Salīdzināt metodes

	Daudzreakciju statistiskā procesa vadība	Daudzreakciju eksperimentu plānošana
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s
Autors	Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)
Tips	Multivariate quality-monitoring procedure	Experimental optimization methodology
Pirmavots	Lowry, C. A., & Montgomery, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800–810.	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
Citi nosaukumi	Multivariate SPC, MSPC, Multi-response SPC, Multivariate statistical process control	Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE
Saistītās	6	4
Kopsavilkums	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.	Multi-response Design of Experiments (MRDoE) extends classical DoE to situations where several response variables must be optimized simultaneously. Rather than tuning factors for a single output, the experimenter fits separate regression or response-surface models for each response, then combines them — most often via Derringer and Suich's desirability function — into a single composite score that guides the search for factor settings satisfying all response targets at once.