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Criterion
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Bucket System.
Agile
Bucket System
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.When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules.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 a large batch of work needs a fast, rough estimate, it sorts items into predefined size buckets. It sorts work by value, risk, and delivery ability. The result is captured as a Bucketed Backlog, Relative Estimates, and Split Candidates.
Complexitydifferent
MediumMediumHighMedium
Timedifferent
30-60 minlaufend30-90 min Setup, danach laufend30-90 min
Participantsdifferent
2-62-121-83-12
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop
Outputdifferent
Smaller Stories, Acceptance Criteria, Split RationaleThroughput Data, Flow Forecast, Slicing RulesForecast Percentiles, Throughput Dataset, Risk CommunicationBucketed Backlog, Relative Estimates, Split Candidates
Tagsno overlap
BacklogIterationDelivery
EstimationForecastingFlow
ForecastingFlowDelivery
EstimationBacklogRelative sizing
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