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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Blameless Postmortem workspace showing the question, observations, and next decision.
DevOps
Blameless Postmortem
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Incident Timeline Analysis.
DevOps
Incident Timeline 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.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.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 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.
Complexitydifferent
HighMediumMediumMedium
Timedifferent
30-90 min Setup, danach laufend30-90 minlaufend60-180 min
Participantsdifferent
1-83-122-123-10
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationPostmortem Doc, Action Items, TimelineThroughput Data, Flow Forecast, Slicing RulesIncident Timeline, Evidence Log, Delay Analysis, Improvement Actions
Tagsno overlap
ForecastingFlowDelivery
Site Reliability EngineeringIncidentLearningReliability
EstimationForecastingFlow
IncidentTimelineSite Reliability Engineering
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