So sánh phương pháp

	Biểu đồ kiểm soát đa đáp ứng	Phương pháp Bề mặt Đáp ứng Đa phản hồi
Lĩnh vực	Thiết kế thí nghiệm	Thiết kế thí nghiệm
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions)	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
Người khởi xướng	Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
Loại	Multivariate statistical process monitoring	Experimental optimization technique
Công trình gốc	Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). McGraw-Hill.	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
Tên gọi khác	multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
Liên quan	6	6
Tóm tắt	A multi-response control chart simultaneously monitors two or more correlated quality characteristics on a single chart, preserving the correlation structure that univariate charts ignore. Built on Hotelling's T² statistic and its time-weighted extensions (MEWMA, MCUSUM), it detects process shifts that would be missed if each response were charted independently. It is the standard tool in manufacturing and service quality when product performance depends on multiple interrelated outputs.	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.