Visual Data Mining for Strategic Import Decision-Making

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Published : 2024-10-15
DOI : https://doi.org/10.37815/rte.v36nE1.1209
Keywords :
Business Intelligence, Clustering, Data Mining, Data Visualization, International Trade
Andres Teodoro Calle Clavijo
https://orcid.org/0009-0009-0104-5458
Luis Tonon-Ordoñez
https://orcid.org/0000-0003-2360-9911
Marcos Orellana
https://orcid.org/0000-0002-3671-9362
Abstract

In international trade, choosing the ideal supplier can be a challenge. Against this background, it is mandatory to identify the countries from which the import of a certain good is most appropriate. Therefore, this paper proposes a model that allows for the analysis and visualization of cost data for imports into Ecuador from 2008-2018. To achieve this, the existing information on imports was analyzed by reviewing the related literature. Then, the K-means algorithm was applied to group countries by tariff heading, considering the FOB value and import costs. Finally, a visualization interface facilitates decision-making based on the obtained information. Consequently, the proposed model is useful for decision-making in imports because it allows data analysis from all countries in conjunction with imported goods, proving to be applicable to a wide range of companies.

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How to Cite
Calle Clavijo, A. T., Tonon-Ordoñez, L., & Orellana, M. (2024). Visual Data Mining for Strategic Import Decision-Making. Revista Tecnológica - ESPOL, 36(E1), 163-176. https://doi.org/10.37815/rte.v36nE1.1209
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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