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Context. This paper presents a method for solving the problem of product's quality assurance at the stage of the initial manufacture process design in accordance with the process-analytical technology for the design of modern certified manufacturing-QbD. The method uses the information technologies of multivariate statistical analysis (MSA) to evaluate the influence of time multivariate critical process parameters (CPPs) on the time product critical quality attributes (CQAs). Preparatory transformation of clusters of critical process (manufacture process) parameters into factors of product critical quality attributes was carried out. Objective. To disclose the method of multivariate statistical analysis for assessing the character and features of the influence of time multivariate critical process parameters on time multivariate critical quality attributes at the design stage of the manufacture process. Method. The method consistently uses: statistical procedures of exploratory multivariate data analysis; transformation the homogeneous observed values matrices of CPPs and product CQAs into data frame (table) with factorized data; construction the regression trees of multivariate CPPs with a multivariate responses (CQAs). The method is implemented the R language packages software. Results. Factorized time multivariate CPPs make it possible to use methods of multivariate statistical analysis for evaluating the influence of CPPs factors on the time multivariate CQAs. Conclusions. This method of statistical analysis, together with statistical multivariate canonical analysis, represents an up-to-date information technology for detailed estimation the influence of time multivariate CPPs objects and some CPPs components on CQAs.

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АННОТАЦИЯ В статье предлагается метод ко-кластеризации стохастических данных многомерных критических параметров процесса (CPPs) с целью оценки влияния обнаруженных факторов на многомерные атрибуты критического качества (CQAs) продукта на стадии первоначального проектирования процесса производства. Метод представляет новый подход к обеспечению качества продукта, который учитывает проблему ко-кластеризации массивов данных CPPs для определения каузальной связи с CQAs. Используется технология неметрического многомерного шкалирования (NMDS) для определения исходных параметров ко-кластеризации. Ключевые слова: качество через дизайн; критические атрибуты качества; ко-кластеризация; многомерный статистический анализ

Context. This paper presents a method for solving the problem of detecting and taking into account the influence of various (external and/or internal) factors on extreme and risky values of the multivariate observed parameters (covariates) of technological and/or diagnostic processes. Taking into account external and internal influence factors on covariates, by analogy with critical process parameters, is a significant addition to the extreme values statistics and the estimations the influence of the variability of process's covariates on the expected losses, i.e. value at risk. Risk-oriented analysis is an actual tool for the data behavior investigation of the multivariate observations of process's parameters. Objective. To disclose a method for detecting and taking into account the factors influence on the distribution functions parameters of the observed extreme values of process's covariates and determine the influence of these distribution functions parameters on estimates of risks values. Method. The method consistently uses: the procedures of multivariate statistical cluster analysis, transformation the matrix of observed extreme values of process's covariates into data frame with factor variables, estimation the extremal index and distribution functions parameters of nonclustered and clustered the observed extreme data of covariates and estimation the risk values on the calculated values of distribution functions parameters. The proposed sequence of actions is aimed at implementing the information technology of statistical causal analysis of the influence of factors on the variability of process's covariates and their risk values due to the application of the clustering procedure for observed multivariate extreme values of covariates. The method is implementing the R-language packages software. Results. Clustering of the multivariate observed extreme values of process's covariates allows to identifying the influence of environmental (manufacturing) factors and estimates the covariates' risky values taking into account of this influence. Conclusions. The method is an information technology of statistical causal analysis of factors influence on the variability of process's covariates and theirs risk values due to the application of the clustering procedure of covariates' multivariate values. The prospect of further research is to improve the methods of causal multivariate statistical analysis of the various factors influence on the exogenous and endogenous parameters of manufacturing and other processes in order to reduce the variability of these parameters and, as a result, minimize the risks.

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