Developing an Artificial Intelligence–based Procurement Model for Forecasting and Inventory Efficiency

Procurement teams increasingly operate in volatile environments characterized by fluctuating demand, uncertain supplier lead times, and rising service-level expectations. These conditions often lead to inefficient inventory outcomes, including stockouts of critical items and excessive accumulation of slow-moving stock, thereby increasing working capital pressure and operational costs. Traditional forecasting and inventory management approaches frequently struggle to capture complex supply chain dynamics such as promotional effects, market disruptions, and global supply variability. This study proposes an AI-enabled predictive procurement model designed to integrate probabilistic demand forecasting, supplier reliability assessment, and multi-echelon inventory optimization within a unified decision-support framework. The model leverages advanced analytics to generate uncertainty-aware demand forecasts, estimate supplier lead-time distributions, and dynamically determine optimal inventory policies, including safety stock levels, reorder points, and order quantities. By embedding these insights into procurement workflows through a policy-driven orchestration layer, the framework ensures that operational constraints—such as minimum order quantities, shelf-life requirements, budget limitations, and sustainability considerations—are systematically incorporated into procurement decisions. Furthermore, the model supports human-in-the-loop governance by allowing decision overrides while maintaining full traceability and integration with enterprise resource planning systems. The proposed approach enhances procurement agility, reduces inventory-related costs, and improves service reliability by transforming fragmented data signals into coordinated, data-driven procurement strategies. The findings demonstrate that AI-enabled predictive procurement can significantly improve demand visibility, supplier performance evaluation, and inventory optimization across complex supply networks.