Bandingkan metode

	Diagram Kendali Respons Ganda	Pengendalian Proses Statistik
Bidang	Desain Eksperimen	Desain Eksperimen
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions)	1924–1931
Pencetus	Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others	Walter A. Shewhart
Tipe	Multivariate statistical process monitoring	Process monitoring and quality control method
Sumber perintis	Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). McGraw-Hill.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand. ISBN: 978-0873890762
Alias	multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart	SPC, statistical quality control, process control charting, Shewhart control
Terkait	6	6
Ringkasan	A multi-response control chart simultaneously monitors two or more correlated quality characteristics on a single chart, preserving the correlation structure that univariate charts ignore. Built on Hotelling's T² statistic and its time-weighted extensions (MEWMA, MCUSUM), it detects process shifts that would be missed if each response were charted independently. It is the standard tool in manufacturing and service quality when product performance depends on multiple interrelated outputs.	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.