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| Criterion |  Delivery Monte Carlo Forecasting |  Operations MORT Analysis |  Agile NoEstimates |  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 a safety-relevant event or a system with high protection requirements, the method examines where controls failed. It exposes both technical and organizational gaps. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | 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. |
Complexitydifferent | High | High | Medium | High |
Timedifferent | 30-90 min Setup, danach laufend | Mehrere Tage bis Wochen | laufend | 2-6 h |
Participantsdifferent | 1-8 | 2-6 | 2-12 | 3-10 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | MORT Worksheets, Findings per Branch, Corrective Actions, Systemic Recommendations | Throughput Data, Flow Forecast, Slicing Rules | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions |
Tagsno overlap | ForecastingFlowDelivery | Root causeSafetySystemicIncident | EstimationForecastingFlow | CausalityIncidentRoot causeTimeline |