विधियों की तुलना करें

	बहु-प्रतिक्रिया सांख्यिकीय प्रक्रिया नियंत्रण	बहु-प्रतिक्रिया प्रयोगों का डिज़ाइन
क्षेत्र	प्रयोगात्मक अभिकल्प	प्रयोगात्मक अभिकल्प
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s
प्रवर्तक	Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)
प्रकार	Multivariate quality-monitoring procedure	Experimental optimization methodology
मौलिक स्रोत	Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800–810.	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
उपनाम	Multivariate SPC, MSPC, Multi-response SPC, Multivariate statistical process control	Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE
संबंधित	6	4
सारांश	Multi-response statistical process control (multivariate SPC) extends classical univariate control charting to processes where two or more correlated quality characteristics must be monitored simultaneously. By treating all responses as a joint distribution, it detects shifts that would be invisible when each response is charted independently, reducing false alarms and improving the sensitivity of process monitoring in manufacturing and service contexts.	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.