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| Criterion |  Decision Making Decision Tree |  Operations PDCA Cycle |  Operations A3 Problem Solving |  Decision Making Constraint Analysis |
|---|---|---|---|---|
Purposedifferent | 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 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 a complex deviation with several people involved, the method bundles problem, analysis, and decision onto one page. It creates a shared working space where causes, countermeasures, and follow-up fit together. | 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 | Medium | Low | Medium | Low |
Timedifferent | 30-90 min | 1 h bis mehrere Wochen | 45-90 min | 30-90 min |
Participantsdifferent | 1-6 | 1-8 | 2-5 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Decision Tree, Option Map, Assumption List | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | A3 Report, Action Plan, Root Cause Analysis | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | DecisionTreeOptions | Continuous improvementLeanExperiments | LeanProblem solvingCoachingOperations | ConstraintsDecisionPlanningOptions |