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| Criterion |  Agile Ideal Days |  Delivery Monte Carlo Forecasting |  Decision Making Delphi Method |  Product Strategy DIBB |
|---|---|---|---|---|
Purposedifferent | 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. | 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 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 | Low | High | High | Low |
Timedifferent | 15-60 min | 30-90 min Setup, danach laufend | 1-4 Wochen | 1-2 h |
Participantsdifferent | 2-9 | 1-8 | 6-30 Experten | 2-8 |
Formatdifferent | Workshop + async | Workshop + async | Async | Workshop + async |
Outputdifferent | Ideal Day Estimates, Assumption Notes, Capacity Caveats | Forecast Percentiles, Throughput Dataset, Risk Communication | Expert Forecast, Consensus Range, Assumption Notes | DIBB document, Belief list, Bet list, Learning report |
Tagsno overlap | EstimationEffortAgile | ForecastingFlowDelivery | ForecastingExpertsDecisionStrategy | StrategyDecisionAssumptionsHypothesis |