Statistical Descriptive Analysis of Portuguese Public Procurement Data from 2015 to 2022

During the tender phase of public construction projects in Portugal, documents that describe the project are mandatorily submitted to open data repositories. However, in their current state, most of these repositories do not allow for benchmarking analysis due to a lack of data treatment and cohesio...

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Bibliographic Details
Main Authors: Luís Jacques de Sousa, Maria Lurdes Simões, João Poças Martins, Luís Sanhudo, Jorge Moreira da Costa
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:CivilEng
Subjects:
Online Access:https://www.mdpi.com/2673-4109/4/3/45
Description
Summary:During the tender phase of public construction projects in Portugal, documents that describe the project are mandatorily submitted to open data repositories. However, in their current state, most of these repositories do not allow for benchmarking analysis due to a lack of data treatment and cohesion. This paper seeks to diagnose the main trends during the public construction project’s tender phase by performing a descriptive statistical analysis on the Portuguese Public Procurement Database (PPPData), a database that compiles 5172 public procurement contracts in Portugal from 2015 to 2022, to respond to the research gap in construction procurement benchmarking. The results of this statistical analysis draw out the main trends, uncover which tender variables can influence budget compliance, and diagnose Portugal’s public procurement in terms of its geographical, temporal, financial, and performance dispersion. This paper concludes that the award criteria are not correlated with final project performance and that multifactor assessment criteria do not necessarily lead to better performance. High-value projects awarded solely with the price award criterion tend to perform worse than those awarded with the multifactor assessment. The study also identified frequent errors and omissions in construction reporting; thus, there is a need for error mitigation tools.
ISSN:2673-4109