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| Criterion |  DevOps Game Day |  Delivery Monte Carlo Forecasting |  Operations Learning Review |
|---|---|---|---|
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. | After a project phase with mixed results, the method makes learning from the individual case reusable. It connects events, decisions, and systemic conditions into robust insights. |
Complexitydifferent | High | High | Medium |
Timedifferent | Halber Tag | 30-90 min Setup, danach laufend | 1-3 h |
Participantsdifferent | 5-20 | 1-8 | 3-10 |
Formatdifferent | Workshop | Workshop + async | Workshop |
Outputdifferent | Simulation Notes, Gaps List, Updated Runbooks | Forecast Percentiles, Throughput Dataset, Risk Communication | Learning Review Notes, System Factors, Improvement Actions |
Tagsno overlap | ResilienceOperationsIncident | ForecastingFlowDelivery | LearningRetrospectiveIncidentOperations |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
