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| Criterion |  Delivery Monte Carlo Forecasting |  Product Strategy DIBB |  Product Discovery Hypothesis Prioritization Canvas |
|---|---|---|---|
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 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. |
Complexitydifferent | High | Low | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 1-2 h | 60-90 min |
Participantsdifferent | 1-8 | 2-8 | 3-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | DIBB document, Belief list, Bet list, Learning report | Prioritization Canvas, Hypothesis Backlog |
Tagsno overlap | ForecastingFlowDelivery | StrategyDecisionAssumptionsHypothesis | ExperimentsPrioritizationDiscoveryHypothesis |
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