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| Criterion |  Agile NoEstimates |  Agile Story Points |  Delivery Monte Carlo Forecasting |  Engineering Kanban |
|---|---|---|---|---|
Purposedifferent | 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. | When teams want to rate complexity rather than hours, it creates a shared, relative scale. It sorts work by value, risk, and delivery ability. The result is captured as Point Estimates, Reference Stories, and Velocity Data. | 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 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 | Medium | Medium | High | Medium |
Timedifferent | laufend | laufend, 1-5 min je Item | 30-90 min Setup, danach laufend | Ongoing |
Participantsdifferent | 2-12 | 3-9 | 1-8 | 2-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Throughput Data, Flow Forecast, Slicing Rules | Point Estimates, Reference Stories, Velocity Data | Forecast Percentiles, Throughput Dataset, Risk Communication | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | EstimationForecastingFlow | EstimationAgileMeasurement | ForecastingFlowDelivery | FlowVisual managementDelivery |