人工智能发展如何提升供应链韧性？

doi:10.3785/j.issn.1008-942X.CN33-6000/C.2023.07.141

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1253 KB) RICH HTML
输出: BibTeX | EndNote (RIS)

摘要人工智能加速了传统供应链的智能化转变，有助于及时发现和预警供应链风险，但在“黑天鹅”事件不断出现的情况下，企业人工智能应用是否提升了供应链韧性仍缺乏系统性证据。着眼于中国企业的视角来评估人工智能这一全球关键性技术变革对供应链韧性的影响，发现：（1）企业人工智能发展有助于缓解采购和分销渠道多样性不足以及资金配置有效性缺乏的问题，提升供应链韧性。（2）人工智能提升供应链韧性的净效应与供需波动偏离程度无关，目前人工智能尚未缓解信息有效性不足所带来的问题。（3）人工智能与供应链韧性的关系受到地域同群效应和企业龙头效应的显著影响。在长三角和珠三角城市群，企业人工智能发展对供应链韧性的影响较为显著，大规模企业、民营企业的人工智能发展更能显著提升供应链韧性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	邓慧慧
	刘宇佳
	王强

关键词 ：人工智能, 供应链韧性, 资源配置, 供应链智能化

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.

Key words： artificial intelligence supply chain resilience resource allocation intelligent supply chain

收稿日期: 2023-07-14

基金资助:国家自然科学基金面上项目（72073023）；对外经济贸易大学区域国别研究联合专项课题（LHZX202306）；对外经济贸易大学杰出青年学者资助项目（20JQ08）

作者简介: 邓慧慧（https：//orcid.org/0000-0001-7741-2271），女，对外经济贸易大学国家对外开放研究院国际经济研究院研究员，博士生导师，对外经济贸易大学区域与城市经济研究中心主任，经济学博士，主要从事区域经济、数字经济研究；；刘宇佳（https：//orcid.org/0000-0002-1551-8991）（通信作者），女，对外经济贸易大学国家对外开放研究院国际经济研究院博士研究生，主要从事世界经济研究；；王强（https：//orcid.org/0009-0007-6905-3994），男，对外经济贸易大学国际经济贸易学院教授，博士生导师，管理学博士，主要从事供应链管理、应用统计研究；

引用本文:

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.

链接本文:

https://www.zjujournals.com/soc/CN/10.3785/j.issn.1008-942X.CN33-6000/C.2023.07.141 或 https://www.zjujournals.com/soc/CN/Y2024/V54/I6/5

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.