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| Criterion |  Delivery Monte Carlo Forecasting |  Product Discovery Dual-Track Agile |  Decision Making Delphi Method |  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 uncertainty and delivery run in parallel, it separates learning work from implementation while keeping both connected. It prevents unvalidated ideas from falling directly into the delivery stream. | 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 | High | Medium | High | Low |
Timedifferent | 30-90 min Setup, danach laufend | Laufend, Wochen bis Monate | 1-4 Wochen | 1-2 h |
Participantsdifferent | 1-8 | 4-10 | 6-30 Experten | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Expert Forecast, Consensus Range, Assumption Notes | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ForecastingFlowDelivery | AgileDiscoveryDelivery | ForecastingExpertsDecisionStrategy | StrategyDecisionAssumptionsHypothesis |