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| Criterion |  Operations Theory of Constraints |  Agile Story Points |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
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. |
Complexitydifferent | High | Medium | High |
Timedifferent | 2-4 h Analyse, laufend | laufend, 1-5 min je Item | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-12 | 3-9 | 1-8 |
Formatsame | 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 |
Tagsno overlap | OperationsConstraintsFlowImprovement | EstimationAgileMeasurement | ForecastingFlowDelivery |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
