Comparer des méthodes

	Contrôle statistique des processus à réponses multiples	Maîtrise Statistique des Procédés
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	1924–1931
Auteur d'origine	Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young	Walter A. Shewhart
Type	Multivariate quality-monitoring procedure	Process monitoring and quality control method
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. ISBN: 978-0873890762
Alias	Multivariate SPC, MSPC, Multi-response SPC, Multivariate statistical process control	SPC, statistical quality control, process control charting, Shewhart control
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.	Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioners to maintain processes in a stable, predictable state and to detect problems early, before defective output reaches customers.