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| Criterion |  Operations Waste Analysis |  Operations PDCA Cycle |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
Purposedifferent | For a process that keeps people busy but creates little value, the method exposes waste. It directs attention to unnecessary movement, waiting times, rework, and overdelivery. | 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. |
Complexitydifferent | Low | Low | High |
Timedifferent | 45-120 min | 1 h bis mehrere Wochen | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-8 | 1-8 | 1-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Waste Map, Prioritized Waste, Improvement Backlog | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | WasteLeanProcess improvement | Continuous improvementLeanExperiments | ForecastingFlowDelivery |
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