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| Criterion |  Delivery Monte Carlo Forecasting |  Knowledge Modeling Concept Mapping |  Agile Ideal Days |
|---|---|---|---|
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. | Concept Mapping arranges terms and relationships in a network that makes subject-matter connections tangible. It fits when a topic needs to be understood structurally rather than linearly. | 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. |
Complexitydifferent | High | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | 1-2 h | 15-60 min |
Participantsdifferent | 1-8 | 1-8 | 2-9 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Concept Map, Relationship Labels, Knowledge Gaps | Ideal Day Estimates, Assumption Notes, Capacity Caveats |
Tagsno overlap | ForecastingFlowDelivery | KnowledgeConceptsMappingSensemaking | EstimationEffortAgile |
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