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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Theory of Constraints |  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 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 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 | High | Low | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 2-4 h Analyse, laufend | 20-45 min | 1-3 h |
Participantsdifferent | 1-8 | 3-12 | 3-12 | 3-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Constraint Map, Improvement Plan, Flow Metrics | Lessons learned, Action items, Event summary | Learning Review Notes, System Factors, Improvement Actions |
Tagsno overlap | ForecastingFlowDelivery | OperationsConstraintsFlowImprovement | LearningOperationsImprovement | LearningRetrospectiveIncidentOperations |