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| Criterion |  Agile T-Shirt Sizing |  Operations Theory of Constraints |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | When effort only needs to be classified roughly, it makes comparability more important than false precision. It helps sort work quickly into manageable sizes. | 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. | 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 | Low | High | Medium | High |
Timedifferent | 15-45 min | 2-4 h Analyse, laufend | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-12 | 3-12 | 2-12 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Size Buckets, Rough Backlog Map, Split Candidates | Constraint Map, Improvement Plan, Flow Metrics | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationAgileRoadmap | OperationsConstraintsFlowImprovement | EstimationForecastingFlow | ForecastingFlowDelivery |