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DOI: 10.14569/IJACSA.2022.0130581
PDF

Demand Forecasting Model using Deep Learning Methods for Supply Chain Management 4.0

Author 1: Loubna Terrada
Author 2: Mohamed El Khaili
Author 3: Hassan Ouajji

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: In the context of Supply Chain Management 4.0, costumers’ demand forecasting has a crucial role within an industry in order to maintain the balance between the demand and supply, thus improve the decision making. Throughout the Supply Chain (SC), a large amount of data is generated. Artificial Intelligence (AI) can consume this data in order to allow each actor in the SC to gain in performance but also to better know and understand the customer. This study is carried out in order to improve the performance of the demand forecasting system of the SC based on Deep Learning methods, including Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) using historical transaction record of a company. The experimental results enable to select the most efficient method that could provide better accuracy than the tested methods.

Keywords: Supply chain management 4.0; demand forecasting; decision making; artificial intelligence; deep learning; Auto-Regressive Integrated Moving Average (ARIMA); Long Short-Term Memory (LSTM)

Loubna Terrada, Mohamed El Khaili and Hassan Ouajji, “Demand Forecasting Model using Deep Learning Methods for Supply Chain Management 4.0” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130581

@article{Terrada2022,
title = {Demand Forecasting Model using Deep Learning Methods for Supply Chain Management 4.0},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130581},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130581},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Loubna Terrada and Mohamed El Khaili and Hassan Ouajji}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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