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| Criterion |  Operations Bottleneck Analysis |  Operations PDCA Cycle |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
Purposedifferent | 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. | 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 | Medium | Low | High |
Timedifferent | 1-3 h | 1 h bis mehrere Wochen | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-8 | 1-8 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | FlowMeasurementConstraints | Continuous improvementLeanExperiments | ForecastingFlowDelivery |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
