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| Criterion |  Operations Bottleneck Analysis |  Delivery Monte Carlo Forecasting |  Product Discovery Dual-Track Agile |  Engineering Kanban |
|---|---|---|---|---|
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. | 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 uncertainty and delivery run in parallel, it separates learning work from implementation while keeping both connected. It prevents unvalidated ideas from falling directly into the delivery stream. | 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 | High | Medium | Medium |
Timedifferent | 1-3 h | 30-90 min Setup, danach laufend | Laufend, Wochen bis Monate | Ongoing |
Participantsdifferent | 3-8 | 1-8 | 4-10 | 2-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Forecast Percentiles, Throughput Dataset, Risk Communication | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | FlowMeasurementConstraints | ForecastingFlowDelivery | AgileDiscoveryDelivery | FlowVisual managementDelivery |