Supply chain management using machine learning, deep learning, and blockchain techniques: A review

Abstract

Existence in a globalized economy presents the most significant challenges that the supply chain management has never experienced before, such as demand in variation, disruption in supply, transparency deficiencies, and issues of sustainability. This review explicitly looks at how machine learning, deep learning and blockchain technologies can be used to transform the activity in the supply chains in a revolutionizing way. The work applies the PRISMA methodology to analyze the current trends of the supply chain systems with respect to intelligent supply chain system systematically and within the framework of new trends and future directions. The machine learning algorithms have amazing performance in demand forecasting, inventory optimization and predictive maintenance, whereas deep learning architectures perform better in complicated pattern recognition, computer vision tasks and natural language processing to supply chain analytics. Distributed ledger systems, smart contracts, and decentralized consensus mechanisms are all innovations that blockchain technology suggests which will bring unprecedented levels of transparency, traceability, and security. According to this review, existing research has a number of gaps such as limited integration frameworks involving each of the three technologies, lack of confirmation of scalability in practice, and little attention to the problems of adoption in small and medium enterprises. The article shows that hybrid solutions that integrate machine learning predictive functionalities with blockchain trust infrastructures have a higher performance on providing end-to-end supply chain visibility and optimization. The potential opportunities are autonomous supply chain orchestration, circular economy enabling and robust network design.