Comparar métodos

	Control Estadístico de Procesos Multirrespuesta	Diseño de experimentos de respuesta múltiple
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s
Autor original	Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)
Tipo	Multivariate quality-monitoring procedure	Experimental optimization methodology
Fuente seminal	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.
Alias	Multivariate SPC, MSPC, Multi-response SPC, Multivariate statistical process control	Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE
Relacionados	6	4
Resumen	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.