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| Criterion |  Operations DMAIC |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |
|---|---|---|---|
Purposedifferent | For a process problem with fluctuating performance, the method brings analysis and improvement into a disciplined sequence. It creates a framework in which numbers, causes, and control come together. | 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 | High | Medium | High |
Timedifferent | 2-12 Wochen | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 3-10 | 2-12 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Project Charter, Measurement Plan, Cause Analysis, Control Plan | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | Continuous improvementQualityProcess improvement | EstimationForecastingFlow | ForecastingFlowDelivery |
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