방법 비교

	다중 응답 공정 능력 분석	다중 반응 반응 표면 방법론
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	1993–1994 (foundational multivariate indices)	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
창시자	Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm)	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
유형	Quantitative quality / process assessment method	Experimental optimization technique
원전	Taam, W., Subbaiah, P., & Liddy, J. W. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3), 339–351.	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
별칭	MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
관련	6	6
요약	Multi-response process capability analysis extends classical single-response capability indices (Cp, Cpk) to situations where a process must simultaneously satisfy specification limits on two or more correlated quality characteristics. Rather than evaluating each response in isolation, it assesses the joint probability that all characteristics fall within their respective tolerance regions, yielding a more realistic picture of overall process performance in multi-characteristic manufacturing and engineering settings.	Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once.