An Exploratory Look at Supply Chains in Japan from Multiscale Network Perspectives

In social network analysis, advances in social networking and computing techniques have increasingly become a popular approach for extracting features and rules of real-world networks. The network language—$$G=\{V, E \}$$G={V,E} provides a common representation of various networks, where G, V, and E denote the system, components, and interactions, respectively. In this study, we employ this emerging technique to discuss supply chains in Japan. We construct the supply network (i.e., system) based on the firms (i.e., components) and their transactional relationships (i.e., interactions). In comparison with the traditional approaches of industrial sectors and regional clusters, this study represents an exploratory look at supply networks, and investigates different scales of supply networks from three perspectives. (1) In the macro-scale perspective, we evaluate the “small-world” separation of supply networks using average path length. (2) In the meso-scale perspective, we detect communities of the supply networks, which can be marked for cross-location and cross-industry features. (3) In the micro-scale perspective, we investigate the “scale-free” nature of supply networks and each community using node degree-prior connections, which can find “hub” firms and simultaneously estimate the robustness of supply networks using a sequential elimination choice strategy of these hubs.

[1]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Giacomo Becattini,et al.  Industrial Sectors and Industrial Districts: Tools for Industrial Analysis , 2002 .

[3]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Thomas Y. Choi,et al.  Structural investigation of supply networks: A social network analysis approach , 2011 .

[5]  M. Newman Communities, modules and large-scale structure in networks , 2011, Nature Physics.

[6]  Svetha Venkatesh,et al.  Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection? , 2016, Heliyon.

[7]  Ichiro Sakata,et al.  Identifying and bridging networks in regional clusters , 2012 .

[8]  Ichiro Sakata,et al.  An analysis of geographical agglomeration and modularized industrial networks in a regional cluster: A case study at Yamagata prefecture in Japan , 2008 .

[9]  Ichiro Sakata,et al.  Machine learning approach for finding business partners and building reciprocal relationships , 2012, Expert Syst. Appl..

[10]  Ichiro Sakata,et al.  Multiscale analysis of interfirm networks in regional clusters , 2010 .

[11]  Marcus A. Bellamy,et al.  The influence of supply network structure on firm innovation , 2014 .

[12]  Yuya Kajikawa,et al.  Extraction of business relationships in supply networks using statistical learning theory , 2016, Heliyon.

[13]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[14]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[15]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Chin-Huang Lin,et al.  Elucidating the industrial cluster effect from a system dynamics perspective , 2006 .

[17]  M. Imase,et al.  Application of Network Analysis Techniques for Japanese Corporate Transaction Network , 2005, 6th Asia-Pacific Symposium on Information and Telecommunication Technologies.