monte-carlo-simulation

Monte Carlo simulation quantifies uncertainty in project costs and schedules by sampling from user-supplied distributions and aggregating results into percentiles and contingency recommendations. It runs 10,000 iterations by default to produce probabilistic risk assessments.

Monte Carlo risk analysis supports triangular, normal, uniform, PERT, and discrete distributions. You provide a model specification with variables, assign a distribution to each, and define a target formula to run the simulation.

Sensitivity analysis identifies variable impact by generating a sensitivity tornado chart from the Monte Carlo simulation results. This visualizes which input distributions have the greatest effect on your target outcome, helping prioritize risk factors.

Monte Carlo simulation for contingency planning generates P10, P50, P80, P90, and P95 percentiles, the probability of meeting a target threshold, a sensitivity tornado, and contingency recommendations to support high-stakes commitments.

Monte Carlo simulation is applicable to revenue forecasting under uncertainty. By applying distributions to revenue variables and running 10,000 iterations, it outputs probability percentiles and the likelihood of meeting revenue targets.

Monte Carlo simulation results are reproducible because the tool enforces a fixed random seed of 42 and runs 10,000 iterations by default. This ensures consistent outputs for percentiles and sensitivity analysis across repeated runs.