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Criterion
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Planning Poker.
Agile
Planning Poker
Paper illustration for Bucket System.
Agile
Bucket System
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 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.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 CandidatesBucketed Backlog, Relative Estimates, Split CandidatesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationForecastingFlow
EstimationAgileRelative sizingTeam
EstimationBacklogRelative sizing
ForecastingFlowDelivery
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