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| Criterion |  Operations PDCA Cycle |  Operations Bottleneck Analysis |  Delivery Monte Carlo Forecasting |  Product Strategy Counter Metrics |
|---|---|---|---|---|
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. | For a flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves throughput. | 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 a headline metric could mask side effects, it adds guardrail signals against unwanted outcomes. It connects customer value, product logic, and decision priorities. The result is captured as a counter-metric list and guardrail definitions. |
Complexitydifferent | Low | Medium | High | Low |
Timedifferent | 1 h bis mehrere Wochen | 1-3 h | 30-90 min Setup, danach laufend | 30-60 min |
Participantsdifferent | 1-8 | 3-8 | 1-8 | 2-6 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Forecast Percentiles, Throughput Dataset, Risk Communication | Counter Metric List, Guardrail Definitions |
Tagsno overlap | Continuous improvementLeanExperiments | FlowMeasurementConstraints | ForecastingFlowDelivery | MetricsMeasurementStrategyExperiments |