60% confidence by May 15 and 80% confidence by May 25, guiding buffer and contingency planning.

PMP Example Question

Which technique provides a probability distribution of project finish dates by repeatedly sampling activity duration estimates from their defined distributions?

  1. Three-point estimating
  2. Decision tree analysis
  3. Monte Carlo simulation
  4. Fast tracking

Correct Answer: C — Monte Carlo simulation

Explanation: Monte Carlo uses random sampling across many iterations to produce a probabilistic range of outcomes, such as a completion date distribution.

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