大数据技术能否提升企业供应链效率？

蔡蒙　谢建国

（南京大学）

摘要：在数字经济时代，大数据技术逐渐成为提升企业供应链管理能力的重要手段。本文构建了企业大数据技术和供应链效率的测度指标，深入探讨了大数据技术如何提升供应链效率。研究结果表明，大数据技术应用提升了供应链效率，其主要作用机制包括创新韧性的增强、企业敏捷响应度的优化以及生产经营效率的提升。经内生性分析和稳健性检验后，本文的主要结论依然成立。进一步分析发现，大数据技术对提升供应链效率的作用在供应链中断风险较低以及供应链集中度较低的企业中更加明显。本文的研究不仅丰富了数字经济背景下企业大数据技术应用对供应链管理的理论和实证研究，也为企业利用大数据技术促进供应链企稳增效提供了政策启示和实证参考。

关键词：大数据技术；供应链效率；创新韧性；敏捷响应度；生产经营效率

作者简介：蔡蒙，南京大学商学院博士研究生，南京，210093；谢建国，南京大学商学院教授、博士生导师，通信作者。

基金项目：国家社会科学基金重点项目“全球化与逆全球化问题研究”（24AZD050）

引用格式：蔡蒙,谢建国.大数据技术能否提升企业供应链效率?[J].经济与管理研究,2026,47(3):85-98.

Does the Application of Big Data Technologies Improve Firms’ Supply Chain Efficiency?

CAI Meng, XIE Jianguo

(Nanjing University, Nanjing 210093)

Abstract: In the era of the digital economy, big data technologies have emerged as a crucial driver for improving firms’ supply chain efficiency. As global supply chain structures grow more complex and external shocks persistently accumulate, enhancing supply chain efficiency has become a vital pathway for firms to maintain stable development. Although existing studies have examined the impact of digital transformation on supply chain performance, systematic research on the mechanisms and causal effects of the application of big data technologies at the firm level remains limited.

This paper constructs indicators of firms’ application of big data technologies and supply chain efficiency, and identifies the causal relationship between the two using a two-way fixed effects model based on data from A-share listed companies in China between 2010 and 2023. The density of long-distance optical cable lines in the city where the firm was registered in 1995 is selected as an instrumental variable to address potential endogeneity issues, which reflects the initial endowment of historical infrastructure and satisfies the requirement of exogeneity.

The empirical results show that the application of big data technologies improves firms’ supply chain efficiency. This conclusion remains valid across multiple tests, including the use of instrumental variable and propensity score matching to address endogeneity issues, as well as altering the sample observation period and employing different clustering levels for standard errors. Further mechanism analysis indicates that this positive effect operates through three main paths: first, strengthening innovation resilience to promote knowledge integration and continuous innovation; second, enhancing firms’ agile responsiveness to improve real-time perception and rapid reaction to external changes; third, improving operational efficiency to achieve optimized resource allocation and process coordination. Heterogeneity analysis reveals that this enhancement is more pronounced among firms facing lower supply chain disruption risk and supply chain concentration, suggesting that its effectiveness is jointly shaped by firms’ operational foundations and supply chain structures.

Overall, this paper provides new empirical evidence for understanding the micro-level impact of big data technologies as a strategic factor of production and offers valuable implications for policy design. In general, policy efforts should clarify differentiated digital development paths for various types of firms and improve digital infrastructure and data circulation systems to provide targeted support for digital upgrading under different supply chain risk conditions. At the same time, firms should be guided to optimize innovation resilience, agile responsiveness, and operational efficiency, forming a sustainable digital operating system, and ultimately achieving comprehensive improvements in supply chain efficiency and overall competitiveness.

Keywords: big data technology; supply chain efficiency; innovation resilience; agile responsiveness; operational efficiency

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