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| Criterion |  Delivery Monte Carlo Forecasting |  Product Strategy DIBB |  Product Discovery Experiment Canvas |  Decision Making Assumption Surfacing |
|---|---|---|---|---|
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 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. | 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. |
Complexitydifferent | High | Low | Low | Low |
Timedifferent | 30-90 min Setup, danach laufend | 1-2 h | 30-60 min | 45-90 min |
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 | DIBB document, Belief list, Bet list, Learning report | Completed Experiment Canvas, Success Metric | Assumption List, Critical Assumptions, Learning Plan |
Tagsno overlap | ForecastingFlowDelivery | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryHypothesis | AssumptionsRiskDecisionDiscovery |