Monte Carlo Simulation Calculator Guide
Use this Monte Carlo simulation calculator to model profit uncertainty, estimate expected profit, confidence intervals, downside risk, value at risk, and the probability of meeting a business target.
How to use the Monte Carlo simulation calculator
Enter the base revenue, cost of goods sold, operating expenses, tax rate, and the uncertainty ranges for revenue, COGS, and operating expenses. Then choose how many simulations to run.
The calculator generates a distribution of possible profit outcomes so you can compare expected profit, median profit, downside percentiles, probability of loss, and probability of meeting the base-case target.
What is Monte Carlo simulation?
Monte Carlo simulation is a forecasting method that runs many possible outcomes using uncertain input assumptions. Instead of relying on one forecast, it shows a probability distribution of results.
In business planning, Monte Carlo analysis is often used for revenue forecasts, cash flow projections, investment decisions, project risk analysis, pricing decisions, and budget planning.
Monte Carlo simulation formula and logic
Each simulation adjusts revenue, COGS, and operating expenses within the selected uncertainty ranges, then calculates gross profit, EBIT, taxes, net profit, net margin, and gross margin.
After all simulations are complete, the calculator sorts the outcomes and calculates mean, median, standard deviation, percentiles, confidence interval, value at risk, expected shortfall, skewness, and kurtosis.
How to interpret the results
The mean is the expected value across all simulated outcomes. The median is the middle result. If mean and median are far apart, the distribution may be skewed by unusually strong or weak outcomes.
The 5th percentile is a downside case where only about 5 percent of outcomes are worse. The 95th percentile is an upside case where only about 5 percent of outcomes are better.
Common Monte Carlo simulation mistakes
The most common mistake is entering unrealistic uncertainty ranges. If the revenue, cost, or expense assumptions are too optimistic, the simulation will still produce optimistic results.
Another mistake is treating Monte Carlo output as a prediction instead of a risk model. The result is only as useful as the assumptions, ranges, and distributions behind it.
- Use ranges based on historical data or defensible planning assumptions.
- Run enough simulations for a stable distribution.
- Review downside percentiles, not only the average result.
- Compare probability of loss with your risk tolerance.
- Update assumptions when market conditions or cost structure changes.