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Procurement Mechanisms for Assortments of Differentiated Products

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  • Daniela Saban

    (Graduate School of Business, Stanford University, Stanford, California 94305)

  • Gabriel Y. Weintraub

    (Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

We consider the problem faced by a procurement agency that runs a mechanism for constructing an assortment of differentiated products with posted prices, from which heterogeneous consumers buy their most preferred alternative. Procurement mechanisms used by large organizations, including framework agreements (FAs), which are widely used in the public sector, often take this form. When choosing the assortment, the procurement agency must optimize the tradeoff between offering a richer menu of products for consumers and offering less variety, hoping to engage the suppliers in more aggressive price competition. We formulate the problem faced by the procurement agency as a mechanism design problem, and we progressively incorporate more complex and often more realistic implementation constraints, including that the allocations should be decentralized (that is, consumers choose what to buy) and that payments must be implemented through linear pricing (in particular, no up-front payments are allowed). We characterize the optimal buying mechanisms that highlight the importance of restricting the entry of close-substitute products to the assortment as a way to increase price competition without much damage to variety. Motivated by the implementation of the Chilean FAs, which are being used to acquire around US$3 billion in goods and services per year, we leverage our characterization of the optimal mechanism to study the design of first-price-auction-type mechanisms that are commonly used in public settings. Our results shed light on simple ways to improve their performance.

Suggested Citation

  • Daniela Saban & Gabriel Y. Weintraub, 2021. "Procurement Mechanisms for Assortments of Differentiated Products," Operations Research, INFORMS, vol. 69(3), pages 795-820, May.
  • Handle: RePEc:inm:oropre:v:69:y:2021:i:3:p:795-820
    DOI: 10.1287/opre.2020.2053
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    References listed on IDEAS

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    Cited by:

    1. Will Ma, 2023. "When Is Assortment Optimization Optimal?," Management Science, INFORMS, vol. 69(4), pages 2088-2105, April.
    2. Santiago R. Balseiro & Antoine Désir, 2023. "Incentive-Compatible Assortment Optimization for Sponsored Products," Management Science, INFORMS, vol. 69(8), pages 4668-4684, August.

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