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| Criterion |  Delivery Monte Carlo Forecasting |  Agile Ideal Days |  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 effort needs to be thought of as real working time, it offers a simple common denominator for comparison. It sorts work by value, risk, and delivery ability. The result is captured as Ideal Day Estimates, Assumption Notes, and Capacity Caveats. | 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 | Low | Low |
Timedifferent | 30-90 min Setup, danach laufend | 15-60 min | 30-60 min |
Participantsdifferent | 1-8 | 2-9 | 3-7 |
Formatdifferent | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Example Map, Acceptance Criteria, Open Questions |
Tagsno overlap | ForecastingFlowDelivery | EstimationEffortAgile | Behavior-Driven DevelopmentRequirementsExamplesRefinement |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
