International Journal of Social Analytics

AI-Driven Optimization Models for E-commerce Supply Chain Operations: Demand Prediction, Inventory Management, and Delivery Time Reduction with Cost Efficiency Considerations

Deepak Kaul

Parker, Colorado

https://orcid.org/0009-0005-7058-0607

Rahul Khurana

Bothell, WA, USA

https://orcid.org/0009-0005-5267-2006

Keywords: AI models, demand forecasting, e-commerce supply chain, inventory management, operational efficiency, reinforcement learning, resilience

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Abstract

E-commerce supply chains have faced immense challenges: increased consumer demand, increased pressure to compete, and the real need to make sure operation efficiency is seamless. Conventional approaches to supply chain management are usually bound by linear models and historic heuristics that cannot fully capture accurate demand predictions, optimal inventory positioning, and delivery time reductions. The aim of this paper is to conduct a technical investigation into applying advanced AI models in e-commerce supply chains in the areas of demand forecasting, inventory management, and reduction of delivery times. We investigate sophisticated AI-driven techniques, including neural networks, deep reinforcement learning, and optimization algorithms to improve real-time responsiveness while reducing operational costs and enhancing overall supply chain resilience. Particular attention is given to cost efficiency by integrating AI models that address the balancing act between meeting high service levels and controlling operational expenses. We discuss various model architectures including, but not limited to, RNNs, LSTMs, and Transformer-based models for demand forecasting; applying reinforcement learning for inventory optimization; and using advanced heuristic search algorithms for last-mile delivery optimization. We further discuss model integration challenges with scalability and some data-related considerations, conclude by recommending future research directions that may help overcome some limitations in the current state of the art and develop more robust, adaptive AI models for e-commerce supply chain optimization.