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| Criterion |  Delivery Monte Carlo Forecasting |  Operations ABC Analysis |  Operations Learning Review |  DevOps Blameless Postmortem |
|---|---|---|---|---|
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. | 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. | 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. | After an incident with damage or a near miss, the method creates a sober field for learning without assigning blame. It directs attention to the course of events, conditions, and effective countermeasures. |
Complexitydifferent | High | Low | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 30-60 min | 1-3 h | 30-90 min |
Participantsdifferent | 1-8 | 1-5 | 3-10 | 3-12 |
Formatdifferent | Workshop + async | Async | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | ABC Classification, Focus Rules, Control List | Learning Review Notes, System Factors, Improvement Actions | Postmortem Doc, Action Items, Timeline |
Tagsno overlap | ForecastingFlowDelivery | PrioritizationOperationsPortfolio | LearningRetrospectiveIncidentOperations | Site Reliability EngineeringIncidentLearningReliability |