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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Causal Factor Analysis |  Delivery Value Stream Mapping |
|---|---|---|---|
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. | For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events. | When delivery work dissolves into wait times, handoffs, and hidden effort, Value Stream Mapping makes the actual flow visible. The method exposes bottlenecks, idle time, and feedback loops so lead time and improvement levers become tangible. |
Complexitydifferent | High | High | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 2-6 h | 1-3 h |
Participantsdifferent | 1-8 | 3-10 | 4-10 |
Formatdifferent | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Current-state map, Future-state map, Bottleneck list |
Tagsno overlap | ForecastingFlowDelivery | CausalityIncidentRoot causeTimeline | LeanFlowWasteDelivery |
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