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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Assumption Mapping |  Decision Making Delphi Method |  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 knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together. | 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 | High | High |
Timedifferent | 30-90 min Setup, danach laufend | 45-60 min | 1-4 Wochen | 1-4 Wochen |
Participantsdifferent | 1-8 | 2-8 | 6-30 Experten | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Assumption map, Test backlog, Risk ranking | Expert Forecast, Consensus Range, Assumption Notes | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | AssumptionsRiskExperimentsValidation | ForecastingExpertsDecisionStrategy | ExperimentsGrowthAnalyticsValidation |