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| Criterion |  Operations Bottleneck Analysis |  Decision Making Decision Tree |  Product Discovery Outcome-Driven Innovation |  Innovation Design Thinking |
|---|---|---|---|---|
Purposedifferent | For a flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves throughput. | 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. | Outcome-Driven Innovation clarifies customer problems, solution opportunities, and evidence. It separates jobs, desired outcomes, importance, satisfaction, and opportunity, and captures results as desired outcome statements, an opportunity landscape, and segments by underserved needs. | 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. |
Complexitydifferent | Medium | Medium | High | High |
Timedifferent | 1-3 h | 30-90 min | Mehrere Wochen | 1 Tag bis mehrere Wochen |
Participantsdifferent | 3-8 | 1-6 | 2-6 researchers plus sample | 4-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Decision Tree, Option Map, Assumption List | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Problem Statement, Prototype, Test Learnings |
Tagsno overlap | FlowMeasurementConstraints | DecisionTreeOptions | OutcomesInnovationResearchQuantitative | InnovationPrototypeResearch |