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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Growth Experiment |  Engineering Kanban |
|---|---|---|---|
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 growth ideas surface quickly, the temptation to build immediately instead of learning is high. A Growth Experiment frames an assumption so target group, lever, and measurement are clear before the first deployment. | 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. |
Complexitydifferent | High | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 1-2 Wochen | Ongoing |
Participantsdifferent | 1-8 | 1-6 | 2-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Experiment card, Result summary, Next bet | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | ForecastingFlowDelivery | MarketingGrowthExperimentsLearning | FlowVisual managementDelivery |
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