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| Criterion |  Operations PDCA Cycle |  Decision Making Decision Tree |  Operations Kaizen Event |  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. | 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. | For a tightly scoped process segment with noticeable waste, the method bundles shared energy for change. It suits situations that call for fast learning loops and visible adjustments. | 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 | 30-90 min | 0.5-5 Tage | 30-90 min |
Participantsdifferent | 1-8 | 1-6 | 4-10 | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Decision Tree, Option Map, Assumption List | Kaizen Charter, Waste List, Improvement Experiments, Standard Work Update | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | Continuous improvementLeanExperiments | DecisionTreeOptions | LeanContinuous improvementOperations | ConstraintsDecisionPlanningOptions |