Professor David Pooe, Dr Shallone Munongo

Abstract

Artificial Intelligence (AI) is reshaping supply chain management, offering transformative capabilities for optimisation, efficiency, and decision-making. The study analysed the use of AI applications in supply chain optimisation (SCO) through a comprehensive bibliometric analysis. Using data from the Web of Science database from 1995-2024, a total of 794 documents were selected to map this field’s intellectual structure and research frontiers. Findings revealed a rapid growth in scholarly interest, with significant interdisciplinary collaborations. Dominant themes include AI-driven demand forecasting, inventory management, logistics optimisation, and risk management. Machine learning algorithms, neural networks, and predictive analytics emerged as key AI methodologies, highlighting their critical role in enhancing supply chain optimisation. Geographically, North America, Europe, and Asia lead in research output, while emerging economies show increasing interest. Citation analysis revealed seminal works and influential authors, offering guidance for future research. However, research gaps and challenges persist, including the need for more empirical studies, integration of AI with big data, blockchain, and the Internet of Things (IoT), and addressing ethical concerns related to AI deployment in supply chains. The study provides valuable insights, emphasises the transformative potential of AI in supply chain optimisation.