Digital-Intelligent Supply Chain Development Path and Resilience Mechanisms: A Case Study of JD Logistics

Abstract

Global supply chains are increasingly transitioning from an efficiency-oriented paradigm toward a more resilient and sustainable model [1][2]. Recent disruptions, including geopolitical conflicts, extreme weather events, and trade policy changes, have exposed structural vulnerabilities in traditional supply chain systems, leading to higher operational risks and recovery costs [1][3]. In this context, digital-intelligent technologies—such as big data analytics, artificial intelligence, and digital twins—have become key enablers of supply chain resilience [4][5]. However, many enterprises, particularly small and medium-sized enterprises (SMEs), continue to face challenges such as data silos, fragmented technology adoption, and limited transformation capabilities [3][6]. This study examines JD Logistics as a representative case to explore the development path of digital-intelligent supply chains. The findings indicate that transformation typically follows a phased process, including infrastructure digitization, data integration, and ecosystem collaboration. Moreover, the integration of AI large models, digital twins, and edge computing forms a “prediction–simulation–execution” closed-loop mechanism that enhances adaptability and resilience. Based on these findings, this paper proposes lightweight transformation strategies for SMEs, offering practical implications for supply chain digitalization.