Result of Digitalization in the Automotive Industry: Total Equipment Effectiveness and Bayesian Analysis

Abstract

Businesses today try to accommodate the fast-developing world through prioritizing high productivity, low cost, customer satisfaction, and most time-saving with the help of digitalization. The automotive industry as one of the greatest in global markets also finds its place in digitalization studies. The company analyzed in this article is producing automotive parts and investing in machines and software to enable digitalization. The aim of the firm is to raise the facilities’ productivity in the digitalization process. The overall equipment efficiency study carried out in practice was carried out in order to observe the productivity change in the unit where the application was made with are proposed the digitalization transformation in the company. This article considers the total equipment effectiveness and Kaizen is applied for the interruptions with negative impacts on productivity. The productivity of the company is significantly increased after the Kaizen application. In order to digitalize the manufacturing processes successfully, more expert opinions are required. The Bayesian network (BN) is used to achieve higher increase at productivity. It has a powerful probability theory eliminating the inconsistent probability. During the implementation phase, the most important part of this article is the employment of components of the total equipment effectiveness as the variable for BN. The utilizing the expert opinions resulted to advance productivity. At the end of the Kaizen study, the productivity is raised from 83% to 85%. According to the results of the studied BN, the required suggestions to the company.