View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Operations Theory of Constraints |  Agile NoEstimates |  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 estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | 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 | Medium | High |
Timedifferent | 2-4 h Analyse, laufend | laufend | laufend, 1-5 min je Item | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-12 | 2-12 | 3-9 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Constraint Map, Improvement Plan, Flow Metrics | Throughput Data, Flow Forecast, Slicing Rules | Point Estimates, Reference Stories, Velocity Data | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | OperationsConstraintsFlowImprovement | EstimationForecastingFlow | EstimationAgileMeasurement | ForecastingFlowDelivery |