Artificial Intelligence for Inventory Optimization :A Strategic Framework for E-Commerce SMEs in the Middle East

Abstract

Growth in e commerce in the Middle East has increased the pressure on small and medium enterprises especially in managing stock during times of changing demand. While artificial intelligence offers tools for better prediction and stock control, there is a lack of clear guidance for small businesses with limited budgets. This study looks at how artificial intelligence can improve stock management for e commerce small businesses in the Middle East. It develops a clear strategy for using these tools based on a review of existing research.

The study used a systematic review of 29 academic papers. It followed the PRISMA process and checked the quality of research using CASP tools. The analysis was based on three main theories including the Technology Organisation Environment framework, Diffusion of Innovations, and the Technology Acceptance Model.

The findings identify four main areas. First, artificial intelligence improves sales predictions and automatic reordering. Second, small businesses face specific barriers like poor data quality and a lack of technical skills. Third, regional factors like long lead times and local market shifts influence success. Fourth, a step by step adoption process is necessary to build trust and reduce risk. The research shows that artificial intelligence helps reduce out of stock situations and lowers costs. However, success depends on having the right data and strong support from management. Finally, the study provides a Strategic AI Adoption Framework. This roadmap helps managers in the Middle East move from small pilot tests to full scale use of artificial intelligence to improve business performance.