Optimal supplier selection through reliability and cost analysis based on the Weibull distribution applied to the automotive industry

Authors

DOI:

https://doi.org/10.26439/ing.ind2026.n50.8672

Keywords:

Weibull distribution, supplier selection, mean time between failures, reliability, shape parameter, scale parameter

Abstract

This paper presents the application of the two-parameter Weibull distribution to a case study to evaluate three suppliers and determine which is the best. Based on each supplier’s failure times, the Weibull shape and scale parameters are estimated, along with the mean time between failures (MTBF) and reliability curve for each supplier. Finally, the annual cost of component consumption is quantified. It is concluded that the second supplier is the best option, as it achieved the highest MTBF of 384,6 hours, exceeding supplier 1 by 28,5 % and supplier 3 by 24,2 %. Additionally, it presented the lowest annual cost of $ 27 579, despite having the highest unit price.

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Author Biographies

  • Juan Manuel Izar Landeta, Facultad de Ingeniería Industrial, Instituto Tecnológico Superior de Rioverde, México

    Doctor en Administración por la Universidad Autónoma de San Luis Potosí e ingeniero industrial por la Universidad de Lima. Es profesor investigador del Instituto Tecnológico Superior de Rioverde, con más de 40 años de experiencia en la academia, tiene 18 libros publicados, más de 125 artículos de investigación y divulgación, más de 80 ponencias en congresos nacionales e internacionales, y es miembro nivel II del Sistema Nacional de Investigadores en México. Sus áreas de especialidad son la administración y la ingeniería, específicamente en investigación de operaciones, estadística, ingeniería económica, gestión y evaluación de proyectos, finanzas, gestión de la calidad, administración de la educación y desempeño de las organizaciones.

  • Ivanna López Reyna, Facultad de Ingeniería Industrial, Instituto Tecnológico Superior de Rioverde, México

    Ingeniera en Mecatrónica por la Universidad Autónoma de San Luis Potosí, campus Rioverde. Se desempeña como docente en el Instituto Tecnológico Superior de Rioverde y en la Facultad de Estudios Profesionales Zona Media. Su experiencia profesional se enfoca en el área de automatización, el diseño asistido por computadora y la aplicación de tecnologías en procesos industriales. Ha participado en la elaboración de trabajos de investigación académica.

References

Abuzaid, A. N., Alateeq, M. M., Madadha, S.-a. M., Al Sharari, F. E. A., & Alsbou, M. K. (2024). The effect of suppliers’ green and traditional selection criteria in supply chain management on purchasing firms’ performance. Sustainability, 16(15), 6276, https://doi.org/10.3390/su16156276

Al-Moisheer, A. S., Sultan, K. S., & Radwan, H. M. M. (2025). A novel adaptable Weibull distribution and its applications. Axioms, 14(7), 490. https://doi.org/10.3390/axioms14070490

Alshanbari, H. M., Ahmad, Z., El-Bagoury, A. A.-A. H., Odhah, O. H., & Rao, G. S. (2024). A new modification of the Weibull distribution: Model, theory, and analyzing engineering data sets. Symmetry, 16(5), 611. https://doi.org/10.3390/sym16050611

Anjos, J. S. (2008, 7-9 de octubre). How to use Weibull as tool for a decision making on field issues [Presentación de escrito]. 2008 SAE Brasil Congress and Exhibit, Sao Paulo, Brasil. https://doi.org/10.4271/2008-36-0192

Cooper, M. (2024, 9 de julio). Understanding supplier selection criteria: Perspectives from procurement professionals in diverse industries. Preprints.org, https://doi.org/10.20944/preprints202407.0744.v1

Dickson, G. W. (1966). An analysis of vendor selection systems and decisions. Journal of Purchasing, 2(1), 5-17. https://doi.org/10.1111/j.1745-493X.1966.tb00818.x

Ferrisi, S., Cappellari, P., Guido, R., Umbrello, D., & Ambrogio, G. (2025). Application of two-parameter Weibull distribution for predictive maintenance: A case study. Procedia Computer Science, 253, 3160-3168. https://doi.org/10.1016/j.procs.2025.02.041

Gallegos Londoño, C. M., García Mora, F. A., & Caicedo Benavides, F. U. (2022). Applications of the Weibull distribution in the study of reliability. Conciencia Digital, 5(3), 48-67. https://doi.org/10.33262/concienciadigital.v5i3.2203

Gondres Torné, I., Báez Prieto, R., Lajes Choy, S., & del Castillo Serpa, A. (2013). Determinación de la confiabilidad en interruptores de potencia: caso de estudio. Ingeniare, 21(2), 271-278. https://doi.org/10.4067/s0718-33052013000200010

Jardine, A. K. S., & Tsang, A. H. C. (2021). Maintenance, Replacement, and Reliability. Theory and Applications (3.ª ed.). CRC Press. https://doi.org/10.1201/9780429021565

Okoh, C., Roy, R., & Mehnen, J. (2017). Predictive maintenance modelling for through-life engineering services. Procedia CIRP, 59, 196-201. https://doi.org/10.1016/j.procir.2016.09.033

Onukwulu, E. C., Odochi-Agho, M., & Eyo-Udo, N. L. (2024). Innovations in supplier evaluation: Frameworks and techniques for supply chain resilience. International Journal of Research and Scientific Innovation, 11(12), 610-623. https://doi.org/10.51244/IJRSI.2024.11120056

Plaskitt, R., Wynn-Jones, C., & Halfpenny, A. (2024). Weibull or lognormal distribution to characterize fatigue life scatter – which is more suitable? – continued. Fatigue of Aircraft Structures, 2024(16), 131-146. https://doi.org/10.2478/fas-2024-0011

Prasetya, M. A., Adhim, F. I., & Al Kindhi, B. (2023). Implementation of Weibull analysis method in designing predictive maintenance for medical mask machine. En 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (pp. 519-527). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICAMIMIA60881.2023.10427705

Qin, S., Wang, B. X., Tsai, T.-R., & Wang, X. (2023). The prediction of remaining useful lifetime for Weibull k-out-of-n load-sharing system. Reliability Engineering & System Safety, 233, 109091. https://doi.org/10.1016/j.ress.2023.109091

Safari, M. A. M., Masseran, N., Majid, M. H. A., & Tajuddin, R. R. M. (2025). Robust estimation of the three parameter Weibull distribution for addressing outliers in reliability analysis. Scientific Reports, 15, 11516. https://doi.org/10.1038/s41598-025-96043-1

Shu, M.-H., Wu, C.-W., Hsu, B.-M., & Wang, T.-C. (2021). Lifetime performance-qualified sampling system under a Weibull distribution with failure-censoring. Quality Engineering, 33(3), 404-416. https://doi.org/10.1080/08982112.2021.1874014

Shu, M.-H., Wu, C.-W., Hsu, B.-M., & Wang, T.-C. (2022). Standardized lifetime-capability and warranty-return-rate-based suppliers qualification and selection with accelerated Weibull-life type II testing data. Communications in Statistics-Theory and Methods, 51(23), 8186-8204. https://doi.org/10.1080/03610926.2021.1890124

Singh, P., & Liao, T. W. (2025). Multi-criteria group decision-making using ambiguous sets, Weibull distribution, and aggregation operators: A case study in optimal vendor selection for office supplies. Systems and Soft Computing, 7, 200283. https://doi.org/10.1016/j.sasc.2025.200283

Sulewski, P., & Drapella, A. (2025). Weibull distribution with linear shape function. Applied Sciences, 15(20), 11222. https://doi.org/10.3390/app152011222

Thavorn, E., & Sudasna-Na-Ayudthya, P. (2022). The effects of Weibull distribution on supplier comparison using lower process capability index: A Case Study. Trends in Sciences, 19(3), 2158. https://doi.org/10.48048/tis.2022.2158

Wang, J., Geng, H., & Li, P. (2025). A software reliability model for open source big data systems based on Weibull-Weibull distribution. Scientific Reports, 15, 14670. https://doi.org/10.1038/s41598-025-98942-9

Wei, Z., Gupta, R. C., Ng, C. M., & Ong, S. H. (2025). A Weibull COMP-Bessel distribution to analyse life testing data. Journal of Statistical Theory and Practice, 19, Artículo 95. https://doi.org/10.1007/s42519-025-00508-5

Wu, T., Blackhurst, J., & O’Grady, P. (2007). Methodology for supply chain disruption analysis. International Journal of Production Research, 45(7), 1665-1682. https://doi.org/10.1080/00207540500362138

Yu, W. (2024). Application of Weibull analysis in the optimization of civil aviation aircraft engineering reliability management. Academic Journal of Engineering and Technology Science, 7(1), 68-73. https://doi.org/10.25236/ajets.2024.070111

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Published

2026-06-15

Issue

No. 50 (2026)

Section

Quality and environment

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Izar Landeta, J. M., & López Reyna, I. (2026). Optimal supplier selection through reliability and cost analysis based on the Weibull distribution applied to the automotive industry. Ingeniería Industrial, 50, 218-234. https://doi.org/10.26439/ing.ind2026.n50.8672