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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Bottleneck Analysis.
Operations
Bottleneck Analysis
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Theory of Constraints.
Operations
Theory of Constraints
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 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 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 a system that does not get faster despite effort, the method directs attention to the bottleneck. It concentrates improvement on the point that actually limits throughput.
Complexitydifferent
HighMediumMediumHigh
Timedifferent
30-90 min Setup, danach laufend1-3 hlaufend2-4 h Analyse, laufend
Participantsdifferent
1-83-82-123-12
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationBottleneck Map, Flow Metrics, Improvement Options, Follow-up MeasuresThroughput Data, Flow Forecast, Slicing RulesConstraint Map, Improvement Plan, Flow Metrics
Tags1 shared
ForecastingFlowDelivery
FlowMeasurementConstraints
EstimationForecastingFlow
OperationsConstraintsFlowImprovement
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