Determining and forecasting oee based on reliability and maintainability using polynomial regression and neural networks

Subject Areas : Maintenance management

Ratna Mayasari ¹ , Robby Marlon Brando ² , Agus Widodo ³ , Ardani Cesario Zuhri ⁴ , Nasril Nasril ⁵ , Mario Ardhany ⁶ , Arwidya Tantri Agtusia ⁷ , Ellia Kristiningrum ⁸

1 - Research Center for Industrial Process and Manufacturing Technology, National Research and Innovation Agency, Jakarta, Indonesia.
2 - Research Center for Industrial Process and Manufacturing Technology, National Research and Innovation Agency, Jakarta, Indonesia.
3 - Research Center for Industrial Process and Manufacturing Technology, National Research and Innovation Agency, Jakarta, Indonesia.
4 - Research Center for Industrial Process and Manufacturing Technology, National Research and Innovation Agency, Jakarta, Indonesia.
5 - Research Center for Industrial Process and Manufacturing Technology, National Research and Innovation Agency, Jakarta, Indonesia.
6 - Research Center for Industrial Process and Manufacturing Technology, National Research and Innovation Agency, Jakarta, Indonesia.
7 - Research Center for Transportation Technology, National Research and Innovation Agency, Jakarta, Indonesia.
8 - Research Centre for Testing Technology and Standard – National Research and Innovation Agency, Jakarta, Indonesia

Received: 2024-09-10 Accepted : 2024-11-10 Published : 2024-11-28

Keywords: OEE, MTBF, MTTR, Regression, Neural network,

Abstract :

Production maintenance ensures equipment remains in good working order, enabling the generation of goods complying with specifications. The purpose of this research was to assess and evaluate the machine performance in one of the plastic industries. This research will determine how machine performance is correlated with OEE, MTBF, and MTTR, optimise the OEE variables and forecast the future OEE values. The relationship between OEE, MTBF, and MTTR has been analysed by linear and non-linear regression using polynomial and artificial neural networks (ANN). Meanwhile, the OEE optimization is performed using SciPy optimizer on linear and nonlinear objective functions, whereas the OEE forecasting employs Convolutional Neural Network (CNN) in addition to the ANN and the polynomials. All regression analysis indicate OEE is well explained by MTBF and MTTR as all R-squared values are above 95%. Specifically, those R-squared values are 98.25%, 97.78%, 97.64%, and 95.56%, for ANN, polynomial degree 3, degree 2 and degree 1, respectively. Furthermore, the optimal value of MTBF is found to be at least 3.706 whereas that of MTTR is at most 0.899 hours to achieve an OEE value of at least 0.85. Lastly, the accuracy of OEE predictions using CNN achieves the best performance by having the lowest RMSE of 0.0156, followed by ANN with an RMSE of 0.0166, and the polynomials with RMSEs of around 0.02.

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