Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многокритериальная методология Six Sigma DMAIC× | Методология поверхности отклика (RSM)× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство≠ | Process / pipeline | Hypothesis test |
| Год появления≠ | 2000s–2010s (applied integration era) | 1951 |
| Автор метода≠ | Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community | George E. P. Box & K. B. Wilson |
| Тип≠ | Process improvement methodology with multi-objective optimization | Second-order polynomial response surface model |
| Основополагающий источник≠ | Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Doubleday. ISBN: 978-0385494090 | Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45. link ↗ |
| Другие названия≠ | MR-DMAIC, multi-response DMAIC, multi-criteria Six Sigma, multi-objective DMAIC | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Связанные≠ | 5 | 7 |
| Сводка≠ | Multi-response Six Sigma DMAIC extends the classic Define-Measure-Analyze-Improve-Control framework to situations where a process must satisfy several quality characteristics simultaneously. Rather than optimizing a single output, the methodology integrates multi-response optimization techniques — such as desirability functions, TOPSIS, or weighted signal-to-noise ratios — within the Analyze and Improve phases to identify factor settings that jointly meet all quality targets. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
| ScholarGateНабор данных ↗ |
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