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| Criterion |  Engineering Kanban |  Growth A/B Testing |  Delivery Monte Carlo Forecasting |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | 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. | 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 experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. |
Complexitydifferent | Medium | High | High | Low |
Timedifferent | Ongoing | 1-4 Wochen | 30-90 min Setup, danach laufend | 30-60 min |
Participantsdifferent | 2-12 | 1-6 | 1-8 | 1-5 |
Formatdifferent | Workshop + async | Async | Workshop + async | Workshop + async |
Outputdifferent | Kanban board, WIP policies, Flow metrics | Experiment results, Decision log, Learning summary | Forecast Percentiles, Throughput Dataset, Risk Communication | Completed Experiment Canvas, Success Metric |
Tagsno overlap | FlowVisual managementDelivery | ExperimentsGrowthAnalyticsValidation | ForecastingFlowDelivery | ExperimentsValidationDiscoveryHypothesis |