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| Criterion |  Operations PDCA Cycle |  Operations Fault Tree Analysis |  Product Discovery Smoke Test |  Growth A/B Testing |
|---|---|---|---|---|
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 critical top event with several possible triggers, the method logically models failure paths. It makes visible which combinations of conditions can lead to damage. | 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. | 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. |
Complexitydifferent | Low | High | Low | High |
Timedifferent | 1 h bis mehrere Wochen | 2-6 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-8 | 3-8 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | Continuous improvementLeanExperiments | RiskRoot causeSafety | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |