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| Criterion |  Delivery Monte Carlo Forecasting |  Agile 4Ls Retrospective |  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. | When a sprint should not only be assessed but also learned from, it captures experience in clear learning categories. It records what worked, what's missing, and what should run differently next time. | 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 | 45–75 min | Ongoing |
Participantsdifferent | 1-8 | Team members involved | 2-12 |
Formatdifferent | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | 4Ls Board, Learning Themes, Improvement Actions | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | ForecastingFlowDelivery | RetrospectiveLearningReflectionTeam | FlowVisual managementDelivery |
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