Data Mining for the Optimization of Industrial Processes in Latin American Manufacturing
DOI:
https://doi.org/10.70577/c7273g40Keywords:
data, industry, mining, models, optimization.Abstract
This study explores the application of data mining and machine learning techniques for industrial process optimization in Latin America, with an emphasis on the context of Industry 4.0. Using simulated data representative of real-life operations, advanced statistical methodologies were implemented, including imputation models, variable selection, principal component analysis (PCA), clustering, and predictive models such as XGBoost and SVM. The results reveal that variables such as lead time, mean time between failures (MTBF), and CO₂ emissions have a direct impact on the defect per million (PPM) rate, highlighting the interrelationship between logistical, maintenance, and environmental factors. The clustering analysis identified three operational profiles differentiated by energy efficiency and quality, facilitating targeted interventions. Despite the high performance of the XGBoost model, possible overfitting is noted, so cross-validation is recommended. Time trends did not show significant seasonality, suggesting a greater influence of internal process variables. The study concludes that the integration of advanced analytics, predictive maintenance, and artificial intelligence can significantly improve competitiveness, sustainability, and quality in Latin American manufacturing environments.
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