thrivve-mc-how-many

To forecast Agile story completion by a target date, run a Monte Carlo simulation using your historical daily throughput data. The skill calculates probabilistic outcomes at specified confidence levels like P50, P85, or P95 to estimate how many stories will be done.

Monte Carlo simulation for Agile throughput forecasting is a method that runs multiple iterations across a planning horizon using historical daily throughput data. It produces probabilistic percentile forecasts, such as P50 or P85, to predict how many tasks will be completed by a future date.

You need at least 10 days of historical daily throughput data to run a Monte Carlo forecast. The simulation uses this history to project future completion rates, and an XMR-style variation check validates the stability of your throughput data before generating the forecast.

Yes, you can set an optional confidence level for your Monte Carlo story forecast. The skill uses this input to generate percentile-based forecasts, allowing you to choose thresholds like P50, P85, or P95 to match your planning risk tolerance.

Yes, the Monte Carlo forecast validates data stability using an XMR-style variation check on your historical throughput. This ensures the daily completion rates are consistent enough to produce reliable probabilistic forecasts for your Agile planning horizon.

The best way to use Monte Carlo simulation for capacity sizing is to input your historical daily throughput, a start date, and a target date. The simulation outputs percentile forecasts that help you set realistic expectations for how many stories your team can deliver within that timeframe.