Research on Factors Influencing DHL's Logistics Business Processes in the Context of Digital Transformation

Abstract

In the context of digital transformation, the logistics industry is undergoing a profound transformation from traditional operational models to intelligent and data-driven systems. This study focuses on DHL logistics business processes and constructs an integrated theoretical framework of "digital technology empowerment → logistics process reconstruction → operational performance improvement". It systematically explores the impact mechanisms of key technologies such as big data, artificial intelligence (AI), Internet of Things (IoT), robotic process automation (RPA), and blockchain on core processes such as warehouse management, transportation scheduling, end of pipe distribution, and cross-border customs clearance. Based on the theory of dynamic capabilities, this study reveals how enterprises can cope with market uncertainty through the ability of "perception capture reconstruction” and uses comparative case analysis to compare the digital paths of DHL and SF Express. At the same time, it focuses on the lagging transformation of small and medium-sized logistics enterprises, and deeply analyzes the moderating effects of market structure, policy environment, and technological ecology on transformation effectiveness.

The study adopts a mixed method, combined with literature review, case analysis, and quantitative evaluation, to quantify the optimization effect of digital technology on business processes using key performance indicators such as operating costs, delivery time, order accuracy, and customer satisfaction. Simultaneously identify practical challenges such as high investment risks, data security vulnerabilities, and talent shortages, and propose targeted solutions. The research results not only enrich the micro level digital transformation theory in the field of logistics management, but also provide logistics enterprises with operable transformation roadmaps, technology selection references, and risk assessment tools. It also provides empirical basis for the government to formulate industry standards and data governance policies, and helps to build an efficient, green, and safe modern intelligent logistics system.