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| Criterion |  Operations PDCA Cycle |  Agile Affinity Estimation |  Product Discovery Experiment Canvas |  Product Discovery Hypothesis Prioritization Canvas |
|---|---|---|---|---|
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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. |
Complexitydifferent | Low | Medium | Low | Medium |
Timedifferent | 1 h bis mehrere Wochen | 30-90 min | 30-60 min | 60-90 min |
Participantsdifferent | 1-8 | 3-12 | 1-5 | 3-8 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Affinity Size Map, Grouped Estimates, Unclear Items | Completed Experiment Canvas, Success Metric | Prioritization Canvas, Hypothesis Backlog |
Tagsno overlap | Continuous improvementLeanExperiments | EstimationBacklogRelative sizing | ExperimentsValidationDiscoveryHypothesis | ExperimentsPrioritizationDiscoveryHypothesis |