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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Bucket System.
Agile
Bucket System
Story Splitting method illustration showing its working structure
Agile
Story Splitting
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 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.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.
Complexitydifferent
HighMediumMedium
Timedifferent
30-90 min Setup, danach laufend30-90 min30-60 min
Participantsdifferent
1-83-122-6
Formatdifferent
Workshop + asyncWorkshopWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationBucketed Backlog, Relative Estimates, Split CandidatesSmaller Stories, Acceptance Criteria, Split Rationale
Tagsno overlap
ForecastingFlowDelivery
EstimationBacklogRelative sizing
BacklogIterationDelivery
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