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Criterion
A paper-based illustration representing WSJF with its core stages and visible working result.
Agile
WSJF
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Purposedifferent
When many tasks compete for the same capacity, it orders work by economic impact. It makes time lost, benefit, and effort comparable 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.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 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
HighHighLowHigh
Timedifferent
45-90 min30-90 min Setup, danach laufend1-2 h1-4 Wochen
Participantsdifferent
3-101-82-86-30 Experten
Formatdifferent
WorkshopWorkshop + asyncWorkshop + asyncAsync
Outputdifferent
Ranked Backlog, Cost-of-Delay AssumptionsForecast Percentiles, Throughput Dataset, Risk CommunicationDIBB document, Belief list, Bet list, Learning reportExpert Forecast, Consensus Range, Assumption Notes
Tagsno overlap
LeanEconomicsSequencePrioritization
ForecastingFlowDelivery
StrategyDecisionAssumptionsHypothesis
ForecastingExpertsDecisionStrategy
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