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| Criterion |  Operations PDCA Cycle |  Engineering Kanban |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
Purposedifferent | 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. | 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. | 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 | Low | Medium | High |
Timedifferent | 1 h bis mehrere Wochen | Ongoing | 30-90 min Setup, danach laufend |
Participantsdifferent | 1-8 | 2-12 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Kanban board, WIP policies, Flow metrics | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | Continuous improvementLeanExperiments | FlowVisual managementDelivery | ForecastingFlowDelivery |
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