Skip to main content
Log in

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

  • Article
  • Published:
The Review of Socionetwork Strategies Aims and scope Submit manuscript

Abstract

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—\(G=\{V, E \}\) 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. Tokyo Shoko Research, Ltd., http://www.tsr-net.co.jp/en.

  2. The firework-like network charts are drawn by an open source network analysis and visualization software—Gephi. https://gephi.github.io/.

References

  1. Barabasi, A. L., & Bonabeau, E. (2003). Scale-free networks. Scientific American, 288(5), 60–69.

    Article  Google Scholar 

  2. Becattini, G. (2002). Industrial sectors and industrial districts: Tools for industrial analysis. European Planning Studies, 10(4), 483–493.

    Article  Google Scholar 

  3. Bellamy, M. A., Ghosh, S., & Hora, M. (2014). The influence of supply network structure on firm innovation. Journal of Operations Management, 32(6), 357–373.

    Article  Google Scholar 

  4. Clauset, A., Newman, M. E. J., & Moore, C. (2005). Finding community structure in very large networks. Physical Review E, 70, 066111.

    Article  Google Scholar 

  5. Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821–7826.

    Article  Google Scholar 

  6. Kajikawa, Y., Mori, J., & Sakata, I. (2012). Identifying and bridging networks in regional clusters. Technological Forecasting and Social Change, 79(2), 252–262.

    Article  Google Scholar 

  7. Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2010). Multiscale analysis of interfirm networks in regional clusters. Technovation, 30(3), 168–180.

    Article  Google Scholar 

  8. Kim, Y., Choi, T. Y., Yan, T., & Dooley, K. (2011). Structural investigation of supply networks: A social network analysis approach. Journal of Operations Management, 29(3), 194–211.

    Article  Google Scholar 

  9. Lin, C. H., Tung, C. M., & Huang, C. T. (2006). Elucidating the industrial cluster effect from a system dynamics perspective. Technovation, 26(4), 473–482.

    Article  Google Scholar 

  10. Mori, J., Kajikawa, Y., Kashima, H., & Sakata, I. (2012). Machine learning approach for finding business partners and building reciprocal relationships. Expert Systems with Applications, 39(12), 10402–10407.

    Article  Google Scholar 

  11. Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69, 066133.

    Article  Google Scholar 

  12. Newman, M. E. J. (2012). Communities, modules and large-scale structure in networks. Nature Physics, 8(1), 25–31.

    Article  Google Scholar 

  13. Sugiyama, K., Honda, O., Ohsaki, H., Imase, M.: Application of network analysis techniques for Japanese corporate transaction network. In: Proceedings of The 6th Asia-Pacific Symposium on Information and Telecommunication Technologies. pp. 387–392 (2005)

  14. Takeda, Y., Kajikawa, Y., Sakata, I., & Matsushima, K. (2008). An analysis of geographical agglomeration and modularized industrial networks in a regional cluster: A case study at Yamagata prefecture in Japan. Technovation, 28(8), 531–539.

    Article  Google Scholar 

  15. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ’small-world’ networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  16. Zuo, Y., Kajikawa, Y., & Mori, J. (2016). Extraction of business relationships in supply networks using statistical learning theory. Heliyon, 2(6), 1–25.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yi Zuo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zuo, Y., Kajikawa, Y. An Exploratory Look at Supply Chains in Japan from Multiscale Network Perspectives. Rev Socionetwork Strat 11, 111–128 (2017). https://doi.org/10.1007/s12626-017-0009-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12626-017-0009-y

Keywords

Navigation