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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Causal Factor Analysis |  Operations 5 Whys |
|---|---|---|---|
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. | For a single, hard-to-explain deviation, the method exposes the causal chain behind the visible symptom. It keeps the cause open until a controllable condition emerges instead of a mere description. |
Complexitydifferent | High | High | Low |
Timedifferent | 30-90 min Setup, danach laufend | 2-6 h | 15-30 min |
Participantsdifferent | 1-8 | 3-10 | 2-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Root cause notes, Countermeasures |
Tagsno overlap | ForecastingFlowDelivery | CausalityIncidentRoot causeTimeline | Root causeIncidentLeanProblem solving |
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