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| Criterion |  Operations Theory of Constraints |  Agile Story Points |  Delivery Monte Carlo Forecasting |  Operations After-Action 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. | When teams want to rate complexity rather than hours, it creates a shared, relative scale. It sorts work by value, risk, and delivery ability. The result is captured as Point Estimates, Reference Stories, and Velocity Data. | 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. |
Complexitydifferent | High | Medium | High | Low |
Timedifferent | 2-4 h Analyse, laufend | laufend, 1-5 min je Item | 30-90 min Setup, danach laufend | 20-45 min |
Participantsdifferent | 3-12 | 3-9 | 1-8 | 3-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Constraint Map, Improvement Plan, Flow Metrics | Point Estimates, Reference Stories, Velocity Data | Forecast Percentiles, Throughput Dataset, Risk Communication | Lessons learned, Action items, Event summary |
Tagsno overlap | OperationsConstraintsFlowImprovement | EstimationAgileMeasurement | ForecastingFlowDelivery | LearningOperationsImprovement |