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| Criterion |  Operations PDCA Cycle |  Delivery Monte Carlo Forecasting |  Operations Waste Analysis |
|---|---|---|---|
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. | 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. | 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. |
Complexitydifferent | Low | High | Low |
Timedifferent | 1 h bis mehrere Wochen | 30-90 min Setup, danach laufend | 45-120 min |
Participantsdifferent | 1-8 | 1-8 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Forecast Percentiles, Throughput Dataset, Risk Communication | Waste Map, Prioritized Waste, Improvement Backlog |
Tagsno overlap | Continuous improvementLeanExperiments | ForecastingFlowDelivery | WasteLeanProcess improvement |
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