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| Criterion |  Knowledge Modeling Concept Mapping |  Delivery Monte Carlo Forecasting |  Decision Making PERT Estimation |  Knowledge Modeling Knowledge Mapping |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | On complex initiatives, a plain average tends to understate just how uncertain the outcome really is. It separates options, evaluation criteria, and open risks. The result is captured as a PERT Estimate, Expected Value, and Risk Notes. | 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. |
Complexitydifferent | Medium | High | Medium | Medium |
Timedifferent | 1-2 h | 30-90 min Setup, danach laufend | 15-45 min | 1-3 h |
Participantsdifferent | 1-8 | 1-8 | 1-8 | 3-12 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Concept Map, Relationship Labels, Knowledge Gaps | Forecast Percentiles, Throughput Dataset, Risk Communication | PERT Estimate, Expected Value, Risk Notes | Knowledge Map, Critical Knowledge Areas, Transfer Plan |
Tagsno overlap | KnowledgeConceptsMappingSensemaking | ForecastingFlowDelivery | EstimationUncertaintyRisk | KnowledgeMappingRisk |