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Criterion
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Planning Poker.
Agile
Planning Poker
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
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 estimates within the team diverge too much, it puts differing expectations on a common test bench. It separates rough complexity from unspoken assumptions.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.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
laufend2-5 min je Item30-90 min30-90 min Setup, danach laufend
Participantsdifferent
2-123-93-121-8
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshopWorkshop + async
Outputdifferent
Throughput Data, Flow Forecast, Slicing RulesRelative Estimates, Assumption Notes, Split CandidatesAffinity Size Map, Grouped Estimates, Unclear ItemsForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationForecastingFlow
EstimationAgileRelative sizingTeam
EstimationBacklogRelative sizing
ForecastingFlowDelivery
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