Monte Carlo Delivery Forecaster
Use Monte Carlo simulation to forecast project delivery dates based on historical throughput data. Get probabilistic estimates with P50, P85, and P95 confidence levels.
Historical Throughput Data
Add at least 2 throughput data points to run a simulation.
What is Monte Carlo Forecasting?
Monte Carlo simulation uses random sampling from your historical throughput data to generate thousands of possible future outcomes. Instead of a single deterministic estimate, you get a probability distribution showing the likelihood of finishing within a given number of sprints. This approach accounts for natural variability in team performance and provides more realistic delivery forecasts.
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How to Use the Monte Carlo Forecaster
Enter Historical Throughput
Add your team's historical throughput data — the number of items completed per sprint or week. The more data points you add, the more accurate your forecast will be.
Set Remaining Work
Enter the total number of remaining items (stories, tasks, tickets) that need to be delivered. Adjust simulation iterations for more or less precision.
Run Simulation & Read Results
Click 'Run Simulation' to execute thousands of randomized iterations. Review the P50, P85, and P95 percentile results and the probability distribution chart to understand your delivery timeline.
Practical Use Cases
Sprint Planning
"Forecast how many sprints your team needs to deliver the remaining backlog, giving stakeholders probabilistic timelines instead of single-point estimates."
Release Date Commitment
"Use the P85 percentile to commit to a delivery date with high confidence, while communicating the P50 as an optimistic target."
Risk Assessment
"Compare the spread between P50 and P95 to understand delivery risk. A wide spread indicates high variability and potential schedule risk."
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Frequently Asked Questions
?What is a Monte Carlo simulation?
A Monte Carlo simulation is a statistical technique that uses random sampling to model the probability of different outcomes. In delivery forecasting, it randomly samples from your historical throughput data thousands of times to predict how long future work might take.
?How many data points do I need?
A minimum of 2 data points is required, but we recommend at least 8-10 sprints or weeks of throughput data for reliable forecasts. More data provides a more representative sample of your team's performance variability.
?What do P50, P85, and P95 mean?
P50 means there is a 50% probability of completing within that many sprints. P85 provides 85% confidence, and P95 gives 95% confidence. Most teams use P85 for external commitments.
?Does this work for Kanban teams?
Absolutely. Use your weekly throughput (items completed per week) instead of sprint-based data. The simulation works the same way regardless of your workflow methodology.
?Is my data private?
Yes. All calculations run entirely in your browser. No data is sent to any server. Your throughput data is optionally saved in your browser's local storage for convenience.
?Is this tool free to use?
Yes, the Monte Carlo Delivery Forecaster is completely free with no registration required. It runs 100% in your browser.
?How many iterations should I use?
10,000 iterations is the recommended default and provides a good balance between accuracy and speed. You can increase to 50,000 for more precision, though the difference is usually minimal.