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Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning

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  • Manuel J. García Rodríguez
  • Vicente Rodríguez Montequín
  • Francisco Ortega Fernández
  • Joaquín M. Villanueva Balsera

Abstract

The largest project managers and adjudicators of a country, both by number of projects and by cost, are public procurement agencies. Therefore, knowing and characterising public procurement announcements (tenders) is fundamental for managing public resources well. This article presents the case of public procurement in Spain, analysing a dataset from 2012 to 2018: 58,337 tenders with a cost of 31,426 million euros. Many studies of public procurement have been conducted globally or theoretically, but there is a dearth of data analysis, especially regarding Spain. A quantitative, graphical, and statistical description of the dataset is presented. Mainly, the analysis is of the relation between the award price and the bidding price. An award price estimator is proposed that uses the random forest regression method. A good estimator would be very useful and valuable for companies and public procurement agencies. It would be a key tool in their project management decision making. Finally, a similar analysis, employing a dataset from European countries, is presented to compare and generalise the results and conclusions. Hence, this is a novel study which fills a gap in the literature.

Suggested Citation

  • Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    • Handle: RePEc:hin:complx:2360610
    DOI: 10.1155/2019/2360610
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    References listed on IDEAS

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    Cited by:

    1. Aristotelis Mavidis & Dimitris Folinas, 2022. "From Public E-Procurement 3.0 to E-Procurement 4.0; A Critical Literature Review," Sustainability, MDPI, vol. 14(18), pages 1-23, September.

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