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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of Hypothesis Prioritization Canvas with a method-specific labelled workspace.
Product Discovery
Hypothesis Prioritization Canvas
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
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.When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog.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
HighMediumHigh
Timedifferent
30-90 min Setup, danach laufend60-90 min1-4 Wochen
Participantsdifferent
1-83-86-30 Experten
Formatdifferent
Workshop + asyncWorkshopAsync
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationPrioritization Canvas, Hypothesis BacklogExpert Forecast, Consensus Range, Assumption Notes
Tagsno overlap
ForecastingFlowDelivery
ExperimentsPrioritizationDiscoveryHypothesis
ForecastingExpertsDecisionStrategy
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Paper illustration of Cost of Delay with its method-specific working model.
Delivery
Cost of Delay
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Paper illustration of Experiment Canvas with a method-specific labelled workspace.
Product Discovery
Experiment Canvas
Paper illustration of a Test Card with four fields for hypothesis, test, metric, and success threshold.
Product Discovery
Test Card