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| Criterion |  Innovation Design Thinking |  Decision Making Decision Tree |  Innovation Design Sprint |  Decision Making Constraint Analysis |
|---|---|---|---|---|
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. | 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 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. | 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 | High | Medium | High | Low |
Timedifferent | 1 Tag bis mehrere Wochen | 30-90 min | 4-5 Tage | 30-90 min |
Participantsdifferent | 4-10 | 1-6 | 5-8 | 2-8 |
Formatdifferent | Workshop | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Problem Statement, Prototype, Test Learnings | Decision Tree, Option Map, Assumption List | Prototype, Test Findings, Decision Rationale | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | InnovationPrototypeResearch | DecisionTreeOptions | Design sprintPrototypeValidationInnovation | ConstraintsDecisionPlanningOptions |