Enterprise AI Analysis
Exploring the relationship between artificial intelligence and resilience in manufacturing industrial chains: mechanisms, effects and empirical evidence
Authors: Sirui Liu, Yang Fu, Hanqi Song & Ping Han | Journal: Scientific Reports | DOI: https://doi.org/10.1038/s41598-025-34829-z
Received: 6 September 2025 | Accepted: 31 December 2025 | Published Online: 06 January 2026
Executive Summary: Key Findings & Strategic Implications
This study comprehensively examines the impact of artificial intelligence (AI) on the resilience of manufacturing industrial chains in China, utilizing panel data from 30 provinces (2012–2023). Key findings indicate a significant direct positive effect of AI on industrial chain resilience, validated through benchmark regressions and robustness tests (H1). AI also indirectly enhances resilience by promoting regional economic development (H2). Urbanization positively moderates this relationship, amplifying AI's enabling effect (H3). Furthermore, the impact of AI exhibits a non-linear threshold characteristic dependent on data element development: beyond a critical threshold, its positive effect intensifies, displaying 'increasing marginal returns' (H4). Regional heterogeneity analysis reveals that AI’s enabling effect is strongest in the East, followed by the West, and least pronounced in the Central region. A 'Matthew effect' is observed, where AI's impact strengthens with higher levels of existing industrial chain resilience. These insights offer crucial policy implications for developing differentiated and coordinated AI promotion strategies to enhance industrial resilience and achieve high-quality development.
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Direct Positive Effect on Resilience (AI Coefficient)
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Model Explanatory Power (R² with AI)
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Urbanization Moderating Effect
Deep Analysis & Enterprise Applications
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Abstract: Manufacturing Resilience and AI
Using panel data from 30 Chinese provinces for the period 2012–2023, this study systematically examines the mechanisms, nonlinear characteristics, and spatial heterogeneity of artificial intelligence's impact on the resilience of manufacturing industrial chains. The results indicate that AI exerts a significant and robust direct positive effect on industrial chain resilience. Furthermore, AI indirectly enhances resilience by promoting regional economic development. The urbanization rate positively moderates this relationship, with a higher urbanization level amplifying Al's enabling effect. A threshold analysis reveals that the influence of AI exhibits nonlinear characteristics based on the development level of data elements; beyond a certain threshold, its positive effect displays a pattern of “marginal increase." Heterogeneity analysis shows that Al's enabling effect varies regionally, being strongest in the east, followed by the west, and least pronounced in the central region. Moreover, this effect intensifies with higher levels of supply chain resilience, suggesting a “Matthew effect” whereby stronger chains benefit more. This study provides theoretical and empirical insights into how digital technologies enhance industrial resilience and offers policy implications for designing differentiated and coordinated AI promotion strategies.
AI's Multi-faceted Pathway to Resilience
AI enhances resilience through direct technological empowerment and indirect economic growth.
Artificial Intelligence Development
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Regional Economic Development
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Improved Industrial Foundational Conditions
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Optimized Industrial Structure & Innovation
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Enhanced Manufacturing Industrial Chain Resilience
| Region | AI Impact Coefficient | Key Characteristics |
|---|---|---|
| Eastern Region | 1.497*** |
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| Western Region | 0.355** |
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| Central Region | 0.190 (Insignificant) |
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The Matthew Effect: Stronger Chains Benefit More
The study reveals that industrial chains with higher baseline resilience experience a progressively stronger enabling effect from AI.
At the 25th percentile (low resilience), the AI coefficient is 0.641. At the 50th percentile, it rises to 1.056, and at the 75th percentile (high resilience), it further increases to 1.327. This demonstrates that AI applications on low-resilience systems yield modest efficiency gains, primarily through localized optimization. However, in highly resilient systems with sophisticated networks, agile structures, and strong technological assimilation, AI performs advanced functions like intelligent forecasting and dynamic scheduling, significantly amplifying its marginal contribution. This creates a positive feedback loop, leading to a 'strong grow stronger' phenomenon.
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Your AI Implementation Roadmap
A structured approach to integrating AI for enhanced manufacturing resilience.
Phase 1: Readiness Assessment & Strategy Definition
Evaluate current industrial chain resilience, digital infrastructure, and data element maturity. Define AI objectives aligned with business goals and identify key intervention points for resilience enhancement.
Phase 2: Pilot Implementation & Mechanism Validation
Deploy AI solutions in targeted segments (e.g., predictive maintenance, agile scheduling) and monitor direct effects. Validate the mediating role of economic development and moderating effects of urbanization.
Phase 3: Scaled Integration & Systemic Optimization
Expand successful AI applications across the industrial chain, focusing on data-driven decision-making and collaborative innovation. Optimize institutional environments and foster talent development for sustained impact.
Phase 4: Continuous Evolution & Matthew Effect Leverage
Establish dynamic monitoring mechanisms to adapt to changing conditions. Leverage the Matthew effect by continuously strengthening already resilient chains, fostering cross-regional learning, and upgrading AI models.
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