How Does Artificial Intelligence Influence Supply Chain Resilience: Evidence from Listed Companies
Deng Huihui1, Liu Yujia1, Wang Qiang2
1.Institute of International Economy, Academy of China Open Economy Studies, University of International Business and Economics, Beijing 100029, China 2.School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China
Abstract:Artificial intelligence (AI) has accelerated the transformation of traditional supply chains into intelligent ones, offering a way to find and predict supply chain risks and avoid disruptions. However, there is little evidence on whether AI applications have improved supply chain resilience (SCR). This study explores the impact of AI on supply chain resilience using a panel dataset from 3,240 listed companies from 2017 to 2021. We explore its mechanism from two aspects: the effectiveness of internal resource allocation and the diversity of external resource structures. The results show that: (1) Artificial intelligence could shorten inventory turnover days and improve supply chain resilience. (2) Artificial intelligence could mitigate the ineffectiveness of capital allocation. When enterprises invest inefficiently, artificial intelligence will have a greater influence on supply chain resilience. Particularly, compared to enterprises that underinvest, AI has a greater potential impact on supply chain resilience in companies that overinvest. (3) The bullwhip effect does not affect the role of artificial intelligence in improving supply chain resilience, suggesting that the effect of AI in enhancing supply chain resilience is independent of the degree of deviation from supply and demand fluctuations. In other words, artificial intelligence doesn’t mitigate the problems posed by the lack of information validity. (4) AI could improve supply chain resilience by alleviating inadequate diversity of external resources. When sourcing and distribution channels are not diverse, AI has a greater impact on supply chain resilience. (5) AI is more likely to improve supply chain resilience for large-scale firms, private firms, and firms in the Pearl River Delta city cluster and the Yangtze River Delta city cluster. However, for MSMEs, SOEs, Sino-foreign joint ventures, and firms in the Beijing-Tianjin-Hebei city cluster, the influence of AI on supply chain resilience is not significant.Our study has three contributions First, existing research is primarily theoretical and lacks empirical tests on the marginal contributions of artificial intelligence. This paper studies the level of AI in enterprises from two lenses, intention and action. And it answers questions like “Does AI improve supply chain resilience” and “How much of an improvement does artificial intelligence make”. Second, considerable studies have analyzed the impact of resource redundancy on supply chain resilience, ignoring the effectiveness of resource allocation. This paper fills this gap and proposes mechanisms of artificial intelligence to improve supply chain resilience from a resource perspective. Furthermore, we incorporate the structural diversity of external resources into the research framework and extend the perspective of resource-based theory in supply chains. Third, this paper further distinguishes differences in the firm size, property rights, and city cluster location. It assists businesses in understanding the true impact and mechanism of AI on supply chain resilience, allowing them to modify their supply chain management strategy accordingly.
邓慧慧, 刘宇佳, 王强. 人工智能发展如何提升供应链韧性?[J]. 浙江大学学报(人文社会科学版), 2024, 54(6): 5-23.
Deng Huihui, Liu Yujia, Wang Qiang. How Does Artificial Intelligence Influence Supply Chain Resilience: Evidence from Listed Companies. JOURNAL OF ZHEJIANG UNIVERSITY, 2024, 54(6): 5-23.
1 Paul S. K., Chowdhury P. & Moktadir M. A. et al., “Supply chain recovery challenges in the wake of COVID-19 pandemic,” Journal of Business Research, Vol. 136 (2021), pp. 316-329. 2 Essuman D., Bruce P. A. & Ataburo H. et al., “Linking resource slack to operational resilience: integration of resource-based and attention-based perspectives,” International Journal of Production Economics, Vol. 254 (2022), https://doi.org/10.1016/j.ijpe.2022.108652. 3 Simchi-Levi D. & Haren P., “How the war in Ukraine is further disrupting global supply chains,” 2022-03-17, https://hbr.org/2022/03/how-the-war-in-ukraine-is-further-disrupting-global-supply-chains, 2022-06-25. 4 吕越、邓利静:《着力提升产业链供应链韧性与安全水平——以中国汽车产业链为例的测度及分析》,《国际贸易问题》2023年第2期,第1-19页。 5 Zhao N., Hong J. & Lau K. H., “Impact of supply chain digitalization on supply chain resilience and performance: a multi-mediation model,” International Journal of Production Economics, Vol. 259 (2023), https://doi.org/10.1016/j.ijpe.2023.108817. 6 Riahi Y., Saikouk T. & Gunasekaran A. et al., “Artificial intelligence applications in supply chain: a descriptive bibliometric analysis and future research directions,” Expert Systems with Applications, Vol. 173 (2021), https://doi.org/10.1016/j.eswa.2021.114702. 7 Singh S., Kumar R. & Panchal R. et al., “Impact of COVID-19 on logistics systems and disruptions in food supply chain,” International Journal of Production Research, Vol. 59, No. 7 (2021), pp. 1993-2008. 8 Kan M., “Drones may one day deliver your Ben & Jerry’s ice cream,” 2020-02-04, https://www.pcmag.com/news/drones-may-one-day-deliver-your-ben-jerrys-ice-cream, 2023-06-25. 9 Dolgui A. & Ivanov D., “5G in digital supply chain and operations management: fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything,” International Journal of Production Research, Vol. 60, No. 2 (2022), pp. 442-451. 10 邓慧慧、刘宇佳、王强:《中国数字技术城市网络的空间结构研究——兼论网络型城市群建设》,《中国工业经济》2022年第9期,第121-139页。 11 杜勇、娄靖、胡红燕:《供应链共同股权网络下企业数字化转型同群效应研究》,《中国工业经济》2023年第4期,第1-20页。 12 杨金玉、彭秋萍、葛震霆:《数字化转型的客户传染效应——供应商创新视角》,《中国工业经济》2022年第8期,第156-174页。 13 李云鹤、蓝齐芳、吴文锋:《客户公司数字化转型的供应链扩散机制研究》,《中国工业经济》2022年第12期,第146-165页。 14 邓慧慧、徐昊、王强:《数字经济与全球制造业增加值贸易网络演进》,《统计研究》2023年第5期,第3-19页。 15 Toorajipour R., Sohrabpour V. & Nazarpour A. et al., “Artificial intelligence in supply chain management: a systematic literature review,” Journal of Business Research, Vol. 122 (2021), pp. 502-517. 16 Küfner T., Uhlemann T. H. J. & Ziegler B., “Lean data in manufacturing systems: using artificial intelligence for decentralized data reduction and information extraction,” Procedia CIRP, Vol. 72 (2018), pp. 219-224. 17 Pournader M., Ghaderi H. & Hassanzadegan A. et al., “Artificial intelligence applications in supply chain management,” International Journal of Production Economics, Vol. 241 (2021), https://doi.org/10.1016/j.ijpe.2021.108250. 18 Ralston P. & Blackhurst J., “Industry 4.0 and resilience in the supply chain: a driver of capability enhancement or capability loss,” International Journal of Production Research, Vol. 58, No. 16 (2020), pp. 5006-5019. 19 Gupta S., Modgil S. & Choi T. M. et al., “Influences of artificial intelligence and blockchain technology on financial resilience of supply chains,” International Journal of Production Economics, Vol. 261 (2023), https://doi.org/10.1016/j.ijpe.2023.108868. 20 McCarthy J., Minsky M. L. & Rochester N. et al., “A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955,” AI Magazine, Vol. 27, No. 4 (2006), https://doi.org/10.1609/aimag.v27i4.1904. 21 Huang M. H. & Rust R. T., “Artificial intelligence in service,” Journal of Service Research, Vol. 21, No. 2 (2018), pp. 155-172. 22 Belhadi A., Kamble S. & Wamba S. F. et al., “Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework,” International Journal of Production Research, Vol. 60, No. 14 (2022), pp. 4487-4507. 23 Klumpp M., “Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements,” International Journal of Logistics Research and Applications, Vol. 21, No. 3 (2018), pp. 224-242. 24 Zhang X., Chan F. T. S. & Adamatzky A. et al., “An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition,” International Journal of Production Research, Vol. 55, No. 1 (2017), pp. 244-263. 25 Wong C. W. Y., Lirn T. C. & Yang C. C. et al., “Supply chain and external conditions under which supply chain resilience pays: an organizational information processing theorization,” International Journal of Production Economics, Vol. 226 (2020), https://doi.org/10.1016/j.ijpe.2019.107610. 26 Hosseini S. & Barker K., “A Bayesian network model for resilience-based supplier selection,” International Journal of Production Economics, Vol. 180 (2016), pp. 68-87. 27 Massari G. F. & Giannoccaro I., “Investigating the effect of horizontal coopetition on supply chain resilience in complex and turbulent environments,” International Journal of Production Economics, Vol. 237 (2021), https://doi.org/10.1016/j.ijpe.2021.108150. 28 Tukamuhabwa B. R., Stevenson M. & Busby J. et al., “Supply chain resilience: definition, review and theoretical foundations for further study,” International Journal of Production Research, Vol. 53, No. 18 (2015), pp. 5592-5623. 29 Namdar J., Li X. & Sawhney R. et al., “Supply chain resilience for single and multiple sourcing in the presence of disruption risks,” International Journal of Production Research, Vol. 56, No. 6 (2018), pp. 2339-2360. 30 Behzadi G., O’Sullivan M. J. & Olsen T. L., “On metrics for supply chain resilience,” European Journal of Operational Research, Vol. 287, No. 1 (2020), pp. 145-158. 31 Cabral I., Grilo A. & Cruz-Machado V., “A decision-making model for lean, agile, resilient and green supply chain management,” International Journal of Production Research, Vol. 50, No. 17 (2012), pp. 4830-4845. 32 Rajesh R., “Forecasting supply chain resilience performance using grey prediction,” Electronic Commerce Research and Applications, Vol. 20 (2016), pp. 42-58. 33 吕越、谷玮、包群:《人工智能与中国企业参与全球价值链分工》,《中国工业经济》2020年第5期,第80-98页。 34 张任之:《数字技术与供应链效率:理论机制与经验证据》,《经济与管理研究》2022年第5期,第60-76页。 35 赵奎、后青松、李巍:《省会城市经济发展的溢出效应——基于工业企业数据的分析》,《经济研究》2021年第3期,第150-166页。 36 陈楠、蔡跃洲:《人工智能技术创新与区域经济协调发展——基于专利数据的技术发展状况及区域影响分析》,《经济与管理研究》2023年第3期,第16-40页。 37 Qi Y., Wang X. & Zhang M. et al., “Developing supply chain resilience through integration: an empirical study on an e-commerce platform,” Journal of Operations Management, Vol. 69, No. 3 (2023), pp. 477-496. 38 Richardson S., “Over-investment of free cash flow,” Review of Accounting Studies, Vol. 11 (2006), pp. 159-189. 39 Shan J., Yang S. & Yang S. et al., “An empirical study of the bullwhip effect in China,” Production and Operations Management, Vol. 23, No. 4 (2014), pp. 537-551. 40 Cachon G. P., Randall T. & Schmidt G. M., “In search of the bullwhip effect,” Manufacturing & Service Operations Management, Vol. 9, No. 4 (2007), pp. 457-479. 41 Kinkel S., Baumgartner M. & Cherubini E., “Prerequisites for the adoption of AI technologies in manufacturing—evidence from a worldwide sample of manufacturing companies,” Technovation, Vol. 110 (2022), https://doi.org/10.1016/j.technovation.2021.102375.