Analysis of machine learning integration into supply chain management - Aguirre Rodríguez, Elen Yanina [UNESP]; Aguirre Rodríguez, Elias Carlos [UNESP]; da Silva, Aneirson Francisco [UNESP]; Rizol, Paloma Maria Silva Rocha [UNESP]; de Carvalho Miranda, Rafael; Marins, Fernando Augusto Silva [UNESP] (Inderscience Publishers, 2024)
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: classification; gaps; machine learning; SCM; supply chain management; trends