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| Criterion |  Agile Affinity Estimation |  Delivery Monte Carlo Forecasting |  Agile Planning Poker |  Product Discovery Dual-Track Agile |
|---|---|---|---|---|
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 estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions. | 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. |
Complexitydifferent | Medium | High | Low | Medium |
Timedifferent | 30-90 min | 30-90 min Setup, danach laufend | 2-5 min je Item | Laufend, Wochen bis Monate |
Participantsdifferent | 3-12 | 1-8 | 3-9 | 4-10 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Forecast Percentiles, Throughput Dataset, Risk Communication | Relative Estimates, Assumption Notes, Split Candidates | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories |
Tagsno overlap | EstimationBacklogRelative sizing | ForecastingFlowDelivery | EstimationAgileRelative sizingTeam | AgileDiscoveryDelivery |