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Criterion
Paper illustration for NoEstimates.
Agile
NoEstimates
Story Splitting method illustration showing its working structure
Agile
Story Splitting
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Purposedifferent
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.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 many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes.
Complexitydifferent
MediumMediumHighMedium
Timedifferent
laufend30-60 min30-90 min Setup, danach laufend30-90 min
Participantsdifferent
2-122-61-83-12
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop
Outputdifferent
Throughput Data, Flow Forecast, Slicing RulesSmaller Stories, Acceptance Criteria, Split RationaleForecast Percentiles, Throughput Dataset, Risk CommunicationAffinity Size Map, Grouped Estimates, Unclear Items
Tagsno overlap
EstimationForecastingFlow
BacklogIterationDelivery
ForecastingFlowDelivery
EstimationBacklogRelative sizing
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