E. DEMINg’S SYSTEM OF PROFOUND KNOWLEDGE AND THE POSSIBILITY OF ITS USE IN RUSSIA
Problem Statement (Relevance): This paper examines the problem of quality in manufacturing, which is of relevance in the Russian Federation. It also substantiates why it is necessary to look into the causes identified of reduced reliability of domestic industrial products. Objectives: To understand the reasons for reverse application of Deming’s system of profound knowledge originally aimed at improving the quality of industrial products. Methods Applied: The provisions of Deming’s system of profound knowledge were applied. Geometric modelling of the system components was carried out using Venn diagrams, as well as methods and aspects of the knowledge and variability theories. To understand how production systems and products function, a method of assessing their state on the basis of the signal-to-noise criterion was implemented. Taguchi methods were used to analyse the quality of production systems. Originality: The authors looked at the causes for reverse application of Deming’s system of profound knowledge. They also proposed an approach to assessing the reliability of technical products using the Taguchi T -criterion. Findings: This paper examines the system of profound knowledge, its structure, components and their capabilities. It identifies the causes of reduced reliability of modern machinery, and examples are given of the cases when reliability was deliberately lowered. It is shown that even though the system proves to be an effective quality improvement tool, it can have reverse application. It is noted that the system of profound knowledge can be used to assess the products and their quality. Practical Relevance: As a result of interpretation of the signal-to-noise criterion, an approach is proposed to assessing the products, according to which not only the level of the design values but also the following characteristics should be considered: transition of the product parameters from being constant to being variable, increase or decrease in the total number of parameters used. Such approach will help prevent both under- or overestimation of the product quality.