Chinese listed manufacturers that adopt digital intelligence technologies show stronger supply-chain security, driven by better risk management and coordination, with biggest gains where digital infrastructure is advanced; improvements at focal firms also lift the security of upstream suppliers and downstream customers.

Main Finding

Using panel data on Chinese A‑share listed manufacturing firms (2012–2024), the authors find that firm-level adoption of digital intelligence technologies (integration of digitalisation and AI/ML/etc.) significantly improves supply chain security (SCS). The effect operates mainly by strengthening firms’ risk‑management capability and by improving supply‑chain coordination. The positive impact is larger for firms located in eastern China and for firms with lower supply‑chain concentration, is amplified where regional digital infrastructure is more developed, and generates positive network spillovers to upstream suppliers and downstream customers.

Key Points

• Research question: Does adoption of digital intelligence technologies increase manufacturing firms’ supply chain security, through what mechanisms, and with what heterogeneity and spillover patterns?
• Core result: Digital intelligence adoption → higher SCS at the firm level (statistically significant).
• Mechanisms identified:
  • Risk management capability: earlier risk identification, improved traceability and authenticity (IoT/blockchain), scenario testing (digital twins) → lower risk probability and faster recovery.
  • Supply‑chain coordination: better planning alignment, dynamic resource allocation, and faster emergency coordination among chain partners.
• Moderation/heterogeneity:
  • Stronger effects in eastern China (regionally advantaged) and in firms with lower supply‑chain concentration.
  • Regional digital infrastructure strengthens the digital intelligence → SCS relationship.
• Network effects: Improvements in SCS for focal (chain‑leading) firms spill over and enhance SCS of upstream suppliers and downstream customers.
• Contributions claimed by authors:
  • Incorporates digital intelligence adoption into SCS research at the micro (firm) level.
  • Clarifies two internal transmission channels (risk management; coordination) and one external moderator (digital infrastructure).
  • Documents cross‑firm spillovers along supply‑chain links.

Data & Methods

• Data: Panel of Chinese A‑share listed manufacturing firms covering 2012–2024. (Exact sample size and variable construction are not included in the excerpt.)
• Empirical approach (as reported):
  • Panel empirical analysis linking firm‑level digital intelligence adoption to a firm‑level SCS measure.
  • Mechanism/mediation analysis to test roles of risk management capability and supply‑chain coordination.
  • Moderation tests using regional digital infrastructure to examine boundary conditions.
  • Heterogeneity analysis across regions (east vs other) and by supply‑chain concentration.
  • Spillover/network analysis to assess effects on upstream and downstream partners.
• Robustness: The paper reports robustness and further analyses, though specific robustness tests and econometric specifications (fixed effects, IV, etc.) are not detailed in the provided excerpt.
• Caveat: This is an unedited manuscript in press; details on variable measurement, model specification, identification strategies, and sample construction should be checked in the final published version.

Implications for AI Economics

• Firm performance and risk management: The paper provides micro‑level evidence that integrating AI/ML and related digital technologies into operations materially improves firms’ ability to manage systemic and idiosyncratic supply‑chain risks—not only by efficiency gains but by enhancing resilience and controllability.
• Complementarity with regional infrastructure: The effectiveness of AI/digital investments depends on external digital infrastructure; regional complementarities matter. This underscores that policy or firm investments in AI should be coordinated with broader digital infrastructure development.
• Strategic targeting and spillovers: Chain‑leading firms can act as conduits of SCS improvements to their partners, suggesting that policies or incentives that encourage AI adoption among key firms could generate wider network benefits (positive externalities).
• Distributional and structural considerations: Heterogeneous effects by region and supply‑chain concentration hint at uneven gains—policies should consider less developed regions and highly concentrated supply chains where returns may differ.
• Research directions for AI economists:
  • Causal identification: use quasi‑experimental or instrumental strategies to pin down causality between AI adoption and SCS.
  • Measurement: refine firm‑level measures of “digital intelligence adoption” and of supply‑chain security (multidimensional indices).
  • General equilibrium and market structure: study how widespread AI adoption reshapes supply‑chain structure, market power, and systemic risk.
  • Cross‑country and firm‑size comparisons: test whether findings generalize beyond Chinese listed manufacturers and to SMEs.
  • Long‑run tradeoffs: investigate potential tradeoffs (e.g., cost, technological dependence, cybersecurity vulnerabilities) and non‑linearities in the relationship between digital intelligence and SCS.

Reference (preprint): Yu Z., Liu H. & Xing C. Digital intelligence technology adoption and supply chain security of manufacturing firms: empirical evidence from China. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41349-x

Note: this summary is based on the unedited article in press; check the final published version for full details and exact empirical specifications.