Krahasoni metodat

	Analiza e Aftësisë së Procesit me Përgjigje të Shumëfishta	Metodologjia e Sipërfaqes së Përgjigjes me Përgjigje të Shumëfishta
Fusha	Dizajni eksperimental	Dizajni eksperimental
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	1993–1994 (foundational multivariate indices)	1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson)
Krijuesi	Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm)	Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework)
Lloji	Quantitative quality / process assessment method	Experimental optimization technique
Burimi themelues	Taam, W., Subbaiah, P., & Liddy, J. W. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3), 339–351.	Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219.
Emërtime të tjera	MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability	Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization
Të lidhura	6	6
Përmbledhja	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.	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.