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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Causal Factor Analysis |
|---|---|---|
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. |
Complexitysame | High | High |
Timedifferent | 30-90 min Setup, danach laufend | 2-6 h |
Participantsdifferent | 1-8 | 3-10 |
Formatsame | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions |
Tagsno overlap | ForecastingFlowDelivery | CausalityIncidentRoot causeTimeline |
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