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| Criterion |  Delivery Monte Carlo Forecasting |  Decision Making Decision Tree |  Product Strategy DIBB |  Decision Making Constraint Analysis |
|---|---|---|---|---|
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. | When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped. |
Complexitydifferent | High | Medium | Low | Low |
Timedifferent | 30-90 min Setup, danach laufend | 30-90 min | 1-2 h | 30-90 min |
Participantsdifferent | 1-8 | 1-6 | 2-8 | 2-8 |
Formatsame | Workshop + async | 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 | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | ForecastingFlowDelivery | DecisionTreeOptions | StrategyDecisionAssumptionsHypothesis | ConstraintsDecisionPlanningOptions |