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| Criterion |  Delivery Monte Carlo Forecasting |  Product Strategy DIBB |  Growth A/B Testing |  Decision Making Delphi Method |
|---|---|---|---|---|
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. | 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 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 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. |
Complexitydifferent | High | Low | High | High |
Timedifferent | 30-90 min Setup, danach laufend | 1-2 h | 1-4 Wochen | 1-4 Wochen |
Participantsdifferent | 1-8 | 2-8 | 1-6 | 6-30 Experten |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | DIBB document, Belief list, Bet list, Learning report | Experiment results, Decision log, Learning summary | Expert Forecast, Consensus Range, Assumption Notes |
Tagsno overlap | ForecastingFlowDelivery | StrategyDecisionAssumptionsHypothesis | ExperimentsGrowthAnalyticsValidation | ForecastingExpertsDecisionStrategy |