← Back to Volume 10, Issue 7

Cost-Benefit Effects and Breakthrough Strategies of AIGC Digital Transformation for Small and Medium Hardware Manufacturing Enterprises

Dafeng Gong, Lili Shi, Wanle Chi
Vol.10, No.7, pp.29-45, 2026 DOI: 10.62051/ijgem.v10n7.04 July 29, 2026
Download PDF
Abstract: Against the intelligent manufacturing and new quality productive forces context, generative AI (AIGC) has reconstructed manufacturing R&D, production, quality inspection and supply chain links. Most existing AIGC transformation studies concentrate on well-resourced large listed manufacturers, with scarce quantitative empirical evidence for traditional hardware small and medium enterprises (SMEs) regarding transformation gains and cost pressures. Taking 41 Wenzhou hardware SMEs from 2021 to 2025 as balanced panel samples, this research adopts a two-way fixed-effects DID model to quantify the causal effects of AIGC adoption. Benchmark regressions prove AIGC significantly boosts SME performance: cutting new product R&D cycles by 32.6%, lowering unit manufacturing costs by 18.3% and raising total factor productivity (TFP) by 14%. Such benefits are more pronounced for medium firms with 20-50 million RMB annual revenue and 8-15 years of operation; placebo, variable substitution and parallel trend tests validate result robustness. Cost measurement identifies three key burdens: one-off equipment expenses 11.37% of annual net profit, annual system operation fees (6.99%) and compound talent costs (8.97%). Major transformation obstacles cover industrial AI talent shortages, mismatched universal AIGC models, limited profit margins and inadequate cluster digital ecosystems. Drawing on cost-benefit results, this paper puts forward dual micro-macro solutions: lightweight SaaS AIGC subscriptions for firms, plus tiered subsidies, shared industrial AI platforms and school-enterprise talent training schemes for governments. This research fills the SME AIGC transformation research and supplies actionable empirical references for industrial clusters and local policymakers.
Keywords: AIGC, Small and Medium Manufacturing Enterprises, Digital Transformation, Cost-Benefit Analysis, New Quality Productive Forces
APA Citation: Dafeng Gong, Lili Shi, Wanle Chi (2026). Cost-Benefit Effects and Breakthrough Strategies of AIGC Digital Transformation for Small and Medium Hardware Manufacturing Enterprises. International Journal of Global Economics and Management, 10(7), 29-45. https://doi.org/10.62051/ijgem.v10n7.04

References

  1. Pan, H., Cheng, M., Wang, R., Zou, N., & Liu, D. (2026). Digital transformation and supply chain resilience in Chinese manufacturing: The mediating role of supply chain risk management. Asia Pacific Journal of Marketing and Logistics, 38(7), 2184–2200. https://doi.org/10.1108/APJML-01-2025-0059
  2. Gu, S., Ma, J., Wang, K., Zhou, C., Zhang, M., & Wei, Z. (2026). How digital transformation enhances firm performance through capability building: An empirical analysis of Chinese manufacturing firms. Technology in Society, 86, 103288. https://doi.org/10.1016/j.techsoc.2026.103288
  3. Zhang, H., Yu, Z., & Liu, H. (2026). Research on the impact of digital transformation in manufacturing enterprises on new quality productive forces. Sustainability, 18(10), 4881. https://doi.org/10.3390/su18104881
  4. Sun, Y. (2026). The impact of digital transformation on the performance of manufacturing enterprises. Journal of Innovation Development, 14(3), 821–826. https://doi.org/10.54097/j4rj2165
  5. Dong, S., Lei, L., & Chen, H. (2026). The impact of artificial intelligence on the new quality transformation of Chinese manufacturing. Sustainability, 18(9), 4196. https://doi.org/10.3390/su18094196
  6. Zhou, C., Zhang, H., Ying, J., He, S., Zhang, C., & Yan, J. (2025). Artificial intelligence and green transformation of manufacturing enterprises. International Review of Financial Analysis, 104, 104330. https://doi.org/10.1016/j.irfa.2025.104330
  7. Ghobakhloo, M., Iranmanesh, M., Vilkas, M., Grybauskas, A., & Amran, A. (2022). Drivers and barriers of Industry 4.0 technology adoption among manufacturing SMEs: A systematic review and transformation roadmap. Journal of Manufacturing Technology Management, 33(6), 1029–1058. https://doi.org/10.1108/jmtm-12-2021-0505
  8. Xu, P., & Li, T. (2026). The effect of technological core executives on industrial AI transformation: Evidence from Chinese manufacturing enterprises. Chinese Management Studies, 20(5), 1449–1472. https://doi.org/10.1108/cms-09-2024-0705
  9. Salinas, L. E. C., Rimachi, J. M., & Reyes, C. J. S. (2026). Digital transformation as a driver of operational sustainability in manufacturing: A systematic review (2015–2025). F1000Research, 15, 544. https://doi.org/10.12688/f1000research.178740.1
  10. Xie, S. (2026). The application and challenges of agile project management in the digital transformation of traditional manufacturing. Management, 9(3). https://doi.org/10.31058/j.mana.2026.93004
  11. Mansouri, A., Bennis, I., Siadat, A., & Akef, F. (2026). Transitioning manufacturing toward circular economy: A systematic review of practices, digital enablers, and implementation barriers. Circular Economy and Sustainability, 6(3), 265. https://doi.org/10.1007/s43615-026-00994-0
  12. Elisa, B., Simone, G., Gianluca, M., & Domenico, C. (2023). Adoption paths of digital transformation in manufacturing SME. International Journal of Production Economics, 255. https://doi.org/10.1016/j.ijpe.2022.108675
  13. Wang, P., & Chen, D. (2026). The impact of firms’ digital transformation and relationship network status on capacity utilization: Evidence from listed manufacturing companies in China. Chinese Management Studies, 20(7), 1936–1969. https://doi.org/10.1108/cms-08-2024-0630
  14. Li, R., & Zheng, J. (2026). How the core–periphery system shapes digital-driven manufacturing transformation: Evidence from a peripheral province of China. Sustainability, 18(12), 6298. https://doi.org/10.3390/su18126298
  15. Zhang, C. (2022). Research on countermeasures for intelligent and digital transformation of manufacturing industry—Take Suzhou City, China as example. Economic Management Review, 3(1). https://doi.org/10.37420/j.emr.2022.002
  16. Liu, C., & Zhang, N. (2025). Research on the impact of digital transformation on the dual innovation performance of the manufacturing industry—Using organizational resilience as a moderating variable. In GBP Proceedings Series (Vol. 13, pp. 227–243). https://doi.org/10.70088/YPE9SE36
  17. Lin, D. (2025). Analysis on the evolution path and influencing factors of financial governance mechanism of manufacturing enterprises under the background of digital transformation. Journal of Economics and Management Science, 8(3), 112. https://doi.org/10.30560/jems.v8n3p112
  18. Wu, S., Hou, J., & Yu, D. (2026). Measurement discipline for sustainable industrial transition: Frontier productivity evidence from Shandong and Jiangsu manufacturing, 2013–2023. Sustainability, 18(12), 5888. https://doi.org/10.3390/su18125888
  19. Z, C., L, F., L, C., & T, G. (2023). Data-driven low-carbon transformation management for manufacturing enterprises: An eco-efficiency perspective. Environmental Science and Pollution Research International, 30(46), 102519–102530.. https://doi.org/10.1007/s11356-023-29573-8
  20. Huang, T., & Wei, F. (2025). Research on the cross-industry integration development of the logistics industry and the manufacturing industry under the background of digital transformation. Modern Economic Management Forum, 3(2). https://doi.org/10.70711/memf.v3i2.8838
  21. Hedan, M., Xinliang, J., & Xin, W. (2022). Digital transformation, ambidextrous innovation and enterprise value: Empirical analysis based on listed Chinese manufacturing companies. Sustainability, 14(15), 9482. https://doi.org/10.3390/su14159482