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Criterion
Paper illustration for Bucket System.
Agile
Bucket System
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Paper illustration for NoEstimates.
Agile
NoEstimates
Purposedifferent
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.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 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.
Complexitydifferent
MediumHighMediumMedium
Timedifferent
30-90 min30-90 min Setup, danach laufend30-60 minlaufend
Participantsdifferent
3-121-82-62-12
Formatdifferent
WorkshopWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Bucketed Backlog, Relative Estimates, Split CandidatesForecast Percentiles, Throughput Dataset, Risk CommunicationSmaller Stories, Acceptance Criteria, Split RationaleThroughput Data, Flow Forecast, Slicing Rules
Tagsno overlap
EstimationBacklogRelative sizing
ForecastingFlowDelivery
BacklogIterationDelivery
EstimationForecastingFlow
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