Examinando por Materia "INDUSTRIA TEXTIL - COLOMBIA) - INNOVACIONES TECNOLÓGICAS"

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    Modelo de predicción de venta en una compañía textil con técnicas de Machine Learning
    (Universidad EAFIT, 2025) Lezcano Echeverri, Jhon Wilder; Puerta Puerta, Henry Daniel
    This study explores the implementation of sales forecasting models in a Colombian textile company, combining traditional techniques with Machine Learning-based approaches. Daily sales data from 187 stores between 2021 and 2025 were analyzed. The methodology followed five stages: (1) exploratory analysis, (2) feature engineering, (3) model implementation, (4) model optimization and fine-tunning, and (5) comparative validation. The models implemented were: Prophet, XGBoost, Random Forest, and regularized Linear Regression. Prophet achieved the best overall performance for units sold (R² = 0.7121), standing out for its ability to capture complex seasonal patterns and adapt to store-level variability. XGBoost demonstrated high accuracy in non-linear scenarios, Random Forest showed robustness to noise, and Linear Regression provided greater interpretability. Feature engineering resulted in 83 variables, including temporal components, trends, volatility, and special effects. A cross-sectional analysis revealed common patterns such as peak underestimation, higher error in smaller stores and weekends, and lower accuracy in predicting monetary values compared to units. The findings confirm that sales forecasting using Machine Learning offers substantial improvements over traditional methods, enhancing operational efficiency, inventory optimization, and financial planning. Prophet is recommended as the primary model, along with the establishment of monthly recalibration cycles to maintain accuracy.