Monte Carlo Forecasting

The method is simpler than its name suggests. Take a team's recent throughput — how many items it completed each week — as a sample of how it really behaves. To forecast finishing some number of items, randomly draw from that history week after week, accumulating completions until the work is done, and record how long it took. Do that thousands of times and you get not one answer but a distribution of outcomes, because each simulated future draws a different sequence of good and bad weeks.

That distribution is the forecast. Rather than a single date that is almost certainly wrong, you read percentiles: there might be a 50% chance of finishing by one date and an 85% chance by a later one. This lets a team commit at a confidence level it chooses — conservative for an external promise, aggressive for an internal stretch — and communicate uncertainty honestly instead of hiding it behind a point estimate.

Its great advantage is that it needs no story points or estimation at all — only the completion data a team already generates. It also naturally accounts for variability, including the bad weeks that single-number plans always forget. The main caveat is that it assumes the future resembles the recent past, so it works best when the team and the type of work are reasonably stable.

Planoda charts throughput per period from completion events, the raw material a Monte Carlo forecast runs on — so delivery questions can be answered with a probability grounded in real history rather than an optimistic guess.