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Criterion
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Story Splitting method illustration showing its working structure
Agile
Story Splitting
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.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.
Complexitydifferent
MediumHighMedium
Timedifferent
laufend30-90 min Setup, danach laufend30-60 min
Participantsdifferent
2-121-82-6
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Throughput Data, Flow Forecast, Slicing RulesForecast Percentiles, Throughput Dataset, Risk CommunicationSmaller Stories, Acceptance Criteria, Split Rationale
Tagsno overlap
EstimationForecastingFlow
ForecastingFlowDelivery
BacklogIterationDelivery
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Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration for Three-Point Estimation.
Decision Making
Three-Point Estimation