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| Criterion |  Product Strategy DIBB |  Agile NoEstimates |  Decision Making Delphi Method |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | 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. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | 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. |
Complexitydifferent | Low | Medium | High | High |
Timedifferent | 1-2 h | laufend | 1-4 Wochen | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-8 | 2-12 | 6-30 Experten | 1-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | DIBB document, Belief list, Bet list, Learning report | Throughput Data, Flow Forecast, Slicing Rules | Expert Forecast, Consensus Range, Assumption Notes | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | StrategyDecisionAssumptionsHypothesis | EstimationForecastingFlow | ForecastingExpertsDecisionStrategy | ForecastingFlowDelivery |