Late or inaccurate road freight deliveries and their relationship to working conditions of drivers: A model of logistic regression

Luis David Berrones Sanz, Patricia Cano-Olivos, Diana Sánchez-Partida, José Luis Martínez Flores


Introduction: Cargo drivers are essential to maintain the efficiency and competitiveness of the supply chain, which subjects them to demands and strong pressures derived from their working conditions. However, it is not clear how their working conditions affect the efficiency of their activities. The objective of this work is to identify how the working conditions of cargo drivers influence on-time delivery of shipments and thus impact the competitiveness of the supply chain.

Methodology: 26,312 shipments over a period of two years were analyzed. These shipments were managed by a manufacturing company with an 80% market share in light construction systems in Mexico. The shipments were identified as correct or incorrect delivery caused by the driver. Due to the binary nature of this variable, a logistic regression was applied to the data, in order to analyze how working conditions were related to the non-compliance of on-time delivery events.

Results: The model generated through the logistic regression provided a 96.3 percent global precision of the sample in predicting the failed delivery of the shipment. Eleven independent variables were obtained from the analyzed shipments, and five of those variables –dissatisfaction, level of stress, time of transit, type of vehicle, and medical coverage­– were strongly associated with the inability of drivers to deliver the cargo on time.

Discussion or Conclusions: Results show a promising tool to provide meaningful interpretations that may be used for future improvements in the development of cargo and freight companies. Results also show the importance of working conditions and the way they influence the compliance of on-time delivery


driver trucking; supply chain; working conditions; logistic regression binary; on-time delivery


Al-Ghamdi, A. S. (2002). Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis & Prevention, 34(6), 729–741.

Álvarez-Cáceres, R. (2007). Estadística aplicada a las ciencias de la salud. Madrid: Díaz de Santos.

Apostolopoulos, Y., Sönmez, S., Shattell, M. M., & Belzer, M. (2010). Worksite-induced morbidities among truck drivers in the United States. AAOHN journal: official journal of the American Association of Occupational Health Nurses, 58(7), 285–296.

Ballou, R. H. (2004). Business logistics/supply chain management: Planning, organizing, and controlling the supply chain (5. ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Bathija, G., Bant, D., Itagimath, S., Lokare, L., Godbole, M., Nekar, M.,. . . Reddi, K. (2014). A study on stress among government city bus drivers in Hubli. International Journal of Biomedical Research, 5(2), 102.

Berrones, L. D. (2017). Choferes del autotransporte de carga en México: investigaciones sobre condiciones laborales y la cadena de suministro. Revista Transporte y Territorio, 1(17), 252–267.

Berrones, L. D., Cano, P., Sánchez, D., & Martínez, J. (2018). Lesiones, enfermedades y accidentes de trabajo de los conductores del autotransporte de carga en México. Acta Universitaria. (Forthcoming).

Bigert, C. (2004). Time trends in the incidence of myocardial infarction among professional drivers in Stockholm 1977-96. Occupational and environmental medicine, 61(12), 987–991.

Blanquart, C., & Burmeister, A. (2009). Evaluating the performance of freight transport: A service approach. European Transport Research Review, 1(3), 135–145.

Chopra, S., & Meindl, P. (2016). Supply chain management: Strategy, planning, and operation (Sixth Edition). Boston: Pearson.

Chun-Chieh, C., Li-Jie, S., Yu-Ling, L., Kuan-Yeng, T., Kwan-Yu, C., Chih-Jung, Y.,. . . Ruey-Hong, W. (2010). Shift work and arteriosclerosis risk in professional bus drivers. Annals of epidemiology, 20(1), 60–66.

DGAF. (2015). Estadística básica del Autotransporte Federal 2015. México: Dirección General de Autotransporte Federal (DGAF); Secretaría de Comunicaciones y Transportes.

Fiuza, M. D., & Rodríguez, J. (2000). La regresión logística: Una herramienta versátil. Nefrología, 20(6), 495–500.

Garcia, L., Dias, C., Pereira, L., Cesar, M., Romero, D., & Leyton, V. (2016). Truck drivers’ traffic accidents in the State of São Paulo: prevalence and predictors. Ciencia & saude coletiva, 21(12), 3757–3767.

Hilakivi, I., Veilahti, J., Asplund, P., Sinivuo, J., Laitinen, L., & Koskenvuo, K. (1989). A sixteen-factor personality test for predicting automobile driving accidents of young drivers. Accident; analysis and prevention, 21(5), 413–418.

INEGI. (2017). Encuesta Nacional de Ocupación y Empleo (ENOE). Población de 15 años y más de edad: Población ocupada. Retrieved from

Lámbarry, F., Trujillo, M., & Cumbres, C. (2016). Stress from an administrative perspective in public transport drivers in Mexico City: Minibus and metrobus. Estudios Gerenciales. Advance online publication.

Magee, C., Stefanic, N., Caputi, P., & Iverson, D. (2011). Occupational factors and sick leave in Australian employees. Journal of occupational and environmental medicine, 53(6), 627–632.

Martínez, J., Moreno, M., Morales, M., Herrera, A., Balbuena, J., Pérez, J.,. . . Zamora, A. (2015). Manual estadístico del sector transporte 2015. Sanfandila, Qro.: Instituto Mexicano del Transporte (IMT).

Martínez, L., Oviedo, O., & Luna, C. (2015). Impact of working conditions on the quality of working life: Case manufacturing sector colombian Caribbean Region. DYNA, 82(194), 194–203.

Mehrjoo, S., & Bashiri, M. (2013). An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: An automotive industry case. Journal of Industrial Engineering International, 9(1), 1–12.

Müller, C. F., Monrad, T., Biering-Sørensen, F., Darre, E., Deis, A., & Kryger, P. (1999). The influence of previous low back trouble, general health, and working conditions on future sick-listing because of low back trouble: A 15-year follow-up study of risk indicators for self-reported sick-listing caused by low back trouble. Spine, 24(15), 1562–1570.

Nogueira, G., Carvalho, D., Siqueira, C., Borges, P., de Almeida, W., Sisinno, L., & Landmann, C. (2016). Alcohol abuse and involvement in traffic accidents in the Brazilian population, 2013. Ciencia & saude coletiva, 21(12), 3777–3786.

Ordaz, E., Maqueda, J., Silva, A., Asúnsolo, Á., Prieto, D., & Olmedo, O. (2007). Salud y Condiciones de Trabajo en el Transporte de Mercancías por Carretera. Madrid, España: Instituto de Salud Carlos III.

Paydar, S., Endut, I. R., & Lajevardi, A. (2013). Environmental determinants of RFID adoption in retail supply chain, a binary logistic regression analysis. In IEEE International Conference on RFID-Technologies and Applications, RFID-TA 2013. Malaysia: IEEE.

Pérez, C. (2004). Técnicas de análisis multivariante de datos: Aplicaciones con SPSS. Madrid: Pearson Prentice Hall.

Sánchez-Gómez, M. G. (2008). Cuantificación y generación de valor en la cadena de suministro extendida. León: Del Blanco.

Shin, S. Y., Lee, C. G., Song, H. S., Kim, S. H., Lee, H. S., Jung, M. S., & Yoo, S. K. (2013). Cardiovascular Disease Risk of Bus Drivers in a City of Korea. Annals of Occupational and Environmental Medicine, 25(1), 34.

Sluis, S., & Giovanni, P. de. (2016). The selection of contracts in supply chains: An empirical analysis. Journal of Operations Management, 41, 1–11.

Suwazono, Y., Dochi, M., Kobayashi, E., Oishi, M., Okubo, Y., Tanaka, K., & Sakata, K. (2008). Benchmark duration of work hours for development of fatigue symptoms in Japanese workers with adjustment for job-related stress. Risk analysis: an official publication of the Society for Risk Analysis, 28(6), 1689–1698.

Van-Der-Beek, A. (2012). World at work: truck drivers. Occupational and environmental medicine, 69(4), 291–295.

Vargas, J. M. (2013). Kilómetro a kilómetro guachicoleándose la vida. El caso del hombre camión en una empresa Queretana. Universidad Autónoma de Querétaro (UAQ), Querétaro.

Villar, E., Delgado, J., & Barrilao, P. (2015). Job Satisfaction Among Spanish Tax Administration Employees: A Logistic Regression Analysis. Journal of Labor Research, 36(2), 210–223.

Zhu, Y., Xie, C., Sun, B., Wang, G. J., & Yan, X. G. (2016). Predicting China's SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models. Sustainability, 8(5), 433.



  • There are currently no refbacks.

Copyright (c) 2018 Nova Scientia

Nova Scientia, year 10, issue 20, May – October 2018, is a biannual journal printed by the Universidad De La Salle Bajío, with its address: Av. Universidad 602, Col. Lomas del Campestre, C. P. 37150, León, Gto. México. Phone: (52) 477 7108500, e-mail: Chief editor: Ph.D. Ramiro Rico Martínez. ISSN 2007 - 0705. Copyright for exclusive use No. 04-2008-092518225500/102, Diffusion rights via computer net 04 - 2008 – 121011584800-203 both granted by the Instituto Nacional del Derecho de Autor.

Editor responsible for updating this issue: Direction of Research Department of the Universidad De La Salle Bajío, last updated on May 25th, 2018.