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