Using neural networks to examine trending keywords in Inventory Control

Abstract

Abstract Inventory control is one of the key areas of research in logistics. Using the SCOPUS database, we have processed 9,829 articles on inventory control using triangulation of statistical methods and machine learning. We have proven the usefulness of the proposed statistical method and Graph Attention Network (GAT) architecture for determining trend-setting keywords in inventory control research. We have demonstrated the changes in the research conducted between 1950 and 2021 by presenting the evolution of keywords in articles. A novelty of our research is the applied approach to bibliometric analysis using unsupervised deep learning. It allows to identify the keywords that determined the high citation rate of the article. The theoretical framework for the intellectual structure of research proposed in the studies on inventory control is general and can be applied to any area of knowledge.