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

TL;DR

This paper explores the use of machine learning and deep learning algorithms to predict overall equipment effectiveness in manufacturing, enabling proactive decision-making to enhance efficiency.

Contribution

It introduces a comparative analysis of multiple ML algorithms, including deep learning, for real-time OEE prediction in manufacturing environments.

Findings

Deep Learning and Random Forest outperform other models in accuracy

ML models enable proactive OEE prediction rather than post-hoc analysis

Models trained on automotive industry data demonstrate practical applicability

Abstract

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.