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| Criterion |  Operations PDCA Cycle |  Agile Affinity Estimation |  Agile Bucket System |  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. | When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. | 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 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 | Medium | Medium | High |
Timedifferent | 1 h bis mehrere Wochen | 30-90 min | 30-90 min | 1-4 Wochen |
Participantsdifferent | 1-8 | 3-12 | 3-12 | 1-6 |
Formatdifferent | Workshop + async | Workshop | Workshop | Async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Affinity Size Map, Grouped Estimates, Unclear Items | Bucketed Backlog, Relative Estimates, Split Candidates | Experiment results, Decision log, Learning summary |
Tagsno overlap | Continuous improvementLeanExperiments | EstimationBacklogRelative sizing | EstimationBacklogRelative sizing | ExperimentsGrowthAnalyticsValidation |