Сравнение методов

	Многомерный анализ пригодности процесса	Статистическое управление процессами
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1993–1994 (foundational multivariate indices)	1924–1931
Автор метода	Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm)	Walter A. Shewhart
Тип	Quantitative quality / process assessment method	Process monitoring and quality control method
Основополагающий источник	Taam, W., Subbaiah, P., & Liddy, J. W. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3), 339–351.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Другие названия	MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability	SPC, statistical quality control, process control charting, Shewhart control
Связанные	6	6
Сводка	Multi-response process capability analysis extends classical single-response capability indices (Cp, Cpk) to situations where a process must simultaneously satisfy specification limits on two or more correlated quality characteristics. Rather than evaluating each response in isolation, it assesses the joint probability that all characteristics fall within their respective tolerance regions, yielding a more realistic picture of overall process performance in multi-characteristic manufacturing and engineering settings.	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.