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| Criterion |  Operations PDCA Cycle |  Delivery Monte Carlo Forecasting |  Decision Making Three-Point Estimation |
|---|---|---|---|
Purposedifferent | 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. | 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. | A single estimate for effort, time, or risk often looks too smooth for the real uncertainty behind it. It separates options, evaluation criteria, and open risks. The result is captured as a Three-Point Estimate, Risk Range, and Assumption Notes. |
Complexitydifferent | Low | High | Medium |
Timedifferent | 1 h bis mehrere Wochen | 30-90 min Setup, danach laufend | 10-30 min je Item |
Participantssame | 1-8 | 1-8 | 1-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | PDCA Log, Experiment Plan, Learning Outcome, Standard Change | Forecast Percentiles, Throughput Dataset, Risk Communication | Three-Point Estimate, Risk Range, Assumption Notes |
Tagsno overlap | Continuous improvementLeanExperiments | ForecastingFlowDelivery | EstimationUncertaintyForecasting |
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Methods with strong topical overlap with the current selection, not yet in the comparison.
