Prediction of operating times in organic coatings using multiple linear regression and decision trees
DOI:
https://doi.org/10.24054/rcta.v2i48.4500Keywords:
decision trees, machine learning, multiple linear regression, organic coatings, prediction, time studiesAbstract
This article presents the development of predictive models for estimating operation times in an industrial process of organic coatings, applied to the registration of new products in a manufacturing plant. The central issue lies in the fact that, before incorporating new products into production lines, the organization must record a preliminary standard time in the information system, even though method and time studies are not yet available. To address this challenge, a database was consolidated from historical records, stopwatch measurements, and attributes associated with each reference. For the analysis, three product families were defined, considering fundamental aspects for their grouping, such as the surface area of the piece (expressed in square decimeters) and the number of units per hanger. Subsequently, two supervised techniques were compared: multiple linear regression and regression decision trees. This required the definition of data-cleaning criteria, training and testing protocols, as well as performance metrics, sensitivity analysis, overfitting diagnostics, and cross-validation against standard times previously defined by the organization. The results show that regression decision trees achieve better overall fit indicators than multiple linear regression across the three evaluated models; however, their use should be understood as a support tool for preliminary estimation rather than as an absolute substitute for method and time studies.
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L. J. M. Meléndez, D. A. S. Chávez, and L. E. T. Mata, “El tiempo estándar y su importancia en las cotizaciones de proyectos de manufactura. Un enfoque de gestión,” NovaRUA, vol. 14, no. 24, pp. 110–122, Jan. 2022, doi: 10.20983/novarua.2022.24.6.
E. Cakit and M. Dagdeviren, “Comparative analysis of machine learning algorithms for predicting standard time in a manufacturing environment,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol. 37, e2, 2023, doi: 10.1017/S0890060422000245.
P. Backus, M. Janakiram, S. Mowzoon, G. C. Runger, and A. Bhargava, “Factory cycle-time prediction with a data-mining approach,” IEEE Transactions on Semiconductor Manufacturing, vol. 19, no. 2, pp. 252-258, 2006, doi: 10.1109/TSM.2006.873400.
Y. Meidan, B. Lerner, G. Rabinowitz, and M. Hassoun, “Cycle-Time Key Factor Identification and Prediction in Semiconductor Manufacturing Using Machine Learning and Data Mining,” IEEE Transactions on Semiconductor Manufacturing, vol. 24, no. 2, pp. 237-248, 2011, doi: 10.1109/TSM.2011.2118775.
A. Ozturk, S. Kayaligil, and N. E. Ozdemirel, “Manufacturing lead time estimation using data mining,” European Journal of Operational Research, vol. 173, no. 2, pp. 683-700, 2006, doi: 10.1016/j.ejor.2005.03.015.
L. Lingitz, V. Gallina, F. Ansari, D. Gyulai, A. Pfeiffer, W. Sihn, and L. Monostori, “Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer,” Procedia CIRP, vol. 72, pp. 1051-1056, 2018, doi: 10.1016/j.procir.2018.03.148.
A. Rokoss, M. Syberg, L. Tomidei, C. Hülsing, J. Deuse, and M. Schmidt, “Case study on delivery time determination using a machine learning approach in small batch production companies,” Journal of Intelligent Manufacturing, vol. 35, pp. 3937–3958, 2024, doi: 10.1007/s10845-023-02290-2 (Springer Nature).
Y. P. Deepthi, P. Kalaga, S. K. Sahu, J. J. Jacob, K. P. S., and Q. Ma, “AI-based machine learning prediction for optimization of copper coating process on graphite powder for green composite fabrication,” International Journal on Interactive Design and Manufacturing, vol. 19, pp. 4123-4130, 2025, doi: 10.1007/s12008-024-02032-5.
K.-J. Flores-Huamán, A. Escudero-Santana, M.-L. Muñoz-Díaz, and P. Cortés, “Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach,” Mathematics, vol. 12, no. 15, art. 2347, 2024, doi: 10.3390/math12152347.
J. J. Paniagua Medina, E. Vargas Rodríguez, and R. Guzmán Cabrera, “Aprendizaje automático y la colección Reuters-21578 en la clasificación de documentos,” Revista Colombiana de Tecnologías de Avanzada (RCTA), vol. 2, no. 40, pp. 39–46, Jul. 2022, doi: 10.24054/rcta.v2i40.2344.
A. A. Rosado Gómez, L. Calderón Benavides, and J. A. Parra, “Comparación empírica de dos modelos de aprendizaje automático generados mediante procesos diferentes,” Revista Colombiana de Tecnologías de Avanzada (RCTA), vol. 1, no. 39, pp. 20–24, 2022, doi: 10.24054/rcta.v1i39.1369.
F. A. Fernández-Gelvez, L. Jaimes-Cerveleón, and L. E. Mendoza, “Modelo de regresión basado en máquinas de aprendizaje utilizando datos estadísticos del café colombiano,” Mundo Fesc, vol. 13, no. S1, pp. 258–272, 2023, doi: 10.61799/2216-0388.1499.
J. C. Gutiérrez Medina, A. Martínez, and P. Alzate, “La Industria 4.0: Tendencias, barreras y retos en la cuarta revolución industrial,” Mundo Fesc, vol. 14, no. 30, pp. 439–448, Sep. 2024, doi: 10.61799/2216-0388.1448.
M. P. Brugés-Peláez, C. A. Parra-Ortega, and J. D. Ramón-Valencia, “Forecasting of particulate material concentration using supervised machine learning,” Respuestas, vol. 29, no. 2, pp. 90–98, May 2024, doi: 10.22463/0122820X.5150.
J. J. Castro-Maldonado, J. A. Patiño-Murillo, and E. Camargo-Casallas, “Aplicación de analítica de datos en la evaluación de los procesos de investigación aplicada y desarrollo experimental para fortalecer las competencias del siglo XXI en una institución de educación no formal,” Respuestas, vol. 27, no. 2, pp. 6–26, May 2022, doi: 10.22463/0122820X.3541.
L. D. Suárez-Riveros, W. Pineda-Ríos, and I. M. Mendivelso-Ramírez, “Técnicas estadísticas y logro de aprendizaje: revisión bibliográfica,” Eco Matemático, vol. 12, no. 2, pp. 112–125, Jul. 2021, doi: 10.22463/17948231.3323.
J. R. Vera-Rozo, J. M. Riesco-Ávila, and A. Pardo-García, “Optimización mediante regresión polinomial del rendimiento líquido de la pirólisis de residuos plásticos recolectados en Norte de Santander,” Eco Matemático, vol. 15, no. 2, pp. 76–82, Jul. 2024, doi: 10.22463/17948231.4999.
A. Bustamante-Limones, C. Rodriguez-Borges, and J. A. Pérez-Rodriguez, “Evaluación del uso de gemelos digitales en los sistemas de producción,” AiBi Revista de Investigación, Administración e Ingeniería, vol. 12, no. 3, pp. 195–204, Sep. 2024, doi: 10.15649/2346030X.4382.
D. Arenas Seleey, C. E. Prieto Triana, y D. C. Chacón López, “Ingeniería de requerimientos e inteligencia artificial: una revisión de literatura”, Rev. Colomb. Tecnol. Avanzada, vol. 1, n.º 39, pp. 101-107, 2022. Disponible: https://doi.org/10.24054/rcta.v1i39.1395
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Boca Raton, FL, USA: Chapman and Hall/CRC, 2017, doi: 10.1201/9781315139470.
G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, An Introduction to Statistical Learning: with Applications in Python. Cham, Switzerland: Springer, 2023, doi: 10.1007/978-3-031-38747-0.
M. Kuhn and K. Johnson, Applied Predictive Modeling. New York, NY, USA: Springer, 2013, doi: 10.1007/978-1-4614-6849-3.
J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, 2001, doi: 10.1214/aos/1013203451.
A. C. Choueiri, D. M. V. Sato, E. E. Scalabrin, and E. A. P. Santos, “An extended model for remaining time prediction in manufacturing systems using process mining,” Journal of Manufacturing Systems, vol. 56, pp. 188-201, 2020, doi: 10.1016/j.jmsy.2020.06.003.
M. Alnahhal, D. Ahrens, and B. Salah, “Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain,” Applied Sciences, vol. 11, no. 21, art. 10105, 2021, doi: 10.3390/app112110105.
Y. Li, Z. Fu, X. Yu, Z. Jin, H. Gong, L. Ma, X. Li, and D. Zhang, “Developing an atmospheric aging evaluation model of acrylic coatings: A semi-supervised machine learning algorithm,” International Journal of Minerals, Metallurgy and Materials, vol. 31, pp. 1617-1627, 2024, doi: 10.1007/s12613-024-2921-9.
W. Chen et al., “Prediction of coating degradation based on ‘Environmental Factors-Physical Property-Corrosion Failure’ two-stage machine learning,” npj Materials Degradation, vol. 9, art. 67, 2025, doi: 10.1038/s41529-025-00614-6.
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