monte-carlo-simulation

name	monte-carlo-simulation
description	Technical instructions for implementing probabilistic forecasting.

Monte Carlo Simulation Skill

Use this skill to implement the "When" and "How Many" forecasting modules.

1. Data Requirements

Input data must be a clean list of Daily Throughput:

• [0, 1, 3, 0, 2, ...] where each number is the count of items moved to 'Done' on a specific day.

2. Algorithm (Sampling with Replacement)

1. Setup: Define backlog_size and iterations (default 10,000).
2. Loop:
   • Randomly pick a throughput value from history.
   • Subtract from remaining backlog.
   • Track total days until backlog <= 0.
3. Distribution: Store the result of each iteration to build a histogram.

3. Interpretation (Confidence Levels)

Report results using percentiles:

• 50% (Median): The coin-flip date. Not for commitment.
• 85% (Professional): High confidence, accounts for variability.
• 95% (Safe): Minimal risk.

4. Implementation Notes

• Use a fast random number generator.
• Cache simulation results if historical data hasn't changed.
• For "How Many", fix the number of days and sum the sampled throughput.