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| Criterion |  Delivery Monte Carlo Forecasting |  Operations ABC Analysis |  Operations Learning Review |  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. | 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 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 | Low | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | 30-60 min | 1-3 h | 20-45 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 | Lessons learned, Action items, Event summary |
Tagsno overlap | ForecastingFlowDelivery | PrioritizationOperationsPortfolio | LearningRetrospectiveIncidentOperations | LearningOperationsImprovement |