Enhancing Supply Chain Efficiency: The Role of Artificial Intelligence in Modern Business Logistics
Keywords:
Artificial Intelligence, supply chain efficiency, organisational information processing theory, dynamic capabilities, T-O-E framework, collaboration, green supply chain.Abstract
Many papers suggest that AI can enhance supply chain efficiency. The causes of this effect have also been explored. At this juncture, it was considered appropriate to review some recent trends in research on AI enhancing supply chain efficiency. Hence, this systematic review aimed to synthesise evidence from the recent literature on the role of AI in enhancing supply chain efficiency. Google Scholar was searched using an appropriate search term to achieve the aim. The identified papers were screened to select 25 papers through the PRISMA flow process using some inclusion and exclusion criteria. The 25 papers were used for this review. The review showed that the enthusiasm for research on the impact of AI on supply chain efficiency declined since 2022. The most discussed topics in the 25 reviewed papers were performance and its efficiency, resilience and mention of COVID. Collaboration, including green collaboration and various types of factors related to AI and supply chains, received less attention. Very few papers use d mixed methods for their research. Organisational information processing theory and dynamic capabilities theory seem to be more relevant to research on AI's impact on supply chain efficiency. The qualities of the reviewed papers were moderate to high. Future research on this topic should focus on supply chain efficiency to a greater extent. Green and sustainable chains, collaborations and risks should receive increased attention. They should use mixed methods more. Country comparisons, especially among developed, emerging and developing countries, may be useful. The best practices of using AI for supply chain efficiency should be studied in such multi-country comparisons. Some limitations of this review were relying on a single database, targeting a fixed number of papers and setting the period as 2020 to 2024 reduces the scope for selecting many good papers. However, as was said above, the quality of the selected papers was good. The period was chosen deliberately to reflect the latest research trends. These trends were discussed above.
