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| Criterion |  Delivery Monte Carlo Forecasting |  Agile Specification by Example |  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 business rules need to become precise, it ties language, example, and test together. It sorts work by value, risk, and delivery ability. The result is captured as an Example Table and Acceptance Tests. | 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 | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | 60-90 min pro Feature | Laufend, Wochen bis Monate | 30-60 min |
Participantsdifferent | 1-8 | 3-6 | 4-10 | 3-7 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Example Table, Acceptance Tests | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Example Map, Acceptance Criteria, Open Questions |
Tagsno overlap | ForecastingFlowDelivery | AgileRequirementsExamplesBehavior-Driven Development | AgileDiscoveryDelivery | Behavior-Driven DevelopmentRequirementsExamplesRefinement |