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| Criterion |  Knowledge Modeling Teach-back |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
Purposedifferent | 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. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | 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 | Low | Medium | High |
Timedifferent | 5-20 min | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-8 | 2-12 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Understanding Check, Clarification Notes, Learning Gaps | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | LearningUnderstandingCommunication | EstimationForecastingFlow | ForecastingFlowDelivery |
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