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| Criterion |  Delivery Monte Carlo Forecasting |  Decision Making Decision Matrix |  Decision Making Decision Tree |  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. | When several options collide with several criteria, comparison quickly becomes subjective. A Decision Matrix makes the trade-off visible and brings weighting, criteria, and options into a shared logic. | 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. | 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 | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | 45-90 min | 30-90 min | 30-90 min |
Participantsdifferent | 1-8 | 2-8 | 1-6 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Decision Matrix, Scoring Rationale, Selected Option | Decision Tree, Option Map, Assumption List | Constraint List, Hard/Soft Classification, Option Impact Notes, Decision Boundaries |
Tagsno overlap | ForecastingFlowDelivery | DecisionCriteriaScoringTradeoffs | DecisionTreeOptions | ConstraintsDecisionPlanningOptions |