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| Criterion |  Decision Making Decision Tree |  Innovation Design Thinking |  Engineering Failure Scenario Analysis |  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. | 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. | 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. | 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 | High | Medium | High |
Timedifferent | 30-90 min | 1 Tag bis mehrere Wochen | 1-3 h | 4-5 Tage |
Participantsdifferent | 1-6 | 4-10 | 3-8 | 5-8 |
Formatdifferent | Workshop + async | Workshop | Workshop | Workshop |
Outputdifferent | Decision Tree, Option Map, Assumption List | Problem Statement, Prototype, Test Learnings | Failure Scenarios, Risk Notes, Control Gaps, Test and Response Actions | Prototype, Test Findings, Decision Rationale |
Tagsno overlap | DecisionTreeOptions | InnovationPrototypeResearch | FailureResilienceRisk | Design sprintPrototypeValidationInnovation |