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Criterion
Paper illustration for Wideband Delphi.
Decision Making
Wideband Delphi
Paper illustration for NoEstimates.
Agile
NoEstimates
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
A single opinion rarely holds up for planning when the future is genuinely uncertain. It separates options, evaluation criteria, and open risks. The result is captured as an Estimate Range, Assumption Log, and Expert Consensus Notes.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 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
HighMediumMediumHigh
Timedifferent
1-4 h or multiple roundslaufend30-90 min30-90 min Setup, danach laufend
Participantsdifferent
4-12 Experten2-123-121-8
Formatdifferent
Workshop + asyncWorkshop + asyncWorkshopWorkshop + async
Outputdifferent
Estimate Range, Assumption Log, Expert Consensus NotesThroughput Data, Flow Forecast, Slicing RulesAffinity Size Map, Grouped Estimates, Unclear ItemsForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationExpertsForecasting
EstimationForecastingFlow
EstimationBacklogRelative sizing
ForecastingFlowDelivery
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