Developing AI-Driven Adaptive Learning Models for Real-Time Supply Chain Optimization

Abstract

Supply chains face increasing volatility from demand fluctuations, supplier disruptions, and global uncertainties, necessitating adaptive solutions for real-time optimization. This study proposes an AI-driven adaptive learning model (ALM) that dynamically adjusts to evolving supply chain conditions using incremental learning and memory-augmented networks. Through simulation-based experiments in three case studies—Singapore’s port logistics, Amsterdam’s retail supply chain, and Boston’s pharmaceutical distribution—we evaluate the model’s efficacy in optimizing inventory, routing, and demand forecasting. Results show that ALMs reduce operational costs by 20–35% and improve service levels by 15–25% compared to static models. The model leverages real-time IoT data and mitigates catastrophic forgetting, ensuring robust performance under disruptions. This research advances supply chain management by offering a scalable, data-driven framework for logistics resilience.