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| Criterion |  Agile Story Points |  Delivery Monte Carlo Forecasting |  Operations After-Action Review |
|---|---|---|---|
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. | 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. | After missions or project phases with unclear outcomes, the method makes visible what actually happened and what can be learned from it. It separates course, effect, and causes so experience turns into solid improvement. |
Complexitydifferent | Medium | High | Low |
Timedifferent | laufend, 1-5 min je Item | 30-90 min Setup, danach laufend | 20-45 min |
Participantsdifferent | 3-9 | 1-8 | 3-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Point Estimates, Reference Stories, Velocity Data | Forecast Percentiles, Throughput Dataset, Risk Communication | Lessons learned, Action items, Event summary |
Tagsno overlap | EstimationAgileMeasurement | ForecastingFlowDelivery | LearningOperationsImprovement |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
