Artificial Intelligence–Driven Demand Forecasting Models for Enhancing Supply Chain Planning Accuracy in Saudi Industrial Sectors

Accurate demand forecasting is a cornerstone of effective supply chain planning; however, traditional forecasting techniques often fail to capture complex market dynamics, nonlinear demand patterns, and rapid environmental changes. The emergence of artificial intelligence (AI) has significantly transformed demand forecasting by enabling advanced learning from large, heterogeneous datasets through machine learning (ML), deep learning (DL), and hybrid analytical models. Within the context of Saudi Arabia’s industrial sectors—shaped by Vision 2030 objectives of economic diversification, industrial localization, and digital transformation—AI-driven demand forecasting presents a strategic mechanism for improving planning accuracy and operational resilience. This study examines the theoretical foundations, model architectures, implementation considerations, and strategic benefits of AI-based demand forecasting in Saudi industrial supply chains. It further discusses integration with digital platforms, real-time analytics, and multi-source data fusion. A conceptual deployment framework is proposed to demonstrate how AI forecasting can enhance planning accuracy, reduce inventory inefficiencies, and support national supply chain development goals.