Technology-Driven Logistics Transformation: Opportunities, Challenges and Countermeasures

DOI: https://doi.org/10.62517/jbdc.202501303

Author(s)

Xiang Huang

Affiliation(s)

Sichuan Land-Sea Cloud Port Development Group Co., Ltd. Chengdu, Sichuan, China

Abstract

With the rapid advancement of global technology, emerging innovations such as big data, artificial intelligence, and the Internet of Things are fundamentally reshaping the development model of the logistics industry. This paper explores the opportunities and challenges brought about by these new technologies in driving logistics transformation. By leveraging big data, logistics enterprises can optimize transportation routes and reduce operating costs; the introduction of artificial intelligence enhances the efficiency of demand forecasting and inventory management; meanwhile, IoT technology provides real-time monitoring and information sharing across logistics processes. The adoption of these technologies not only improves logistics efficiency and service quality but also offers significant competitive advantages in the market. However, during the transformation process, logistics enterprises also face challenges including high costs of technology implementation, concerns regarding data security and privacy protection, as well as mismatches in organizational structure and management practices. This paper proposes countermeasures to address these challenges, such as optimizing cost control, strengthening data protection, and adjusting organizational frameworks, in order to promote the sustainable development of the logistics industry.

Keywords

Logistics Transformation; Big Data; Artificial Intelligence; Internet of Things; Efficiency Improvement

References

[1] Wang Y. “Smart Supply Chain Management Based on IoT Identification Technology.” Industrial Innovation Research, 2025, (14): 48-50. [2] Tian M, and Wu Y M. “Research on the Path to Optimizing Logistics Costs and Improving Operational Efficiency Driven by New Quality Productivity.” Logistics Technology, 2025, 48(14): 13-15. [3] Ying X H. “CIoT Anomaly Detection in Smart Logistics Based on Deep Learning.” Electronic Design Engineering, 2025, 33(13): 11-16. [4] Liu L, and Liu J. “Research on the Development Strategies and Trends of Smart Logistics under the Background of Big Data.” Logistics Technology, 2025, 48(14): 50-52+57. [6] Chen Y M, Chen J H, Ma, Nan, et al. “Industry Digital Transformation Driven by Smart Terminals: Innovative Architecture and Logistics Practice.” Logistics Technology, 2025, 48(13): 38-43. [7] Huang X Y, Li G S, Cen X H, et al. “Construction of a Smart Logistics Warehouse Management System in Guangxi Based on Artificial Intelligence Technology.” Enterprise Technology and Development, 1-5. [8] Li J, and Wang J. “Application of Digital Twin Technology in the Field of Logistics Management: A Meta-Analysis Perspective.” Logistics Research, 2025, (03): 47-53. [9] Wu Y. “The Impact of Smart Logistics Systems Integrating Artificial Intelligence and IoT on the Green Economy.” China Navigation Weekly, 2025, (24): 68-70. [10] Guo Y S. “Analysis of the Application of Smart Logistics Technologies in Supply Chain Visual Management.” China Logistics and Procurement, 2025, (12): 74-75. [11] Fu Y, Zhu J. Operation mechanisms for intelligent logistics system: a blockchain perspective. IEEE Access, 2019, 7: 144202-144213. [12] Chen W, Men Y, Fuster N, et al. Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability, 2024, 16(21): 9145. [13] Lei N. Intelligent logistics scheduling model and algorithm based on Internet of Things technology. Alexandria Engineering Journal, 2022, 61(1): 893-903. [14] Gumzej R. Intelligent logistics systems in E-commerce and transportation. Math. Biosci. Eng, 2023, 20: 2348-2363. [15] Wang J, Lim M K, Zhan Y, et al. An intelligent logistics service system for enhancing dispatching operations in an IoT environment. Transportation Research Part E: Logistics and Transportation Review, 2020, 135: 101886.