MATEC Web Conf.
Volume 218, 2018The 1st International Conference on Industrial, Electrical and Electronics (ICIEE 2018)
|Number of page(s)||7|
|Section||Industrial And Engineering Applications|
|Published online||26 October 2018|
Prediction of Grinding Work Roll Demand in a Job Shop Company By using Artificial Neural Network and ARIMA Method
Department of Industrial Engineering, Engineering Faculty, Universitas Sultan Ageng Tirtayasa, Banten, Indonesia 42435
2 Production System Laboratory, Department of Industrial Engineering, Engineering Faculty, Universitas Sultan Ageng Tirtayasa, Banten, Indonesia 42435
Corresponding author: firstname.lastname@example.org
This study concern about forecasting grinding work roll demand in a job shop company located in industrial area in Cilegon. This factory main production is fabrication, which accepts various orders from other companies especially from the company around. Grinding Work Roll is one of those products that frequently request by customer. Although the order is frequent but the volume is fluctuation month by month. This situation drives the company to face the problem in preparing the resources required in fabrication process specially in scheduling the operators. To cope with this problem, we proposed to apply two robust forecasting methods, Artificial Neural Network and ARIMA to help in prediction the grinding work roll demand so as the company could make a good plan for the production process. The best architecture for ANN is obtained through applying Taguchi Method which applies Levenberg-Marquardt algorithm as Training Function. The best number for hidden layer is 10, while Momentum is 0.9. The Prediction result shows that ANN predicts better than ARIMA Method according to the lower Mean Square Error (MSE). MSE Value for ANN is 0.002 while for ARIMA MSE is 0.0043. From this study, we experienced that by applying Taguchi method could improve the performance of Artificial Neural Network.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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