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The overall equipment effectiveness (OEE) is a performance measurement metricwidely used. Its calculation provides to the managers the possibility toidentify the main losses that reduce the machine effectiveness and then takethe necessary decisions in order to improve the situation. However, thiscalculation is done a-posterior which is often too late. In the presentresearch, we implemented different Machine Learning algorithms namely; Supportvector machine, Optimized Support vector Machine (using Genetic Algorithm),Random Forest, XGBoost and Deep Learning to predict the estimate OEE value. Thedata used to train our models was provided by an automotive cable productionindustry. The results show that the Deep Learning and Random Forest are moreaccurate and present better performance for the prediction of the overallequipment effectiveness in our case study.

Artificial Intelligence and Machine Learning to Predict and Improve Efficiency in Manufacturing Industry