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Criterion
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration for Affinity Estimation.
Agile
Affinity Estimation
Purposedifferent
When knowledge is distributed and direct dominance should be avoided, groups quickly fall under the sway of their opinion leaders. The Delphi Method gathers assessments iteratively and gradually brings expert judgments closer together.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 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.
Complexitydifferent
HighHighMedium
Timedifferent
1-4 Wochen30-90 min Setup, danach laufend30-90 min
Participantsdifferent
6-30 Experten1-83-12
Formatdifferent
AsyncWorkshop + asyncWorkshop
Outputdifferent
Expert Forecast, Consensus Range, Assumption NotesForecast Percentiles, Throughput Dataset, Risk CommunicationAffinity Size Map, Grouped Estimates, Unclear Items
Tagsno overlap
ForecastingExpertsDecisionStrategy
ForecastingFlowDelivery
EstimationBacklogRelative sizing
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Often compared together

Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration for Wideband Delphi.
Decision Making
Wideband Delphi
Paper illustration of Cost of Delay with its method-specific working model.
Delivery
Cost of Delay