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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Dual-Track Agile |  Agile Example Mapping |
|---|---|---|---|
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 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 a story carries business rules and exceptions, it brings clarity before implementation. It makes examples, open questions, and boundaries visible enough that the logic becomes jointly sound. |
Complexitydifferent | High | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | Laufend, Wochen bis Monate | 30-60 min |
Participantsdifferent | 1-8 | 4-10 | 3-7 |
Formatdifferent | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Example Map, Acceptance Criteria, Open Questions |
Tagsno overlap | ForecastingFlowDelivery | AgileDiscoveryDelivery | Behavior-Driven DevelopmentRequirementsExamplesRefinement |
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