قارن الطرق

	مخطط التحكم متعدد الاستجابات	تصميم التجارب متعدد الاستجابات
المجال	التصميم التجريبي	التصميم التجريبي
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions)	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s
صاحب الطريقة	Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)
النوع	Multivariate statistical process monitoring	Experimental optimization methodology
المصدر التأسيسي	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.
الأسماء البديلة	multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart	Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE
ذات صلة	6	4
الملخص	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 Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once.