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| Criterion |  Agile Story Points |  Facilitation Dot Estimation |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
Purposedifferent | When teams want to rate complexity rather than hours, it creates a shared, relative scale. It sorts work by value, risk, and delivery ability. The result is captured as Point Estimates, Reference Stories, and Velocity Data. | When size or effort can only be estimated roughly, Dot Estimation condenses the group's experience into a quick range. It turns individual contributions into a visible selection. The result is captured as an Effort Heatmap, Risk Signals, and Discussion Targets. | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. |
Complexitydifferent | Medium | Low | High |
Timedifferent | laufend, 1-5 min je Item | 5-20 min | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-9 | 3-20 | 1-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Point Estimates, Reference Stories, Velocity Data | Effort Heatmap, Risk Signals, Discussion Targets | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationAgileMeasurement | EstimationEffortRisk | ForecastingFlowDelivery |
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