What is Monte Carlo simulation?
Monte Carlo simulation is a computational method that models uncertain inputs as ranges (probability distributions), then runs the calculation thousands of times with randomly drawn values. The outputs form a distribution of possible results.
How a Monte Carlo simulation works
- Build a model of the outcome (e.g. annual profit).
- Identify uncertain inputs (demand, price, cost).
- Give each a range or distribution based on data.
- Run the model thousands of times with random inputs.
- Analyse the results: average, range, and probabilities (e.g. P(profit > target)).
Example
A business plans a new outlet. Demand may be 300–500 covers a day, average bill ₹350–₹450, food cost 30–36%. 10,000 simulations show expected monthly profit of ₹4.1 lakh, with a 15% chance of a loss in the first six months — useful for deciding how much buffer cash to keep.
Common mistakes to avoid
- Using ranges based on hope rather than data.
- Ignoring correlations between inputs.
- Presenting only the average and hiding the range.
How DOOfy helps
Simulations are only as good as the ranges you feed them. AIMOS One provides real historical variation in demand, prices and costs from your own operations, so simulation inputs reflect reality.
Frequently asked questions
Why is it called Monte Carlo?
It is named after the Monte Carlo casino, because the method relies on random sampling, like games of chance.
Do I need special software?
Simple simulations can be done in a spreadsheet; larger models use statistical or analytics tools.