AI-DRIVEN SALES AND DISTRIBUTION PLANNING FOR SUSTAINABLE LOGISTICS OPERATIONS IN SAUDI ARABIAS FMCG INDUSTRY

Saudi Arabias fast-moving consumer goods (FMCG) sector depends on reliable replenishment across diverse outlets, dense urban markets and long intercity freight corridors. Conventional sales planning often treats demand forecasts, distributor allocation, inventory deployment and vehicle dispatch as separate activities. This review examines how artificial intelligence (AI) can connect these decisions while supporting the sustainability priorities of Saudi Vision 2030 and the National Transport and Logistics Strategy. A structured critical narrative approach integrates peer-reviewed studies on predictive demand planning, machine learning, operations research, logistics performance and responsible deployment with official Saudi policy sources. The proposed framework links validated data, hierarchical demand forecasting, stock-aware order planning, constrained vehicle-routing optimization and an auditable sustainability scorecard. It identifies important distinctions between predictive accuracy and operational value: a better sales forecast may fail to reduce freight emissions if minimum shipment quantities, delivery windows or incentives prevent execution. Illustrative scenarios demonstrate trade-offs rather than report observed Saudi company results.