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| Criterion |  Knowledge Modeling Knowledge Mapping |  Delivery Monte Carlo Forecasting |  Product Discovery Experiment Canvas |  Product Discovery Hypothesis Prioritization Canvas |
|---|---|---|---|---|
Purposedifferent | Knowledge Mapping makes knowledge, gaps, and transfer paths visible across a field. It fits when expertise should not just exist but also be findable and transferable. | 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. | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. |
Complexitydifferent | Medium | High | Low | Medium |
Timedifferent | 1-3 h | 30-90 min Setup, danach laufend | 30-60 min | 60-90 min |
Participantsdifferent | 3-12 | 1-8 | 1-5 | 3-8 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop |
Outputdifferent | Knowledge Map, Critical Knowledge Areas, Transfer Plan | Forecast Percentiles, Throughput Dataset, Risk Communication | Completed Experiment Canvas, Success Metric | Prioritization Canvas, Hypothesis Backlog |
Tagsno overlap | KnowledgeMappingRisk | ForecastingFlowDelivery | ExperimentsValidationDiscoveryHypothesis | ExperimentsPrioritizationDiscoveryHypothesis |