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| Criterion |  Delivery Monte Carlo Forecasting |  DevOps Game Day |  Agile NoEstimates |  Operations Theory of Constraints |
|---|---|---|---|---|
Purposedifferent | 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. | In preparing for rare incidents, the method tests response capability under controlled conditions. It shows where assumptions about stability, roles, and recovery are too optimistic. | 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. | 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. |
Complexitydifferent | High | High | Medium | High |
Timedifferent | 30-90 min Setup, danach laufend | Halber Tag | laufend | 2-4 h Analyse, laufend |
Participantsdifferent | 1-8 | 5-20 | 2-12 | 3-12 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Simulation Notes, Gaps List, Updated Runbooks | Throughput Data, Flow Forecast, Slicing Rules | Constraint Map, Improvement Plan, Flow Metrics |
Tagsno overlap | ForecastingFlowDelivery | ResilienceOperationsIncident | EstimationForecastingFlow | OperationsConstraintsFlowImprovement |