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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
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
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.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.
Complexitydifferent
MediumHighHighHigh
Timedifferent
30-90 min1-4 h or multiple rounds30-90 min Setup, danach laufend1-4 Wochen
Participantsdifferent
3-124-12 Experten1-86-30 Experten
Formatdifferent
WorkshopWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
Affinity Size Map, Grouped Estimates, Unclear ItemsEstimate Range, Assumption Log, Expert Consensus NotesForecast Percentiles, Throughput Dataset, Risk CommunicationExpert Forecast, Consensus Range, Assumption Notes
Tagsno overlap
EstimationBacklogRelative sizing
EstimationExpertsForecasting
ForecastingFlowDelivery
ForecastingExpertsDecisionStrategy
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