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| Criterion |  Product Discovery Dual-Track Agile |  Operations Bottleneck Analysis |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | When uncertainty and delivery run in parallel, it separates learning work from implementation while keeping both connected. It prevents unvalidated ideas from falling directly into the delivery stream. | For a flow that backs up at one point, the method looks for the capacity limit with the greatest leverage. It explains why extra effort elsewhere barely improves throughput. | 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. | 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 | 1-3 h | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 4-10 | 3-8 | 2-12 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Bottleneck Map, Flow Metrics, Improvement Options, Follow-up Measures | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | AgileDiscoveryDelivery | FlowMeasurementConstraints | EstimationForecastingFlow | ForecastingFlowDelivery |