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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Learning Review |  Operations Theory of Constraints |
|---|---|---|---|
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. | 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. | 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. |
Complexitydifferent | High | Medium | High |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | 2-4 h Analyse, laufend |
Participantsdifferent | 1-8 | 3-10 | 3-12 |
Formatdifferent | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Learning Review Notes, System Factors, Improvement Actions | Constraint Map, Improvement Plan, Flow Metrics |
Tagsno overlap | ForecastingFlowDelivery | LearningRetrospectiveIncidentOperations | OperationsConstraintsFlowImprovement |
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