Monte Carlo Simulation uses repeated random sampling from input distributions to estimate the range, probability, and risk of possible outcomes.
Definition
Monte Carlo Simulation is an analytical method that models uncertainty by repeatedly sampling from defined input distributions and calculating resulting outputs. Instead of a single point estimate, it produces a range of possible outcomes and probabilities.
It is useful when several uncertain inputs combine to affect cost, lead time, demand, reliability, inventory, capacity, or risk.
History
Monte Carlo methods developed in mathematical statistics and computing, with early use in scientific and defense applications. As computing became accessible, the method spread into engineering, finance, supply chain, quality, and project risk analysis.
When to Use
Use Monte Carlo Simulation when decisions depend on uncertainty, variation, multiple interacting assumptions, or risk probability. It is valuable for tolerance stack-up, project duration, demand planning, inventory, cost risk, and reliability estimates.
Step-by-Step
- Define the output decision or risk.
- Identify uncertain inputs and relationships.
- Choose realistic distributions for inputs.
- Run many simulated trials.
- Review output distribution, percentiles, and probabilities.
- Perform sensitivity analysis.
- Validate assumptions with data or experts.
- Use results to choose controls, buffers, or actions.
Examples
- Project risk: Activity durations are simulated to estimate completion probability.
- Inventory: Demand and lead-time variation estimate stockout risk.
- Quality: Dimensional variation is simulated for assembly fit.
Common Pitfalls
- Using guessed distributions without review.
- Confusing model precision with truth.
- Ignoring correlations between inputs.
- No sensitivity analysis.
- Too few trials for stable estimates.
- No validation against real outcomes.