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| Criterion |  Agile Affinity Estimation |  Operations PDCA Cycle |  Product Discovery Fake Door Test |  Agile Bucket System |
|---|---|---|---|---|
Purposedifferent | 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. | 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 demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. | 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. |
Complexitydifferent | Medium | Low | Medium | Medium |
Timedifferent | 30-90 min | 1 h bis mehrere Wochen | 1-5 Tage | 30-90 min |
Participantsdifferent | 3-12 | 1-8 | Nutzertraffic | 3-12 |
Formatdifferent | Workshop | Workshop + async | Async | Workshop |
Outputdifferent | Affinity Size Map, Grouped Estimates, Unclear Items | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Click Data, Interest Signal, Learning Decision | Bucketed Backlog, Relative Estimates, Split Candidates |
Tagsno overlap | EstimationBacklogRelative sizing | Continuous improvementLeanExperiments | ValidationExperimentsDemandDiscovery | EstimationBacklogRelative sizing |