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| Criterion |  Delivery Monte Carlo Forecasting |  Agile Bucket System |  Product Discovery Dual-Track Agile |  Agile Ideal Days |
|---|---|---|---|---|
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 a large batch of work needs a fast, rough estimate, it sorts items into predefined size buckets. It sorts work by value, risk, and delivery ability. The result is captured as a Bucketed Backlog, Relative Estimates, and Split Candidates. | 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 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. |
Complexitydifferent | High | Medium | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | 30-90 min | Laufend, Wochen bis Monate | 15-60 min |
Participantsdifferent | 1-8 | 3-12 | 4-10 | 2-9 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Bucketed Backlog, Relative Estimates, Split Candidates | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Ideal Day Estimates, Assumption Notes, Capacity Caveats |
Tagsno overlap | ForecastingFlowDelivery | EstimationBacklogRelative sizing | AgileDiscoveryDelivery | EstimationEffortAgile |