Partial productivity measurement: validation for a transdisciplinary model
Palabras clave:
Operations research, production engineering, resources management, measuring methods, mathematical models
Resumen
Labor productivity refers to the efficiency with which resources are used to obtain goods or services. Its calculation involves methods that include precise variables representing the performance of productive units; however, it is necessary to understand the outcomes when integrating social and human variables into the measurement process. The objective of this study is to describe the relevance of a new transdisciplinary model based on the partial measurement of productivity in an industrial company, applying a traditional indicator model, the Data Envelopment Analysis (DEA) technique, and a categorical model. The research focused on the work centers involved in raw material transformation, and the workforce analyzed consisted of operators who remained constant during the eight months of the study. The results indicate significant differences among the methods, showing large fluctuations in productivity values across periods. The inclusion of transdisciplinary variables in a categorical model increases productivity values compared to the DEA model. The high contrast between traditional models is due to the partial use of information to measure the indicator. There is evidence to confirm the proposed objective and to support the continued development of a model for measuring productivity that integrates transdisciplinary variables.
Citas
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Demuner-Flores, M. D. R., Saavedra-García, M. L., & Cortes Castillo, M. D. R. (2022). Business performance, resilience and innovation in SMEs. Investigación Administrativa, 51(130). https://doi.org/10.35426/iav51n130.01
Diabat, A., Shetty, U., & Pakkala, T. P. M. (2015). Improved efficiency measures through directional distance formulation of data envelopment analysis. Annals of Operations Research, 229(1), 325-346. https://doi.org/10.1007/s10479-013-1470-9
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Fontalvo-Herrera, T. J., & De La Hoz-Granadillo, E. (2020). Cluster method–discriminant analysis–data envelopment analysis to classify and evaluate business efficiency. Entramado, 16(2), 46-55. https://doi.org/10.18041/1900-3803/entramado.2.6437
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Moreno-Rodríguez, D. C. (2022). Productivity in Service (Vol. 53). Editorial de la Universidad Pedagógica y Tecnológica de Colombia-UPTC.
Muñoz Choque, A. M. (2021). Estudio de tiempos y su relación con la productividad. Revista Enfoques, 5(17), 40–54. https://doi.org/10.33996/revistaenfoques.v5i17.104
Palange, A., & Dhatrak, P. (2021). Lean manufacturing a vital tool to enhance productivity in manufacturing. Materials Today: Proceedings, 46, 729-736. https://doi.org/10.1016/j.matpr.2020.12.193
Ramírez, G. G., Magaña, D. E., & Ojeda, R. N. (2022). Productividad, aspectos que benefician a la organización. Revisión sistemática de la producción científica. Trascender, contabilidad y gestión, 8(20), 189–208. https://doi.org/10.36791/tcg.v8i20.166
Riaño-Henao, C. A., & Larrea-Serna, O. L. (2021). Data envelopment analysis and its applications in sustainability. Ingeniare, (31), 11-19. https://doi.org/10.18041/1909-2458/ingeniare.31.8934
Tang, W., He, H., & Tu, X. M. (2023). Applied categorical and count data analysis. Chapman and Hall/CRC. https://doi.org/10.1201/9781003109815
Trizano-Hermosilla, I. (2017). Evaluation of reliability estimation under asymmetric, congeneric, categorical data conditions and in the presence of multidimensionality [Tesis de doctorado, Universidad Autónoma de Madrid]. http://hdl.handle.net/10486/678525
Ulloa-Pimienta, A. R., Sánchez-Trinidad, A. del C., & Balcazar-Sosa, M. T. de J. (2023). La productividad en la empresa de la industria de la transformación. Revista de Investigaciones Universidad Del Quindio, 35(1), 236–247. https://doi.org/10.33975/riuq.vol35n1.1156
Vartia, L. (2008). How do taxes affect investment and productivity? An industry-level analysis of OECD countries. OECD Economics Department Working Papers, (656), 1-42. http://doi.org/10.1787/230022721067
Yong-Chung, F., García-Salirrosas, E. E., Bonilla-Bermeo, J. D., & Medina de la Cruz, R. M. (2024). Capital psicológico en trabajadores profesionales peruanos: análisis de factores determinantes. Revista Venezolana De Gerencia, 29(108), 1615-1629. https://doi.org/10.52080/rvgluz.29.108.9
Ahmed, A., Page, J., & Olsen, J. (2020). Enhancing Six Sigma methodology using simulation techniques: Literature review and implications for future research. International Journal of Lean Six Sigma, 11(1), 211–232. https://doi.org/10.1108/ijlss-03-2018-0033
Barradas Martinez, M. D. R., Rodríguez Lázaro, J., & Maya Espinoza, I. (2021). Desempeño organizacional. Una revisión teórica de sus dimensiones y forma de medición. RECAI Revista de Estudios En Contaduría, Administración e Informática, 21. https://doi.org/10.36677/recai.v10i28.15678
Buitrago, O. Y., Espitia, A. A., & Molano, L. (2017). Análisis envolvente de datos para la medición de la eficiencia en instituciones de educación superior: una revisión del estado del arte. Revista Científica General José María Córdova, 15(19), 147. https://doi.org/10.21830/19006586.84
Cequea, M. M., Monroy, C. R., & Bottini, M. A. N. (2011). The productivity from a human perspective: Dimensions and factors. Intangible Capital, 7(2), 549-584. http://dx.doi.org/10.3926/ic.2011.v7n2.p549-584
DANE. (2025). Productivity. National Administrative Department of Statistics – DANE. https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/productividad
Demuner-Flores, M. D. R., Saavedra-García, M. L., & Cortes Castillo, M. D. R. (2022). Business performance, resilience and innovation in SMEs. Investigación Administrativa, 51(130). https://doi.org/10.35426/iav51n130.01
Diabat, A., Shetty, U., & Pakkala, T. P. M. (2015). Improved efficiency measures through directional distance formulation of data envelopment analysis. Annals of Operations Research, 229(1), 325-346. https://doi.org/10.1007/s10479-013-1470-9
Dror, R., Baumer, G., Shlomov, S., & Reichart, R. (2018, julio). The hitchhiker’s guide to testing statistical significance in natural language processing. En Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1383-1392). https://doi.org/10.18653/v1/P18-1128
Elia, V., Gnoni, M. G., & Tornese, F. (2017). Measuring circular economy strategies through index methods: A critical analysis. Journal of Cleaner Production, 142, 2741-2751. https://doi.org/10.1016/j.jclepro.2016.10.196
Fernández, Ó. (2023). Analysis of the efficiency of listed companies in the NBI index [Tesis de doctorado, Universitat Politècnica de València. Riunet Repositorio Institucional]. https://riunet.upv.es/handle/10251/19505
Filippini, O. S., Regules, V., Gualdoni, G. H., Gamarra, J., Huarita, S., Depiante, I., & Delfino, V. (2018). Strategies for the analysis of binary categorical data. Trabajo presentado en el XLVI Coloquio Argentino de Estadística y 4.ª Conferencia en Educación Estadística, Río Cuarto, Argentina. https://www.unirioeditora.com.ar/wp-content/uploads/2018/10/978-987-688-265-1.pdf
Fontalvo-Herrera, T. J., & De La Hoz-Granadillo, E. (2020). Cluster method–discriminant analysis–data envelopment analysis to classify and evaluate business efficiency. Entramado, 16(2), 46-55. https://doi.org/10.18041/1900-3803/entramado.2.6437
Franco-López, J. A., Uribe-Gómez, J. A., & Agudelo-Vallejo, S. (2021). Key factors in productivity assessment: a case study. Revista CEA, 7(15), Artículo e1800. https://doi.org/10.22430/24223182.1800
Jaimes, L., Luzardo, M., & Rojas, M. D. (2018). Factores Determinantes de la Productividad Laboral en Pequeñas y Medianas Empresas de Confecciones del Área Metropolitana de Bucaramanga, Colombia. CIT Informacion Tecnologica, 29(5), 175–186. https://doi.org/10.4067/s0718-07642018000500175
Kaydos, W. (2020). Operational performance measurement: increasing total productivity. CRC Press. https://doi.org/10.4324/9780367802103
Kloke, J., & McKean, J. (2024). Nonparametric statistical methods using R. Chapman and Hall/CRC. https://doi.org/10.1201/9781003423546
Kozai, T., Uraisami, K., Kai, K., & Hayashi, E. (2022). Chapter 12 - Productivity: Definition and application. En Plant Factory Basics, Applications and Advances (pp. 197-216). Academic Press. https://doi.org/10.1016/B978-0-323-85152-7.00009-4
Modrak, V., Helo, P. T., & Matt, D. (2018). Complexity measures and models in supply chain networks. Complexity, 2018, Artículo 7015927. https://doi.org/10.1155/2018/7015927
Moreno-Rodríguez, D. C. (2022). Productivity in Service (Vol. 53). Editorial de la Universidad Pedagógica y Tecnológica de Colombia-UPTC.
Muñoz Choque, A. M. (2021). Estudio de tiempos y su relación con la productividad. Revista Enfoques, 5(17), 40–54. https://doi.org/10.33996/revistaenfoques.v5i17.104
Palange, A., & Dhatrak, P. (2021). Lean manufacturing a vital tool to enhance productivity in manufacturing. Materials Today: Proceedings, 46, 729-736. https://doi.org/10.1016/j.matpr.2020.12.193
Ramírez, G. G., Magaña, D. E., & Ojeda, R. N. (2022). Productividad, aspectos que benefician a la organización. Revisión sistemática de la producción científica. Trascender, contabilidad y gestión, 8(20), 189–208. https://doi.org/10.36791/tcg.v8i20.166
Riaño-Henao, C. A., & Larrea-Serna, O. L. (2021). Data envelopment analysis and its applications in sustainability. Ingeniare, (31), 11-19. https://doi.org/10.18041/1909-2458/ingeniare.31.8934
Tang, W., He, H., & Tu, X. M. (2023). Applied categorical and count data analysis. Chapman and Hall/CRC. https://doi.org/10.1201/9781003109815
Trizano-Hermosilla, I. (2017). Evaluation of reliability estimation under asymmetric, congeneric, categorical data conditions and in the presence of multidimensionality [Tesis de doctorado, Universidad Autónoma de Madrid]. http://hdl.handle.net/10486/678525
Ulloa-Pimienta, A. R., Sánchez-Trinidad, A. del C., & Balcazar-Sosa, M. T. de J. (2023). La productividad en la empresa de la industria de la transformación. Revista de Investigaciones Universidad Del Quindio, 35(1), 236–247. https://doi.org/10.33975/riuq.vol35n1.1156
Vartia, L. (2008). How do taxes affect investment and productivity? An industry-level analysis of OECD countries. OECD Economics Department Working Papers, (656), 1-42. http://doi.org/10.1787/230022721067
Yong-Chung, F., García-Salirrosas, E. E., Bonilla-Bermeo, J. D., & Medina de la Cruz, R. M. (2024). Capital psicológico en trabajadores profesionales peruanos: análisis de factores determinantes. Revista Venezolana De Gerencia, 29(108), 1615-1629. https://doi.org/10.52080/rvgluz.29.108.9
Publicado
2026-06-03
Cómo citar
Monsalve Fonnegra, G. P., Navarro Becerra, Y., & Sandoval Jure, H. E. (2026). Partial productivity measurement: validation for a transdisciplinary model. Revista Venezolana De Gerencia, 31(15), e31e1533. Recuperado a partir de http://www.produccioncientifica.luz.edu.ve/index.php/rvg/article/view/45656
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