IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v28y2009i4p637-644.html
   My bibliography  Save this article

—PIN Optimal Distribution of Auction Vehicles System: Applying Price Forecasting, Elasticity Estimation, and Genetic Algorithms to Used-Vehicle Distribution

Author

Listed:
  • Jie Du

    (J.D. Power and Associates, Troy, Michigan 48098)

  • Lili Xie

    (J.D. Power and Associates, Troy, Michigan 48098)

  • Stephan Schroeder

    (J.D. Power and Associates, Troy, Michigan 48098)

Abstract

In addition to retailing new vehicles, automotive manufacturers in the United States sell millions of vehicles through leasing and to fleet customers every year. The majority of these vehicles are returned to the automotive manufacturers at the end of the contracted term and must be “remarketed.” In 2007, about 10 million used vehicles were sold at more than 400 auctions in the United States. Large consigners face decisions every day about when, where, and at what price to offer these vehicles, which has significant financial implications for their profitability. To address the challenges of the distribution process, (), a division of J.D. Power and Associates, developed the PIN Optimal Distribution of Auction Vehicles System (ODAV), an automated decision optimization system that helps remarketers maximize profits through the most advantageous distribution of their auction vehicles. At the core of the system is a combination of three models that determine the distribution of the vehicles on a daily basis: a nearest neighbor linear regression model for short-term auction price forecasting; an autoregressive integrated moving average time-series analysis model for volume-price elasticity; and a genetic algorithm optimizer for vehicle distribution. Since its launch in 2003, PIN has been providing ODAV services on a daily basis, and to date, more than two million vehicles have been distributed through this system. In this paper, we will describe the PIN ODAV System, its implementation, and the business impact by using as an example the experience with our largest client, Chrysler Group LLC.

Suggested Citation

  • Jie Du & Lili Xie & Stephan Schroeder, 2009. "—PIN Optimal Distribution of Auction Vehicles System: Applying Price Forecasting, Elasticity Estimation, and Genetic Algorithms to Used-Vehicle Distribution," Marketing Science, INFORMS, vol. 28(4), pages 637-644, 07-08.
  • Handle: RePEc:inm:ormksc:v:28:y:2009:i:4:p:637-644
    DOI: 10.1287/mksc.1080.0470
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1080.0470
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.1080.0470?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. Jiang, Yuanchun & Shang, Jennifer & Liu, Yezheng, 2013. "Optimizing shipping-fee schedules to maximize e-tailer profits," International Journal of Production Economics, Elsevier, vol. 146(2), pages 634-645.
    2. Born, Alexander & Kovachka, Nikoleta & Lessmann, Stefan & Seow, Hsin-Vonn, 2018. "Price Management in the Used-Car Market: An Evaluation of Survival Analysis," IRTG 1792 Discussion Papers 2018-065, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Korbinian Dress & Stefan Lessmann & Hans-Jorg von Mettenheim, 2017. "Residual Value Forecasting Using Asymmetric Cost Functions," Papers 1707.02736, arXiv.org.
    4. Dress, Korbinian & Lessmann, Stefan & von Mettenheim, Hans-Jörg, 2018. "Residual value forecasting using asymmetric cost functions," International Journal of Forecasting, Elsevier, vol. 34(4), pages 551-565.
    5. Lessmann, Stefan & Voß, Stefan, 2017. "Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy," International Journal of Forecasting, Elsevier, vol. 33(4), pages 864-877.
    6. Gary L. Lilien & John H. Roberts & Venkatesh Shankar, 2013. "Effective Marketing Science Applications: Insights from the ISMS-MSI Practice Prize Finalist Papers and Projects," Marketing Science, INFORMS, vol. 32(2), pages 229-245, March.
    7. Fadi Al-Turjman & Adedoyin A. Hussain & Sinem Alturjman & Chadi Altrjman, 2022. "Vehicle Price Classification and Prediction Using Machine Learning in the IoT Smart Manufacturing Era," Sustainability, MDPI, vol. 14(15), pages 1-11, July.

    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:ormksc:v:28:y:2009:i:4:p:637-644. 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.