Сравнение на методи

	Анализ на възможностите на процеса с подпомагане на оптимизацията	Методология на повърхността на отклика (RSM)
Област	Планиране на експеримента	Планиране на експеримента
Семейство	Process / pipeline	Hypothesis test
Година на възникване	1986–2000s	1951
Създател	V. E. Kane (capability indices, 1986); integrated with optimization frameworks by quality engineering researchers in the 1990s–2000s	George E. P. Box & K. B. Wilson
Тип	Quantitative engineering method	Second-order polynomial response surface model
Основополагащ източник	Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41–52.	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Други названия	OA-PCA, optimization-integrated capability analysis, capability-constrained process optimization, process capability with optimization	RSM, Central Composite Design, Box-Behnken Design, CCD
Свързани	5	7
Резюме	Optimization-assisted process capability analysis combines classical capability indices (Cp, Cpk, Cpm) with mathematical optimization to identify process parameter settings that simultaneously satisfy engineering specifications and maximize process capability. Rather than simply measuring whether a process is capable, it prescribes the control factor levels — mean, variance, tolerances — that push capability above a target threshold. It is widely applied in manufacturing, chemical processing, and quality engineering contexts where multiple process variables must be tuned jointly.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.