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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Communication Plan
Delivery
Communication Plan
Paper illustration for Constraint Analysis.
Decision Making
Constraint Analysis
Decision Tree method illustration showing its working structure
Decision Making
Decision Tree
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.When many people need to be informed in parallel and messages would otherwise arrive unevenly or too late, a Communication Plan orders communication reliably. Channels, audiences, and cadences interlock so important information doesn't get lost.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.
Complexitydifferent
HighLowLowMedium
Timedifferent
30-90 min Setup, danach laufend45-90 min30-90 min30-90 min
Participantsdifferent
1-82-62-81-6
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationCommunication Plan, Channel Matrix, Message CadenceConstraint List, Hard/Soft Classification, Option Impact Notes, Decision BoundariesDecision Tree, Option Map, Assumption List
Tagsno overlap
ForecastingFlowDelivery
CommunicationStakeholdersDeliveryPlanning
ConstraintsDecisionPlanningOptions
DecisionTreeOptions
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