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| Criterion |  Delivery Monte Carlo Forecasting |  Systems Thinking Leverage Points |  Systems Thinking Future Reality Tree |
|---|---|---|---|
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. | Leverage Points show exactly where interventions in a system produce a disproportionate effect. It draws relationships, patterns, and feedback loops. The result is captured as a leverage map and action strategy. | A Future Reality Tree shows how desired actions are meant to lead to a better system state. The method checks whether a path of change holds together logically before time flows into implementation. |
Complexitysame | High | High | High |
Timedifferent | 30-90 min Setup, danach laufend | Half day | 2-4 h |
Participantsdifferent | 1-8 | 3-12 | 3-8 |
Formatdifferent | Workshop + async | Workshop | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Leverage Map, Action Strategy | Future Reality Tree, Negative Branches, Assumption List, Improved Injections |
Tagsno overlap | ForecastingFlowDelivery | Systems thinkingChangeStrategy | Theory of ConstraintsSystems thinkingChange |
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