International Journal of Economics, Finance and Management Sciences

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The Statistical Feature Analysis and Simulation Study of Supply Chain Based on Fixed Spread Risk Probability

Received: 08 October 2013    Accepted:     Published: 10 November 2013
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Abstract

Understanding supply chain network are important for modeling the spread of risks in enterprise nodes. This study characterizes the supply chain risk network of the spread of several nodes. To identify the rule of the movement of risk nodes, several parameters describing these properties are measured (degree, risk, the number of risk nodes, average risk, average path length and average clustering). The simulation results indicate: (1) this risk network has small-world and scale-free property; (2) the basic topological characteristics on static network displayed a regular change; (3) the characteristics of the spread of risk is measured by risk distribution which obeys a double power law and average risk which has a negative correlation with the number of risk node. In summation, this paper tries to analyze the risk spread of several nodes in supply chain network from macroscopic perspective.

DOI 10.11648/j.ijefm.20130106.18
Published in International Journal of Economics, Finance and Management Sciences (Volume 1, Issue 6, December 2013)
Page(s) 318-322
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Supply Chain, Complex Network, Risk Spread, Fixed Probability, Degree Distribution, Risk Distribution, Average Path Length, Average Clustering

References
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[3] Bollobas B, Riordan O. Robustness and vulnerability of scale-free random graphs[J]. InternetMath, 2003, 1: 1-35.
[4] A.E.Motter. Cascade Control and defense in Complex Networks[J].Phys Rev Lett,2004,93: 098701.
[5] Christopher M, Peck H. Building the resilient supply chain[J]. International Journal of Logistics Management, The, 2004, 15(2): 1-14.
[6] Klibi W, Martel A, Guitouni A. The design of robust value-creating supply chain networks: a critical review[J]. European Journal of Operational Research, 2010, 203(2): 283-293.
[7] Vahdani B, Tavakkoli-Moghaddam R, Modarres M, et al. Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model[J]. Transportation Research Part E: Logistics and Transportation Review, 2012, 48(6): 1152-1168.
[8] Longinidis P, Georgiadis M C. Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty[J]. Computers & Chemical Engineering, 2012.
[9] Klibi W, Martel A. Scenario-based supply chain network risk modeling[J]. European Journal of Operational Research, 2012.
[10] Tang C S. Robust strategies for mitigating supply chain disruptions[J]. International Journal of Logistics: Research and Applications, 2006, 9(1): 33-45.
[11] Pishvaee M S, Rabbani M, Torabi S A. A robust optimization approach to closed-loop supply chain network design under uncertainty[J]. Applied Mathematical Modelling, 2011, 35(2): 637-649.
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[14] Schönlein M, Makuschewitz T, Wirth F, et al. Measurement and optimization of robust stability of multiclass queueing networks: Applications in dynamic supply chains[J]. European Journal of Operational Research, 2013.
Author Information
  • Department of Economics and Management, North China Electric Power University, Baoding 071003, China

  • Department of Economics and Management, North China Electric Power University, Baoding 071003, China

  • Department of Economics and Management, North China Electric Power University, Baoding 071003, China

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  • APA Style

    Lei Wen, Mingfang Guo, Yachao Shi. (2013). The Statistical Feature Analysis and Simulation Study of Supply Chain Based on Fixed Spread Risk Probability. International Journal of Economics, Finance and Management Sciences, 1(6), 318-322. https://doi.org/10.11648/j.ijefm.20130106.18

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    ACS Style

    Lei Wen; Mingfang Guo; Yachao Shi. The Statistical Feature Analysis and Simulation Study of Supply Chain Based on Fixed Spread Risk Probability. Int. J. Econ. Finance Manag. Sci. 2013, 1(6), 318-322. doi: 10.11648/j.ijefm.20130106.18

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    AMA Style

    Lei Wen, Mingfang Guo, Yachao Shi. The Statistical Feature Analysis and Simulation Study of Supply Chain Based on Fixed Spread Risk Probability. Int J Econ Finance Manag Sci. 2013;1(6):318-322. doi: 10.11648/j.ijefm.20130106.18

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  • @article{10.11648/j.ijefm.20130106.18,
      author = {Lei Wen and Mingfang Guo and Yachao Shi},
      title = {The Statistical Feature Analysis and Simulation Study of Supply Chain Based on Fixed Spread Risk Probability},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {1},
      number = {6},
      pages = {318-322},
      doi = {10.11648/j.ijefm.20130106.18},
      url = {https://doi.org/10.11648/j.ijefm.20130106.18},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijefm.20130106.18},
      abstract = {Understanding supply chain network are important for modeling the spread of risks in enterprise nodes. This study characterizes the supply chain risk network of the spread of several nodes. To identify the rule of the movement of risk nodes, several parameters describing these properties are measured (degree, risk, the number of risk nodes, average risk, average path length and average clustering). The simulation results indicate: (1) this risk network has small-world and scale-free property; (2) the basic topological characteristics on static network displayed a regular change; (3) the characteristics of the spread of risk is measured by risk distribution which obeys a double power law and average risk which has a negative correlation with the number of risk node. In summation, this paper tries to analyze the risk spread of several nodes in supply chain network from macroscopic perspective.},
     year = {2013}
    }
    

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    T1  - The Statistical Feature Analysis and Simulation Study of Supply Chain Based on Fixed Spread Risk Probability
    AU  - Lei Wen
    AU  - Mingfang Guo
    AU  - Yachao Shi
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    DO  - 10.11648/j.ijefm.20130106.18
    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
    SP  - 318
    EP  - 322
    PB  - Science Publishing Group
    SN  - 2326-9561
    UR  - https://doi.org/10.11648/j.ijefm.20130106.18
    AB  - Understanding supply chain network are important for modeling the spread of risks in enterprise nodes. This study characterizes the supply chain risk network of the spread of several nodes. To identify the rule of the movement of risk nodes, several parameters describing these properties are measured (degree, risk, the number of risk nodes, average risk, average path length and average clustering). The simulation results indicate: (1) this risk network has small-world and scale-free property; (2) the basic topological characteristics on static network displayed a regular change; (3) the characteristics of the spread of risk is measured by risk distribution which obeys a double power law and average risk which has a negative correlation with the number of risk node. In summation, this paper tries to analyze the risk spread of several nodes in supply chain network from macroscopic perspective.
    VL  - 1
    IS  - 6
    ER  - 

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