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| Criterion |  Delivery Monte Carlo Forecasting |  Delivery Value Stream Mapping |  Product Discovery Assumption Mapping |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
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 assumptions still sit unordered in the room, it weighs uncertainty against leverage. It makes visible which hypotheses should be checked first. | 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 | High | Medium | Medium | Low |
Timedifferent | 30-90 min Setup, danach laufend | 1-3 h | 45-60 min | 30-60 min |
Participantsdifferent | 1-8 | 4-10 | 2-8 | 1-5 |
Formatdifferent | Workshop + async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Forecast Percentiles, Throughput Dataset, Risk Communication | Current-state map, Future-state map, Bottleneck list | Assumption map, Test backlog, Risk ranking | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ForecastingFlowDelivery | LeanFlowWasteDelivery | AssumptionsRiskExperimentsValidation | ExperimentsValidationDiscoveryHypothesis |