Use these tips to build reliable Monte Carlo simulations and get actionable
results in Workspace.
Monte Carlo simulation is only as good as the model behind it. Using realistic distributions, validated equations, and appropriate iteration counts reduces the risk of drawing conclusions from assumptions that do not reflect the real system.
Refer to the following guidelines to ensure that your Monte Carlo simulation results are
accurate, interpretable, and actionable.
Start with a simple model-
- Start with the most important inputs and outputs in your system. A simple model is easier to validate and interpret, and it often reveals the primary drivers of performance.
- You can add complexity later as needed.
Choose input distributions carefully-
- If you have historical data that represents expected future performance, use it to help select appropriate distributions.
- If historical data is not available, rely on process knowledge or subject-matter experts.
Enter specification limits whenever
possible-
- Add upper or lower specification limits to calculate capability metrics and percent out-of-specification. These measures are often more useful for decision-making than averages alone.
- Even if final specifications are not yet approved, enter preliminary or target limits to evaluate risk, percent out-of-specification, and relative capability early in the analysis.
Interpret sensitivity analysis results
strategically-
- Inputs with steeply sloped lines have the greatest impact on output variation and are strong candidates for tighter control.
- Inputs with flat lines have little effect on variability and may allow for relaxed tolerances.
Organize complex simulations with groups-
- For large or complex models, use groups to organize inputs and outputs by function or process step.
- Groups make models easier to manage, review, and communicate to others.
Expect small differences between simulation
runs-
- Because Monte Carlo simulations rely on random sampling, results will vary slightly each time you run the simulation.
- Focus on overall trends, ranges, and comparisons rather than exact values.
Duplicate models to explore scenarios-
- Keep a baseline version of your simulation while testing alternative scenarios, such as new input settings or reduced variability.
- Compare results across models to help you evaluate tradeoffs and choose the best option.
