Revolutionizing the future of supply chain management : a deep dive into how artificial intelligence can enhance efficiency and optimization

Abstract

The current corporate world is one which can be characterised as being dynamic in nature and as a result companies are looking at rapid technological advancements with the goal of evolving their managerial strategies. So much so that MIT Sloan professor Retsef Levi once said – “There are times when there is a major disruption that suddenly implies what worked so far is not going to work anymore and you need a new playbook.” (Stackpole (2021)) Investigating the transformative potential role which could be played by Artificial Intelligence (AI) reveals its potential to redefine and optimize supply chain management (SCM) whilst also improving performance amidst the ever-increasing competitive corporate environment. Furthermore, the use of the Wallas-Four-Stage Model and a pragmatic qualitative approach by conducting in-depth target interviews allowed the study to aid in the development of strategies which can be used to integrate AI into modern complex supply chains in attempts to achieve competitive advantages. This all underscores the study’s contribution to advancing both academic understanding and practical applications of AI in SCM. This study is focused on 27 firms based in Malta and addresses three core research questions: the role of AI in current SCM processes, its economic, environmental, and social impacts, and the challenges that hinder AI adoption in SCM. The key findings reveal that AI can enhance SCM efficiency, contributing to environmental goals by reducing waste and emissions. Nonetheless, it should be also noted that aside from the previously mentioned benefits, AI’s implementation also may give rise to certain challenges such as high investment costs, data integration issues, and employee resistance persist. The study concludes with strategic recommendations for phased AI adoption, comprehensive training, and transparent communication to ease the transition and maximize AI's potential in SCM, underscoring the study's contribution to both academic understanding and practical applications. Furthermore, this study also addresses key research gaps relating to the lack of information on the challenges associated with the adoption of AI, the scarcity of comparative studies relating to the obstacles faced when integrating AI and the underexplored human-AI dynamic in decision-making.