Monte Carlo Simulation

September 2, 2025
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What is Monte Carlo Simulation?

Monte Carlo simulation is a mathematical technique used in project management to estimate the probability of different outcomes when there is uncertainty in forecasts or decision-making. It utilizes random sampling and statistical modelling to predict a range of possible outcomes, enabling project managers to assess risk and plan more effectively. The method was developed during World War II and takes its name from the Monte Carlo Casino due to its reliance on chance and probability.

In project management, the project team often uses this simulation during the risk analysis phase to understand how changes in key variables, such as cost or time, can affect the overall success of a project. By running thousands of simulations using different random values within specified ranges, teams can develop a more comprehensive understanding of likely outcomes and their associated probabilities. This method enables more confident decision-making, even in the face of uncertainty.

Key Points

  • Monte Carlo simulation uses random sampling and repeated calculations to estimate risk and forecast outcomes.
  • It helps identify the probability of meeting specific project objectives, such as deadlines or budgets.
  • The method facilitates a deeper understanding of uncertainty in complex projects.
  • It provides a range of possible results instead of a single estimate.
  • Software tools often automate the process, allowing for faster and more accurate analysis.

Related Terms

  • Risk analysis involves assessing potential uncertainties in a project, and teams often utilize Monte Carlo simulation to support this process.
  • Contingency planning benefits from this simulation by estimating the likelihood of scenarios that may require backup strategies.
  • Sensitivity analysis complements Monte Carlo simulation by showing how changes in one variable impact outcomes.
  • Quantitative risk analysis frequently uses Monte Carlo simulation to provide numerical estimates of project risks.
  • Project forecasting can be improved through the use of simulation techniques that account for variable factors.

Monte Carlo Simulation: Example

A construction company planning a highway project uses Monte Carlo simulation to predict whether it can complete the job within 18 months. The team defines key variables such as weather delays, material availability, and labour costs, each with a range of possible values. They run 10,000 simulations with different random inputs. The results indicate a 75% chance of completing the project within the timeline and a 25% risk of delay, which helps the team justify adding a buffer to their schedule.

Monte Carlo Simulation: Best Practices

  • Clearly define input variables and assign realistic probability distributions.
  • Use reliable historical data to inform simulation assumptions.
  • Run a sufficient number of simulations (typically thousands) to obtain meaningful results.
  • Review outputs with stakeholders to support transparent decision-making.
  • Combine with other risk management tools for a balanced approach.

Additional Resources

Monte Carlo Simulation - A Guide to the Project Management Body of Knowledge (PMBOK Guide)     Monte Carlo Simulation - PROJECT RISK ANALYSIS MADE RIDICULOUSLY SIMPLE

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