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| Criterion |  Agile NoEstimates |  Innovation Lean Startup |  Product Strategy Lean Canvas |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | 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. | For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight. | When an early product bet still has too many open points, it brings target group, problem, and assumptions into a single view. It condenses the idea so core risks become nameable. | 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. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | laufend | Wochen bis Monate je Lernzyklus | 45-90 min | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-12 | 2-8 | 1-6 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Throughput Data, Flow Forecast, Slicing Rules | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Lean Canvas, Core Assumptions, Experiment Backlog | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationForecastingFlow | LeanStartupValidationMVP | StartupLeanAssumptionsBusiness model | ForecastingFlowDelivery |