AI-Driven Supply Chain Optimization: A Sustainable Framework for Enhancing Operational Efficiency, Traceability, and Market Integration
DOI:
https://doi.org/10.54741/MJAR/6.3.2026.305Keywords:
artificial intelligence, supply chain optimization, operational efficiency, traceability, sustainability, market integration, predictive analytics, digital supply chainAbstract
Artificial Intelligence (AI) has emerged as a transformative technology in modern supply chain management by improving operational efficiency, enhancing transparency, and supporting sustainable business practices. The increasing complexity of global supply chain networks, rising customer expectations, and growing sustainability concerns have accelerated the adoption of AI-driven technologies such as machine learning, predictive analytics, intelligent automation, big data analytics, blockchain integration, and Internet of Things (IoT)-enabled systems. This study reviews the existing literature on AI-driven supply chain optimization and develops a sustainable framework for enhancing operational efficiency, traceability, and market integration within supply chain systems. The review identifies that AI technologies significantly improve demand forecasting, inventory management, transportation planning, logistics coordination, and real-time operational monitoring, thereby reducing operational inefficiencies and improving supply chain responsiveness. The study further reveals that AI-enabled traceability systems strengthen supply chain transparency, supplier monitoring, product authentication, and regulatory compliance across global supply networks. In addition, AI-supported sustainable logistics practices contribute to reducing fuel consumption, carbon emissions, operational waste, and resource inefficiencies. The findings also indicate that AI-driven digital platforms improve collaboration, information sharing, and coordination among suppliers, manufacturers, distributors, retailers, and customers, thereby strengthening market integration and operational synchronization. However, the review identifies limited integrated research combining operational efficiency, sustainability, traceability, and market integration within a unified AI-driven supply chain framework. Therefore, the study proposes a comprehensive sustainable AI-driven supply chain optimization framework that integrates intelligent technologies with operational and sustainability objectives. The study contributes to the growing body of knowledge on digital supply chain transformation and provides valuable implications for researchers, managers, policymakers, and industry practitioners seeking to develop resilient, transparent, and sustainable supply chain ecosystems.
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