Advertisement

A Heuristic Lotting Method for Electronic Reverse Auctions

  • Uzay Kaymak
  • Jean Paul Verkade
  • Hubert A. B. te Braake
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2869)

Abstract

An increasing number of commercial companies are using online reverse auctions for their sourcing activities. In reverse auctions, multiple suppliers bid for a contract from a buyer for selling goods and/or services. Usually, the buyer has to procure multiple items, which are typically divided into lots for auctioning purposes. By steering the composition of the lots, a buyer can increase the attractiveness of its lots for the suppliers, which can then make more competitive offers, leading to larger savings for the procuring party. In this paper, a clustering-based heuristic lotting method is proposed for reverse auctions. Agglomerative clustering is used for determining the items that will be put in the same lot. A suitable metric is defined, which allows the procurer to incorporate various approaches to lotting. The proposed lotting method has been tested for the procurement activities of a consumer packaged goods company. The results indicate that the proposed strategy leads to 2–3% savings, while the procurement experts confirm that the lots determined by the proposed method are acceptable given the procurement goals.

Keywords

Combinatorial Auction Online Auction Reverse Auction Linkage Algorithm Hierarchical Cluster Technique 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jap, S.D.: Online reverse auctions: issues, themes and prospects for the future. Journal of the Academy of Marketing Science 30, 506–525 (2002)CrossRefGoogle Scholar
  2. 2.
    Minahan, T., Howarth, F., Vigoroso, M.: Making e–sourcing strategic. Research report, Aberdeen Group, Boston (2002) Google Scholar
  3. 3.
    Emiliani, M.L.: Business-to-business online auctions: key issues for purchasing process improvement. Supply Chain Management 5, 176–186 (2000)CrossRefGoogle Scholar
  4. 4.
    Tully, S.: The B2B tool that really is changing the world. Fortune 141, 132–145 (2000)Google Scholar
  5. 5.
    Teich, J.E., Wallenius, H., Wallenius, J., Zaitsev, A.: Designing electronic auctions: an internetbased hybrid procedure combining aspects of negotiations and auctions. Electronic Commerce Research 1, 301–314 (2001)CrossRefGoogle Scholar
  6. 6.
    Müller, R.: Auctions – the big winner among trading mechanisms for the internet economy. Merit–infonomics research memorandum series. University of Maastricht, MERIT – Maastricht Economic Research Insititute on Innovation and Technology (2001) (2001–2016)Google Scholar
  7. 7.
    Klemperer, P.: Auction theory: a guide to the literature. Journal of Economic Surveys 13, 227–286 (1999)CrossRefGoogle Scholar
  8. 8.
    Wedel, M., Kamakura, W.A.: Market Segmentation: conceptual and methodological foundations. Kluwer, Boston (1998)Google Scholar
  9. 9.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function. Plenum Press, NewYork (1981)zbMATHGoogle Scholar
  10. 10.
    Johnson, R.A., Wichem, D.W.: Applied Multivariate Statistical Analysis. Prentice Hall, New Jersey (1982)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Uzay Kaymak
    • 1
  • Jean Paul Verkade
    • 1
  • Hubert A. B. te Braake
    • 1
  1. 1.Faculty of EconomicsErasmus University RotterdamRotterdamThe Netherlands

Personalised recommendations