Enhanced Demand-Supply Matching in E-Commerce Using Deep Learning Techniques

Authors- Assistant Professor Dr. Pankaj Malik, Pavitra Singh, Zainab Abbas, Harshita Jain, Madhvi Bhawsar

Abstract-E-commerce companies face ongoing challenges in accurately matching supply with volatile demand, influenced by seasonal trends, promotions, and rapid shifts in consumer behavior. Traditional demand-supply matching methods often fall short in addressing the complexity and scale of e-commerce data, necessitating more sophisticated approaches. This paper proposes a deep learning-based framework for demand-supply matching, leveraging models such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Transformer models to capture intricate temporal patterns and dependencies in e-commerce demand. By integrating diverse data sources—historical sales data, customer behavior insights, and external factors—this approach dynamically predicts demand patterns and guides supply allocations with higher accuracy. Our experiments reveal that deep learning models significantly outperform traditional methods in demand forecasting metrics, such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), particularly in handling large-scale e-commerce datasets. Furthermore, the study demonstrates the potential of these models to reduce stockouts, optimize inventory levels, and enhance order fulfillment efficiency. This research provides a foundational step toward applying deep learning for demand-supply matching in e-commerce, highlighting both the performance gains and the challenges associated with model interpretability and real-time deployment. Future work will explore reinforcement learning integration and the use of multimodal data for further improvements.

DOI: /10.61463/ijset.vol.12.issue6.345