Predicting the impact of type changes on Overall Equipment Effectiveness (OEE) through machine learning

Abstract

Nowadays, Industry 4.0 and the Smart Manufacturing environment are increasingly taking advantage of Artificial Intelligence. There are more and more sensors, cameras, vision systems and barcodes in the production area, as a result of which the number of data recorded during manufacturing and assembly operations is growing extremely fast. The interpretation and processing of such production-type data by humans is believed less effective. In the Big Data domain, machine learning is playing an increasingly important role within data mining. This paper focuses on the product change processes of semi-automatic assembly line batch production and examines the impact of changes on Overall Equipment Effectiveness (OEE) and attempts to determine future values through supervised machine learning. Using decision tree, the effect on the OEE value can be predicted with an accuracy of up to 1%. The presented data and conclusions come from a real industrial environment, so the obtained results are proven in practice.