  The overall equipment effectiveness (OEE) is a performance measurement metric
widely used. Its calculation provides to the managers the possibility to
identify the main losses that reduce the machine effectiveness and then take
the necessary decisions in order to improve the situation. However, this
calculation is done a-posterior which is often too late. In the present
research, we implemented different Machine Learning algorithms namely; Support
vector machine, Optimized Support vector Machine (using Genetic Algorithm),
Random Forest, XGBoost and Deep Learning to predict the estimate OEE value. The
data used to train our models was provided by an automotive cable production
industry. The results show that the Deep Learning and Random Forest are more
accurate and present better performance for the prediction of the overall
equipment effectiveness in our case study.
