The smart manufacturing revolution: how industrial robotics reshape supplier networks

Weiping Li et al.

China Journal of Accounting Research2026https://doi.org/10.1016/j.cjar.2026.100468article
AJG 2ABDC A
Weight
0.50

Abstract

Supplier diversification is a crucial corporate strategy for ensuring uninterrupted production and secure supply chains. This study investigates how industrial robots reshape corporate suppliers’ allocation strategies. Using data from China’s A-share listed firms during 2012–2022, we find that extensive robot adoption significantly reduces supplier concentration. This shift is primarily driven by enhanced market power and expanded product diversity. The diversification effect is more pronounced in regions with less developed market institutions characterized by pronounced government intervention, underdeveloped private sectors and weak legal systems, as well as within more technology-intensive and competitive industries and among firms with a larger share of low-skilled workers. We identify a trade-off of automation-driven supplier diversification: enhanced firm performance and supply chain resilience but reduced inventory efficiency and increased transaction costs. Our findings offer valuable insights for managers and policymakers seeking to optimize automation investments and improve robotics integration into supply chain management.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1016/j.cjar.2026.100468

Or copy a formatted citation

@article{weiping2026,
  title        = {{The smart manufacturing revolution: how industrial robotics reshape supplier networks}},
  author       = {Weiping Li et al.},
  journal      = {China Journal of Accounting Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.cjar.2026.100468},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

The smart manufacturing revolution: how industrial robotics reshape supplier networks

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.