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| Criterion |  Delivery Monte Carlo Forecasting |  Agile Story Points |  Engineering Kanban |  Product Discovery Dual-Track Agile |
|---|---|---|---|---|
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 teams want to rate complexity rather than hours, it creates a shared, relative scale. It sorts work by value, risk, and delivery ability. The result is captured as Point Estimates, Reference Stories, and Velocity Data. | When work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable. | 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. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | laufend, 1-5 min je Item | Ongoing | Laufend, Wochen bis Monate |
Participantsdifferent | 1-8 | 3-9 | 2-12 | 4-10 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Point Estimates, Reference Stories, Velocity Data | Kanban board, WIP policies, Flow metrics | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories |
Tagsno overlap | ForecastingFlowDelivery | EstimationAgileMeasurement | FlowVisual managementDelivery | AgileDiscoveryDelivery |