IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v34y2004i3p191-205.html
   My bibliography  Save this article

Inventory Decisions in Dell's Supply Chain

Author

Listed:
  • Roman Kapuscinski

    (University of Michigan Business School, Ann Arbor, Michigan 48109)

  • Rachel Q. Zhang

    (Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853)

  • Paul Carbonneau

    (McKinsey & Company, 3 Landmark Square, Stamford, Connecticut 06901)

  • Robert Moore

    (Dell Inc., Mail Stop 6363, Austin, Texas 78682)

  • Bill Reeves

    (Dell Inc., Mail Stop 6363, Austin, Texas 78682)

Abstract

The Tauber Manufacturing Institute (TMI) is a partnership between the engineering and business schools at the University of Michigan. In the summer of 1999, a TMI team spent 14 weeks at Dell Inc. in Austin, Texas, and developed an inventory model to identify inventory drivers and quantify target levels for inventory in the final stage of Dell's supply chain, the revolvers or supplier logistics centers (SLC). With the information and analysis provided by this model, Dell's regional materials organizations could tactically manage revolver inventory while Dell's worldwide commodity management could partner with suppliers in improvement projects to identify inventory drivers and to reduce inventory. Dell also initiated a pilot program for procurement of XDX (a disguised name for one of the major components of personal computers (PCs)) in the United States to institutionalize the model and promote partnership with suppliers. Based on the model predictions, Dell launched e-commerce and manufacturing initiatives with its suppliers to lower supply-chain-inventory costs by reducing revolver inventory by 40 percent. This reduction would raise the corresponding inventory turns by 67 percent. Net Present Value (NPV) calculations for XDX alone suggest $43 million in potential savings. To ensure project longevity, Dell formed the supply-chain-optimization team and charged it with incorporating the model into a strategic redesign of Dell's business practices and supervising improvement projects the model identified.

Suggested Citation

  • Roman Kapuscinski & Rachel Q. Zhang & Paul Carbonneau & Robert Moore & Bill Reeves, 2004. "Inventory Decisions in Dell's Supply Chain," Interfaces, INFORMS, vol. 34(3), pages 191-205, June.
  • Handle: RePEc:inm:orinte:v:34:y:2004:i:3:p:191-205
    DOI: 10.1287/inte.1030.0068
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.1030.0068
    Download Restriction: no

    File URL: https://libkey.io/10.1287/inte.1030.0068?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Erica L. Plambeck, 2008. "Asymptotically Optimal Control for an Assemble-to-Order System with Capacitated Component Production and Fixed Transport Costs," Operations Research, INFORMS, vol. 56(5), pages 1158-1171, October.
    2. Lin, Junyi & Naim, Mohamed M. & Spiegler, Virginia L.M., 2020. "Delivery time dynamics in an assemble-to-order inventory and order based production control system," International Journal of Production Economics, Elsevier, vol. 223(C).
    3. ElHafsi, Mohsen & Fang, Jianxin & Hamouda, Essia, 2020. "A novel decomposition-based method for solving general-product structure assemble-to-order systems," European Journal of Operational Research, Elsevier, vol. 286(1), pages 233-249.
    4. Barros, Oscar & Weber, Richard & Reveco, Carlos, 2021. "Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation," Operations Research Perspectives, Elsevier, vol. 8(C).
    5. Kai Huang, 2014. "Benchmarking non-first-come-first-served component allocation in an assemble-to-order system," Annals of Operations Research, Springer, vol. 223(1), pages 217-237, December.
    6. Abhilasha Prakash Katariya & Sıla Çetinkaya & Eylem Tekin, 2014. "Cyclic Consumption and Replenishment Decisions for Vendor-Managed Inventory of Multisourced Parts in Dell’s Supply Chain," Interfaces, INFORMS, vol. 44(3), pages 300-316, June.
    7. Yingdong Lu & Jing-Sheng Song & Yao Zhao, 2010. "No-Holdback Allocation Rules for Continuous-Time Assemble-to-Order Systems," Operations Research, INFORMS, vol. 58(3), pages 691-705, June.
    8. Olof Stenius & Ayşe Gönül Karaarslan & Johan Marklund & A. G. de Kok, 2016. "Exact Analysis of Divergent Inventory Systems with Time-Based Shipment Consolidation and Compound Poisson Demand," Operations Research, INFORMS, vol. 64(4), pages 906-921, August.

    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:inm:orinte:v:34:y:2004:i:3:p:191-205. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.