Big Data Analytics in Supply Chain Optimization and Risk Management: A case study of Amazon

Shenzhen Xinhangdao, Dongguan, China, 523000

^* Corresponding author: 1814010111@stu.hrbust.edu.cn

Abstract

This study aims to explore the application of big data technology in supply chain management, especially its role in coping with complex market demand and supply chain risks. Starting from both theoretical and practical case levels, the study systematically comprehends the key technologies of big data in supply chain management, including application scenarios such as demand forecasting, inventory management, production optimization, and supplier management. Through empirical analysis with Amazon as a case study, the study reveals how big data analytics can significantly improve the agility, efficiency, and risk resistance of the supply chain, which is manifested in the improvement of inventory turnover, reduction of supply chain cost, and optimization of logistics efficiency. A series of optimization strategies are proposed. The study systematically comprehends the core technologies of big data in supply chain management. This study analyzes their practical application effects in demand forecasting, inventory management, production optimization, logistics and distribution, and supplier management scenarios.