Probabilistic Forecasting with Monte Carlo Simulations
Configure your parameters and click "Generate Forecast"
This tool runs 500 Monte Carlo simulations using a triangular distribution, which acknowledges that extreme values (very low or very high throughput) are less likely than values near your "most likely" estimate, creating more realistic project completion timelines with confidence intervals.
Traditional project estimation asks you to predict a single completion date. This approach assumes everything goes according to plan - no surprises, no variability, perfect execution. Reality is messier: some weeks you complete more work, some weeks less. Scope evolves. Risks materialize. Single-point estimates are consistently wrong.
Monte Carlo simulation embraces uncertainty. Instead of one estimate, this tool runs your project 500 times with different combinations of variables. Each simulation randomly samples from your throughput data, scope ranges, and risk factors. The result: a probability distribution showing realistic completion dates with measurable confidence levels.
For each of 500 simulations, the tool:
After 500 trials, results are sorted to calculate percentiles: 50% (median), 85% (recommended for commitments), and 95% (high confidence).
When using three-point estimates, the tool applies a triangular distribution. This means values near your "most likely" estimate occur more frequently than extreme values (pessimistic/optimistic). This creates more realistic forecasts than uniform distribution where all values are equally likely. The math: for random value U between 0 and 1, if U ≤ (mode-min)/(max-min), return min + √(U × (max-min) × (mode-min)), otherwise return max - √((1-U) × (max-min) × (max-mode)).
500 trials provides statistically significant results while running quickly in your browser (typically under 1 second). This sample size produces stable percentile calculations - running 1000 or 10,000 simulations would yield similar results but take longer. The Central Limit Theorem ensures our probability distribution converges to the true distribution with 500 samples.
This tool builds upon the foundational work of Troy Magennis, a pioneer in applying Monte Carlo simulation and statistical methods to agile project forecasting. Troy's work at Focused Objective has educated thousands of practitioners worldwide on data-driven forecasting techniques.
Troy's Throughput Forecaster spreadsheet and other freely-available tools demonstrated how probabilistic methods could replace traditional single-point estimates with realistic confidence intervals. His emphasis on historical data, Monte Carlo simulation, and transparent methodologies has shaped modern approaches to project forecasting in agile environments.
While this web-based tool is an independent implementation, we gratefully acknowledge Troy's intellectual contributions to the field. His educational materials and spreadsheet tools served as invaluable references during development.
Learn More from Troy Magennis:
Monte Carlo simulation is a probabilistic technique that runs hundreds of simulations using random sampling to predict project completion dates with confidence intervals. Instead of giving you a single deadline that's likely wrong, it provides a probability distribution showing realistic completion ranges based on your data.
Use historical data if you have tracked past sprint throughput - it's significantly more accurate because it reflects actual team performance including all hidden factors. Use three-point estimates only for new teams, new types of work, or when historical data isn't available. Aim for 8-12 weeks of historical data for best results.
For external stakeholder commitments, use the 85% confidence level - it provides high confidence without excessive padding. Use 50% only for internal planning (but expect 50% chance of delay). Reserve 95% for critical deadlines where failure has severe consequences.
The tool runs 500 trial simulations. Each trial: (1) Randomizes story count within your range, (2) Applies split factor for scope creep, (3) Evaluates each risk probabilistically, (4) Simulates weekly progress using randomized throughput until project completes. Results are sorted to calculate percentiles (50th, 85th, 95th) showing probability distribution of completion dates.
Split factor accounts for stories that get split into multiple smaller stories during development. A factor of 1.5 means for every 10 planned stories, you'll actually complete 15 stories worth of work. Typical values: 1.0-1.2 for well-groomed backlogs, 1.2-1.5 for normal uncertainty, 1.5-2.0 for high uncertainty or new domains.
Team capacity accounts for time spent on non-project work. Typical values: 100% for dedicated teams with no other responsibilities, 70-80% for primary project with some maintenance, 50-60% for teams with significant other responsibilities. Track actual time for one week if unsure - you'll likely find attention is more fragmented than expected.
Traditional estimation assumes everything goes to plan - it produces single-point estimates that are consistently wrong. Monte Carlo acknowledges reality: throughput varies week to week, scope changes, risks materialize. By modeling these variables probabilistically across hundreds of scenarios, you get realistic ranges with measurable confidence levels instead of false precision.
Yes, Monte Carlo estimation works for any agile methodology. For Scrum teams, use story points or story counts completed per sprint. For Kanban teams, use throughput (items completed per week) from your cumulative flow diagram. The key is consistent measurement - whatever units you track, use those consistently.
Include 2-5 meaningful risks that might add work: legacy code refactoring, regulatory requirements, integration issues, additional platform support. Each risk needs probability (0-100%) and impact range (min/max stories added). Don't use 100% probability - if something is certain, add it to your scope range instead of modeling as a risk.
Monte Carlo simulations typically provide 85-95% accuracy when based on quality historical data, significantly outperforming traditional estimation methods. Accuracy depends on input data quality - using actual past throughput yields much better results than estimates. The more historical data you have, the more accurate your forecasts become.
Yes, this tool is completely free to use with no registration or account required. All calculations run entirely in your browser - your project data never leaves your device. We can't see or access any of your project information, story counts, or throughput data.