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| Criterion |  Delivery Monte Carlo Forecasting |  Agile Ideal Days |  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 effort needs to be thought of as real working time, it offers a simple common denominator for comparison. It sorts work by value, risk, and delivery ability. The result is captured as Ideal Day Estimates, Assumption Notes, and Capacity Caveats. | 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 | Low | High | Low |
Timedifferent | 30-90 min Setup, danach laufend | 15-60 min | 1-4 Wochen | 1-2 h |
Participantsdifferent | 1-8 | 2-9 | 6-30 Experten | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Expert Forecast, Consensus Range, Assumption Notes | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | ForecastingFlowDelivery | EstimationEffortAgile | ForecastingExpertsDecisionStrategy | StrategyDecisionAssumptionsHypothesis |