JRM Vol.31 No.4 pp. 546-557
doi: 10.20965/jrm.2019.p0546


Centralized Business-to-Business Networks in the Japanese Textile and Apparel Industry: Using Network Analysis and an Agent-Based Model

Yusaku Ogai*1, Yoshiyuki Matsumura*1, Yusuke Hoshino*2, Toshiyuki Yasuda*3, and Kazuhiro Ohkura*4

*1Shinshu University
3-15-1 Tokida, Ueda, Nagano 386-8567, Japan

*2Musashino University
3-3-3 Ariake, Koto, Tokyo 135-8181, Japan

*3University of Toyama
3190 Gofuku, Toyama 930-8555, Japan

*4Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan

February 21, 2019
April 26, 2019
August 20, 2019
Business-to-Business (B2B) networks, innovation of ICT and logistics technology, complex systems, scale-free networks, agent-based modeling

This study deals with the estimation of the changes that occur in the Business-to-Business (B2B) networks in the Japanese textile and apparel industry by applying datasets of about 2000 companies from 2011/2012 to 2015/2016. Network analysis was used to examine the properties of the B2B networks. A factor of innovation in information and communications technology (ICT) and logistics technology was introduced into an agent-based model to demonstrate changes occurring in the related structures of B2B networks. The agent-based model was designed and tested based on qualitative information on Japanese textile and apparel industries. Consequently, network analysis revealed power-law properties and the structures of centralized hub companies. Moreover, in the simulation experiments, the centralizations of the networks generated by the agent-based model due to innovation in ICT and logistics technology were illustrated. Therefore, one of the predicted cases regarding changes that occur in the B2B networks was explained as centralizations to hub companies.

Changes of B2B networks demonstrated by an agent-based model

Changes of B2B networks demonstrated by an agent-based model

Cite this article as:
Y. Ogai, Y. Matsumura, Y. Hoshino, T. Yasuda, and K. Ohkura, “Centralized Business-to-Business Networks in the Japanese Textile and Apparel Industry: Using Network Analysis and an Agent-Based Model,” J. Robot. Mechatron., Vol.31 No.4, pp. 546-557, 2019.
Data files:
  1. [1] M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” Swarm Intelligence, Vol.7, No.1, pp. 1-41, 2013.
  2. [2] R. Albert and A.-L. Barabási, “Statistical mechanics of complex networks,” Reviews of Modern Physics, Vol.74, No.1, pp. 47-97, 2002.
  3. [3] A.-L. Barabási and E. Bonabeau, “Scale-Free Networks,” Scientific American, Vol.288, No.5, pp. 60-69, 2003.
  4. [4] D. J. Watts and S. H. Strogatz, “Collective dynamics of ’small-world’ networks,” Nature, Vol.393, No.4, pp. 440-442, 1998.
  5. [5] A. Mandes and P. Winler, “Complexity and model comparison in agent based modeling of financial markets,” J. Econ. Interact Coord., Vol.12, Issue 3, pp. 469-506, 2017.
  6. [6] N. Gilbert and K. G. Troitzsch, “Simulation for the social scientist,” Open University Press, 2nd edition, 1999.
  7. [7] T. C. Schelling, “Dynamic models of segregation,” J. of Mathematical Sociology, Vol.1, pp. 143-186, 1971.
  8. [8] A. Gordon, “The Evolution of Labor Relations in Japan: Heavy Industry, 1853-1955,” Harvard University Asia Center, Harvard University, 1985.
  9. [9] H. Yamawaki, “The Evolution and Structure of Industrial Cluster in Japan,” Small Business Economics, Vol.18, Issue 1-3, pp. 121-140, 2002.
  10. [10] P. L. Robertson and R. N. Langlois, “Innovation, Networks, and Vertical Integration,” Forthcoming in Research Policy, Vol.24, No.4, pp. 543-562, 1994.
  11. [11] T. Moyaux, B. Chaib-draa, and S. D’Amours, “Supply Chain Management and Multi agent Systems: An Overview,” B. Chaib-draa and J. P. Müller (Eds.), “Multiagent based Supply Chain Management,” pp. 1-27, Springer, 2006.
  12. [12] D. Turker and C. Altuntas, “Sustainable supply chain management in the fast fashion industry: An analysis of corporate reports,” European Management J., Vol.32, No.1, pp. 837-849, 2014.
  13. [13] S. W. Kim, “Organizational structures and the performance of supply chain management,” Int. J. Production Economics, Vol.106, No.2, pp. 323-345, 2007.
  14. [14] T. Urakami, “Specialty store strategy within Japanese apparel wholesalers: and empirical analysis,” J. of Fashion Marketing and Management: An Int. J., Vol.14, No.4, pp. 634-647, 2010.
  15. [15] Senisangyo kouzoukaizen jigyoukyoukai (The official institution of Japanese textile and apparel industry), “Apparel sangyou gairon (The introduction of apparel industry),” Senisangyo kouzoukaizen jigyoukyoukai, 1996.
  16. [16] M. Bruce, L. Daly, and N. Towers, “Lean or agile: A solution for supply chain management in the textiles and clothing industry?,” Textiles and Pear Science, Vol.24, No.2, pp. 151-170, 2004.
  17. [17] Senken-shimbun company, “Fashion Business Data Bank,” 2011/2012, 2013/2014, 2015/2016 (in Japanese).
  18. [18] C. W. Reynolds, “Flocks, Herds and Schools: A Distributed Behavioral Model,” Computer Graphics, Vol.21, No.4, pp. 25-34, 1987.
  19. [19] J. J. Lewer and H. Van den Berg, “A gravity model of immigration,” Economics Letters, Vol.99, No.1, pp. 164-167, 2008.
  20. [20] X. Wang, H. Li, H. Yao, Z. Chen, and Q. Guan, “Network feature and influence factors of global nature graphite trade competition,” Resources Policy, Vol.60, pp. 153-161, 2019.
  21. [21] P. Kaluza, A. Kölzsch, M. T. Gastner, and B. Blasius, “The complex network of global cargo ship movements,” J. of the Royal Society Interface, Vol.7, No.48, pp. 1093-1103, 2010.
  22. [22] A. Allard, M. Ángeles Serrano, G. Garcia-Perez, and M. Boguna, “The geometric nature of weights in real complex networks,” Nature Communications, Vol.8, 14103, pp. 1-8, 2017.
  23. [23] H. Ahn and J.-H. Park, “The structural effects of sharing function on Twitter networks: Focusing on the retweet function,” J. of Information Science, Vol.41, No.3, pp. 354-365, 2015.
  24. [24] M. Del Vicario, Q. Zhang, A. Bessi, F. Zollo, A. Scala, G. Caldarelli, and W. Quattrociocchi, “Structural Patterns of the Occupy Movement on Facebook,” Association for the Advancement of Artificial Intelligence, Vol.1501, 07203, pp. 1-8, 2015.
  25. [25] A. Fabrikant, E. Koutsoupias, and C. Papadimitriou, “Heuristically optimized trade-offs: a new paradigm for power laws in the internet,” Proc. of the 29th Int. Colloquium on Automata, Languages, and Programming (ICALP), pp. 110-122, 2002.
  26. [26] R. M. D’Souza, C. Borgs, J. T. Chayes, N. Berger, and R. D. Kleinberg, “Emergence of tempered preferential attachment from optimization,” Proc. of the National Academy of Sciences April 2007, Vol.104, No.15, pp. 6112-6117, 2007.

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