IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/25126.html
   My bibliography  Save this paper

Modeling Overstock

Author

Listed:
  • Fernandes, Rui
  • Gouveia, Borges
  • Pinho, Carlos

Abstract

Two main problems have been emerging in supply chain management: the increasing pressure to reduce working capital and the growing variety of products. Most of the popular indicators have been developed based on a controlled environment. A new indicator is now proposed, based on the uncertainty of the demand, the flexibility of the supply chains, the evolution of the products lifecycle and the fulfillment of a required service level. The model to support the indicator will be developed within the real options approach.

Suggested Citation

  • Fernandes, Rui & Gouveia, Borges & Pinho, Carlos, 2010. "Modeling Overstock," MPRA Paper 25126, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:25126
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/25126/1/MPRA_paper_25126.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Lusa, Amaia & Corominas, Albert & Muñoz, Norberto, 2008. "A multistage scenario optimisation procedure to plan annualised working hours under demand uncertainty," International Journal of Production Economics, Elsevier, vol. 113(2), pages 957-968, June.
    2. Mukhopadhyay, Samar K. & Ma, Huafan, 2009. "Joint procurement and production decisions in remanufacturing under quality and demand uncertainty," International Journal of Production Economics, Elsevier, vol. 120(1), pages 5-17, July.
    3. Pindyck, Robert S, 1988. "Irreversible Investment, Capacity Choice, and the Value of the Firm," American Economic Review, American Economic Association, vol. 78(5), pages 969-985, December.
    4. Stephen C. Graves & Sean P. Willems, 2000. "Optimizing Strategic Safety Stock Placement in Supply Chains," Manufacturing & Service Operations Management, INFORMS, vol. 2(1), pages 68-83, June.
    5. Hemmelmayr, Vera & Doerner, Karl F. & Hartl, Richard F. & Savelsbergh, Martin W.P., 2010. "Vendor managed inventory for environments with stochastic product usage," European Journal of Operational Research, Elsevier, vol. 202(3), pages 686-695, May.
    6. L. Wade, 1988. "Review," Public Choice, Springer, vol. 58(1), pages 99-100, July.
    7. Handfield, Robert & Warsing, Don & Wu, Xinmin, 2009. "(Q,r) Inventory policies in a fuzzy uncertain supply chain environment," European Journal of Operational Research, Elsevier, vol. 197(2), pages 609-619, September.
    8. Ryu, Seung-Jin & Tsukishima, Takahiro & Onari, Hisashi, 2009. "A study on evaluation of demand information-sharing methods in supply chain," International Journal of Production Economics, Elsevier, vol. 120(1), pages 162-175, July.
    9. Graman, Gregory A., 2010. "A partial-postponement decision cost model," European Journal of Operational Research, Elsevier, vol. 201(1), pages 34-44, February.
    10. Matuyama, Keisuke & Sumita, Tomofumi & Wakayama, Daiki, 2009. "Periodic forecast and feedback to maintain target inventory level," International Journal of Production Economics, Elsevier, vol. 118(1), pages 298-304, March.
    11. Borgonovo, E. & Peccati, L., 2007. "Global sensitivity analysis in inventory management," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 302-313, July.
    12. Sodhi, ManMohan S. & Tang, Christopher S., 2009. "Modeling supply-chain planning under demand uncertainty using stochastic programming: A survey motivated by asset-liability management," International Journal of Production Economics, Elsevier, vol. 121(2), pages 728-738, October.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    overstock; stock management; real options;

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:25126. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter) or (Rebekah McClure). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.