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| Criterion |  Delivery Monte Carlo Forecasting |  Growth Growth Experiment |  Engineering Kanban |  Agile NoEstimates |
|---|---|---|---|---|
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. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. |
Complexitydifferent | High | Medium | Medium | Medium |
Timedifferent | 30-90 min Setup, danach laufend | 1-2 Wochen | Ongoing | laufend |
Participantsdifferent | 1-8 | 1-6 | 2-12 | 2-12 |
Formatsame | Workshop + async | 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 | Throughput Data, Flow Forecast, Slicing Rules |
Tagsno overlap | ForecastingFlowDelivery | MarketingGrowthExperimentsLearning | FlowVisual managementDelivery | EstimationForecastingFlow |