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| Criterion |  Delivery Monte Carlo Forecasting |  Knowledge Modeling Feynman Technique |  Engineering Kanban |
|---|---|---|---|
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. | The Feynman Technique checks understanding by explaining a topic so that it stays easy for others to understand. It makes knowledge gaps visible before they become costly in everyday work. | When work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable. |
Complexitydifferent | High | Low | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 20-60 min | Ongoing |
Participantsdifferent | 1-8 | 1 | 2-12 |
Formatdifferent | Workshop + async | Async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Plain-language Explanation, Knowledge Gaps, Study Notes | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | ForecastingFlowDelivery | LearningKnowledgeUnderstanding | FlowVisual managementDelivery |
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