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Criterion
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Paper illustration for Planning Poker.
Agile
Planning Poker
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
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 estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions.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.
Complexitydifferent
MediumLowMediumHigh
Timedifferent
30-60 min2-5 min je Itemlaufend30-90 min Setup, danach laufend
Participantsdifferent
2-63-92-121-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Smaller Stories, Acceptance Criteria, Split RationaleRelative Estimates, Assumption Notes, Split CandidatesThroughput Data, Flow Forecast, Slicing RulesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
BacklogIterationDelivery
EstimationAgileRelative sizingTeam
EstimationForecastingFlow
ForecastingFlowDelivery
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