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| Criterion |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |  Product Discovery Dual-Track Agile |  Engineering Kanban |
|---|---|---|---|---|
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. | 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 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. | When work sits in too many parallel streams, throughput suffers and no one sees the bottlenecks. Kanban makes the flow of work visible and limits overload so a system becomes calmer and more predictable. |
Complexitydifferent | Medium | High | Medium | Medium |
Timedifferent | laufend | 30-90 min Setup, danach laufend | Laufend, Wochen bis Monate | Ongoing |
Participantsdifferent | 2-12 | 1-8 | 4-10 | 2-12 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication | Discovery Backlog, Delivery Backlog, Experiment-Ergebnisse, Validierte Stories | Kanban board, WIP policies, Flow metrics |
Tagsno overlap | EstimationForecastingFlow | ForecastingFlowDelivery | AgileDiscoveryDelivery | FlowVisual managementDelivery |