Monte Carlo simulation

Monte Carlo simulation runs thousands of scenarios to predict the range of possible outcomes for your project. The results help you set realistic expectations, size contingency budgets, and communicate risk to stakeholders with confidence.

Where to find simulation results

Monte Carlo results appear on the Project dashboard. Navigate to Dashboards Project, select a project from the dropdown, then scroll to the simulation section.

What you see

S-curve visualization (cumulative distribution)
P50, P85, and P95 percentile values
Cost impact ranges based on EMV
Sensitivity analysis showing top risk contributors

How Monte Carlo uses EMV

Each risk in your project has an EMV (Expected Monetary Value), calculated as probability multiplied by financial impact. A single EMV gives you a useful number, but it assumes all risks are independent and averages out the uncertainty.

Monte Carlo goes further. It runs thousands of simulations where each risk either occurs or doesn't, based on its probability. The result is a distribution of total cost outcomes rather than a single number. This tells you not just the expected cost, but how much it could realistically vary.

Example:

A project with 15 risks has a total deterministic EMV of €1.2M. Monte Carlo shows P50 at €950K, P85 at €1.8M, and P95 at €2.4M. The difference between your base estimate and P85 (€600K) is the recommended contingency budget.

Understanding percentiles

Percentiles tell you the probability of achieving a certain outcome. Think of them as confidence levels for your estimates.

P50

50% confidence

There is a 50% chance the actual outcome will be at or below this value. This is the median result.

If P50 cost is €950K, half the simulations came in at €950K or less.

P85

85% confidence

There is an 85% chance the actual cost will be at or below this value. Use P85 for contingency budgets.

If P85 cost is €1.8M, 85% of simulations stayed at €1.8M or less.

P95

95% confidence

There is a 95% chance the actual cost will be at or below this value. Use P95 for management reserve.

If P95 cost is €2.4M, only 5% of simulations exceeded €2.4M.

Reading the S-curve

The S-curve (cumulative distribution function) plots outcome values on the x-axis against cumulative probability on the y-axis. A steeper curve means more certainty in your estimates. Flat sections indicate ranges of high uncertainty.

X-axis: Total cost exposure (€)
Y-axis: Cumulative probability (0-100%)
Steep sections: High certainty around those values
Flat sections: High uncertainty in that range

Practical applications

Apply Monte Carlo results to make better decisions about contingencies and planning.

Set contingency budgets

Use P85 for planning contingency and P95 for management reserve. The difference between your deterministic estimate and the P85 value is the recommended contingency amount.

Communicate risk to stakeholders

Share the range from P50 to P95 with stakeholders. This sets realistic expectations and shows the full spectrum of possible outcomes rather than a single-point estimate.

Focus on high-impact risks

Review the sensitivity analysis to identify which risks contribute most to overall uncertainty. Focus your measure effort on these risks first for the greatest reduction in exposure.