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Criterion
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Paper illustration for PERT Estimation.
Decision Making
PERT Estimation
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
When a story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify.On complex initiatives, a plain average tends to understate just how uncertain the outcome really is. It separates options, evaluation criteria, and open risks. The result is captured as a PERT Estimate, Expected Value, and Risk Notes.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
30-60 min15-45 min30-90 min Setup, danach laufend
Participantsdifferent
2-61-81-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Smaller Stories, Acceptance Criteria, Split RationalePERT Estimate, Expected Value, Risk NotesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
BacklogIterationDelivery
EstimationUncertaintyRisk
ForecastingFlowDelivery
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Paper illustration for Three-Point Estimation.
Decision Making
Three-Point Estimation
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation