Monte Carlo Simulation for Probabilistic Schedule Analysis
Configure Monte Carlo analysis to estimate the probability of meeting deadline and budget targets.
Apply Monte Carlo simulation to the project schedule and budget to generate probability distributions for completion dates and determine contingency reserves using statistical analysis.
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Prompt objective
Apply Monte Carlo simulation to the project schedule and budget to generate probability distributions for completion dates and determine contingency reserves using statistical analysis.
Real use case
A clean energy startup needs to deliver a 5MW solar installation in 14 months to secure an $18 million contract with the utility provider. The project manager wants to know the realistic probability of on-time delivery, accounting for uncertainties in panel imports and environmental permitting.
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Prompt
Configure a Monte Carlo simulation for the following project: Project: [PROJECT NAME] Planned deterministic duration: [MONTHS/DAYS] Deterministic budget: [AMOUNT IN USD] Number of activities on critical path: [QUANTITY] Activities with highest uncertainty: [LIST WITH OPTIMISTIC/LIKELY/PESSIMISTIC ESTIMATES] Assumed distribution: [TRIANGULAR / PERT / NORMAL] Number of iterations: [1,000 / 5,000 / 10,000] The analysis must include: 1) Simulation configuration: - Input table: activity, distribution, parameters (min, likely, max) - Correlations between activities (if applicable) - Documented simulation assumptions 2) Schedule results: - Histogram of completion date distribution - Percentiles: P10, P25, P50, P75, P85, P90, P95 - Probability of meeting the planned date (%) - Date with 85% confidence - Activities contributing most to variability (tornado chart) 3) Cost results: - Total cost distribution - Cost percentiles: P50, P85, P95 - Value at risk (difference P85 - P50) 4) Sensitivity analysis: - Top 5 risk drivers (activities/costs with highest impact) - Correlation coefficient of each driver to the final outcome 5) Recommendations: - Recommended schedule buffer (P85 - deterministic) - Contingency reserve (P85 - baseline budget) - Actions to reduce variability in top drivers - Recommended re-simulation frequency 6) Simplified Python script to reproduce the simulation Format: technical report with input/output tables, text-based histograms, tornado chart, and executive recommendations.
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