Salīdzināt metodes

	Daudzreakciju statistiskā procesa vadība	Vairākuzdevumu atbildes virsmas metodoloģija
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1947 (Hotelling's T²); mature multivariate SPC framework 1980s–2000s	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
Autors	Harold Hotelling (T² statistic); extended by Alt, Lowry, Montgomery, Mason & Young	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
Tips	Multivariate quality-monitoring procedure	Experimental optimization technique
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 RSM, MRSM, Multi-objective RSM, Multiple response optimization
Saistītās	6	6
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 Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once.