Monte Carlo Simulation Calculator
Use the Monte Carlo simulation calculator to model uncertainty in a process, forecast, or business equation. Define uncertain input variables as probability distributions, combine them in one or more output equations, and simulate many possible outcomes.
Start by entering the X variables that influence your result. For each variable, choose a distribution such as normal, uniform, triangular, lognormal, exponential, or discrete, and enter the required parameters. Then define the Y output with an equation that uses the input variable names. Optional lower and upper specification limits can be added to estimate defect rates, PPM, and capability measures.
The model diagram helps you check whether the selected inputs are connected to the intended outputs. After running the simulation, the result table summarizes the mean, standard deviation, minimum, maximum, specification-limit risk, Ppk, normality check, histogram, and sensitivity of the output to each input.
When to use Monte Carlo simulation
Monte Carlo simulation is useful when a result depends on uncertain inputs and a single best-case or average-case calculation is not enough. Typical use cases include process capability with variable inputs, financial forecasts, risk analysis, product tolerance studies, demand planning, and any model where uncertainty should be shown as a range of possible outcomes.
How to use the calculator
- Enter each uncertain input as an X variable and select its distribution.
- Check the distribution parameters and add discrete values if needed.
- Define one or more Y outputs with equations such as Revenue = Visitors * ConversionRate * OrderValue.
- Add optional lower or upper specification limits when you want defect percentages or PPM.
- Review the model diagram and run the simulation with the desired number of iterations.
- Interpret the histogram, summary statistics, specification-limit results, and sensitivity table.
The simulation output should be interpreted as an approximation based on the distributions and equations you provide. Better assumptions about the input distributions usually lead to more useful risk estimates.