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Selected:Monte Carlo ForecastingIncident Timeline AnalysisCausal Factor AnalysisBlameless Postmortem
| Criterion |  Delivery Monte Carlo Forecasting |  DevOps Incident Timeline Analysis |  Operations Causal Factor Analysis |  DevOps Blameless Postmortem |
|---|---|---|---|---|
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 incident with an unclear sequence, the method makes the timeline precisely visible. It separates perception, reaction, and delay so cause and effect become more clearly readable. | 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. | After an incident with damage or a near miss, the method creates a sober field for learning without assigning blame. It directs attention to the course of events, conditions, and effective countermeasures. |
Complexitydifferent | High | Medium | High | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 60-180 min | 2-6 h | 30-90 min |
Participantsdifferent | 1-8 | 3-10 | 3-10 | 3-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Incident Timeline, Evidence Log, Delay Analysis, Improvement Actions | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Postmortem Doc, Action Items, Timeline |
Tagsno overlap | ForecastingFlowDelivery | IncidentTimelineSite Reliability Engineering | CausalityIncidentRoot causeTimeline | Site Reliability EngineeringIncidentLearningReliability |