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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Learning Review |  DevOps Game Day |  Operations After-Action Review |
|---|---|---|---|---|
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. | 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. | 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 missions or project phases with unclear outcomes, the method makes visible what actually happened and what can be learned from it. It separates course, effect, and causes so experience turns into solid improvement. |
Complexitydifferent | High | Medium | High | Low |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | Halber Tag | 20-45 min |
Participantsdifferent | 1-8 | 3-10 | 5-20 | 3-12 |
Formatdifferent | Workshop + async | Workshop | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Learning Review Notes, System Factors, Improvement Actions | Simulation Notes, Gaps List, Updated Runbooks | Lessons learned, Action items, Event summary |
Tagsno overlap | ForecastingFlowDelivery | LearningRetrospectiveIncidentOperations | ResilienceOperationsIncident | LearningOperationsImprovement |