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| Criterion |  Knowledge Modeling Event Modeling |  Operations PDCA Cycle |  Product Discovery Experiment Canvas |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | Event Modeling connects business workflows with commands, events, and views into one coherent mental model. It helps design behavior, UI, and technical slices from the same underlying logic. | 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 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 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. |
Complexitydifferent | Medium | Low | Low | Medium |
Timedifferent | 2-6 h | 1 h bis mehrere Wochen | 30-60 min | 1-5 Tage |
Participantsdifferent | 2-8 | 1-8 | 1-5 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Event Model, UI Flow, Commands, Read Models | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Completed Experiment Canvas, Success Metric | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | EventsBlueprintDomain-Driven DesignBehavior | Continuous improvementLeanExperiments | ExperimentsValidationDiscoveryHypothesis | ValidationExperimentsDemandDiscovery |