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| Criterion |  Delivery Monte Carlo Forecasting |  Operations Fault Tree Analysis |  Operations Causal Factor Analysis |  Operations MORT 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 a critical top event with several possible triggers, the method logically models failure paths. It makes visible which combinations of conditions can lead to damage. | 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 safety-relevant event or a system with high protection requirements, the method examines where controls failed. It exposes both technical and organizational gaps. |
Complexitysame | High | High | High | High |
Timedifferent | 30-90 min Setup, danach laufend | 2-6 h | 2-6 h | Mehrere Tage bis Wochen |
Participantsdifferent | 1-8 | 3-8 | 3-10 | 2-6 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Fault Tree, Critical Paths, Cause Hypotheses, Control Actions | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | MORT Worksheets, Findings per Branch, Corrective Actions, Systemic Recommendations |
Tagsno overlap | ForecastingFlowDelivery | RiskRoot causeSafety | CausalityIncidentRoot causeTimeline | Root causeSafetySystemicIncident |