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| Criterion |  Operations ABC Analysis |  Delivery Monte Carlo Forecasting |  DevOps Game Day |  Operations Learning Review |
|---|---|---|---|---|
Purposedifferent | With many similar objects of differing economic significance, the method prioritizes effort by effect. It separates what needs regular steering from what rarely needs attention. | 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. | 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 | Low | High | High | Medium |
Timedifferent | 30-60 min | 30-90 min Setup, danach laufend | Halber Tag | 1-3 h |
Participantsdifferent | 1-5 | 1-8 | 5-20 | 3-10 |
Formatdifferent | Async | Workshop + async | Workshop | Workshop |
Outputdifferent | ABC Classification, Focus Rules, Control List | Forecast Percentiles, Throughput Dataset, Risk Communication | Simulation Notes, Gaps List, Updated Runbooks | Learning Review Notes, System Factors, Improvement Actions |
Tagsno overlap | PrioritizationOperationsPortfolio | ForecastingFlowDelivery | ResilienceOperationsIncident | LearningRetrospectiveIncidentOperations |