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Criterion
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration for Bottleneck Analysis.
Operations
Bottleneck Analysis
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
For decisions with follow-on paths and dependencies, a linear comparison is often not enough. A Decision Tree shows how options branch under conditions and which consequences hang on each branch.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.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
30-90 min1-3 h30-90 min Setup, danach laufend
Participantsdifferent
1-63-81-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Decision Tree, Option Map, Assumption ListBottleneck Map, Flow Metrics, Improvement Options, Follow-up MeasuresForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
DecisionTreeOptions
FlowMeasurementConstraints
ForecastingFlowDelivery
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Often compared together

Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration for Constraint Analysis.
Decision Making
Constraint Analysis
Paper illustration of Cost of Delay with its method-specific working model.
Delivery
Cost of Delay
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method