Conference Paper

Artificial Intelligence Models for Overall Equipment Effectiveness Prediction: A Case Study in an Assembly Manufacturing Company

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Abstract

Overall Equipment Effectiveness (OEE) stands as a key performance metric widely adopted in the manufacturing industry, aiding in enhancing productivity. This metric offers a comprehensive overview to higher management, enabling them to identify equipment-related losses. With the advancements in Industry 4.0 technologies, the Manufacturing Execution System facilitates real-time data collection, enhancing production efficiency and reducing manufacturing costs. However, the real-time depiction of OEE often fails to provide decision-makers with timely insights to conduct their tasks effectively. This study formulated machine learning models to tackle this challenge by forecasting the OEE for the upcoming working shift. Firstly, the historical dataset encompasses 31 features collected and processed to estimate the OEE value. Then, prominent machine learning models were utilized as prediction models: Linear Regression, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. The results show that the Extreme Gradient Boosting performs well for the OEE prediction with accuracy in training higher than 99% and testing nearly 90%. Our study illustrates an actionable knowledge-discovery process using a real-world data mining approach for the manufacturing industry, potentially applicable to other sectors.

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  • S Nakajima
S. Nakajima, TPM tenkai. Tokyo: Japan Institute of Plant Maintenance, 1982.
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  • T He
  • M Benesty
  • V Khotilovich
  • Y Tang
  • H Cho
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