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| Criterion |  Delivery Monte Carlo Forecasting |  Agile NoEstimates |  Agile Bucket System |  Agile Story Splitting |
|---|---|---|---|---|
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 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 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 a story becomes too large for a clean flow, it breaks scope down along value and risk. It shapes the work into a form that ships earlier and is easier to verify. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | laufend | 30-90 min | 30-60 min |
Participantsdifferent | 1-8 | 2-12 | 3-12 | 2-6 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Throughput Data, Flow Forecast, Slicing Rules | Bucketed Backlog, Relative Estimates, Split Candidates | Smaller Stories, Acceptance Criteria, Split Rationale |
Tagsno overlap | ForecastingFlowDelivery | EstimationForecastingFlow | EstimationBacklogRelative sizing | BacklogIterationDelivery |