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| Criterion |  Operations PDCA Cycle |  Decision Making Decision Tree |  Decision Making Constraint Analysis |  Product Discovery MVP Test Matrix |
|---|---|---|---|---|
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. | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. | When several minimal variants compete to answer the same question, it makes comparing and choosing between them cleaner. It separates problem, assumption, solution, and evidence. The result is captured as a Test Matrix and a Test Plan. |
Complexitydifferent | Low | Medium | Low | Medium |
Timedifferent | 1 h bis mehrere Wochen | 30-90 min | 30-90 min | 45-75 min |
Participantsdifferent | 1-8 | 1-6 | 2-8 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Decision Tree, Option Map, Assumption List | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Test Matrix, Test Plan |
Tagsno overlap | Continuous improvementLeanExperiments | DecisionTreeOptions | ConstraintsDecisionPlanningOptions | ExperimentsValidationDiscoveryOptions |