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| Criterion |  Knowledge Modeling Knowledge Mapping |  Agile Ideal Days |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
Purposedifferent | Knowledge Mapping makes knowledge, gaps, and transfer paths visible across a field. It fits when expertise should not just exist but also be findable and transferable. | 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. | 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 | Low | High |
Timedifferent | 1-3 h | 15-60 min | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-12 | 2-9 | 1-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Knowledge Map, Critical Knowledge Areas, Transfer Plan | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | KnowledgeMappingRisk | EstimationEffortAgile | ForecastingFlowDelivery |
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