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| Criterion |  Operations DMAIC |  Delivery Monte Carlo Forecasting |  Engineering Kanban |  Operations Bottleneck Analysis |
|---|---|---|---|---|
Purposedifferent | For a process problem with fluctuating performance, the method brings analysis and improvement into a disciplined sequence. It creates a framework in which numbers, causes, and control come together. | 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. | 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. |
Complexitydifferent | High | High | Medium | Medium |
Timedifferent | 2-12 Wochen | 30-90 min Setup, danach laufend | Ongoing | 1-3 h |
Participantsdifferent | 3-10 | 1-8 | 2-12 | 3-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Project Charter, Measurement Plan, Cause Analysis, Control Plan | Forecast Percentiles, Throughput Dataset, Risk Communication | Kanban board, WIP policies, Flow metrics | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures |
Tagsno overlap | Continuous improvementQualityProcess improvement | ForecastingFlowDelivery | FlowVisual managementDelivery | FlowMeasurementConstraints |