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| Criterion |  Agile Story Points |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |  Product Discovery Dual-Track Agile |
|---|---|---|---|---|
Purposedifferent | When teams want to rate complexity rather than hours, it creates a shared, relative scale. It sorts work by value, risk, and delivery ability. The result is captured as Point Estimates, Reference Stories, and Velocity Data. | 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. | 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. |
Complexitydifferent | Medium | Medium | High | Medium |
Timedifferent | laufend, 1-5 min je Item | laufend | 30-90 min Setup, danach laufend | Laufend, Wochen bis Monate |
Participantsdifferent | 3-9 | 2-12 | 1-8 | 4-10 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Point Estimates, Reference Stories, Velocity Data | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories |
Tagsno overlap | EstimationAgileMeasurement | EstimationForecastingFlow | ForecastingFlowDelivery | AgileDiscoveryDelivery |