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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
Paper illustration for Bottleneck Analysis.
Operations
Bottleneck Analysis
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
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 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.When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.
Complexitydifferent
HighMediumMediumHigh
Timedifferent
30-90 min Setup, danach laufend30-90 min1-3 h1-4 Wochen
Participantsdifferent
1-81-63-86-30 Experten
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationDecision Tree, Option Map, Assumption ListBottleneck Map, Flow Metrics, Improvement Options, Follow-up MeasuresExpert Forecast, Consensus Range, Assumption Notes
Tagsno overlap
ForecastingFlowDelivery
DecisionTreeOptions
FlowMeasurementConstraints
ForecastingExpertsDecisionStrategy
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