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| Criterion |  Operations Fault Tree Analysis |  Product Discovery Concierge MVP |  Product Strategy DIBB |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | For a critical top event with several possible triggers, the method logically models failure paths. It makes visible which combinations of conditions can lead to damage. | When an idea can first fail or grow through genuine hands-on support, it relies on manual work instead of automation. It shows whether user value holds up even under manual execution. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | High | Medium | Low | Low |
Timedifferent | 2-6 h | 1-4 Wochen | 1-2 h | 30-60 min |
Participantsdifferent | 3-8 | 3-10 Kunden | 2-8 | 1-5 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Concierge Learnings, Service Blueprint, MVP Risks | DIBB document, Belief list, Bet list, Learning report | Completed Experiment Canvas, Success Metric |
Tagsno overlap | RiskRoot causeSafety | MVPValidationServiceDiscovery | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryHypothesis |