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Open AccessArticle
Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach
Departamento de Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Cm. de los Descubrimientos, s/n, 41092 Seville, Spain
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Mathematics 2024, 12(15), 2347; https://doi.org/10.3390/math12152347 (registering DOI)
Submission received: 14 June 2024
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Revised: 23 July 2024
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Accepted: 25 July 2024
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Published: 27 July 2024
Abstract
This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available.
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MDPI and ACS Style
Flores-Huamán, K.-J.; Escudero-Santana, A.; Muñoz-Díaz, M.-L.; Cortés, P.
Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach. Mathematics 2024, 12, 2347.
https://doi.org/10.3390/math12152347
AMA Style
Flores-Huamán K-J, Escudero-Santana A, Muñoz-Díaz M-L, Cortés P.
Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach. Mathematics. 2024; 12(15):2347.
https://doi.org/10.3390/math12152347
Chicago/Turabian Style
Flores-Huamán, Kenny-Jesús, Alejandro Escudero-Santana, María-Luisa Muñoz-Díaz, and Pablo Cortés.
2024. "Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach" Mathematics 12, no. 15: 2347.
https://doi.org/10.3390/math12152347
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