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| Criterion |  Delivery Monte Carlo Forecasting |  Delivery Value Stream Mapping |  Product Discovery Smoke Test |  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 delivery work dissolves into wait times, handoffs, and hidden effort, Value Stream Mapping makes the actual flow visible. The method exposes bottlenecks, idle time, and feedback loops so lead time and improvement levers become tangible. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | 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 | Low | High |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | 1-5 Tage | 1-4 Wochen |
Participantsdifferent | 1-8 | 4-10 | Nutzertraffic | 1-6 |
Formatdifferent | Workshop + async | Workshop | Async | Async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Current-state map, Future-state map, Bottleneck list | Interest Metrics, Conversion Signal, Learning Note | Experiment results, Decision log, Learning summary |
Tagsno overlap | ForecastingFlowDelivery | LeanFlowWasteDelivery | ValidationExperimentsDemandGrowth | ExperimentsGrowthAnalyticsValidation |