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Criterion
Paper illustration for Waste Analysis.
Operations
Waste Analysis
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
Purposedifferent
For a process that keeps people busy but creates little value, the method exposes waste. It directs attention to unnecessary movement, waiting times, rework, and overdelivery.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.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.
Complexitydifferent
LowHighHighLow
Timedifferent
45-120 min1-4 Wochen30-90 min Setup, danach laufend1-2 h
Participantsdifferent
2-86-30 Experten1-82-8
Formatdifferent
WorkshopAsyncWorkshop + asyncWorkshop + async
Outputdifferent
Waste Map, Prioritized Waste, Improvement BacklogExpert Forecast, Consensus Range, Assumption NotesForecast Percentiles, Throughput Dataset, Risk CommunicationDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
WasteLeanProcess improvement
ForecastingExpertsDecisionStrategy
ForecastingFlowDelivery
StrategyDecisionAssumptionsHypothesis
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