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	Analiza Zdolności Procesu Wielowymiarowego	Projektowanie Doświadczeń
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1993–1994 (foundational multivariate indices)	1935
Twórca	Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm)	Ronald A. Fisher
Typ	Quantitative quality / process assessment method	Experimental planning framework
Źródło pierwotne	Taam, W., Subbaiah, P., & Liddy, J. W. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3), 339–351.	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Inne nazwy	MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability	DOE, experimental design, factorial experimentation, planned experimentation
Pokrewne	6	3
Podsumowanie	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.	Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.