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| Criterion |  Engineering Kanban |  Delivery Monte Carlo Forecasting |  Agile Affinity Estimation |
|---|---|---|---|
Purposedifferent | 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. | 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 many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. |
Complexitydifferent | Medium | High | Medium |
Timedifferent | Ongoing | 30-90 min Setup, danach laufend | 30-90 min |
Participantsdifferent | 2-12 | 1-8 | 3-12 |
Formatdifferent | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Kanban board, WIP policies, Flow metrics | Forecast Percentiles, Throughput Dataset, Risk Communication | Affinity Size Map, Grouped Estimates, Unclear Items |
Tagsno overlap | FlowVisual managementDelivery | ForecastingFlowDelivery | EstimationBacklogRelative sizing |
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