Methoden vergelijken

	Multi-respons Six Sigma DMAIC	Response Surface Methodology (RSM)
Vakgebied	Experimenteel ontwerp	Experimenteel ontwerp
Familie	Process / pipeline	Hypothesis test
Jaar van ontstaan	2000s–2010s (applied integration era)	1951
Grondlegger	Extension of Six Sigma DMAIC (Motorola/Mikel Harry); multi-response adaptation developed by quality engineering community	George E. P. Box & K. B. Wilson
Type	Process improvement methodology with multi-objective optimization	Second-order polynomial response surface model
Oorspronkelijke bron	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.
Aliassen	MR-DMAIC, multi-response DMAIC, multi-criteria Six Sigma, multi-objective DMAIC	RSM, Central Composite Design, Box-Behnken Design, CCD
Verwant	5	7
Samenvatting	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.