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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
A paper-based illustration representing Bounded Context Canvas with its core stages and visible working result.
Domain Modeling
Bounded Context Canvas
Anonymous individual judgments pass through feedback loops to form a distribution.
Decision Making
Delphi Method
Paper illustration of DIBB with its method-specific working model.
Product Strategy
DIBB
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 a business context is still loosely outlined, it shapes language, responsibility, and integration space. It clarifies what belongs together and where a boundary needs to hold.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.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
HighMediumHighLow
Timedifferent
30-90 min Setup, danach laufend1-3 h1-4 Wochen1-2 h
Participantsdifferent
1-83-86-30 Experten2-8
Formatdifferent
Workshop + asyncWorkshop + asyncAsyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationContext Canvas, Glossary, Integration NotesExpert Forecast, Consensus Range, Assumption NotesDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
ForecastingFlowDelivery
Domain-Driven DesignBoundariesModeling
ForecastingExpertsDecisionStrategy
StrategyDecisionAssumptionsHypothesis
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