Abstract
This study aims to identify the key factors contributing to time and cost deviations in educational infrastructure projects in Colombia, using a data-driven approach based on open government datasets. By examining 175 procurement records from Colombia’s public data platform, the research seeks to uncover patterns and variables that influence project performance, offering a transparent alternative to traditional expert-based assessments. The results show an average time deviation of 77.73% and a cost deviation of 22.17%, with a significant correlation between both metrics. Project type, contract value, and process type were significant for both deviations. Additional cost-related factors included initial duration, award growth, and number of bidders, while time deviations were influenced by project intensity and time suspended. These findings highlight the need for integrated planning and procurement strategies to improve efficiency in public infrastructure delivery. Although the methodology ensures robustness, limitations include the focus on finalized educational projects and the exclusion of qualitative factors such as stakeholder behavior and site-specific challenges. The insights are valuable for policymakers, public sector managers, and researchers seeking to enhance infrastructure outcomes through evidence-based decision-making and better resource allocation.
| Translated title of the contribution | Análisis de desviaciones en tiempo y costo en proyectos de infraestructura educativa: un enfoque basado en datos utilizando la plataforma pública de datos de Colombia |
|---|---|
| Original language | English |
| Article number | 3505 |
| Pages (from-to) | 1-22 |
| Number of pages | 22 |
| Journal | Buildings |
| Volume | 15 |
| Issue number | 19 |
| DOIs | |
| State | Published - 28 Sep 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- educational infrastructure
- cost overrun
- time overruns
- public procurement
- project performance
- data-driven analysis
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