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| Criterion |  Delivery Monte Carlo Forecasting |  Agile NoEstimates |  Product Discovery Dual-Track Agile |  Agile Story Points |
|---|---|---|---|---|
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 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. | 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 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. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | laufend | Laufend, Wochen bis Monate | laufend, 1-5 min je Item |
Participantsdifferent | 1-8 | 2-12 | 4-10 | 3-9 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Throughput Data, Flow Forecast, Slicing Rules | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Point Estimates, Reference Stories, Velocity Data |
Tagsno overlap | ForecastingFlowDelivery | EstimationForecastingFlow | AgileDiscoveryDelivery | EstimationAgileMeasurement |