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| Criterion |  Delivery Monte Carlo Forecasting |  Agile Affinity Estimation |  Agile NoEstimates |  Agile Story Splitting |
|---|---|---|---|---|
Purposedifferent | 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. | When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. | 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. | When a story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 30-90 min | laufend | 30-60 min |
Participantsdifferent | 1-8 | 3-12 | 2-12 | 2-6 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Affinity Size Map, Grouped Estimates, Unclear Items | Throughput Data, Flow Forecast, Slicing Rules | Smaller Stories, Acceptance Criteria, Split Rationale |
Tagsno overlap | ForecastingFlowDelivery | EstimationBacklogRelative sizing | EstimationForecastingFlow | BacklogIterationDelivery |