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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Three-Point Estimation.
Decision Making
Three-Point Estimation
Paper illustration for Story Points.
Agile
Story Points
Paper illustration for Bottleneck Analysis.
Operations
Bottleneck 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.A single estimate for effort, time, or risk often looks too smooth for the real uncertainty behind it. It separates options, evaluation criteria, and open risks. The result is captured as a Three-Point Estimate, Risk Range, and Assumption Notes.When teams want to rate complexity rather than hours, it creates a shared, relative scale. It sorts work by value, risk, and delivery ability. The result is captured as Point Estimates, Reference Stories, and Velocity Data.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.
Complexitydifferent
HighMediumMediumMedium
Timedifferent
30-90 min Setup, danach laufend10-30 min je Itemlaufend, 1-5 min je Item1-3 h
Participantsdifferent
1-81-83-93-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationThree-Point Estimate, Risk Range, Assumption NotesPoint Estimates, Reference Stories, Velocity DataBottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures
Tagsno overlap
ForecastingFlowDelivery
EstimationUncertaintyForecasting
EstimationAgileMeasurement
FlowMeasurementConstraints
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