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| Criterion |  Operations DMAIC |  Operations PDCA Cycle |  Delivery Monte Carlo Forecasting |  Engineering Kanban |
|---|---|---|---|---|
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. | For an improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode. | 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 | High | Low | High | Medium |
Timedifferent | 2-12 Wochen | 1 h bis mehrere Wochen | 30-90 min Setup, danach laufend | Ongoing |
Participantsdifferent | 3-10 | 1-8 | 1-8 | 2-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Project Charter, Measurement Plan, Cause Analysis, Control Plan | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Forecast Percentiles, Throughput Dataset, Risk Communication | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | Continuous improvementQualityProcess improvement | Continuous improvementLeanExperiments | ForecastingFlowDelivery | FlowVisual managementDelivery |