An inverse-optimization-based auction mechanism to support a multi-attribute RFQ process

126 Pages·2001·2.6 MB·English

by  Wein, Lawrence M

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WorkingPaper4226-01
December 2001
AN INVERSE-OPTIMIZATION-BASED AUCTION MECHANISM
TO SUPPORT A MULTI-ATTRIBUTE RFQ PROCESS
LawrenceM.Wein,DamianR. Beil
©2001byLawrenceM.Wein,DamianR.Beil.Allrightsreserved.
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AN INVERSE-OPTIMIZATION-BASED AUCTION MECHANISM
TO SUPPORT A MULTI-ATTRIBUTE RFQ PROCESS
Daniian R. Beil and Lawrence M. Wein
Operations Research Center, MIT, Cambridge, MA 02142, [email protected]
Sloan School ofManagement, MIT, Cambridge, MA 02142, [email protected]
Abstract
Weconsider a manufacturer who uses areverse, or procurement, auction to determine
whichsuppUer will be awarded acontract. Each bidconsistsofapriceand asetofnon-price
attributes (e.g., quality, lead time). The manufacturer is assumed to know the parametric
form of the suppliers' cost functions (in terms of the non-price attributes), but has no
prior information on the parameter values. We construct a multi-round open-ascending
auction mechanism, where the manufacturer announces aslightly different scoring rule (i.e.,
afunction that ranks the bids in terms ofthe price and non-price attributes) in each round.
Via inverse optimization, the manufacturer uses the bids from the first several rounds to
learn the suppliers' cost functions, and then in the final round chooses a scoring rule that
attempts to maximize his own utility. Under the assumption that suppliers submit their
myopic best-response bids in the last round, and do not distort their bids in the earlier
rounds (i.e., theychoosetheirminimum-cost bid to achieve anygivenscore), our mechanism
indeed maximizes the manufacturer's utility within the open-ascending format. We also
discuss several enhancements that improve the robustness ofour mechanism with respect to
the model's informational and behavioral assumptions.
December 6, 2001

INTRODUCTION
1.
Although the market for onhne business-to-business auctions is enormous (estimated
at $746B in 2004 by Kafka et al. 2000), the price-only auctions that dominate the current
eCommerce landscape severely hinder the range ofproducts that can be auctioned over the
Internet. In particular, within the industrial procurement setting, many low-cost standard-
ized items are being transacted by current online procurement (or reverse) auctions, while
high-value complex items are still being procured via the traditional Request for Quotes
(RFQ) process. An RFQ process allows thesale to bedetermined by avarietyofattributes,
involving not only price, but quality, lead time, contract terms, supplier reputation, and
incumbent switching costs. It also lets the manufacturer reveal his preferences and permits
the suppliers to compete on their own specialized dimensions. Consequently, eMarketplaces
arecurrentlybeingdeveloped topartiallyautomatetheRFQ process; i.e, tocreateaneRFQ
process (see Kafka et al. for examples).
This paper was stimulated by about eight hours ofdiscussions (during the fall of2000
and winterof2001) with theChiefTechnologyOfficer (CTO) ofFrictionless Commerce, who
was seeking help with designing a multi-attribute eRFQ mechanism. The CTO described
the company's multi-attribute procurement software (we are not at liberty to discuss its
details) and the perceived needs and preferences oftheir customers (i.e., the manufacturers
whoowntheirsoftware) and thesuppliercompanies (i.e., thepotential bidders) withrespect
tovariousaspectsofbothtraditionalandelectronicRFQprocesses. Thisinformationhelped
to guide our eRFQ design and our assumptions about supplier behavior. After receiving a
rough first draft of this paper, the CTO shared its ideas with several customers, and their
impressions are briefly summarized in §4.
The appropriate mathematical context for this setting is the multi-attribute, or mul-
tidimensional, auction; consequently, we often refer to the manufacturer as the auctioneer