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Criterion
Paper illustration for NoEstimates.
Agile
NoEstimates
Blameless Postmortem workspace showing the question, observations, and next decision.
DevOps
Blameless Postmortem
Paper illustration for Incident Timeline Analysis.
DevOps
Incident Timeline Analysis
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
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.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.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.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.
Complexitydifferent
MediumMediumMediumHigh
Timedifferent
laufend30-90 min60-180 min30-90 min Setup, danach laufend
Participantsdifferent
2-123-123-101-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Throughput Data, Flow Forecast, Slicing RulesPostmortem Doc, Action Items, TimelineIncident Timeline, Evidence Log, Delay Analysis, Improvement ActionsForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationForecastingFlow
Site Reliability EngineeringIncidentLearningReliability
IncidentTimelineSite Reliability Engineering
ForecastingFlowDelivery
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