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| Criterion |  Delivery Monte Carlo Forecasting |  Agile Story Splitting |  Operations Theory of Constraints |
|---|---|---|---|
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 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. | For a system that does not get faster despite effort, the method directs attention to the bottleneck. It concentrates improvement on the point that actually limits throughput. |
Complexitydifferent | High | Medium | High |
Timedifferent | 30-90 min Setup, danach laufend | 30-60 min | 2-4 h Analyse, laufend |
Participantsdifferent | 1-8 | 2-6 | 3-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Smaller Stories, Acceptance Criteria, Split Rationale | Constraint Map, Improvement Plan, Flow Metrics |
Tagsno overlap | ForecastingFlowDelivery | BacklogIterationDelivery | OperationsConstraintsFlowImprovement |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
