Decision making in industry 4.0 scenarios supported by imbalanced data classification

Abstract

In the last years Data Science has emerged as one of the main technological enablers in many business sectors, including the manufacturing industry. Process engineers, who traditionally resorted to engineering tools for troubleshooting, have now embraced the support of data analysis to unveil complex patterns between process parameters and the quality of products and/or the performance of the production assets in plant. This work elaborates on a practical methodology to conduct data analysis within an industrial environment. The most important contribution of the proposed method is to focus on the importance of hypothesis generation dynamics among multidisciplinary experts in the process, prior to data capture itself. To exemplify the practical utility of this prescribed procedure, evidences from a real industrial case study are provided, departing from the dynamic generation of the hypothesis around the reduction of defects in the delivered products. Interestingly, this process leads to a imbalanced data classification problem, for which an extensive benchmark of supervised learning algorithm and balancing preprocessing techniques is performed to accurately predict whether parts are defective. Insights are drawn from this analysis so as to yield recommended parameter values for different stages of the production process, thereby achieving a lower defective rate and ultimately, a higher manufacturing quality of the industrial process.