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| Criterion |  Agile NoEstimates |  Operations PDCA Cycle |  Operations Kaizen Event |  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. | For an improvement that still has to prove itself in everyday work, the method runs it through small learning loops. It connects planning, checking, and standardization into a repeatable learning mode. | For a tightly scoped process segment with noticeable waste, the method bundles shared energy for change. It suits situations that call for fast learning loops and visible adjustments. | 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 | laufend | 1 h bis mehrere Wochen | 0.5-5 Tage | 30-90 min Setup, danach laufend |
Participantsdifferent | 2-12 | 1-8 | 4-10 | 1-8 |
Formatdifferent | Workshop + async | Workshop + async | Workshop | Workshop + async |
Outputdifferent | Throughput Data, Flow Forecast, Slicing Rules | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Kaizen Charter, Waste List, Improvement Experiments, Standard Work Update | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | EstimationForecastingFlow | Continuous improvementLeanExperiments | LeanContinuous improvementOperations | ForecastingFlowDelivery |