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| Criterion |  Delivery Monte Carlo Forecasting |  Engineering Kanban |  Growth A/B Testing |
|---|---|---|---|
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 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 two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. |
Complexitydifferent | High | Medium | High |
Timedifferent | 30-90 min Setup, danach laufend | Ongoing | 1-4 Wochen |
Participantsdifferent | 1-8 | 2-12 | 1-6 |
Formatdifferent | Workshop + async | Workshop + async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Kanban board, WIP policies, Flow metrics | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | FlowVisual managementDelivery | ExperimentsGrowthAnalyticsValidation |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
