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| Criterion |  Engineering Failure Scenario Analysis |  Delivery Monte Carlo Forecasting |  Product Discovery Assumption Mapping |  Growth A/B Testing |
|---|---|---|---|---|
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. | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. | When assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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. |
Complexitydifferent | Medium | High | Medium | High |
Timedifferent | 1-3 h | 30-90 min Setup, danach laufend | 45-60 min | 1-4 Wochen |
Participantsdifferent | 3-8 | 1-8 | 2-8 | 1-6 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Async |
Outputdifferent | Failure Scenarios, Risk Notes, Control Gaps, Test and Response Actions | Forecast Percentiles, Throughput Dataset, Risk Communication | Assumption map, Test backlog, Risk ranking | Experiment results, Decision log, Learning summary |
Tagsno overlap | FailureResilienceRisk | ForecastingFlowDelivery | AssumptionsRiskExperimentsValidation | ExperimentsGrowthAnalyticsValidation |