The impact mechanism of artificial intelligence on the resilience of manufacturing supply chains and empirical examination

Purpose This paper aims to discuss how to improve supply chain resilience (SCR) in supply chain management, especially how to respond to emergency events and a complicated environment, by analyzing the impact mechanism of artificial intelligence (AI) on the resilience of the manufacturing supply chains. It aims to provide support for the digital transformation of manufacturing industry and the enhancement of SCR by exploring AI's mechanisms and applications across different types of enterprises. Design/methodology/approach Based on the policy of the National AI Innovation and Application Pioneer Zone, an empirical analysis of data from A-share listed manufacturing companies between 2011 and 2023 is conducted using a multi-period difference-in-differences (DID) model to assess AI's impact on the resilience of manufacturing supply chains. Leveraging mediation and moderation models, this paper explores how manufacturing enterprises enhance resilience through AI-driven optimization of resource allocation, AI-powered promotion of technological innovation, and other AI-enabled pathways. Findings The research findings indicate that AI significantly enhances SCR, with this conclusion holding true even after multiple robustness checks. Mechanism analysis reveals that AI mainly boosts SCR by improving total factor productivity and promoting continuous technological innovation, which in turn enhances the supply chain's ability to recover and adapt under external shocks. Further analysis shows that enterprise agility plays a significant moderating role in the relationship between AI and SCR, meaning that the positive effect is more pronounced in companies with higher agility. Heterogeneity analysis demonstrates that AI's impact on SCR is more significant in large-scale enterprises, private enterprises, and those with lower levels of pollution. Research limitations/implications This study has some limitations. The sample is focused on listed manufacturing companies and the conclusions should be applied cautiously to small and medium-sized enterprises. The service industry or other countries/regions is mainly measured based on enterprise-level indicators and does not directly characterize the supply chain network structure and node dependencies. The long-term dynamic effects of AI on resilience still require verification with data over a longer period. Practical implications First, at the theoretical level, the findings of this paper suggest that AI not only influences business performance by improving routine operational efficiency but, more importantly, enhances a company's resilience and adaptability in the face of shocks through total factor productivity improvement and continuous technological innovation. This extends the study of AI's economic consequences from an “efficiency-oriented” perspective to a “resilience-oriented” one, enriching the application of the resource-based view and dynamic capabilities theory in the context of digital technologies. Second, from a practical perspective, the results suggest that business managers, when promoting AI applications, should view AI as a strategic tool to enhance SCR, rather than simply as a means of cost reduction and efficiency improvement. Managers should focus on optimizing internal resource allocation, increasing R&D investment, and enhancing organizational agility to amplify AI's resilience-empowering effects. Third, at the policy level, this paper provides empirical evidence for governments seeking to enhance the security of industrial and supply chains through policy tools such as the AI Innovation Pioneer Zone. Policymakers should pay attention to enterprise heterogeneity, avoid one-size-fits-all technology support strategies, and enhance system-level SCR by promoting data sharing and industry collaboration. From a broader societal perspective, AI, by enhancing SCR, helps to reduce the impact of extreme events on economic operations and employment stability, and may also promote environmental sustainability through resource allocation optimization and efficiency improvement. Social implications It provides valuable insights and specific recommendations for policymakers, researchers, and practitioners. Policymakers can help enterprises enhance SCR by increasing support for AI R&D, offering funding and tax incentives, and promoting AI applications in areas such as production scheduling, inventory management, and transportation optimization. Additionally, policies should consider enterprise heterogeneity and design differentiated support policies for enterprises of different sizes and life cycle stages to ensure AI's positive impact across more enterprises. Researchers can further explore the intrinsic relationship between AI and SCR based on the framework of mechanisms proposed in this paper, especially the heterogeneous effects in different market environments and policy contexts. Business managers should focus on how to optimize resource allocation, enhance technological innovation capabilities, and increase enterprise agility through AI, thereby improving SCR, especially in responding effectively to sudden events and long-term uncertainties, and recovering quickly. Originality/value This paper systematically reveals the causal effects, mechanisms, and applicable conditions of AI on SCR at the micro-enterprise level, expanding the intersection of research on the economic consequences of AI and SCR. The research findings not only provide new empirical evidence for relevant theoretical studies but also offer targeted decision-making references for policymakers in promoting AI applications, enhancing the security of industrial and supply chains, and for business managers to strengthen SCR through intelligent means.