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| Criterion |  DevOps Game Day |  Delivery Monte Carlo Forecasting |  Agile NoEstimates |  Operations Theory of Constraints |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | 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 | Halber Tag | 30-90 min Setup, danach laufend | laufend | 2-4 h Analyse, laufend |
Participantsdifferent | 5-20 | 1-8 | 2-12 | 3-12 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Simulation Notes, Gaps List, Updated Runbooks | Forecast Percentiles, Throughput Dataset, Risk Communication | Throughput Data, Flow Forecast, Slicing Rules | Constraint Map, Improvement Plan, Flow Metrics |
Tagsno overlap | ResilienceOperationsIncident | ForecastingFlowDelivery | EstimationForecastingFlow | OperationsConstraintsFlowImprovement |