AI-Driven Inventory Optimization in Supply Chains: A Comprehensive Review on Reducing Stockouts and Mitigating Overstock Risks

Abstract

Inventory optimization has become an instrumental element of supply chain management, which is attained at anchoring cost efficiency with product availability. Conventional inventory models occasionally struggle with accuracy and adaptability within continuously changing environments. Contributing a holistic review of AI-driven inventory optimization approaches, aimed on their role to lower stockouts and mitigate overstock risks throughout distinct supply chain settings, has been key deliverables of this study. A qualitative literature review has been executed, through synthesizing peer-reviewed articles, industry reports and case studies related to AI applications within inventory management. Focus has been hinged on comparing AI-powered approaches with classical inventory models. AI technologies, such as machine learning, predictive analytics and deep learning, have been observed to increase automate replenishment, support multi-echelon and demand forecasting within inventory optimization. Case studies from renowned organizations (Walmart, Amazon, and Zara) elaborated the potential improvements into responsiveness, customer satisfaction and cost efficiency. Though, setbacks such as data integration issues, limited AI literacy and high implementation costs persist. AI-driven inventory systems provide adaptive and scalable solutions to address current supply chain issues. Regardless of barriers remain, the advantages of decreased stock imbalances and increased operational agility crafted AI as a necessitate tool to build inventory management strategies in future.