Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa

Introducción: Las Micro, Pequeñas y Medianas Empresas (Mipymes) cuentan con serios problemas a la hora de evaluar de manera integral sus procesos logísticos, afectando de esta forma la toma acertada de decisiones generando un efecto negativo en el desempeño general de la empresa. Objetivo: Este trab...

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Autores:
Márquez Gutiérrez, Mateo
Carmona González, Guillermo Leon
Castro Zuluaga, Carlos Alberto
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
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Acceso en línea:
https://hdl.handle.net/11323/12275
https://doi.org/10.17981/ingecuc.18.1.2022.14
Palabra clave:
Maturity Models
Logistic Processes
Fuzzy Inference System
Fuzzy Logic
MSMEs
Modelos de Madurez
Procesos Logísticos
Sistema de Inferencia Difusa
Lógica Difusa
Mipymes
Rights
openAccess
License
INGE CUC - 2021
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repository_id_str
dc.title.spa.fl_str_mv Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa
dc.title.translated.eng.fl_str_mv Evaluation proposal of the logistic maturity of Micro, Small and Medium Enterprises based on fuzzy logic
title Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa
spellingShingle Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa
Maturity Models
Logistic Processes
Fuzzy Inference System
Fuzzy Logic
MSMEs
Modelos de Madurez
Procesos Logísticos
Sistema de Inferencia Difusa
Lógica Difusa
Mipymes
title_short Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa
title_full Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa
title_fullStr Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa
title_full_unstemmed Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa
title_sort Propuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusa
dc.creator.fl_str_mv Márquez Gutiérrez, Mateo
Carmona González, Guillermo Leon
Castro Zuluaga, Carlos Alberto
dc.contributor.author.spa.fl_str_mv Márquez Gutiérrez, Mateo
Carmona González, Guillermo Leon
Castro Zuluaga, Carlos Alberto
dc.subject.eng.fl_str_mv Maturity Models
Logistic Processes
Fuzzy Inference System
Fuzzy Logic
MSMEs
topic Maturity Models
Logistic Processes
Fuzzy Inference System
Fuzzy Logic
MSMEs
Modelos de Madurez
Procesos Logísticos
Sistema de Inferencia Difusa
Lógica Difusa
Mipymes
dc.subject.spa.fl_str_mv Modelos de Madurez
Procesos Logísticos
Sistema de Inferencia Difusa
Lógica Difusa
Mipymes
description Introducción: Las Micro, Pequeñas y Medianas Empresas (Mipymes) cuentan con serios problemas a la hora de evaluar de manera integral sus procesos logísticos, afectando de esta forma la toma acertada de decisiones generando un efecto negativo en el desempeño general de la empresa. Objetivo: Este trabajo tiene como objetivo principal proponer una metodología para evaluar integralmente la madurez de los procesos logísticos en Mipymes, que apoye una acertada toma de decisiones. Metodología: Los conceptos de modelos de madurez y de teoría de conjuntos difusos son la base para desarrollar la metodología de evaluación integral para las pequeñas organizaciones. Resultados: Los principales resultados incluyen un sistema de inferencia difusa (FIS) para la evaluación integral de los procesos logísticos de Mipymes, basado en el criterio de tomadores de decisiones expertos de grandes organizaciones. Asimismo, se plantea una matriz para clasificar el desempeño logístico de la empresa según el resultado obtenido por el FIS. Estos resultados son puestos a prueba en un selecto grupo de Mipymes manufactureras. Conclusiones: Los desarrollos son producto de investigaciones y experiencias de los autores en los temas del presente trabajo. La metodología desarrollada muestra resultados positivos en su implementación con un grupo de Mipymes. Finalmente, la metodología desarrollada permite llevar la experiencia de expertos al ambiente de Mipymes.
publishDate 2021
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dc.date.available.none.fl_str_mv 2021-11-02 00:00:00
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dc.date.issued.none.fl_str_mv 2021-11-02
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dc.relation.references.spa.fl_str_mv J. W. M. Bertrand and J.C.Fransoo, J. C. Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management., vol 22, no 2, pp. 241-264, 2002
M. Tracey, “The Importance of Logistics Efficiency to Customer Service and Firm Performance,” Int. J. Logist. Manag., vol. 9, no. 2, pp. 65–81, Jul. 1998 https://doi.org/10.1108/09574099810805843.
Green, K. W., Whitten, D., & Inman, R. A.. The impact of logistics performance on organizational performance in a supply chain context.Supply Chain Management: An International Journal., vol. 13, no, pp. 317 –327, 2008.
Departamento Nacional de Planeación, “Encuesta Nacional Logística,” 2018.
T. Tokar, “Behavioural research in logistics and supply chain management,” Int. J. Logist. Manag., vol. 21, no. 1, pp. 89–103, 2010 https://doi.org/10.1108/09574091011042197.
R. Puertas, L. Martí, and L. García, “Logistics performance and export competitiveness: European experience,” Empirica, vol. 41, no. 3, pp. 467–480, 2014 https://doi.org/10.1007/s10663-013-9241-z.
L. Martí, R. Puertas and L. García. The importance of the Logistics Performance Index in international trade. Applied economics, vol. 46, no 24, pp. 2982-2992, 2014.
L. J. Branicki, B. Sullivan-Taylor, and S. R. Livschitz, “How entrepreneurial resilience generates resilient SMEs,” Int. J. Entrep. Behav. Res., vol. 24, no. 7, pp. 1244–1263, 2018 https://doi.org/10.1108/IJEBR-11-2016-0396.
E. Pérez-Mergarejo, I. Pérez-Vergara, and Y. Rodríguez-Ruíz, “Maturity models and the suitability of its application in small and medium enterprises,” Ing. Ind., vol. 35, no. 2, pp. 149–160, 2014 https://doi.org/10.1016/j.jag.2015.12.005.
CMMI Product Team, “CMMI for Development, Version 1.3: Improving Processed for Better Products and Services,” Carnegie Mellon Univ. Softw. Eng. Inst., no. November, 2010 https://doi.org/CMU/SEI-2010-TR-033 ESC-TR-2010-033.
L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets Syst., vol. 1, no. 1, pp. 3–28, Jan. 1978. https://doi.org/10.1016/0165-0114(78)90029-5.
H.-J. Zimmermann,. Fuzzy set theory—and its applications. Springer Science & Business Media, 2011.
W. P. Wang, “A fuzzy linguistic computing approach to supplier evaluation,” Appl. Math. Model., vol. 34, no. 10, pp. 3130–3141, 2010 https://doi.org/10.1016/j.apm.2010.02.002.
L. A. Zadeh, “Fuzzy sets,” Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh pp. 394–432, 1996.
F. Herrera and L. Martínez, “A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 31, no. 2, pp. 227–234, 2001 https://doi.org/10.1109/3477.915345.
L. Osiro, F. R. Lima-Junior, and L. C. R. Carpinetti, “A fuzzy logic approach to supplier evaluation for development,” Int. J. Prod. Econ., vol. 153, pp. 95–112, 2014 https://doi.org/10.1016/j.ijpe.2014.02.009.
F. Ahmed, L. F. Capretz, and J. Samarabandu, “Fuzzy inference system for software product family process evaluation,” Inf. Sci. (Ny)., vol. 178, no. 13, pp. 2780–2793, 2008 https://doi.org/10.1016/j.ins.2008.03.002.
C. B. Pedroso, L. D. D. R. Calache, F. R. L. Junior, A. L. da Silva, and L. C. R. Carpinetti, “Proposal of a model for sales and operations planning (S&OP) maturity evaluation,” Production, vol. 27, pp. 1–17, 2017 https://doi.org/10.1590/0103-6513.20170024.
A. V. N. dos Santos, L. B. Felix, and J. G. V. Vieira, “Estudo da logística de distribuição física de um laticínio utilizando lógica fuzzy,” Production, vol. 22, no. 3, pp. 576–583, Jun. 2012 https://doi.org/10.1590/S0103-65132012005000036.
E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Int. J. Man. Mach. Stud., vol. 7, no. 1, pp. 1–13, 1975 https://doi.org/10.1016/S0020-7373(75)80002-2.
A. Amindoust, S. Ahmed, A. Saghafinia, and A. Bahreininejad, “Sustainable supplier selection: A ranking model based on fuzzy inference system,” Appl. Soft Comput., vol. 12, no. 6, pp. 1668–1677, Jun. 2012 https://doi.org/10.1016/j.asoc.2012.01.023.
F. R. L. Junior, L. Osiro, and L. C. R. Carpinetti, “A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules,” Appl. Soft Comput. J., vol. 13, no. 10, pp. 4133–4147, 2013 https://doi.org/10.1016/j.asoc.2013.06.020.
Y. E. Chan, N. Bhargava, and C. T. Street, “Having arrived: The homogeneity of high-growth small firms,” J. Small Bus. Manag., vol. 44, no. 3, pp. 426–440, 2006 https://doi.org/10.1111/j.1540-627X.2006.00180.x.
R. Gélinas, and Y. Bigras. The characteristics and features of SMEs: favorable or unfavorable to logistics integration?. Journal of Small Business Management., vol. 42, no 3, pp. 263-278, 2004.
T. A. Chin, A. B. A, Hamid, A. Rasli & R. Baharun, Adoption of supply chain management in SMEs. Procedia-Social and Behavioral Sciences, vol. 65, p. 614-619., 2012
A. Engelen, F. Heinemann, and M. Brettel, “Cross-cultural entrepreneurship research: Current status and framework for future studies,” J. Int. Entrep., vol. 7, no. 3, pp. 163–189, 2009 https://doi.org/10.1007/s10843-008-0035-5.
B. Asdecker, B., and V.Felch, Development of an Industry 4.0 maturity model for the delivery process in supply chains. Journal of Modelling in Management. vol. 13 No. 4, pp. 840-883, 2018
C. B. Pedroso, L. D. D. R. Calache, F. R. L. Junior, A. L. da Silva, and L. C. R. Carpinetti, “Proposal of a model for sales and operations planning (S&OP) maturity evaluation,” Production, vol. 27, pp. 1–17, 2017 https://doi.org/10.1590/0103-6513.20170024.
R. G. G. Caiado, L. F. Scavarda , L.O. Gavião, P. Ivson, D. L. de Mattos Nascimento and J.A. Garza-Reyes, A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management. International Journal of Production Economics, 231, 107883, 2021.
Pérez-Mergarejo, E., Pérez-Vergara, I., & Rodríguez-Ruíz, Y. (2014). Maturity models and the suitability of its application in small and medium enterprises. Ingeniería Industrial, 35(2), 149–160. https://www.researchgate.net/publication/284177930%0AModelos
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spelling Márquez Gutiérrez, MateoCarmona González, Guillermo LeonCastro Zuluaga, Carlos Alberto2021-11-02 00:00:002024-04-09T20:17:52Z2021-11-02 00:00:002024-04-09T20:17:52Z2021-11-020122-6517https://hdl.handle.net/11323/12275https://doi.org/10.17981/ingecuc.18.1.2022.1410.17981/ingecuc.18.1.2022.142382-4700Introducción: Las Micro, Pequeñas y Medianas Empresas (Mipymes) cuentan con serios problemas a la hora de evaluar de manera integral sus procesos logísticos, afectando de esta forma la toma acertada de decisiones generando un efecto negativo en el desempeño general de la empresa. Objetivo: Este trabajo tiene como objetivo principal proponer una metodología para evaluar integralmente la madurez de los procesos logísticos en Mipymes, que apoye una acertada toma de decisiones. Metodología: Los conceptos de modelos de madurez y de teoría de conjuntos difusos son la base para desarrollar la metodología de evaluación integral para las pequeñas organizaciones. Resultados: Los principales resultados incluyen un sistema de inferencia difusa (FIS) para la evaluación integral de los procesos logísticos de Mipymes, basado en el criterio de tomadores de decisiones expertos de grandes organizaciones. Asimismo, se plantea una matriz para clasificar el desempeño logístico de la empresa según el resultado obtenido por el FIS. Estos resultados son puestos a prueba en un selecto grupo de Mipymes manufactureras. Conclusiones: Los desarrollos son producto de investigaciones y experiencias de los autores en los temas del presente trabajo. La metodología desarrollada muestra resultados positivos en su implementación con un grupo de Mipymes. Finalmente, la metodología desarrollada permite llevar la experiencia de expertos al ambiente de Mipymes.Introduction: Micro, Small and Medium Enterprises (MSMEs) have serious problems when it comes to comprehensively assess their logistics processes, thus affecting the correct decision making, generating a negative effect on the overall performance of the company. Objective: This paper has as main objective to propose a methodology to fully evaluate the maturity of the logistic processes in MSMEs, which supports a successful decision making. Methodology: The concepts of maturity models and fuzzy set theory are the basis for developing the comprehensive evaluation methodology for small organizations. Results: The main results include a Fuzzy Inference System (FIS) for the comprehensive evaluation of MSME’s logistic processes, based on the criteria of expert decision makers of large organizations. Likewise, a matrix is ​​proposed to classify the logistic performance of the company according to the result obtained by the FIS. These results are tested in a select group of manufacturing MSMEs. Conclusions: The developments are the product of research and experiences of the authors in the topics of this paper. The developed methodology showed positive results in its implementation with a group of MSMEs. Finally, the developed methodology allowed to bring the experience of experts to the environment of MSMEs.application/pdftext/htmltext/xmlspaUniversidad de la CostaINGE CUC - 2021http://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/2885Maturity ModelsLogistic ProcessesFuzzy Inference SystemFuzzy LogicMSMEsModelos de MadurezProcesos LogísticosSistema de Inferencia DifusaLógica DifusaMipymesPropuesta de evaluación de la madurez logística de Micro, Pequeñas y Medianas Empresas basado en lógica difusaEvaluation proposal of the logistic maturity of Micro, Small and Medium Enterprises based on fuzzy logicArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inge CucJ. W. M. Bertrand and J.C.Fransoo, J. C. Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management., vol 22, no 2, pp. 241-264, 2002M. Tracey, “The Importance of Logistics Efficiency to Customer Service and Firm Performance,” Int. J. Logist. Manag., vol. 9, no. 2, pp. 65–81, Jul. 1998 https://doi.org/10.1108/09574099810805843.Green, K. W., Whitten, D., & Inman, R. A.. The impact of logistics performance on organizational performance in a supply chain context.Supply Chain Management: An International Journal., vol. 13, no, pp. 317 –327, 2008.Departamento Nacional de Planeación, “Encuesta Nacional Logística,” 2018.T. Tokar, “Behavioural research in logistics and supply chain management,” Int. J. Logist. Manag., vol. 21, no. 1, pp. 89–103, 2010 https://doi.org/10.1108/09574091011042197.R. Puertas, L. Martí, and L. García, “Logistics performance and export competitiveness: European experience,” Empirica, vol. 41, no. 3, pp. 467–480, 2014 https://doi.org/10.1007/s10663-013-9241-z.L. Martí, R. Puertas and L. García. The importance of the Logistics Performance Index in international trade. Applied economics, vol. 46, no 24, pp. 2982-2992, 2014.L. J. Branicki, B. Sullivan-Taylor, and S. R. Livschitz, “How entrepreneurial resilience generates resilient SMEs,” Int. J. Entrep. Behav. Res., vol. 24, no. 7, pp. 1244–1263, 2018 https://doi.org/10.1108/IJEBR-11-2016-0396.E. Pérez-Mergarejo, I. Pérez-Vergara, and Y. Rodríguez-Ruíz, “Maturity models and the suitability of its application in small and medium enterprises,” Ing. Ind., vol. 35, no. 2, pp. 149–160, 2014 https://doi.org/10.1016/j.jag.2015.12.005.CMMI Product Team, “CMMI for Development, Version 1.3: Improving Processed for Better Products and Services,” Carnegie Mellon Univ. Softw. Eng. Inst., no. November, 2010 https://doi.org/CMU/SEI-2010-TR-033 ESC-TR-2010-033.L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets Syst., vol. 1, no. 1, pp. 3–28, Jan. 1978. https://doi.org/10.1016/0165-0114(78)90029-5.H.-J. Zimmermann,. Fuzzy set theory—and its applications. Springer Science & Business Media, 2011.W. P. Wang, “A fuzzy linguistic computing approach to supplier evaluation,” Appl. Math. Model., vol. 34, no. 10, pp. 3130–3141, 2010 https://doi.org/10.1016/j.apm.2010.02.002.L. A. Zadeh, “Fuzzy sets,” Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh pp. 394–432, 1996.F. Herrera and L. Martínez, “A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 31, no. 2, pp. 227–234, 2001 https://doi.org/10.1109/3477.915345.L. Osiro, F. R. Lima-Junior, and L. C. R. Carpinetti, “A fuzzy logic approach to supplier evaluation for development,” Int. J. Prod. Econ., vol. 153, pp. 95–112, 2014 https://doi.org/10.1016/j.ijpe.2014.02.009.F. Ahmed, L. F. Capretz, and J. Samarabandu, “Fuzzy inference system for software product family process evaluation,” Inf. Sci. (Ny)., vol. 178, no. 13, pp. 2780–2793, 2008 https://doi.org/10.1016/j.ins.2008.03.002.C. B. Pedroso, L. D. D. R. Calache, F. R. L. Junior, A. L. da Silva, and L. C. R. Carpinetti, “Proposal of a model for sales and operations planning (S&OP) maturity evaluation,” Production, vol. 27, pp. 1–17, 2017 https://doi.org/10.1590/0103-6513.20170024.A. V. N. dos Santos, L. B. Felix, and J. G. V. Vieira, “Estudo da logística de distribuição física de um laticínio utilizando lógica fuzzy,” Production, vol. 22, no. 3, pp. 576–583, Jun. 2012 https://doi.org/10.1590/S0103-65132012005000036.E. H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Int. J. Man. Mach. Stud., vol. 7, no. 1, pp. 1–13, 1975 https://doi.org/10.1016/S0020-7373(75)80002-2.A. Amindoust, S. Ahmed, A. Saghafinia, and A. Bahreininejad, “Sustainable supplier selection: A ranking model based on fuzzy inference system,” Appl. Soft Comput., vol. 12, no. 6, pp. 1668–1677, Jun. 2012 https://doi.org/10.1016/j.asoc.2012.01.023.F. R. L. Junior, L. Osiro, and L. C. R. Carpinetti, “A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules,” Appl. Soft Comput. J., vol. 13, no. 10, pp. 4133–4147, 2013 https://doi.org/10.1016/j.asoc.2013.06.020.Y. E. Chan, N. Bhargava, and C. T. Street, “Having arrived: The homogeneity of high-growth small firms,” J. Small Bus. Manag., vol. 44, no. 3, pp. 426–440, 2006 https://doi.org/10.1111/j.1540-627X.2006.00180.x.R. Gélinas, and Y. Bigras. The characteristics and features of SMEs: favorable or unfavorable to logistics integration?. 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Ingeniería Industrial, 35(2), 149–160. https://www.researchgate.net/publication/284177930%0AModelos194180118https://revistascientificas.cuc.edu.co/ingecuc/article/download/2885/4300https://revistascientificas.cuc.edu.co/ingecuc/article/download/2885/4651https://revistascientificas.cuc.edu.co/ingecuc/article/download/2885/4652Núm. 1 , Año 2022 : (Enero - Junio)PublicationOREORE.xmltext/xml2729https://repositorio.cuc.edu.co/bitstreams/f71d298b-ae2c-4344-a5ba-6e50271a1065/download73058c9e47a8bad98db98a140b1e46e9MD5111323/12275oai:repositorio.cuc.edu.co:11323/122752024-09-17 10:53:11.948http://creativecommons.org/licenses/by-nc-nd/4.0INGE CUC - 2021metadata.onlyhttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.co

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