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| Criterion |  Delivery Monte Carlo Forecasting |  Decision Making Assumption Surfacing |  Product Discovery Experiment Canvas |  Product Strategy DIBB |
|---|---|---|---|---|
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. | Plans often contain assumptions that were never openly stated and remain dangerous precisely because of that. Assumption Surfacing makes these silent premises visible and prioritizes which of them carry the initiative or could sink it. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | 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 | Low | Low | Low |
Timedifferent | 30-90 min Setup, danach laufend | 45-90 min | 30-60 min | 1-2 h |
Participantsdifferent | 1-8 | 2-8 | 1-5 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Assumption List, Critical Assumptions, Learning Plan | Completed Experiment Canvas, Success Metric | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ForecastingFlowDelivery | AssumptionsRiskDecisionDiscovery | ExperimentsValidationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis |