methodatlas
Compare

View methods side by side.

Choose up to four methods. Add them using the search and share the comparison by copying its link.

Criterion
Paper illustration for Monte Carlo Forecasting.
Delivery
Monte Carlo Forecasting
A paper-based illustration representing WSJF with its core stages and visible working result.
Agile
WSJF
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 many tasks compete for the same capacity, it orders work by economic impact. It makes time lost, benefit, and effort comparable together.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
HighHighHighLow
Timedifferent
30-90 min Setup, danach laufend45-90 min1-4 Wochen1-2 h
Participantsdifferent
1-83-106-30 Experten2-8
Formatdifferent
Workshop + asyncWorkshopAsyncWorkshop + async
Outputdifferent
Forecast Percentiles, Throughput Dataset, Risk CommunicationRanked Backlog, Cost-of-Delay AssumptionsExpert Forecast, Consensus Range, Assumption NotesDIBB document, Belief list, Bet list, Learning report
Tagsno overlap
ForecastingFlowDelivery
LeanEconomicsSequencePrioritization
ForecastingExpertsDecisionStrategy
StrategyDecisionAssumptionsHypothesis
Add more methods