Comparer des méthodes

	Contrôle statistique des processus à réponses multiples	Diagramme de contrôle
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s	1924 (first use); 1931 (seminal book)
Auteur d'origine	Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young	Walter A. Shewhart (Bell Labs)
Type	Multivariate quality-monitoring procedure	Statistical monitoring and control technique
Source fondatrice	Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800–810.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.
Alias	Multivariate SPC, MSPC, Multi-response SPC, Multivariate statistical process control	Shewhart chart, process-behavior chart, SPC chart, quality control chart
Apparentées	6	6
Résumé	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.	A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time.