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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Paper illustration of a calm four-quadrant Assumption Map with hypothesis cards and emphasis on important assumptions with little evidence.
Product Discovery
Assumption Mapping
Paper illustration of Riskiest Assumption Test with a method-specific labelled workspace.
Product Discovery
Riskiest Assumption Test
Purposedifferent
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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first.When an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report.
Complexitydifferent
HighLowMediumMedium
Timedifferent
30-90 min Setup, danach laufend1-2 h45-60 min1-2 Wochen pro Iteration
Participantsdifferent
1-82-82-82-6
Formatsame
Workshop + asyncWorkshop + asyncWorkshop + asyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationDIBB document, Belief list, Bet list, Learning reportAssumption map, Test backlog, Risk rankingPrioritized Assumption List, Test Plan, Results Report
Tagsno overlap
ForecastingFlowDelivery
StrategyDecisionAssumptionsHypothesis
AssumptionsRiskExperimentsValidation
ExperimentsValidationDiscoveryAssumptions
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