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Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
Paper illustration of Request for Comments (RFC) with its method-specific working model.
Architecture
Request for Comments
Paper illustration for DACI
Decision Making
DACI
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.An RFC clarifies architecture context, options, and consequences. It structures technical context, alternatives, tradeoffs, open questions, and review feedback, and captures the result as an RFC document, reviewer comments, and a decision rationale.In cross-functional decisions, responsibilities quickly blur between approval, influence, and execution. DACI arranges these roles so decision pressure, contribution, and information stay cleanly separated.
Complexitydifferent
HighMediumLow
Timedifferent
30-90 min Setup, danach laufend1-3 Wochen vom Draft bis zur Entscheidung30-60 min
Participantsdifferent
1-83-20 Reviewer2-10
Formatdifferent
Workshop + asyncAsyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationRFC document, Reviewer comments, Decision rationale, Follow-up ADR or ticketsDACI Matrix, Decision Owner, Decision Log
Tagsno overlap
ForecastingFlowDelivery
ArchitectureDecisionAsyncGovernance
DecisionRolesGovernanceAlignment
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Often compared together

Methods with strong topical overlap with the current selection, not yet in the comparison.

Paper illustration of Decision Rights Mapping with its method-specific working model.
Decision Making
Decision Rights Mapping
Paper illustration for RAPID
Decision Making
RAPID
Paper illustration of two considered options leading to a decision record with a marked status and visible consequences.
Architecture
Architecture Decision Record
Architecture Inception Canvas method illustration showing its working structure
Architecture
Architecture Inception Canvas