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| Criterion |  Decision Making Decision Tree |  Decision Making Constraint Analysis |  Innovation Design Thinking |  Innovation Design Sprint |
|---|---|---|---|---|
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. | 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. | 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 the product question is unclear and time pressure is high, the method takes a team from an open idea to a testable solution. It connects problem understanding, decision, and learning in one compact format, lowering the risk of spending a lot of energy on a mere guess. |
Complexitydifferent | Medium | Low | High | High |
Timedifferent | 30-90 min | 30-90 min | 1 Tag bis mehrere Wochen | 4-5 Tage |
Participantsdifferent | 1-6 | 2-8 | 4-10 | 5-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries | Problem Statement, Prototype, Test Learnings | Prototype, Test Findings, Decision Rationale |
Tagsno overlap | DecisionTreeOptions | ConstraintsDecisionPlanningOptions | InnovationPrototypeResearch | Design sprintPrototypeValidationInnovation |