Toward a Theory of Industrial Supply Networks: A Multi-Level Perspective via Network Analysis
AbstractIn most supply chains (SCs), transaction relationships between suppliers and customers are commonly considered to be an extrapolation from a linear perspective. However, this traditional linear concept of an SC is egotistic and oversimplified and does not sufficiently reflect the complex and cyclical structure of supplier-customer relationships in current economic and industrial situations. The interactional relationships and topological characteristics between suppliers and customers should be analyzed using supply networks (SNs) rather than traditional linear SCs. Therefore, this paper reconceptualizes SCs as SNs in complex adaptive systems (CAS), and presents three main contributions. First, we propose an integrated framework of CAS network by synthesizing multi-level network analysis from the network-, community- and vertex-perspective. The CAS perspective enables us to understand the advances of SN properties. Second, in order to emphasize the CAS properties of SNs, we conducted a real-world SN based on the Japanese industry and describe an advanced investigation of SN theory. The CAS properties help in enriching the SN theory, which can benefit SN management, community economics and industrial resilience. Third, we propose a quantitative metric of entropy to measure the complexity and robustness of SNs. The results not only support a specific understanding of the structural outcomes relevant to SNs, but also deliver efficient and effective support to the management and design of SNs. View Full-Text
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Zuo, Y.; Kajikawa, Y. Toward a Theory of Industrial Supply Networks: A Multi-Level Perspective via Network Analysis. Entropy 2017, 19, 382.
Zuo Y, Kajikawa Y. Toward a Theory of Industrial Supply Networks: A Multi-Level Perspective via Network Analysis. Entropy. 2017; 19(8):382.Chicago/Turabian Style
Zuo, Yi; Kajikawa, Yuya. 2017. "Toward a Theory of Industrial Supply Networks: A Multi-Level Perspective via Network Analysis." Entropy 19, no. 8: 382.
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