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Choosing the most economically advantageous tender using a multi-criteria decision analysis approach

Author

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  • Juha-Matti Lehtonen
  • Kai Virtanen

Abstract

Purpose - The purpose of this paper is to propose and test a multi-criteria decision analysis (MCDA) approach based on an additive value function (AVF) to select the most economically advantageous tender under European Union public procurement regulations. Design/methodology/approach - A case study in which the AVF tender evaluation model is constructed by the procurement personnel and the results of the original, real-life public procurement evaluation model are compared to those discovered by the MCDA approach. Findings - The AVF model captures the preferences of the procurement authority in a more reliable and transparent manner than commonly used evaluation models based on scoring formulas. Practical implications - While commonly used in public procurement, relative scoring formulas can neither present the preferences of a procurement unit accurately nor do they enable bidders to draft bids according to these preferences. The proposed MCDA approach can achieve both. Originality/value - The contribution of this paper is threefold. First, the successful construction of the AVF model with procurement personnel is introduced. Second, the model is used in an actual, real-life case. Third, a thoughtful comparison of features, structures and results of the AVF model and the evaluation model using scoring formulas is presented.

Suggested Citation

  • Juha-Matti Lehtonen & Kai Virtanen, 2022. "Choosing the most economically advantageous tender using a multi-criteria decision analysis approach," Journal of Public Procurement, Emerald Group Publishing Limited, vol. 22(2), pages 164-179, March.
  • Handle: RePEc:eme:jopppp:jopp-06-2021-0040
    DOI: 10.1108/JOPP-06-2021-0040
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    Citations

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    Cited by:

    1. Ibrahim Mutambik, 2024. "The Sustainability of Smart Cities: Improving Evaluation by Combining MCDA and PROMETHEE," Land, MDPI, vol. 13(9), pages 1-28, September.

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