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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Assumption Mapping |  Decision Making Risk Matrix |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | 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 a project faces many possible disruptions, it quickly becomes unclear which risks deserve attention first. It separates options, evaluation criteria, and open risks. The result is captured as a Risk Matrix and a Top Risk List. | 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 | High | Medium | Low | High |
Timedifferent | 30-90 min Setup, danach laufend | 45-60 min | 30-60 min | 1-4 Wochen |
Participantsdifferent | 1-8 | 2-8 | 3-10 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Assumption map, Test backlog, Risk ranking | Risk Matrix, Top Risk List | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | AssumptionsRiskExperimentsValidation | RiskDecisionPrioritizationAssessment | ExperimentsGrowthAnalyticsValidation |