Statistical Process Control in Big Data Environment

Abstract

Big data analysis tools are an inevitable part of instruments and methods for monitoring and predicting the longitudinal performance of the processes in the production systems of the future, based on the deep automatization and overall digitalization. From this point of view statistical process control (SPC) will continue to be very effective method for meeting these goals. But there must be done some modifications. This paper deals with such possible modifications of SPC. In the first part of the paper the stress is put on various methods that can be integrated into SPC to meet new challenges in collecting, analysing and interpreting data (control charts for high yield processes, multivariable approaches, profile monitoring, data mining tools including machine learning methods, nonparametric control charts). SW for the selected discussed methods is also mentioned. The second part of the paper is devoted to the nonparametric methods of SPC and to the methodology of their practical application.

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