The Impact of High-Level Smart Factory Recognition on Supply Chain Stability: Evidence from a Quasi-Natural Experiment among Chinese A-Share Manufacturing Firms
DOI:
https://doi.org/10.6981/FEM.202611_7(11).0003Keywords:
High-level Smart Factory Recognition; Supply Chain Stability; Supply Chain Restructuring; Difference-in-Differences; Smart Manufacturing.Abstract
With the development of smart manufacturing, smart factory recognition becomes a key policy for the transformation and upgrading of the manufacturing industry. This work takes China's A-share manufacturing listed companies from 2019 to 2025 as a sample, and takes the firms selected into the list of excellent level and leading level smart factories in 2024-2025 as a treatment group, and uses the difference-in-differences method to examine the impact of smart factory recognition on the stability of the firms’ supply chain. The research results show that high-level smart factories have been identified as reducing the stability of the supply chain in the sense of measurement in the short term, but this does not mean that their supply chain capacity will decline immediately, but that firms have carried out stronger supply chain restructuring in the short term. After adopting different sample processing methods and recognition tests, the conclusion is still robust.
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[1] Alcácer, V., & Cruz Machado, V. (2019). Scanning the Industry 4.0: A literature review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal, 22, 899–919. https://doi.org/10.1016/j.jestch.2019.01.006
[2] Hozdić, E. (2015). Smart factory for Industry 4.0: A review. International Journal of Modern Manufacturing Technologies, 7, 28–35.
[3] Niu, Z., & Zheng, L. (2026). Smart manufacturing and productivity improvement of manufacturing firms: From the dual perspectives of labor and division of labor. Collected Essays on Finance and Economics. Advance online publication. https://doi.org/10.13762/j.cnki.cjlc.20260320.004
[4] Su, B., Li, P., Qu, G., Liu, Z., Lu, Y., & Zhou, Y. (2026). A ten year review and future outlook of smart manufacturing development in China. Strategic Study of Chinese Academy of Engineering. Advance online publication. https://link.cnki.net/urlid/11.4421.G3.20260318.1520.006
[5] Ostrovsky, M. (2008). Stability in supply chain networks. American Economic Review, 98(3), 897–923. https://doi.org/10.1257/aer.98.3.897
[6] Ouyang, Y. (2007). The effect of information sharing on supply chain stability and the bullwhip effect. European Journal of Operational Research, 182, 1107–1121. https://doi.org/10.1016/j.ejor.2006.09.037
[7] Bai, X., Zhou, K. Z., Jiang, W., Li, Y., & Chen, X. (2025). Smart manufacturing and supply chain resilience: Evidence from an emerging market. Journal of Business Research, 196, 115421. https://doi.org/10.1016/j.jbusres.2025.115421
[8] Ma, L., Wang, H., He, C., & Sui, X. (2025). Can intelligent development improve manufacturing firms’ supply chain resilience? International Review of Economics & Finance, 97, 103753. https://doi.org/10.1016/j.iref.2024.103753
[9] Shi, H., Liu, Y., & Yang, Y. (2024). Enterprise digital transformation and supply chain stability. Finance Research Letters, 63, 105299. https://doi.org/10.1016/j.frl.2024.105299
[10] Wang, F., & Liu, X. (2025). Smart manufacturing policy, knowledge restructuring, and supply chain resilience. Business and Management Journal, 47, 154–170. https://doi.org/10.19616/j.cnki.bmj.2025.04.009
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