Comparer des méthodes

	Analyse de Capabilité de Procédé Multi-Réponse	Plan d'expériences multi-réponses
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Process / pipeline
Année d'origine	1993–1994 (foundational multivariate indices)	1980 (desirability function formalization); DoE roots from Fisher, 1920s–1930s
Auteur d'origine	Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm)	Derringer & Suich (desirability function); Montgomery (systematic DoE integration)
Type	Quantitative quality / process assessment method	Experimental optimization methodology
Source fondatrice	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.
Alias	MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability	Multi-response DoE, Multiple-response optimization, Multi-objective DoE, MRDoE
Apparentées	6	4
Résumé	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 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.