Enhancing Demand Forecasting Accuracy, Inventory Performance, and Supply Chain Efficiency in Saudi Arabia’s Public Pharmaceutical Sector through Artificial Intelligence.

Abstract

This study examined the role of Artificial Intelligence (AI) in improving demand forecasting and inventory performance in pharmaceutical supply chains in Saudi Arabia’s public healthcare sector. A cross-sectional survey of 155 professionals was conducted, and data were analysed using descriptive statistics, correlation, regression, and mediation/moderation tests. Results showed that AI integration explained 71% of the variability in demand forecasting accuracy (β = 0.82, p < .001). AI adoption also predicted 69% of the variability in inventory performance (β=0.82, p < .001), with significant effects on stock turnover (β=0.83, p < .001), lead time reduction (β=0.81, p < .001), and waste minimisation (β=0.83, p < .001). Organisational capabilities mediated the link between AI adoption and supply chain performance, confirming the importance of digital infrastructure and analytics competency. Barriers such as resistance, regulatory issues, and data quality challenges were reported, but did not significantly moderate the relationship between AI integration and demand forecasting accuracy. These findings confirm that AI improves efficiency, reduces waste, and strengthens resilience in pharmaceutical supply chains. Therefore, AI adoption aligns with Saudi Arabia’s Vision 2030 healthcare reforms.