thrivve-mc-when

Monte Carlo forecasting predicts backlog completion dates by running simulations on your historical daily throughput data. You input your remaining story count and a desired confidence level to receive probabilistic percentile-based completion dates.

Monte Carlo throughput forecasting provides completion dates across multiple confidence levels, specifically P25, P50, P85, P95, and P99. This allows you to plan for uncertainty by choosing a percentile that matches your project's risk tolerance.

Monte Carlo backlog forecasting requires a minimum of 10 days of historical daily throughput data. This baseline ensures sufficient data variation for the simulation to generate accurate, percentile-based completion date predictions.

Yes, Monte Carlo forecasting includes automatic data validation and an XMR variation check. This assesses your historical throughput stability before forecasting, ensuring your agile team's data consistency supports reliable probabilistic predictions.

Estimating when remaining backlog stories will finish is best achieved through probabilistic forecasting. By applying Monte Carlo simulation to your historical throughput, you reduce reliance on single-point deadlines and obtain confident completion dates.