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| Criterion |  Engineering Failure Scenario Analysis |  Product Discovery Outcome-Driven Innovation |  Growth A/B Testing |  Innovation Design Sprint |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | 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 | High | High |
Timedifferent | 1-3 h | Mehrere Wochen | 1-4 Wochen | 4-5 Tage |
Participantsdifferent | 3-8 | 2-6 researchers plus sample | 1-6 | 5-8 |
Formatdifferent | Workshop | Workshop + async | Async | Workshop |
Outputdifferent | Failure Scenarios, Risk Notes, Control Gaps, Test and Response Actions | Desired outcome statements, Opportunity landscape, Segmentation by underserved needs, Innovation hypotheses | Experiment results, Decision log, Learning summary | Prototype, Test Findings, Decision Rationale |
Tagsno overlap | FailureResilienceRisk | OutcomesInnovationResearchQuantitative | ExperimentsGrowthAnalyticsValidation | Design sprintPrototypeValidationInnovation |