Analysis of the Effect of Big Data on Supply Chain Optimization: A Case Study of JD Logistics' Freight and Warehousing Operations

Abstract

In the context of the deep integration of the digital economy, supply chain management is confronted with challenges such as demand fluctuations and information asymmetry. Big data technology is emerging as the core driving force for the precise transformation of supply chains. This paper takes JD Logistics as the research object and systematically analyzes the application paths and optimization effects of big data in the e-commerce supply chain. The research starts from the characteristics of supply chain big data, summarizes the development process and technical architecture of JD Logistics, and focuses on the specific practices of big data in inventory management, intelligent warehousing operations, and delivery optimization. Through a combination of case analysis and literature research, it demonstrates the significant role of big data technology in reducing JD’s warehousing costs and improving delivery efficiency, with a single delivery cost decreasing by 8.5%, unit warehousing cost decreasing by 12%, and order fulfillment rate increasing to 97%. The study shows that the supply chain management driven by big data achieves the unity of cost reduction, efficiency improvement, and service quality enhancement, providing a feasible practical path for the digital transformation of e-commerce logistics enterprises.