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| Criterion |  Delivery Monte Carlo Forecasting |  Decision Making Decision Tree |  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. | For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch. | 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 | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | 30-90 min | 1-2 h |
Participantsdifferent | 1-8 | 1-6 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Decision Tree, Option Map, Assumption List | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ForecastingFlowDelivery | DecisionTreeOptions | StrategyDecisionAssumptionsHypothesis |
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