thrivve-mc-how-many

What problem does it solve?

Forecasts how many stories or tasks will be completed by a future date using Monte Carlo simulation based on historical daily throughput data, enabling probabilistic planning and capacity sizing.

Core Features & Use Cases

• Monte Carlo forecasting over a user-defined horizon using historical throughput data.
• Handles input: at least 10 days of throughput data, optional start date, target date, and confidence level.
• Provides percentile-based forecasts (P50, P85, P95) and readable summaries for planning.
• Use case: you want to know how many stories you can expect by a deadline given recent throughput.

Quick Start

Run a Monte Carlo forecast using your historical throughput to estimate how many stories will be completed by a target date at a chosen confidence level.