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| Criterion |  Product Discovery Dual-Track Agile |  Agile Affinity Estimation |  Delivery Monte Carlo Forecasting |  Agile NoEstimates |
|---|---|---|---|---|
Purposedifferent | 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. | 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 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. |
Complexitydifferent | Medium | Medium | High | Medium |
Timedifferent | Laufend, Wochen bis Monate | 30-90 min | 30-90 min Setup, danach laufend | laufend |
Participantsdifferent | 4-10 | 3-12 | 1-8 | 2-12 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Affinity Size Map, Grouped Estimates, Unclear Items | Forecast Percentiles, Throughput Dataset, Risk Communication | Throughput Data, Flow Forecast, Slicing Rules |
Tagsno overlap | AgileDiscoveryDelivery | EstimationBacklogRelative sizing | ForecastingFlowDelivery | EstimationForecastingFlow |