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Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

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  • Antonio Lorenzo-Espejo

    (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)

  • Alejandro Escudero-Santana

    (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)

  • María-Luisa Muñoz-Díaz

    (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)

  • Alicia Robles-Velasco

    (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)

Abstract

This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability.

Suggested Citation

  • Antonio Lorenzo-Espejo & Alejandro Escudero-Santana & María-Luisa Muñoz-Díaz & Alicia Robles-Velasco, 2022. "Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7779-:d:848098
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    References listed on IDEAS

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