AI AND BIG DATA ANALYTICS FOR LOCAL CONTENT OPTIMIZATION IN SAUDI INDUSTRIAL SUPPLY CHAINS

Saudi Arabias industrial transformation under Vision 2030 increasingly depends on the ability of public and private organizations to convert local-content objectives into efficient, measurable and commercially sustainable supply - chain decisions. Conventional local - content programs frequently rely on static supplier lists, periodic procurement reports and manual category assessments, making it difficult to respond to rapid changes in demand, supplier capability, cost, technology and supply risk. This paper develops an artificial intelligence (AI) and big-data analytics framework for optimizing local content across Saudi industrial supply chains. Using a structured review and conceptual framework methodology, the study integrates recent literature on AI-enabled supply chains, big-data analytics, Industry 4.0, supplier development and supply-chain resilience with official Saudi Vision 2030 and merchandise-trade evidence. The proposed framework contains six decision modules: spend intelligence, demand forecasting, supplier-capability analytics, localization opportunity prediction, supply-risk analytics and local-content performance optimization.