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| Criterion |  Operations DMAIC |  Operations PDCA Cycle |  Delivery Monte Carlo Forecasting |  Delivery Value Stream Mapping |
|---|---|---|---|---|
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 delivery work dissolves into wait times, handoffs, and hidden effort, Value Stream Mapping makes the actual flow visible. The method exposes bottlenecks, idle time, and feedback loops so lead time and improvement levers become tangible. |
Complexitydifferent | High | Low | High | Medium |
Timedifferent | 2-12 Wochen | 1 h bis mehrere Wochen | 30-90 min Setup, danach laufend | 1-3 h |
Participantsdifferent | 3-10 | 1-8 | 1-8 | 4-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Project Charter, Measurement Plan, Cause Analysis, Control Plan | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Forecast Percentiles, Throughput Dataset, Risk Communication | Current-state map, Future-state map, Bottleneck list |
Tagsno overlap | Continuous improvementQualityProcess improvement | Continuous improvementLeanExperiments | ForecastingFlowDelivery | LeanFlowWasteDelivery |