Data–Driven Techniques in Logistics & Supply Chain Management: A Literature Review

Abstract

The importance of supply chain management to business operations and social growth cannot be overstated. Today’s supply chains are very different from those of a few years ago and continually change in a highly competitive climate. Investing in technology that can handle the sheer complexity of dynamic supply chain operations is necessary. Current supply chain solutions cannot completely mitigate the risks of inefficiencies at various supply chain stages. Several functional supply chain applications based on Machine Learning (ML) have appeared in recent years; however, few studies have analyzed data-driven logistics and supply chain management applications. Robotics, machine learning, and natural language processing are potential supply chain transformation enablers. This paper offers a thorough and up-to-date literature review that examines what researchers have done regarding data-driven techniques in the supply chain context and identifies what needs further exploration. We reviewed 135 research articles published between 2008 and 2022 on the Scopus database and created a classification of the research material on data-driven logistics and supply chain management. This comprehensive literature evaluation will enable researchers and business administrators to undertake innovation initiatives better and redirect money and human resource efforts.