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American Journal of Business and Operations Research
Volume 2 , Issue 1, PP: 24-38 , 2021 | Cite this article as | XML | Html |PDF

Title

A Survey on Machine Learning Techniques for Supply Chain Management

  Amal F.Abd El-Gawad 1 * ,   Shereen Zaki 2 ,   Esraa Kamal 3

1  Faculty of Engineering, Zagazig University, EGYPT
    (amgawad2001@yahoo.com)

2  Department of Decision Support, University of Zagazig, EGYPT
    (szsoliman@zu.edu.eg)

3  Faculty of Computers and Informatics, Zagazig University, EGYPT
    (esraakamal183@gmail.com)

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Doi   :   https://doi.org/10.54216/AJBOR.020103

Received December 10, 2020 Revised February 10, 2021 Accepted April 14, 2021

Abstract :

Machine learning arose from the increasing ability of machines to handle large amounts of data over the last two decades, and some machines could also identify hidden patterns and complicated associations that humans couldn't, allowing them to make rational and precise decisions, especially for disruptive and discontinuous data. In several areas of decision-making, machines could produce more reliable outcomes than humans and have already begun to replace them. Machine learning, which is widely recognized as a breakthrough technology, has recently made significant progress in improving supply chain management processes and efficiency. From planning to delivery, machine learning may be applied at different stages of the supply chain management process. Machine learning types are supervised, unsupervised, reinforcement. Each type has many tools which are discussed below in detail. This paper presents a detailed survey on machine learning techniques for supply chain management including supply chain and supply chain management interpretation, machine learning definition, its types, and some algorithms that belong to it.

Keywords :

Machine learning , Supply chain management , Supervised learning , Unsupervised learning , Reinforcement  learning

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Cite this Article as :
Style #
MLA Amal F.Abd El-Gawad , Shereen Zaki, Esraa Kamal. "A Survey on Machine Learning Techniques for Supply Chain Management." American Journal of Business and Operations Research, Vol. 2, No. 1, 2021 ,PP. 24-38 (Doi   :  https://doi.org/10.54216/AJBOR.020103)
APA Amal F.Abd El-Gawad , Shereen Zaki, Esraa Kamal. (2021). A Survey on Machine Learning Techniques for Supply Chain Management. Journal of American Journal of Business and Operations Research, 2 ( 1 ), 24-38 (Doi   :  https://doi.org/10.54216/AJBOR.020103)
Chicago Amal F.Abd El-Gawad , Shereen Zaki, Esraa Kamal. "A Survey on Machine Learning Techniques for Supply Chain Management." Journal of American Journal of Business and Operations Research, 2 no. 1 (2021): 24-38 (Doi   :  https://doi.org/10.54216/AJBOR.020103)
Harvard Amal F.Abd El-Gawad , Shereen Zaki, Esraa Kamal. (2021). A Survey on Machine Learning Techniques for Supply Chain Management. Journal of American Journal of Business and Operations Research, 2 ( 1 ), 24-38 (Doi   :  https://doi.org/10.54216/AJBOR.020103)
Vancouver Amal F.Abd El-Gawad , Shereen Zaki, Esraa Kamal. A Survey on Machine Learning Techniques for Supply Chain Management. Journal of American Journal of Business and Operations Research, (2021); 2 ( 1 ): 24-38 (Doi   :  https://doi.org/10.54216/AJBOR.020103)
IEEE Amal F.Abd El-Gawad, Shereen Zaki, Esraa Kamal, A Survey on Machine Learning Techniques for Supply Chain Management, Journal of American Journal of Business and Operations Research, Vol. 2 , No. 1 , (2021) : 24-38 (Doi   :  https://doi.org/10.54216/AJBOR.020103)
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