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

Sousa, Luís Jacques de; Simões, M. Lurdes; Martins, João Poças; Sanhudo, Luís; Costa, Jorge Moreira da

doi:10.3390/civileng4030045

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…In a previous study, a descriptive statistical analysis was conducted on PPPData (Luís Jacques de Sousa et al. , 2023).…”

Section: Discussionmentioning

confidence: 99%

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Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

Jacques de Sousa,

Poças Martins,

Sanhudo

2024

ECAM

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PurposeFactors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.Design/methodology/approachIn this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.FindingsThe findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.Practical implicationsThe model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.Social implicationsAlthough the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.Originality/valuePrevious research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

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“…In a previous study, a descriptive statistical analysis was conducted on PPPData (Luís Jacques de Sousa et al. , 2023).…”

Section: Discussionmentioning

confidence: 99%

“…An initial descriptive statistical analysis performed in Luís Jacques de Sousa et al. (2023), on the dataset showed that the dataset is highly unbalanced regarding the output feature (the “Project Performance” class).…”

Section: Methodsmentioning

confidence: 99%

Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

Jacques de Sousa,

Poças Martins,

Sanhudo

2024

ECAM

Self Cite

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The State of the Sustainability Factor for Construction Procurement: The Case of Portugal

Jacques De Sousa,

Poças Martins,

Sanhudo

2023

Lecture Notes in Civil Engineering

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Additional Costs of Public Works Contracts in Portugal—Descriptive Statistical Analysis in Light of the Quality of Information

Almeida,

Calejo

2024

CivilEng

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The aim of this work is to analyse the quality and transparency of data on the additional costs of public works. The problem identified is the lack of detailed and accessible information that allows for an adequate analysis of the performance and final state of public works, especially in relation to prices and deadlines. This is a case study carried out in Portugal, in which information from public works contracts with a closing date in 2022 was analysed. The data were extracted from the public access portal, responsible for making available and publishing information on the execution of public works contracts. The information was subjected to a statistical treatment process seeking to identify answers to transparency issues. The originality of this study lies in the quantitative and statistical approach applied to the evaluation of the transparency of the data made available on the portal, contributing to the debate on improving public management. The results indicate the need to expand the content available on the portal since the information provided does not allow for an analysis of the final state and performance of the works carried out, especially those relating to price and deadline, which in turn limits the construction of forecasting models and performance indicators. Corrective measures are proposed that include information that allows for answering questions about transparency and that allows for the construction and analysis of statistics and indicators, contributing to identifying the need for improvements in legislation, and the adoption of mechanisms that can improve, correct and or reinforce actions with an impact on the management of public resources.

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

Cited by 3 publications

References 20 publications

Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

Predicting construction project compliance with machine learning model: case study using Portuguese procurement data

The State of the Sustainability Factor for Construction Procurement: The Case of Portugal

Additional Costs of Public Works Contracts in Portugal—Descriptive Statistical Analysis in Light of the Quality of Information

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