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| Criterion |  Agile Story Points |  Agile NoEstimates |  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 estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | 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 | Medium | High |
Timedifferent | laufend, 1-5 min je Item | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-9 | 2-12 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Point Estimates, Reference Stories, Velocity Data | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationAgileMeasurement | EstimationForecastingFlow | ForecastingFlowDelivery |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
