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Financial Risk Management for Designing Multi-echelon Supply Chain Networks Under Demand Uncertainty

Received: 20 December 2015     Published: 21 December 2015
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Abstract

This paper presents a methodology to include financial risk management for the design of multiproduct, multi-echelon supply chain networks under uncertainty. The method is in the framework of two-stage stochastic programming. Definitions of financial risk and downside risk are adapted. Using these definitions, financial risk management constraints are introduced and a new two-stage stochastic programming model is established. Case studies illustrate the applicability of such financial risk management. Trade-offs between expected cost and risk are also analyzed.

Published in Automation, Control and Intelligent Systems (Volume 3, Issue 6)
DOI 10.11648/j.acis.20150306.13
Page(s) 112-117
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), 2015. Published by Science Publishing Group

Keywords

Supply Chain, Financial Risk Management, Downside Risk, Uncertainty

References
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[2] P. Tsiakis, N. Shah, C. C. Pantelides, Design of multi-echelon supply chain networks under demand uncertainty. Industrial & Engineering Chemistry Research, 2001, 40 (16): 3585-3604.
[3] T. Santoso, S. Ahmed, M. Goetschalckx, A. Shapiro, A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 2005, 167 (1): 96-115.
[4] A. M. Geoffrion, G. W. Graves, Multi-commodity distribution system design by benders decomposition. Management Science, 1974, 20: 822-844.
[5] C. H. Aikens, facility location models for distribution planning. European Journal of Operational Research, 1985, 22 (3): 263-279.
[6] C. J. Vidal, M. Goetschalckx, Strategic production-distribution models: A critical review with emphasis on global supply chain models. European Journal of Operational Research, 1997, 98 (1): 1-18.
[7] T. Assavapokee, W. Wongthatsanekorn, Reverse production system infrastructure design for electronic products in the state of Texas. Computers & Industrial Engineering, 2012, 62(1): 129-140.
[8] D. C. Cafaro, I. E. Grossmann, Strategic planning, design, and development of the shale gas supply chain network. AIChE Journal, 2014, 60(6): 2122-2142.
[9] M. A. Kalaitzidou, P. Longinidis, P. Tsiakis, M. C. Georgiadis, Optimal Design of Multiechelon Supply Chain Networks with Generalized Production and Warehousing Nodes. Industrial & Engineering Chemistry Research, 2014, 53(33): 13125-13138.
[10] I. Heckmann, T. Comes, S. Nickel, A critical review on supply chain risk–Definition, measure and modeling. Omega, 2015, 52: 119-132.
[11] M. Talaei, B. F. Moghaddam, M. S. Pishvaee, A. Bozorgi-Amiri, S. Gholamnejad, A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of Cleaner Production, 2015.
[12] G. Cairns, P. Goodwin, G. Wright, A decision-analysis-based framework for analysing stakeholder behaviour in scenario planning. European Journal of Operational Research, 2016, 249(3): 1050-1062.
[13] A. Barbaro, M. J. Bagajewicz, Managing financial risk in planning under uncertainty. AIChE Journal, 2004, 50 (5): 963-989.
[14] F. You, J. M. Wassick, I. E. Grossmann, Risk management for a global supply chain planing uncder uncertainty: modes and algorithms. AIChE Journal, 2009, 55: 931-946.
[15] S. R. Cardoso, A. P. Barbosa-Póvoa, S. Relvas. Integrating Financial Risk Measures into the Design and Planning of Closed-loop Supply Chains. Computers & Chemical Engineering, 2016, 85: 105-123.
Cite This Article
  • APA Style

    De Gu, Jishuai Wang. (2015). Financial Risk Management for Designing Multi-echelon Supply Chain Networks Under Demand Uncertainty. Automation, Control and Intelligent Systems, 3(6), 112-117. https://doi.org/10.11648/j.acis.20150306.13

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    ACS Style

    De Gu; Jishuai Wang. Financial Risk Management for Designing Multi-echelon Supply Chain Networks Under Demand Uncertainty. Autom. Control Intell. Syst. 2015, 3(6), 112-117. doi: 10.11648/j.acis.20150306.13

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    AMA Style

    De Gu, Jishuai Wang. Financial Risk Management for Designing Multi-echelon Supply Chain Networks Under Demand Uncertainty. Autom Control Intell Syst. 2015;3(6):112-117. doi: 10.11648/j.acis.20150306.13

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  • @article{10.11648/j.acis.20150306.13,
      author = {De Gu and Jishuai Wang},
      title = {Financial Risk Management for Designing Multi-echelon Supply Chain Networks Under Demand Uncertainty},
      journal = {Automation, Control and Intelligent Systems},
      volume = {3},
      number = {6},
      pages = {112-117},
      doi = {10.11648/j.acis.20150306.13},
      url = {https://doi.org/10.11648/j.acis.20150306.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20150306.13},
      abstract = {This paper presents a methodology to include financial risk management for the design of multiproduct, multi-echelon supply chain networks under uncertainty. The method is in the framework of two-stage stochastic programming. Definitions of financial risk and downside risk are adapted. Using these definitions, financial risk management constraints are introduced and a new two-stage stochastic programming model is established. Case studies illustrate the applicability of such financial risk management. Trade-offs between expected cost and risk are also analyzed.},
     year = {2015}
    }
    

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    T1  - Financial Risk Management for Designing Multi-echelon Supply Chain Networks Under Demand Uncertainty
    AU  - De Gu
    AU  - Jishuai Wang
    Y1  - 2015/12/21
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    T2  - Automation, Control and Intelligent Systems
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    AB  - This paper presents a methodology to include financial risk management for the design of multiproduct, multi-echelon supply chain networks under uncertainty. The method is in the framework of two-stage stochastic programming. Definitions of financial risk and downside risk are adapted. Using these definitions, financial risk management constraints are introduced and a new two-stage stochastic programming model is established. Case studies illustrate the applicability of such financial risk management. Trade-offs between expected cost and risk are also analyzed.
    VL  - 3
    IS  - 6
    ER  - 

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Author Information
  • Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, China

  • Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China

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