Sustainable Supply Chain Operations Through Artificial Intelligence: Pathways to EcoEfficient Logistics - Sheikh, Abdullah; Rinvee, Tajbiha Mehonaj; Sheikh, Md Shakil (ExcelingTech, 2025)
Global supply chains face unprecedented volatility and environmental pressure. This paper proposes a conceptual framework that leverages artificial intelligence (AI) to achieve eco-efficient logistics, aligning competitive performance with environmental goals. The framework explains how AI-driven demand forecasting tools, dynamic routing, warehouse optimization, reverse logistics, and supply chain transparency directly lead to quantifiable results such as fuel savings, carbon emissions reduction, and waste reduction. The findings demonstrate that AI-based analytics can optimize critical functions like dynamic routing and predictive forecasting, leading to reduced fuel consumption, lower carbon emissions, and enhanced operational resilience. The methodology involved extracting common themes and applications from these real-world examples to validate and ground the proposed conceptual model, ensuring it is both theoretically sound and practically applicable. The paper concludes that AI is not only a technical tool but also a strategic path essential to a sustainable, competitive and truly sustainable future for global logistics.