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JACIII Vol.22 No.6 pp. 933-942
doi: 10.20965/jaciii.2018.p0933
(2018)

Paper:

Effects of Trade Environment in Decentralized Inter-Organizational Business Structures Through Agent Simulation

Taisei Mukai and Takao Terano

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology
4259 Nagatsuda-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

Received:
November 21, 2017
Accepted:
July 23, 2018
Published:
October 20, 2018
Keywords:
computational intelligence, agent simulation, inter-firm network, supply network
Abstract

The objective of this research is to investigate the features of an inter-firm trade structure model. We have developed this model through agent-based simulation. In order to adapt to rapid changes in the business market, each firm must deal with changes in requirements and the required volume changes. Thus, the model has two combined functions. The first function allows a firm to freely trade with other firms as a decentralized trade structure. The second function allows a firm to mediate other firms as a centralized trade structure. The combined model has worked well in various test cases so far. However, other trading environments have not been fully investigated. Therefore, this study focuses on the detailed environmental conditions: (1) the number of similar firms and (2) the number of procurement items, and deals with intensive experiments and detailed analysis of the two functions of the model in the agent-based simulations. The simulation results suggest that the two functions in the combined model work particularly well under the conditions of a trading environment in which there exist both (1) a large number of similar business firms and (2) firms with a certain number of procurement items, compared to the decentralized trade structure, where each firm’s size is neither particularly large nor small.

The decentralized inter-firm trade model with the centralized structures

The decentralized inter-firm trade model with the centralized structures

Cite this article as:
T. Mukai and T. Terano, “Effects of Trade Environment in Decentralized Inter-Organizational Business Structures Through Agent Simulation,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.6, pp. 933-942, 2018.
Data files:
References
  1. [1] W. Davidow and M. S. Malone, “The virtual corporation: Structuring and revitalizing the corporation for the 21st century,” Harpercollins, 1992.
  2. [2] I. Kaneko and K. Imai, “A network view of the firm,” Hitotsubashi-Stanford, 1987.
  3. [3] T. Mukai and T. Terano, “Modeling decentralized inter-organizational business structures through agent-based simulation,” World Automation Congress (WAC), pp. 1-8, 2016.
  4. [4] T. W. Malone, “Modeling coordination in organizations and markets,” Management Scince, Vol.33, No.10, pp. 1317-1332, 1987.
  5. [5] M. Wang, J. Liu, H. Wang, W. K. Cheung, and X. Xie, “On-demand e-supply chain integration: A multi-agent constraint-based approach,” Expert Systems with applications, Vol.34, No.4, pp. 2683-2692, 2008.
  6. [6] B. Behdani, K. H. van Dam, and Z. Lukszo, “Agent-Based Models of Supply Chains,” Agent-Based Modelling of Socio-Technical Systems, pp. 151-180, 2013.
  7. [7] Y. Tanimizu, Y. Shimizu, K. Iwamura, and N. Sugimura, “Modeling and simulation of closed-loop supply chains considering economic efficiency,” IFIP Int. Conf. on Advances in Production Management Systems, pp. 461-468, 2013.
  8. [8] M. C. Chou, G. A. Chua, C.-P. Teo, and H. Zheng, “Design for process flexibility: Efficiency of the long chain and sparse structure,” Operations research, Vol.58, No.1, pp. 43-58, 2010.
  9. [9] S. Iravani, M. P. Van Oyen, and K. T. Sims, “Structural flexibility: A new perspective on the design of manufacturing and service operations,” Management Scince, Vo.51, No.2, pp. 151-166, 2005.
  10. [10] W. Miura, H. Takayasu, and M. Takayasu, “Effect of Coagulation of Nodes in an Evolving Complex Network,” Physical Review Letters, Vol.108, No.16, pp. 1-4, 2012.
  11. [11] H. Goto, E. Viegas, H. J. Jensen, H. Takayasu, and M. Takayasu, “Appearance of Unstable Monopoly State Caused by Selective and Concentrative Mergers in Business Networks,” Scientific Reports, Vol.7, No.1, Article No.5064, 2017.
  12. [12] H. Chesbrough, “The era of open innovation,” MIT Sloan Management Review, Vol.44, No.3, 2003.
  13. [13] T. Araújo and R. V. Mendes, “Innovation and self-organization in a multi-agent model,” Advances in Complex Systems, Vol.12, No.2, pp. 233-253, 2009.
  14. [14] J. Gu and Y. Chen, “Simulation and Experimental Study of Knowledge Management in Organization,” Developments in Business Simulation and Experiential Learning, Vol.42, pp. 269-279, 2015.
  15. [15] B. Pátkai, “Analogy Based Methodology for Complex Adaptive Production Network Modelling,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.4, pp. 399-408, 2005.
  16. [16] H. L. Lee, “The triple-A supply chain,” Harvard business review, Vol.82, No.10, pp.102-113, 2004.
  17. [17] Y. Kim, T. Y. Choi, T. Yan, and K. Dooley, “Structural investigation of supply networks: A social network analysis approach,” J. of Operations Management, Vol.29, No.3, pp. 194-211, 2011.
  18. [18] J. A. C. Baum, T. J. Rowley, A. V. Shipilov, and Y.-T. Chuang, “Dancing with strangers: Aspiration performance and the search for underwriting syndicate partners,” Administrative Science Quarterly, Vol.50, No.4, pp. 536-575, 2005.
  19. [19] T. Choi and Y. Hong, “Unveiling the structure of supply networks: case studies in Honda, Acura, and DaimlerChrysler,” J. of Operations Management, Vol.20, No.5, pp. 469-493, 2002.
  20. [20] M. Ryall and O. Sorenson, “Brokers and competitive advantage,” Management Scince, Vol.53, No.4, pp. 566-583, 2007.
  21. [21] D. J. Teece, G. Pisano, and A. Shuen, “Dynamic capabilities and strategic management,” Strategic Management J., Vol.18, No.7, pp. 509-533, 1997.
  22. [22] I. Okada and T. Ohta, “Psychological Personality and Organizational Performance with MAS Simulation,” Agent-based Approaches in Economic and Social Complex Systems, pp. 35-46, 2002.
  23. [23] K. Akagi, T. Ohsato, and H. Deguchi, “Input-output table constructed with private business data and its algebraic description,” IEEE/SICE Int. Symp. on System Integration (SII), pp. 339-344, 2015.
  24. [24] J. A. Durlak, “How to Select, Calculate, and Interpret Effect Sizes,” J. of Pediatric Psychology, Vol 34, No.9, pp. 917-928, 2009

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