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| Criterion |  Agile Story Splitting |  Agile NoEstimates |  Product Discovery Dual-Track Agile |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | When a story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify. | 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. | 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. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | 30-60 min | laufend | Laufend, Wochen bis Monate | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-6 | 2-12 | 4-10 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Smaller Stories, Acceptance Criteria, Split Rationale | Throughput Data, Flow Forecast, Slicing Rules | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | BacklogIterationDelivery | EstimationForecastingFlow | AgileDiscoveryDelivery | ForecastingFlowDelivery |