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| Criterion |  Agile Affinity Estimation |  Engineering Kanban |  Agile Story Splitting |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | 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. | 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 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. | 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 | Medium | Medium | Medium | High |
Timedifferent | 30-90 min | Ongoing | 30-60 min | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-12 | 2-12 | 2-6 | 1-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | Kanban board, WIP policies, Flow metrics | Smaller Stories, Acceptance Criteria, Split Rationale | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationBacklogRelative sizing | FlowVisual managementDelivery | BacklogIterationDelivery | ForecastingFlowDelivery |