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Supply Chain Analysis Based on Community Detection of Multi-Layer Weighted Networks

Yingqiu Zhu, Ruiyi Wang, Mingfei Feng, Lei Qin, Ben-Chang Shia and Ming-Chih Chen (081438@mail.fju.edu.tw)
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Yingqiu Zhu: School of Statistics, University of International Business and Economics, Beijing 100029, China
Ruiyi Wang: School of Statistics, University of International Business and Economics, Beijing 100029, China
Mingfei Feng: Education Foundation & Board of Trustees, University of International Business and Economics, Beijing 100029, China
Lei Qin: School of Statistics, University of International Business and Economics, Beijing 100029, China
Ben-Chang Shia: Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan
Ming-Chih Chen: Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan

Mathematics, 2024, vol. 12, issue 22, 1-21

Abstract: As the economic environment becomes more complex, improving supply chain resilience is critical for the effective operation and long-term sustainability of businesses. Real-world supply chains, which consist of various components such as goods, warehouses, and plants, often feature intricate network structures that pose challenges for resilience analysis. This paper addresses these challenges by proposing a framework for studying supply chains using multi-layer network community detection. The complex multi-mode supply chain network is transformed into single-mode, multi-layer weighted networks. A multi-layer weighted community detection method is proposed for identifying local clusters within these networks, revealing interconnected groups that highlight flexibility and redundancy in production capabilities across different plants and goods. An empirical study utilizing real data demonstrates that this clustering method effectively detects indirect capacity links between plants and goods. The insights derived from this method are useful for strategic capacity management, allowing businesses to respond more effectively to supply shortages and unexpected increases in demand.

Keywords: resilience analysis; supply chain; community detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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