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| Criterion |  Delivery Monte Carlo Forecasting |  Engineering Kanban |  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. | When work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable. | 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 | Medium | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | Ongoing | 1-3 h | 20-45 min |
Participantsdifferent | 1-8 | 2-12 | 3-10 | 3-12 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Kanban board, WIP policies, Flow metrics | Learning Review Notes, System Factors, Improvement Actions | Lessons learned, Action items, Event summary |
Tagsno overlap | ForecastingFlowDelivery | FlowVisual managementDelivery | LearningRetrospectiveIncidentOperations | LearningOperationsImprovement |