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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 for Planning Poker.
Agile
Planning Poker
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.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 estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions.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
MediumHighLowMedium
Timedifferent
30-60 min30-90 min Setup, danach laufend2-5 min je Item30-90 min
Participantsdifferent
2-61-83-93-12
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop
Outputdifferent
Smaller Stories, Acceptance Criteria, Split RationaleForecast Percentiles, Throughput Dataset, Risk CommunicationRelative Estimates, Assumption Notes, Split CandidatesBucketed Backlog, Relative Estimates, Split Candidates
Tagsno overlap
BacklogIterationDelivery
ForecastingFlowDelivery
EstimationAgileRelative sizingTeam
EstimationBacklogRelative sizing
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