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Criterion
Paper illustration for Constraint Analysis.
Decision Making
Constraint Analysis
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Bottleneck Analysis.
Operations
Bottleneck Analysis
Purposedifferent
When an initiative stalls, the limit often lies not in the idea but in hard or soft boundary conditions. Constraint Analysis separates these limits and shows which of them can actually be shaped.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.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.
Complexitydifferent
LowMediumHighMedium
Timedifferent
30-90 min30-90 min30-90 min Setup, danach laufend1-3 h
Participantsdifferent
2-81-61-83-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Constraint List, Hard/Soft Classification, Option Impact Notes, Decision BoundariesDecision Tree, Option Map, Assumption ListForecast Percentiles, Throughput Dataset, Risk CommunicationBottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures
Tagsno overlap
ConstraintsDecisionPlanningOptions
DecisionTreeOptions
ForecastingFlowDelivery
FlowMeasurementConstraints
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