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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Dual-Track Agile |  Agile Affinity Estimation |  Agile Ideal Days |
|---|---|---|---|---|
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 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. | 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 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 | High | Medium | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | Laufend, Wochen bis Monate | 30-90 min | 15-60 min |
Participantsdifferent | 1-8 | 4-10 | 3-12 | 2-9 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Affinity Size Map, Grouped Estimates, Unclear Items | Ideal Day Estimates, Assumption Notes, Capacity Caveats |
Tagsno overlap | ForecastingFlowDelivery | AgileDiscoveryDelivery | EstimationBacklogRelative sizing | EstimationEffortAgile |