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Criterion
Paper illustration for Bottleneck Analysis.
Operations
Bottleneck Analysis
Paper illustration for ABC Analysis
Operations
ABC Analysis
Paper illustration for NoEstimates.
Agile
NoEstimates
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.With many similar objects of differing economic significance, the method prioritizes effort by effect. It separates what needs regular steering from what rarely needs attention.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.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
MediumLowMediumHigh
Timedifferent
1-3 h30-60 minlaufend30-90 min Setup, danach laufend
Participantsdifferent
3-81-52-121-8
Formatdifferent
Workshop + asyncAsyncWorkshop + asyncWorkshop + async
Outputdifferent
Bottleneck Map, Flow Metrics, Improvement Options, Follow-up MeasuresABC Classification, Focus Rules, Control ListThroughput Data, Flow Forecast, Slicing RulesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
FlowMeasurementConstraints
PrioritizationOperationsPortfolio
EstimationForecastingFlow
ForecastingFlowDelivery
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