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| Criterion |  Agile Affinity Estimation |  Delivery Monte Carlo Forecasting |  Agile Ideal Days |
|---|---|---|---|
Purposedifferent | 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. | 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 effort needs to be thought of as real working time, it offers a simple common denominator for comparison. It sorts work by value, risk, and delivery ability. The result is captured as Ideal Day Estimates, Assumption Notes, and Capacity Caveats. |
Complexitydifferent | Medium | High | Low |
Timedifferent | 30-90 min | 30-90 min Setup, danach laufend | 15-60 min |
Participantsdifferent | 3-12 | 1-8 | 2-9 |
Formatdifferent | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Forecast Percentiles, Throughput Dataset, Risk Communication | Ideal Day Estimates, Assumption Notes, Capacity Caveats |
Tagsno overlap | EstimationBacklogRelative sizing | ForecastingFlowDelivery | EstimationEffortAgile |
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