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Criterion
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Paper illustration for Wideband Delphi.
Decision Making
Wideband Delphi
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Purposedifferent
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.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.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
MediumHighHigh
Timedifferent
30-90 min1-4 h or multiple rounds30-90 min Setup, danach laufend
Participantsdifferent
3-124-12 Experten1-8
Formatdifferent
WorkshopWorkshop + asyncWorkshop + async
Outputdifferent
Affinity Size Map, Grouped Estimates, Unclear ItemsEstimate Range, Assumption Log, Expert Consensus NotesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationBacklogRelative sizing
EstimationExpertsForecasting
ForecastingFlowDelivery
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