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| Criterion |  Agile Affinity Estimation |  Product Discovery Dual-Track Agile |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
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. | When uncertainty and delivery run in parallel, it separates learning work from implementation while keeping both connected. It prevents unvalidated ideas from falling directly into the delivery stream. | 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 | 30-90 min | Laufend, Wochen bis Monate | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-12 | 4-10 | 1-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationBacklogRelative sizing | AgileDiscoveryDelivery | ForecastingFlowDelivery |
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