Krahasoni metodat

	Diagrama e Kontrollit me Përgjigje Shumëshe	Metodologjia e Sipërfaqes së Përgjigjes me Përgjigje të Shumëfishta
Fusha	Dizajni eksperimental	Dizajni eksperimental
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions)	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
Krijuesi	Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
Lloji	Multivariate statistical process monitoring	Experimental optimization technique
Burimi themelues	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.
Emërtime të tjera	multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
Të lidhura	6	6
Përmbledhja	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.