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| Criterion |  DevOps ChatOps |  Product Strategy DIBB |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | For operational work in a distributed team, the method links communication with direct system control. It records decisions right where the signal, the context, and the execution come together. | 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. | 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 | Low | Medium | High |
Timedifferent | Ongoing | 1-2 h | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-20 | 2-8 | 2-12 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Chat Transcript, Action Log, Automations | DIBB document, Belief list, Bet list, Learning report | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | CollaborationEngineeringIncident | StrategyDecisionAssumptionsHypothesis | EstimationForecastingFlow | ForecastingFlowDelivery |