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| Criterion |  Delivery Monte Carlo Forecasting |  Delivery Value Stream Mapping |  Knowledge Modeling Teach-back |
|---|---|---|---|
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 delivery work dissolves into wait times, handoffs, and hidden effort, Value Stream Mapping makes the actual flow visible. The method exposes bottlenecks, idle time, and feedback loops so lead time and improvement levers become tangible. | Teach-back checks whether an explanation actually got through to the other person. The method makes misunderstandings visible early and strengthens shared understanding in advisory and learning situations. |
Complexitydifferent | High | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | 5-20 min |
Participantsdifferent | 1-8 | 4-10 | 2-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Current-state map, Future-state map, Bottleneck list | Understanding Check, Clarification Notes, Learning Gaps |
Tagsno overlap | ForecastingFlowDelivery | LeanFlowWasteDelivery | LearningUnderstandingCommunication |
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