Efficient supply chain management has emerged as a crucial determinant of organizational performance in the contemporary dynamic corporate environment. The incorporation of nascent technology, such as machine learning and blockchain, is revolutionizing how enterprises manage their supply chain operations. By examining extensive datasets, machine learning algorithms can predict future demand, optimize inventory levels, and improve the planning of routes. By discerning regularities and deviations within datasets, these algorithms facilitate enterprises in making well-informed choices and managing potential hazards. Additionally, the utilization of machine learning facilitates the automation of monotonous jobs, hence mitigating the occurrence of human fallibility and augmenting the overall efficacy of supply chain operations. The utilization of blockchain technology, renowned for its decentralized and unalterable ledger system, effectively tackles several critical issues encountered in the realm of supply chain management.
TopI. Introduction
The current era, often referred to as the “fourth industrial revolution”, is characterized by the advancement of digitization, robotics, communication technology, and Artificial Intelligence (AI) (Pandey, B. K. et al., 2021a). In this age, machines are gaining the ability to make decisions autonomously, replacing the need for human cognitive processes. Machine Learning (ML) (Meslie, Y. et al., 2021) is a technology that focuses on the creation and implementation of computer algorithms that can acquire knowledge from experience. Machine learning (ML) has its roots in the advancements made in machine capabilities over the past two decades. During this time, machines have become increasingly adept at processing vast amounts of input data (Wenzel, H. et al., 2019) some cases, machines have even demonstrated the ability to uncover concealed patterns and intricate relationships, enabling them to make sound and dependable decisions. This capability is particularly valuable in situations where humans struggle to navigate disruptive and discontinuous information. The research indicates that machines have demonstrated superior accuracy compared to humans in several decision-making domains, leading to their increasing substitution in areas such as cancer prediction (Tripathi, R. P. et al., 2023) and prognosis, drug development, and genetics and genomics.
The contemporary business landscape is characterized by intense competition, driven by the rapid advancement of information technology, the process of economic globalization, and the heightened expectations of customers. As a result, companies have been compelled to make significant adjustments to their supply chain management (SCM) practices. These changes underscore the growing significance of competition at the supply chain level, rather than solely at the company level. Supply Chain Management (SCM) (Malhotra, P. et al., 2021) refers to the dynamic integration of various operations within a supply chain, spanning from the initial suppliers to the ultimate end-users. The primary objective of SCM is to deliver a comprehensive range of services, goods, and information that not only enhances customer value (Saxena, A. et al., 2021) but also facilitates the attainment of a sustainable competitive advantage. In the current era of big data, a substantial volume of interactive data is regularly generated, gathered, and stored inside many process industries. These data serve (Kumar, M. S. et al., 2021) as a significant asset in facilitating process operation, control, and design. The strategic utilization of these data, along with the extraction and analysis of information and knowledge derived from them, holds significant potential for advantageous outcomes. The significant increase in data volume from many aspects of supply chain management has compelled firms to devise and execute novel technologies capable of efficiently and intelligently analyzing extensive data sets. This is necessary as conventional decision support systems are inadequate in handling big data securely (Pandey, B. K. et al., 2023) and effectively. Therefore, within the context of the big data era, professionals in the field of supply chain management are actively pursuing strategies to effectively manage and utilize large volumes of data to achieve intelligent (Pramanik, S. et al., 2023) and efficient supply chain operations.