A data analytics approach to improve the international supply of metal inputs in the metal-mechanical sector in Colombia

Lina M. Lozano-Suarez, Fabian A. Torres-Cardenas and Eduardo Rangel Díaz

International Journal of Data Analysis Techniques and Strategies, 2025, vol. 17, issue 2, 121-139

Abstract: The metal-mechanical sector is vital to Colombia's industry, significantly contributing to economic development. To ensure its growth, this sector must enhance competitiveness, particularly in managing metal supplies, often imported. Analysing imports is crucial, but data from DIAN is unprocessed and provided in extensive Excel microdata packages, requiring processing. This study proposes a data analytics approach combining descriptive and predictive analyses. Descriptive analysis using DIAN's 2023 data identifies key import factors: major supplier countries, main customs entries, locations of top importers, and common transport modes. Predictive analysis using regression, decision trees, and k-NN models predicts import quantities based on free on board (FOB) value, with regression showing the highest accuracy. This approach helps companies understand factors affecting imports, such as transportation, customs management, cargo handling, and preparation, facilitating better decision-making and competitiveness.

Keywords: data analytics; supply chain; machine learning; international supply; regression model; decision tree; k-NN; metal-mechanical sector; CRISP-DM; dashboard. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:17:y:2025:i:2:p:121-139

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