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Criterion
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Paper illustration for Three-Point Estimation.
Decision Making
Three-Point Estimation
Paper illustration of a four-column Kanban board with limited ongoing work, a visible blocker and a review loop.
Engineering
Kanban
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.A single estimate for effort, time, or risk often looks too smooth for the real uncertainty behind it. It separates options, evaluation criteria, and open risks. The result is captured as a Three-Point Estimate, Risk Range, and Assumption Notes.When work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable.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
MediumMediumMediumHigh
Timedifferent
30-60 min10-30 min je ItemOngoing30-90 min Setup, danach laufend
Participantsdifferent
2-61-82-121-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Smaller Stories, Acceptance Criteria, Split RationaleThree-Point Estimate, Risk Range, Assumption NotesKanban board, WIP policies, Flow metricsForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
BacklogIterationDelivery
EstimationUncertaintyForecasting
FlowVisual managementDelivery
ForecastingFlowDelivery
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