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| Criterion |  Decision Making Delphi Method |  Delivery Monte Carlo Forecasting |  Product Discovery Hypothesis Prioritization Canvas |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | 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. | 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 many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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. |
Complexitydifferent | High | High | Medium | Low |
Timedifferent | 1-4 Wochen | 30-90 min Setup, danach laufend | 60-90 min | 1-2 h |
Participantsdifferent | 6-30 Experten | 1-8 | 3-8 | 2-8 |
Formatdifferent | Async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Expert Forecast, Consensus Range, Assumption Notes | Forecast Percentiles, Throughput Dataset, Risk Communication | Prioritization Canvas, Hypothesis Backlog | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ForecastingExpertsDecisionStrategy | ForecastingFlowDelivery | ExperimentsPrioritizationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis |