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| Criterion |  Innovation Design Thinking |  Decision Making Constraint Analysis |  Product Strategy ICE Scoring |  Decision Making Decision Tree |
|---|---|---|---|---|
Purposedifferent | For complex user problems with an uncertain cause, the method connects observation, interpretation, and experiment into a learning cycle. It keeps the focus on real needs and avoids premature solution language. This produces robust decisions for product and service. | 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 ideas need sorting quickly, it condenses impact, confidence, and effort into one compact score. It connects customer value, product logic, and decision priorities. The result is captured as an ICE table and top-ideas list. | 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 | High | Low | Low | Medium |
Timedifferent | 1 Tag bis mehrere Wochen | 30-90 min | 30-60 min | 30-90 min |
Participantsdifferent | 4-10 | 2-8 | 2-8 | 1-6 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Problem Statement, Prototype, Test Learnings | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | ICE Table, Top Idea List | Decision Tree, Option Map, Assumption List |
Tagsno overlap | InnovationPrototypeResearch | ConstraintsDecisionPlanningOptions | PrioritizationScoringGrowthDecision | DecisionTreeOptions |