A Monte Carlo Simulation is a quantitative risk analysis method that evaluates the impact of uncertainty on project outcomes by running a large number of simulations through a mathematical or computer model. Each simulation randomly selects values for uncertain variables within defined probability distributions, resulting in a range of possible outcomes and associated probabilities.
Purpose and Characteristics
- Incorporates Uncertainty – Models variability in inputs to reveal risk exposure.
- Generates Probability Distributions – Outputs a range of results with likelihoods.
- Supports Decision Making – Provides data for evaluating best- and worst-case scenarios.
- Requires Repetition – Often runs thousands of simulations to create reliable data.
Common Use Cases
- Estimating project cost or schedule ranges
- Evaluating risk-adjusted return on investment
- Analyzing decision trees and complex trade-offs
- Prioritizing risks based on outcome variability
Example Scenario
A project schedule has uncertain task durations. Instead of relying on single-point estimates, a Monte Carlo simulation runs 10,000 iterations using optimistic, most likely, and pessimistic durations to estimate the probability of completing the project by a target date.
Mermaid Diagram: Monte Carlo Simulation Conceptual Flow
flowchart LR A[Define Input Ranges<br>e.g. Cost, Time] --> B[Assign Probability Distributions] B --> C[Sample Random Input] C --> D[Run Simulation Model] D --> E[Store Output] E --> F{More Iterations?} F -->|Yes| C F -->|No| G[Analyze Outcome Distribution]
Why Monte Carlo Simulation Matters
- Reveals Probabilities – Goes beyond deterministic estimates to show likelihoods of success or failure.
- Improves Risk Awareness – Quantifies uncertainty in both cost and schedule planning.
- Enables Informed Choices – Provides evidence-based support for project risk responses and contingency planning.
See also: Risk Management, Decision Tree Analysis, Multipoint Estimating, Schedule Forecasts.