Сравнение методов

	Многомерный статистический контроль процессов	Статистическое управление процессами
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s	1924–1931
Автор метода	Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young	Walter A. Shewhart
Тип	Multivariate quality-monitoring procedure	Process monitoring and quality control method
Основополагающий источник	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
Другие названия	Multivariate SPC, MSPC, Multi-response SPC, Multivariate statistical process control	SPC, statistical quality control, process control charting, Shewhart control
Связанные	6	6
Сводка	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.