Title: Analysis of machine learning integration into supply chain management

Authors: Elen Yanina Aguirre Rodríguez; Elias Carlos Aguirre Rodríguez; Aneirson Francisco da Silva; Paloma Maria Silva Rocha Rizol; Rafael de Carvalho Miranda; Fernando Augusto Silva Marins

Addresses: Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 – Guaratinguetá, SP, Brazil ' Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 – Guaratinguetá, SP, Brazil ' Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 – Guaratinguetá, SP, Brazil ' Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 – Guaratinguetá, SP, Brazil ' Federal University of Itajubá (UNIFEI), Av. BPS, 1303 – Itajubá, MG, Brazil ' Sao Paulo State University (UNESP), Av. Dr. Ariberto Pereira da Cunha, 333 – Guaratinguetá, SP, Brazil

Abstract: The application of machine learning (ML) techniques in supply chain (SC) processes has been gaining popularity over the last years, because ML significantly helps making the SC faster and more efficient, automatising its processes, improving decision making, and mitigating risks, among other benefits that results in cost savings or more profits. The goal of this work was to analyse the existing studies about the integration of ML into supply chain management (SCM), exploring gaps and trends, from a bibliometric analysis of the articles published. The analysis consisted of assessing the total number of published documents between 2000 and 2020. The main contribution of this research was the identification of significant details about the studies conducted involving the integration of ML and SCM, which will help in the development of new studies in this important area.

Keywords: machine learning; supply chain management; SCM; classification; trends; gaps.

DOI: 10.1504/IJLSM.2024.136856

International Journal of Logistics Systems and Management, 2024 Vol.47 No.3, pp.327 - 355

Received: 21 Dec 2020
Accepted: 30 Jul 2021
Published online: 23 Feb 2024 *

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