Definition
Delivery forecasting
Delivery forecasting is projecting when work will finish from a team's observed throughput rather than from a chosen deadline. It fits a trend to the remaining-scope trajectory and extrapolates the completion date — and, in its probabilistic form, returns a range with confidence levels instead of a single date, so commitments are grounded in evidence rather than optimism.
Key takeaways
- Delivery forecasting projects a completion date from observed throughput, not from a wished-for deadline.
- Its probabilistic form returns a range with confidence levels (e.g. P50/P85) rather than a single date.
- An honest forecast abstains — 'no trend' or 'insufficient data' — instead of fabricating a confident date.
- Planoda surfaces a least-squares forecast on cycles and insights with an at-risk verdict against the deadline.
Setting a date is an act of hope; forecasting one is an act of measurement. The technique reads how scope has actually been burning down — the real remaining-work series from a cycle or project — and projects where that trajectory reaches zero. A simple version fits a least-squares line for one expected date; a robust version runs a Monte Carlo over the historical throughput distribution to produce a band, typically a P50 'likely' and a P85 'safe' date.
An honest forecast also knows when to abstain. If scope isn't shrinking or there are too few data points, it reports 'no trend' or 'insufficient data' rather than inventing a confident date — because a fabricated forecast is worse than none. The discipline it enforces is cultural as much as statistical: it replaces 'when do you think you'll be done?' with 'here's what the data says, at what confidence.'
Planoda computes a least-squares delivery forecast from the live burndown series and surfaces it on cycle and insights views with an at-risk verdict against the deadline — the foundation for the probabilistic Monte Carlo band — so projected dates come from the team's real pace.
Related terms
- Delivery forecastA delivery forecast projects when work will actually finish, based on the team's observed throughput rather than a wished-for deadline. Instead of asking everyone to re-estimate, it fits a trend to the remaining-scope trajectory and extrapolates the completion date — and says so honestly when the data won't support a projection.
- Monte Carlo ForecastingMonte Carlo forecasting predicts delivery by running thousands of simulated futures from a team's historical throughput, producing a probability rather than a single date. Instead of 'we'll finish on the 20th,' it answers 'an 85% chance of finishing by the 24th.' It replaces false-precision estimates with honest, evidence-based ranges drawn from how the team actually performs.
- ThroughputThroughput is the number of work items a team completes in a given period — issues finished per week, for example. It is the simplest flow metric: a direct count of output over time. Tracked across periods, throughput reveals a team's real delivery capacity and is the basis for probabilistic, estimate-free forecasting.
- VelocityVelocity is the average amount of work a team completes per cycle, measured in issues or story points. By tracking it over several cycles, teams forecast how much they can realistically take on next. Velocity is a planning aid for a specific team over time — never a target to maximize or a way to compare teams against each other.
- Cycle (Sprint)A cycle — often called a sprint — is a fixed, repeating time-box, usually one or two weeks, during which a team commits to a focused set of work and aims to finish it. Cycles create a regular cadence for planning, focus, and review, turning an open-ended backlog into shippable increments.