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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Bottleneck Analysis |  Operations After-Action Review |  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 flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves throughput. | After missions or project phases with unclear outcomes, the method makes visible what actually happened and what can be learned from it. It separates course, effect, and causes so experience turns into solid improvement. | 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 | Medium | Low | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | 20-45 min | 1-3 h |
Participantsdifferent | 1-8 | 3-8 | 3-12 | 3-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Lessons learned, Action items, Event summary | Learning Review Notes, System Factors, Improvement Actions |
Tagsno overlap | ForecastingFlowDelivery | FlowMeasurementConstraints | LearningOperationsImprovement | LearningRetrospectiveIncidentOperations |