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| Criterion |  Operations PDCA Cycle |  Decision Making Constraint Analysis |  Engineering Failure Scenario Analysis |  Decision Making Decision Tree |
|---|---|---|---|---|
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 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. | In critical processes, it is not enough to plan only for the normal case. Failure Scenario Analysis looks at the path into failure and shows which failures, chains, and control gaps cause the most damage. | 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. |
Complexitydifferent | Low | Low | Medium | Medium |
Timedifferent | 1 h bis mehrere Wochen | 30-90 min | 1-3 h | 30-90 min |
Participantsdifferent | 1-8 | 2-8 | 3-8 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Failure Scenarios, Risk Notes, Control Gaps, Test and Response Actions | Decision Tree, Option Map, Assumption List |
Tagsno overlap | Continuous improvementLeanExperiments | ConstraintsDecisionPlanningOptions | FailureResilienceRisk | DecisionTreeOptions |