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An Enterprise Risk Management Model for Supply Chains

Part of the Springer Optimization and Its Applications book series (SOIA,volume 30)

Summary

The design of an optimal supply chain rarely considers uncertainty within the modeling framework. This omission is due to several factors, including tradition, model size, and the difficulty in measuring the stochastic parameters. We show that a stochastic program provides an ideal framework for optimizing a large supply chain in the face of an uncertain future. The goal is to reduce disruptions and to minimize expected costs under a set of plausible scenarios. We illustrate the methodology with a global production problem possessing currency movements.

Keywords

  • Supply Chain
  • Stochastic Program
  • Debt Ratio
  • Exchange Rate Movement
  • Payout Ratio

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-0-387-88617-6_5
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Refereneces

  1. Dempster MAH, Thompson RT (1998) Parallellization and aggregation of nested Benders decomposition. Annals of Operations Research 81:163–187

    MATH  CrossRef  MathSciNet  Google Scholar 

  2. Dye S, Tomasgard A, Wallace SW (2000) Two-stage service provision by branch and bound. 35th ORSNZ Conference 221/7

    Google Scholar 

  3. Geoffrion AM (1969) An improved implicit enumeration approach for integer programming. Operations Research 17:437–454

    MATH  CrossRef  Google Scholar 

  4. Goetschalckx M, Vidal CJ, Dogan K (2002) Modeling and design of global logistics systems: a review of integrated strategic and tactical models and design algorithms. European Journal of Operational Research 143: 1-18

    MATH  CrossRef  Google Scholar 

  5. Higle JL, Rayco B, Sen S (2006) Stochastic scenario decomposition for multistage stochastic programs. To appear in Operations Research

    Google Scholar 

  6. Higle JL, Sen S (1996) Stochastic decomposition: a statistical method for large scale stochastic linear programming. Kluwer Academic Publishers, Dordrecht

    MATH  Google Scholar 

  7. Jarrow R, Purnanandam A (2004) The valuation of a firm’s investment opportunities: a reduced form credit risk perspective. Working paper, Cornell University

    Google Scholar 

  8. Mulvey JM, Erkan HG (2005) Decentralized risk management for global property and casulty insurance companies. In: Wallace SW, Ziemba WT (eds) Applications of Stochastic Programming. SIAM Mathematical Series on Optimization 503-530

    Google Scholar 

  9. Mulvey JM, Erkan HG (2006) Applying CVaR for decentralized risk management of financial companies. Journal of Banking and Finance 30:627–644

    CrossRef  Google Scholar 

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Correspondence to John M. Mulvey .

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© 2009 Springer-Verlag US

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Mulvey, J.M., Erkan, H.G. (2009). An Enterprise Risk Management Model for Supply Chains. In: Chaovalitwongse, W., Furman, K., Pardalos, P. (eds) Optimization and Logistics Challenges in the Enterprise. Springer Optimization and Its Applications, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-88617-6_5

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