Monte Carlo Simulation
Monte Carlo simulation is a quantitative risk analysis technique that uses random sampling of probability distributions for cost and schedule estimates to model possible project outcomes and calculate the probability of achieving targets.
Explanation
Monte Carlo simulation runs thousands of iterations of a project model, each time randomly selecting values from the probability distributions assigned to uncertain variables (activity durations, costs, etc.). The result is a probability distribution of possible project outcomes rather than a single deterministic estimate.
For schedule analysis, the simulation might show that there is a 50% probability of completing the project by June 30 and an 85% probability of completing by July 15. For cost analysis, it might reveal that a $2 million budget has only a 40% chance of being sufficient, while $2.3 million provides 90% confidence. These insights directly inform contingency reserve calculations.
The simulation requires three-point estimates (optimistic, most likely, pessimistic) or other probability distributions for each uncertain element. The output is typically displayed as an S-curve (cumulative probability distribution) or histogram. It is the most commonly used technique in Perform Quantitative Risk Analysis.
Key Points
- •Runs thousands of iterations using random sampling from probability distributions
- •Produces S-curves showing probability of meeting cost/schedule targets
- •Requires three-point estimates or defined probability distributions
- •Directly informs contingency reserve amounts
Exam Tip
Monte Carlo simulation answers "What is the probability of finishing by date X or within budget Y?" It does not identify individual risks—it models aggregate uncertainty.
Frequently Asked Questions
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Risk Management
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