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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Theory of Constraints |  Operations Learning Review |
|---|---|---|---|
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 a system that does not get faster despite effort, the method directs attention to the bottleneck. It concentrates improvement on the point that actually limits throughput. | After a project phase with mixed results, the method makes learning from the individual case reusable. It connects events, decisions, and systemic conditions into robust insights. |
Complexitydifferent | High | High | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 2-4 h Analyse, laufend | 1-3 h |
Participantsdifferent | 1-8 | 3-12 | 3-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Constraint Map, Improvement Plan, Flow Metrics | Learning Review Notes, System Factors, Improvement Actions |
Tagsno overlap | ForecastingFlowDelivery | OperationsConstraintsFlowImprovement | LearningRetrospectiveIncidentOperations |
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