방법 비교

	최적화 지원 식스 시그마 DMAIC	반응 표면 분석법 (RSM)
분야	실험설계	실험설계
계열	Process / pipeline	Hypothesis test
기원 연도	1990s–2000s (integration period)	1951
창시자	Six Sigma: Motorola (Bill Smith, Mikel Harry, 1986); optimization integration formalized in engineering literature through the 1990s–2000s	George E. P. Box & K. B. Wilson
유형	Process improvement framework with embedded optimization	Second-order polynomial response surface model
원전	Antony, J., & Banuelas, R. (2002). Key ingredients for the effective implementation of Six Sigma program. Measuring Business Excellence, 6(4), 20-27.	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.
별칭	Optimization-integrated DMAIC, DMAIC with optimization, Six Sigma optimization framework, Opt-DMAIC	RSM, Central Composite Design, Box-Behnken Design, CCD
관련	5	7
요약	Optimization-assisted Six Sigma DMAIC embeds formal mathematical optimization — response surface methods, metaheuristics, or multi-objective solvers — into the Improve phase of the DMAIC cycle. Rather than relying solely on engineering judgment or one-factor-at-a-time trials, the approach uses designed experiments to build a predictive model of the process and then applies an optimization algorithm to locate factor settings that best satisfy quality, cost, or multiple competing performance targets simultaneously.	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.