thrivve-mc-when

What problem does it solve?

Monte Carlo-based forecasting helps teams estimate when a backlog of remaining stories will be completed given historical throughput and a chosen confidence level, reducing reliance on single-point deadlines.

Core Features & Use Cases

• Probabilistic forecasts show completion dates at multiple confidence levels (P25, P50, P85, P95, P99) for planning under uncertainty.
• Automatic data validation and a variation check (XMR) to assess throughput stability before forecasting.
• Real-world use: forecast 100 remaining stories with 15 days of throughput data to determine the date by which you can be X% confident to finish.

Quick Start

Run the Monte Carlo script with your throughput history, the number of remaining stories, and an optional confidence level to obtain forecast dates.