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| Criterion |  Operations PDCA Cycle |  Growth A/B Testing |  Agile Bucket System |  Product Discovery Smoke Test |
|---|---|---|---|---|
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. | 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 a large batch of work needs a fast, rough estimate, it sorts items into predefined size buckets. It sorts work by value, risk, and delivery ability. The result is captured as a Bucketed Backlog, Relative Estimates, and Split Candidates. | 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 | Low | High | Medium | Low |
Timedifferent | 1 h bis mehrere Wochen | 1-4 Wochen | 30-90 min | 1-5 Tage |
Participantsdifferent | 1-8 | 1-6 | 3-12 | Nutzertraffic |
Formatdifferent | Workshop + async | Async | Workshop | Async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Experiment results, Decision log, Learning summary | Bucketed Backlog, Relative Estimates, Split Candidates | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | Continuous improvementLeanExperiments | ExperimentsGrowthAnalyticsValidation | EstimationBacklogRelative sizing | ValidationExperimentsDemandGrowth |