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| Criterion |  Operations Bottleneck Analysis |  Growth A/B Testing |  Decision Making Constraint Analysis |  Product Discovery Smoke Test |
|---|---|---|---|---|
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. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | Medium | High | Low | Low |
Timedifferent | 1-3 h | 1-4 Wochen | 30-90 min | 1-5 Tage |
Participantsdifferent | 3-8 | 1-6 | 2-8 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Experiment results, Decision log, Learning summary | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | FlowMeasurementConstraints | ExperimentsGrowthAnalyticsValidation | ConstraintsDecisionPlanningOptions | ValidationExperimentsDemandGrowth |