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| Criterion |  Agile NoEstimates |  Engineering Kanban |  Product Strategy DIBB |  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. | 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. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | 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 | Low | High |
Timedifferent | laufend | Ongoing | 1-2 h | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-12 | 2-12 | 2-8 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Throughput Data, Flow Forecast, Slicing Rules | Kanban board, WIP policies, Flow metrics | DIBB document, Belief list, Bet list, Learning report | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationForecastingFlow | FlowVisual managementDelivery | StrategyDecisionAssumptionsHypothesis | ForecastingFlowDelivery |