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| Criterion |  Delivery Monte Carlo Forecasting |  Agile Story Splitting |  Engineering Kanban |  Agile Affinity Estimation |
|---|---|---|---|---|
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 story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify. | 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. | 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 | High | Medium | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 30-60 min | Ongoing | 30-90 min |
Participantsdifferent | 1-8 | 2-6 | 2-12 | 3-12 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Smaller Stories, Acceptance Criteria, Split Rationale | Kanban board, WIP policies, Flow metrics | Affinity Size Map, Grouped Estimates, Unclear Items |
Tagsno overlap | ForecastingFlowDelivery | BacklogIterationDelivery | FlowVisual managementDelivery | EstimationBacklogRelative sizing |