Complex Network-Based Resilience Assessment of the Integrated Circuit Industry Chain

Chuang Wang (), Tianyi Zhang, Jing Jia, Jin Wang and Shan Ren
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Chuang Wang: Collaborative Innovation Center for Modern Post, Xi’an University of Posts & Telecommunications, Xi’an 710061, China
Tianyi Zhang: Collaborative Innovation Center for Modern Post, Xi’an University of Posts & Telecommunications, Xi’an 710061, China
Jing Jia: Department of State-Owned Asset Management, Xianyang Normal University, Xianyang 712000, China
Jin Wang: Collaborative Innovation Center for Modern Post, Xi’an University of Posts & Telecommunications, Xi’an 710061, China
Shan Ren: Collaborative Innovation Center for Modern Post, Xi’an University of Posts & Telecommunications, Xi’an 710061, China

Sustainability, 2024, vol. 16, issue 12, 1-23

Abstract: With the improvement of social production efficiency and the enhancement of the supply chain system, the traditional linear supply chain model is gradually evolving into a more complex and dynamic industrial chain network. This article uses complex network theory combined with the basic attributes of the industrial chain and supply chain to conduct a comprehensive and in-depth analysis of the integrated circuit industry chain. Firstly, a cooperative network model of the integrated circuit industry chain in Shaanxi Province is established based on the supply chain relationships of enterprises. Secondly, the study analyzes the basic characteristics of the collaborative network model. Thirdly, this study explores the efficiency, resilience, and innovation capacity of industrial chains using a novel set of indexes: the industry chain efficiency index (ICEI), the industry chain resilience index (ICRI), and the industry chain innovation capability index (ICICI). By employing principal component analysis (PCA), the study provides a comprehensive evaluation of industrial chain performance. The findings reveal that the ICEI highlights the critical importance of average path length and network density, showing that shorter paths and higher density are associated with greater efficiency. The ICRI emphasizes the roles of average degree and standard deviation, indicating that higher connectivity and lower variability contribute to resilience. The ICICI identifies the clustering coefficient and network density as key factors, suggesting that tight-knit networks foster innovation. These results offer significant insights into the dynamics of industrial chain collaboration and provide practical recommendations for enhancing supply chain management. Finally, the effectiveness of the proposed method is demonstrated through a case study. The results of the case study indicate the following: (1) Key Enterprises’ Identification: The analysis identified key enterprises like Samsung Semiconductor and HT-tech with the highest betweenness centrality, highlighting their crucial intermediary roles within the network; (2) Efficiency and Innovation Assessment: Compared with foreign-owned and other immigrant businesses, local businesses generally perform below average in terms of efficiency and resilience, indicating that there is room for improvement in technology adoption and innovation capabilities.

Keywords: industrial chain; complex network theory; integrated circuit industry; industrial chain cooperation; industrial chain resilience assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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