Application of Deep Learning Techniques in Managing Supply Chain: A Bibliometric Analysis

Abstract

Deep learning has become the driving force behind many contemporary technologies and has been successfully applied in many fields. The core objective of this study is to comprehend, investigate, and stipulate significant insights into the deep learning applications in various supply chain management functions through a bibliometric analysis. The literature for the study is gathered from the Scopus database. A bibliometric analysis is conducted using R Studio. It has been found that researchers from China are publishing the most research findings in this area. The study observed drift in concentrated areas of the field from 2007 to 2022. This analysis introduces a paradigm shift in trend techniques. Compared to using computer simulation, long- and short-term memory and reinforcement learning techniques are frequently used for the analysis. Also, the evolution of behavioral research in supply chain-related areas is given prominence compared to research related to agriculture and human interventions, which predominated.