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Criterion
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of a four-column Kanban board with limited ongoing work, a visible blocker and a review loop.
Engineering
Kanban
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.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 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.
Complexitydifferent
MediumHighMedium
Timedifferent
30-60 min30-90 min Setup, danach laufendOngoing
Participantsdifferent
2-61-82-12
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Smaller Stories, Acceptance Criteria, Split RationaleForecast Percentiles, Throughput Dataset, Risk CommunicationKanban board, WIP policies, Flow metrics
Tags1 shared
BacklogIterationDelivery
ForecastingFlowDelivery
FlowVisual managementDelivery
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Value Stream Mapping workspace showing the question, observations, and next decision.
Delivery
Value Stream Mapping
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration for Bottleneck Analysis.
Operations
Bottleneck Analysis