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IJMLC 2020 Vol.10(4): 513-518 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.4.966

Improving Production Forecasting in the Supply Chain for the TFT-LCD Industry in Taiwan

Yi-Hui Liang

Abstract—Forecasting accuracy significantly influences supply chain risk. Supply chain production forecasting is very difficult because it involves numerous upstream suppliers and the volatility caused by the bullwhip effect through the supply chain. The generalized autoregressive conditional heteroskedastic (GARCH) model can handle data with time-varying volatility. Consequently, this study is separated into two parts for the purpose of supply chain forecasting the production of Taiwan's TFT-LCD industry from the perspectives of the upstream and downstream supply chain. In the first part, three upstream components, including a backlight module, a glass substrate, and color filter productions were utilized to forecast TFT-LCD production combing recurrent neural networks and genetic algorithms. In the second part, the GARCH model was used for TFT-LCD production prediction. The forecasting results offer valuable references for the TFT-LCD industry. Managers can consult the results when engaging in supply chain forecasting.

Index Terms—Forecasting, neural network, supply chain management, GARCH.

Yi-Hui Liang is with the Department of Information Management, I-Shou University (ISU), Kaohsiung City, Taiwan (e-mail: german@isu.edu.tw).

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Cite: Yi-Hui Liang, "Improving Production Forecasting in the Supply Chain for the TFT-LCD Industry in Taiwan," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 513-518, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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