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| Criterion |  Operations Bottleneck Analysis |  Engineering Kanban |  Decision Making Decision Tree |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | For a flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves 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. | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | 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 | 1-3 h | Ongoing | 30-90 min | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-8 | 2-12 | 1-6 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Kanban board, WIP policies, Flow metrics | Decision Tree, Option Map, Assumption List | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | FlowMeasurementConstraints | FlowVisual managementDelivery | DecisionTreeOptions | ForecastingFlowDelivery |