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Criterion
Paper illustration for Wideband Delphi.
Decision Making
Wideband Delphi
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Paper illustration for NoEstimates.
Agile
NoEstimates
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.DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report.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.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
HighLowMediumHigh
Timedifferent
1-4 h or multiple rounds1-2 hlaufend30-90 min Setup, danach laufend
Participantsdifferent
4-12 Experten2-82-121-8
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Estimate Range, Assumption Log, Expert Consensus NotesDIBB document, Belief list, Bet list, Learning reportThroughput Data, Flow Forecast, Slicing RulesForecast Percentiles, Throughput Dataset, Risk Communication
Tagsno overlap
EstimationExpertsForecasting
StrategyDecisionAssumptionsHypothesis
EstimationForecastingFlow
ForecastingFlowDelivery
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