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Criterion
Paper illustration for Bottleneck Analysis.
Operations
Bottleneck Analysis
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
For a flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves throughput.When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes.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
MediumMediumHigh
Timedifferent
1-3 h30-90 min30-90 min Setup, danach laufend
Participantsdifferent
3-83-121-8
Formatdifferent
Workshop + asyncWorkshopWorkshop + async
Outputdifferent
Bottleneck Map, Flow Metrics, Improvement Options, Follow-up MeasuresAffinity Size Map, Grouped Estimates, Unclear ItemsForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
FlowMeasurementConstraints
EstimationBacklogRelative sizing
ForecastingFlowDelivery
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