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| Criterion |  Operations PDCA Cycle |  Decision Making Decision Matrix |  Decision Making Decision Tree |  Decision Making Constraint Analysis |
|---|---|---|---|---|
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 several options collide with several criteria, comparison quickly becomes subjective. A Decision Matrix makes the trade-off visible and brings weighting, criteria, and options into a shared logic. | 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. |
Complexitydifferent | Low | Medium | Medium | Low |
Timedifferent | 1 h bis mehrere Wochen | 45-90 min | 30-90 min | 30-90 min |
Participantsdifferent | 1-8 | 2-8 | 1-6 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Decision Matrix, Scoring Rationale, Selected Option | Decision Tree, Option Map, Assumption List | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | Continuous improvementLeanExperiments | DecisionCriteriaScoringTradeoffs | DecisionTreeOptions | ConstraintsDecisionPlanningOptions |