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| Criterion |  Delivery Monte Carlo Forecasting |  Agile NoEstimates |  Operations Theory of Constraints |  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 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. | For a system that does not get faster despite effort, the method directs attention to the bottleneck. It concentrates improvement on the point that actually limits throughput. | 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 | Medium | High | Medium |
Timedifferent | 30-90 min Setup, danach laufend | laufend | 2-4 h Analyse, laufend | Ongoing |
Participantsdifferent | 1-8 | 2-12 | 3-12 | 2-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Throughput Data, Flow Forecast, Slicing Rules | Constraint Map, Improvement Plan, Flow Metrics | Kanban board, WIP policies, Flow metrics |
Tags1 shared | ForecastingFlowDelivery | EstimationForecastingFlow | OperationsConstraintsFlowImprovement | FlowVisual managementDelivery |