Supply Chain System Optimization Model for Logistics Enterprises in the Internet of Things Era Based on Improved ECC Algorithm

Abstract

In this article, a new method to optimize the supply chain system (SCS) of logistics companies in the era of Internet of Things (IoT) using an improved elliptic curve cryptography (ECC) algorithm was put forward.The focus of this research was to address the challenges faced by logistics organizations in adapting to the complexity of modern supply chains and meeting the growing demands of customers. The research aimed to improve the efficiency, security and reliability of supply chain management systems by utilizing advances in ECC algorithms.By conducting comprehensive performance tests of the proposed system, including interface testing, joggle testing, and system response time analysis, the study demonstrated the superiority of the system over traditional systems. The research findings indicated that the system based on the improved ECC algorithm outperformed traditional systems in terms of interface effectiveness, usability, and system response time. The improved ECC algorithm enhanced the security of the encryption process, making it more resistant to unauthorized access, and improved the system’s overall data processing load capacity. Additionally, the system demonstrated superior performance in managing logistics data and information, facilitating better decision-making and efficiency in logistics operations. Overall, this research contributes to the advancement of supply chain management in the IoT era by providing a practical and efficient solution for logistics enterprises to optimize their supply chain systems. The innovative application of the improved ECC algorithm offers a secure, reliable, and efficient approach to managing logistics operations, addressing the challenges posed by the complexities of modern supply chains.

Keywords:

• Logistics Enterprise Supply Chain System
• Elliptic Curve Cryptography Algorithm
• Internet of Things Era
• Model Optimization

EMJ Focus Areas:

• Supply Chain Management

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Xiaopeng Yang

Xiaopeng Yang was born in Weifang, Shandong. China,1987. He graduated from Kyung Hee University in South Korea with a doctor’s degree. Postdoctoral Researcher in Management. He is now working in The School of Economics and Management of Weifang University. His research interest covers supply chain management.Guided students to win provincial and national first prizes multiple times in the National College Student Smart Supply Chain Innovation and Entrepreneurship Challenge and the National College Business Elite Challenge. Mainly teaches courses such as Transportation Management, Logistics Management, Supply Chain Management, and Big Data Analysis.

Mengmeng Linghu

Mengmeng Linghu is a graduate of Supply Chain and Logistics Management from the University of Warwick in the United Kingdom. With nearly a decade of hands-on experience in sales management and operations, she has developed a strong expertise in these domains. Linghu currently serves as a teacher of Business English at a university, where she primarily instructs the course “International Logistics Management.” Her research interests lie at the intersection of industrial clusters and innovative supply chain management practices.