Gestión de impagos bajo incertidumbre en pequeñas y medianas empresas: aplicaciones con redes neuronales
Palabras clave:
Gestión de impagos, expertizaje, lógica difusa, redes neuronales
Resumen
El objetivo de la investigación es desarrollar herramientas de vanguardia que ofrece la lógica difusa como la técnica del expertizaje que alimente un grafo de redes neuronales que conduzca a determinar las mejores acciones, estrategias o políticas de gestión que permitan tomar decisiones eficientes frente a los impagos por parte de clientes de las pequeñas y medianas empresas manufactureras de la ciudad de Cuenca Ecuador. En el plano metodológico, la investigación es de tipo explicativo, con enfoque cuantitativo, cuyo propósito es acotar la incertidumbre, subjetividad, vaguedad e imprecisión en la información obtenida de los expertos funcionarios de tesorería, directivos y gerentes de las empresas estudiadas. Los resultados evidencian las tres acciones más importantes a considerar, “Acción judicial”, “Denegación de compra” y “Tercera notificación personal”, representan el máximo grado de presunción o la posibilidad más alta de ocurrencia de esta acción. A partir del desarrollo de estas herramientas de vanguardia, los directivos y gerentes de las pymes podrán tomar decisiones eficientes y correctivas con la finalidad de reducir la brecha de impagos por los clientes, garantizando una mayor estabilidad financiera y un crecimiento sostenible de las organizaciones.
Citas
Achanga, P., Shehab, E., Roy, R., & Nelder, G. (2012). A fuzzy-logic advisory system for lean manufacturing within SMEs. International Journal of Computer Integrated Manufacturing, 25(9), 839-852. https://doi.org/10.1080/0951192X.2012.665180
Agrawal, R., Asokan, P., & Vinodh, S. (2017). Benchmarking fuzzy logic and ANFIS approaches for leanness evaluation in an Indian SME: a case study. Benchmarking: An International Journal, 24(4), 973-993. https://doi.org/10.1108/BIJ-06-2016-0083
Aguiar, F. (2004), Teoría de la decisión e incertidumbre: modelos normativos y descriptivos. Empiria Revista de metodología de ciencias sociales, (8), 139-160.
Aguilar, A. (2015). Políticas de acceso para el crédito de las Pymes. Revista del observatorio Pyme, 1 (1), 2-5.
Almadi, A. I., Al Mamlook, R. E., Almarhabi, Y., Ullah, I., Jamal, A., & Bandara, N. (2022). A fuzzy-logic approach based on driver decision-making behavior modeling and simulation. Sustainability, 14(14), 8874. https://doi.org/10.3390/su14148874
Andriani, M., Samadhi, T. A., Siswanto, J., & Suryadi, K. (2019). Knowledge management strategy: an organisational development approach. Business process management journal, 25(7), 1474-1490. https://doi.org/10.1108/BPMJ-07-2018-0191
Ávila, P., Mendoza, A., Pinargote, H., y Fernández, L. (2019). Decisiones de financiamiento de las pymes ecuatorianas. SUPLEMENTO CICA, 3 (8), 03-66
Bams, D., Pisa, M., & Wolff, C. (2015). Ripple effects from industry defaults. Centre for Economic Policy Research.
Bar-Gill, O., & Ben-Shahar, O. (2016). Optimal Defaults in Consumer Markets. The Journal of Legal Studies, 45(S2), S137-S161.
Bartholdy, J., & Mateus, C. (2008). Financing of SME’s: an asset side story. Available at SSRN 1098347. http://dx.doi.org/10.2139/ssrn.1098347
Besné, A. G., Luna, D., Cobos, A., Lameiras, D., Ortiz-Moreno, H., & Güereca, L. P. (2018). A methodological framework of eco-efficiency based on fuzzy logic and Life Cycle Assessment applied to a Mexican SME. Environmental Impact Assessment Review, 68, 38-48. https://doi.org/10.1016/j.eiar.2017.10.008
Boloș, M. I., Bradea, I. A., & Delcea, C. (2019). A fuzzy logic algorithm for optimizing the investment decisions within companies. Symmetry, 11(2), 186. https://doi.org/10.3390/sym11020186
Byukusenge, E., Munene, J., & Orobia, L. (2016). Knowledge management and business performance: Mediating effect of innovation. Journal of Business and Management Sciences, 4(4), 82–92. https://nru.uncst.go.ug/handle/123456789/6025
Carneiro, A. (2000). How Does Knowledge Management Influence Innovation and Competitiveness? Journal of Knowledge Management, 4(2), 87-98. https://doi.org/10.1108/13673270010372242
Casanovas M, Fernández A. (2003). La gestión de la tesorería en la incertidumbre. España-Madrid: Ediciones Pirámide.
Córdova, J. F. D., Molina, E. C., & López, P. N. (2017). Fuzzy logic and financial risk. A proposed classification of financial risk to the cooperative sector. Contaduría y administración, 62(5), 1687-1703. https://doi.org/10.1016/j.cya.2017.10.001
Díaz Rodríguez, H. E., Sosa Castro, M., & Cabello Rosales, A. (2019). Determinantes del endeudamiento de los hogares en México: un análisis con redes neuronales. Problemas del desarrollo, 50(199), 115-140.
Dolling, O. R., & Varas, E. A. (2002). Artificial neural networks for streamflow prediction. Journal of hydraulic research, 40(5), 547-554. https://doi.org/10.1080/00221680209499899
Franco, M. D. C., Gómez, F., y Serrano, K. (2019). Determinantes del acceso al crédito para la PYME del Ecuador. Conrado, 15(67), 295-303.
García-Muiña, F. E., González-Sánchez, R., Ferrari, A. M., Volpi, L., Pini, M., Siligardi, C., & Settembre-Blundo, D. (2019). Identifying the equilibrium point between sustainability goals and circular economy practices in an Industry 4.0 manufacturing context using eco-design. Social sciences, 8(8), 241. https://doi:10.3390/socsci8080241
Gubareva, E. A., & Shemyakova, E. (2021). Neural networks in the digital economy. Socio-economic Systems: Paradigms for the Future, 827-835. https://doi.org/10.1007/978-3-030-56433-9_87
Herghiligiu, I. V., Robu, I. B., Pislaru, M., Vilcu, A., Asandului, A. L., Avasilcăi, S., & Balan, C. (2019). Sustainable environmental management system integration and business performance: A balance assessment approach using fuzzy logic. Sustainability, 11(19), 5311. https://doi.org/10.3390/su11195311
Indaryanti, T. R., Lestari, R., & Fitriah, E. (2020, September). Intellectual Capital Disclosure and CSR Disclosure on Company Performance. In International Conference on Management, Accounting, and Economy (ICMAE 2020) (pp. 81-85). Atlantis Press.
Kaufmann, A., y Gil-Aluja, J. (1986). Introducción de la teoría de los subconjuntos borrosos a la gestión de las empresas. Santiago de Compostela, España. Milladoiro.
Kaufmann, A., & Gil-Aluja, J. (1987). Técnicas operativas de gestión para el tratamiento de la incertidumbre. Barcelona: Hispano-europea.
Kaufmann, A., y Gil-Aluja J. (1989). Modelos para la investigación de efectos olvidados. Milladoiro.
Kottaridi, C., Louloudi, K., & Karkalakos, S. (2019). Human capital, skills and competencies: Varying effects on inward FDI in the EU context. International Business Review, 28(2), 375-390. https://doi.org/10.1016/j.ibusrev.2018.10.008
Kosko, B. (1995). Pensamiento borroso: la nueva ciencia de la lógica borrosa. Barcelona, España: Editorial Crítica.
Kusa, R., Duda, J., & Suder, M. (2021). Explaining SME performance with fsQCA: The role of entrepreneurial orientation, entrepreneur motivation, and opportunity perception. Journal of Innovation, 6(4), 234-245. https://doi.org/10.1016/j.jik.2021.06.001
Lee, C. S., & Wong, K. Y. (2017). A fuzzy logic-based knowledge management performance measurement system for SMEs. Cybernetics and Systems, 48(4), 277-302. https://doi.org/10.1080/01969722.2017.1284532
Leoshchenko, S., Oliinyk, A., Subbotin, S., & Zaiko, T. (2020). Using recurrent neural networks for data-centric business. In Data-Centric Business and Applications: Evolvements in Business Information Processing and Management. Springer International Publishing 3, 73-91. https://doi.org/10.1007/978-3-030-35649-1_4
Luna Altamirano, K. A., Sarmiento Espinoza, W. H., y Andrade Cordero, C. F. (2019). Matriz de efectos olvidados: Caso sector industrial de Cuenca-Ecuador. Revista de Ciencias Sociales, 25(2), 96-111. https://doi.org/10.31876/rcs.v25i2.27339
Luna Altamirano, K. A., Sarmiento Espinoza, W. H., & Andrade Pesantez, D. J. (2022). Financial ratios with fuzzy logic approach. New estimation perspective. Revista Venezolana De Gerencia, 27(99), 959-972. https://doi.org/10.52080/ rvgluz.27.99.7
Macwan, N., & Sajja, P. S. (2014). Fuzzy logic: an effective user interface tool for decision support system. International Journal of Engineering Science and Innovative Technology (IJESIT), 3(3), 278-283.
Maes, J., & Sels, L. (2014). SMEs’ radical product innovation: The role of internally and externally oriented knowledge capabilities. Journal of Small Business Management, 52(1), 141-163. https://doi.org/10.1111/jsbm.12037
Mählmeyer, V., Rampeltshammer, L., & Hertwig, M. (2017). European Works Councils during the financial and economic crisis: Activation, stagnation, or disintegration? European Journal of Industrial Relations, 23(3), 225–242. doi:10.1177/0959680116685951
Manjarres, D., Landa-Torres, I., & Andonegui, I. (2017). An Intelligent Decision Support System for Assessing the Default Risk in Small and Medium-Sized Enterprises. In Artificial Intelligence and Soft Computing: 16th International Conference, ICAISC 2017, Zakopane, Poland, June 11-15, 2017, Proceedings, Part II 16 (533-542). Springer International Publishing.
Medina, S. (2006). Estado de la cuestión acerca del uso de la lógica difusa en problemas financieros. Cuadernos de Administración, 32 (19). 195-223.
Meier, J., Andor, M. A., Doebbe, F., Haddaway, N., & Reisch, L. A. (2021). Can green defaults reduce meat consumption?. Food Policy, 110, 102298. http://dx.doi.org/10.2139/ssrn.3903160
Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital signal processing, 73, 1-15. https://doi.org/10.1016/j.dsp.2017.10.011
Muhamediyeva, D., & Abdul-Azalova, M. (2022). Application of the theory of fuzzy logic for analysis of management systems of business processes of an enterprise. Scientific Collection InterConf, 22 (113), 467-471. https://doi.org/10.51582/interconf.19-20.06.2022.049
Niemeyer, J., Rudolf, S., Kvaratskhelia,L. Mennenga, M., & Herrmann, C. (2022). A creativity-driven Case-Based Reasoning Approach for the systematic Engineering of Sustainable Business Models. Procedia CIRP, 105, 470-475, https://doi.org/10.1016/j.procir.2022.02.078.
Olarte, J. (2006). Incertidumbre y evaluación de riesgos financieros. Scientia Et Technica, 12(32), 347- 350. https://www.redalyc.org/articulo.oa?id=84911652061
Pérez, F. O., & Fernández, H. (2007). Las redes neuronales y la evaluación del riesgo de crédito. Revista Ingenierías Universidad de Medellín, 6(10), 77-91. https://revistas.udem.edu.co/index.php/ingenierias/article/view/225
Pérez, I., y Melero, R. (2006). Evaluación de aspirantes a docentes en la Facultad de Ingeniería de la Universidad del Valle del Momboy mediante un modelo difuso de soporte de decisiones. Revista Telos, 8(3), 454- 474. https://ojs.urbe.edu/index.php/telos/article/view/2703
Piñeiro-Chousa, J., López-Cabarcos, M., Romero-Castro, N., & Pérez-Pico, A. (2020). Innovation, entrepreneurship and knowledge in the business scientific field: Mapping the research front. Journal of Business Research, 115, 475-485. https://doi.org/10.1016/j.jbusres.2019.11.045
Reig, J., y González, J. (2002). Modelo borroso de control de gestión de materiales. Revista Española de Financiación y Contabilidad, 31(12). 431-459.
Rhee, J., Park, T., & Lee, D. H. (2010). Drivers of innovativeness and performance for innovative SMEs in South Korea: Mediation of learning orientation. Technovation, 30(1), 65-75. https://doi.org/10.1016/j.technovation.2009.04.008
Rico, M., y Tinto, J. (2008). Matemática borrosa: Algunas aplicaciones en las ciencias económicas, administrativas y contables. Contaduría Universidad de Antioquia, (52) 199-214. https://doi.org/10.17533/udea.rc.2169
Rosenbusch, N., Brinckmann, J., & Bausch, A. (2011). Is innovation always beneficial? A meta-analysis of the relationship between innovation and performance in SMEs. Journal of business Venturing, 26(4), 441-457. https://doi.org/10.1016/j.jbusvent.2009.12.002
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Müller, K. R. (2016). Evaluating the visualization of what a deep neural network has learned. IEEE transactions on neural networks and learning systems, 28(11), 2660-2673. https://doi: 10.1109/TNNLS.2016.2599820.
Sarvamangala, D. R., & Kulkarni, R. V. (2022). Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence, 15(1), 1-22. https://doi.org/10.1007/s12065-020-00540-3
Shanahan, J. G. (2000). Fuzzy Logic. En Soft Computing for Knowledge Discovery (pp. 67–91). Springer US.
Singh, S., Olugu, E. U., & Fallahpour, A. (2014). Fuzzy-based sustainable manufacturing assessment model for SMEs. Clean Technologies and Environmental Policy, 16, 847-860. https://doi.org/10.1007/s10098-013-0676-5
Szalacha-Jarmużek, J., Polasik, M., & Jakubowska, M. (2022). The Institutionalization of “Cashless Poland.” Values, Norms, Sanctions and Grand Narrations in the SMEs’ Perspective on the Adoption of Cashless Payments. Polish Sociological Review, 217(1), 115-138. http://dx.doi.org/10.2139/ssrn.3802643
Uhlaner, L. M., van Stel, A., Duplat, V., & Zhou, H. (2013). Disentangling the effects of organizational capabilities, innovation and firm size on SME sales growth. Small Business Economics, 41, 581-607. https://doi.org/10.1007/s11187-012-9455-7
Uzun, B., Uzun Ozsahin, D., & Duwa, B. (2021). Fuzzy logic and fuzzy based multi criteria decision analysis. Application of multi-criteria decision analysis in environmental and civil engineering, 47-56.
Vidyadhar, R., Sudeep Kumar, R., Vinodh, S., & Antony, J. (2016). Application of fuzzy logic for leanness assessment in SMEs: a case study. Journal of Engineering, Design and Technology, 14(1), 78-103. https://doi.org/10.1108/JEDT-05-2014-0029
Yadav, H. B., Kumar, S., Kumar, Y., & Yadav, D. K. (2018). A fuzzy logic based approach for decision making. Journal of Intelligent & Fuzzy Systems, 35(2), 1531-1539.
Yang, C. W., Fang, S. C., & Lin, J. L. (2010). Organizational knowledge creation strategies: A conceptual framework. International Journal of Information Management, 30(3), 231-238. https://doi.org/10.1016/j.ijinfomgt.2009.08.005
Zadeh, L. (1965). “Fuzzy Sets”. Information and Control. 8 (4). Cambridge, Inglaterra, 338-353.
Zou, J., Han, Y., & So, S. S. (2009). Overview of artificial neural networks. Artificial neural networks: methods and applications, 14-22. https://doi.org/10.1007/978-1-60327-101-1_2
Agrawal, R., Asokan, P., & Vinodh, S. (2017). Benchmarking fuzzy logic and ANFIS approaches for leanness evaluation in an Indian SME: a case study. Benchmarking: An International Journal, 24(4), 973-993. https://doi.org/10.1108/BIJ-06-2016-0083
Aguiar, F. (2004), Teoría de la decisión e incertidumbre: modelos normativos y descriptivos. Empiria Revista de metodología de ciencias sociales, (8), 139-160.
Aguilar, A. (2015). Políticas de acceso para el crédito de las Pymes. Revista del observatorio Pyme, 1 (1), 2-5.
Almadi, A. I., Al Mamlook, R. E., Almarhabi, Y., Ullah, I., Jamal, A., & Bandara, N. (2022). A fuzzy-logic approach based on driver decision-making behavior modeling and simulation. Sustainability, 14(14), 8874. https://doi.org/10.3390/su14148874
Andriani, M., Samadhi, T. A., Siswanto, J., & Suryadi, K. (2019). Knowledge management strategy: an organisational development approach. Business process management journal, 25(7), 1474-1490. https://doi.org/10.1108/BPMJ-07-2018-0191
Ávila, P., Mendoza, A., Pinargote, H., y Fernández, L. (2019). Decisiones de financiamiento de las pymes ecuatorianas. SUPLEMENTO CICA, 3 (8), 03-66
Bams, D., Pisa, M., & Wolff, C. (2015). Ripple effects from industry defaults. Centre for Economic Policy Research.
Bar-Gill, O., & Ben-Shahar, O. (2016). Optimal Defaults in Consumer Markets. The Journal of Legal Studies, 45(S2), S137-S161.
Bartholdy, J., & Mateus, C. (2008). Financing of SME’s: an asset side story. Available at SSRN 1098347. http://dx.doi.org/10.2139/ssrn.1098347
Besné, A. G., Luna, D., Cobos, A., Lameiras, D., Ortiz-Moreno, H., & Güereca, L. P. (2018). A methodological framework of eco-efficiency based on fuzzy logic and Life Cycle Assessment applied to a Mexican SME. Environmental Impact Assessment Review, 68, 38-48. https://doi.org/10.1016/j.eiar.2017.10.008
Boloș, M. I., Bradea, I. A., & Delcea, C. (2019). A fuzzy logic algorithm for optimizing the investment decisions within companies. Symmetry, 11(2), 186. https://doi.org/10.3390/sym11020186
Byukusenge, E., Munene, J., & Orobia, L. (2016). Knowledge management and business performance: Mediating effect of innovation. Journal of Business and Management Sciences, 4(4), 82–92. https://nru.uncst.go.ug/handle/123456789/6025
Carneiro, A. (2000). How Does Knowledge Management Influence Innovation and Competitiveness? Journal of Knowledge Management, 4(2), 87-98. https://doi.org/10.1108/13673270010372242
Casanovas M, Fernández A. (2003). La gestión de la tesorería en la incertidumbre. España-Madrid: Ediciones Pirámide.
Córdova, J. F. D., Molina, E. C., & López, P. N. (2017). Fuzzy logic and financial risk. A proposed classification of financial risk to the cooperative sector. Contaduría y administración, 62(5), 1687-1703. https://doi.org/10.1016/j.cya.2017.10.001
Díaz Rodríguez, H. E., Sosa Castro, M., & Cabello Rosales, A. (2019). Determinantes del endeudamiento de los hogares en México: un análisis con redes neuronales. Problemas del desarrollo, 50(199), 115-140.
Dolling, O. R., & Varas, E. A. (2002). Artificial neural networks for streamflow prediction. Journal of hydraulic research, 40(5), 547-554. https://doi.org/10.1080/00221680209499899
Franco, M. D. C., Gómez, F., y Serrano, K. (2019). Determinantes del acceso al crédito para la PYME del Ecuador. Conrado, 15(67), 295-303.
García-Muiña, F. E., González-Sánchez, R., Ferrari, A. M., Volpi, L., Pini, M., Siligardi, C., & Settembre-Blundo, D. (2019). Identifying the equilibrium point between sustainability goals and circular economy practices in an Industry 4.0 manufacturing context using eco-design. Social sciences, 8(8), 241. https://doi:10.3390/socsci8080241
Gubareva, E. A., & Shemyakova, E. (2021). Neural networks in the digital economy. Socio-economic Systems: Paradigms for the Future, 827-835. https://doi.org/10.1007/978-3-030-56433-9_87
Herghiligiu, I. V., Robu, I. B., Pislaru, M., Vilcu, A., Asandului, A. L., Avasilcăi, S., & Balan, C. (2019). Sustainable environmental management system integration and business performance: A balance assessment approach using fuzzy logic. Sustainability, 11(19), 5311. https://doi.org/10.3390/su11195311
Indaryanti, T. R., Lestari, R., & Fitriah, E. (2020, September). Intellectual Capital Disclosure and CSR Disclosure on Company Performance. In International Conference on Management, Accounting, and Economy (ICMAE 2020) (pp. 81-85). Atlantis Press.
Kaufmann, A., y Gil-Aluja, J. (1986). Introducción de la teoría de los subconjuntos borrosos a la gestión de las empresas. Santiago de Compostela, España. Milladoiro.
Kaufmann, A., & Gil-Aluja, J. (1987). Técnicas operativas de gestión para el tratamiento de la incertidumbre. Barcelona: Hispano-europea.
Kaufmann, A., y Gil-Aluja J. (1989). Modelos para la investigación de efectos olvidados. Milladoiro.
Kottaridi, C., Louloudi, K., & Karkalakos, S. (2019). Human capital, skills and competencies: Varying effects on inward FDI in the EU context. International Business Review, 28(2), 375-390. https://doi.org/10.1016/j.ibusrev.2018.10.008
Kosko, B. (1995). Pensamiento borroso: la nueva ciencia de la lógica borrosa. Barcelona, España: Editorial Crítica.
Kusa, R., Duda, J., & Suder, M. (2021). Explaining SME performance with fsQCA: The role of entrepreneurial orientation, entrepreneur motivation, and opportunity perception. Journal of Innovation, 6(4), 234-245. https://doi.org/10.1016/j.jik.2021.06.001
Lee, C. S., & Wong, K. Y. (2017). A fuzzy logic-based knowledge management performance measurement system for SMEs. Cybernetics and Systems, 48(4), 277-302. https://doi.org/10.1080/01969722.2017.1284532
Leoshchenko, S., Oliinyk, A., Subbotin, S., & Zaiko, T. (2020). Using recurrent neural networks for data-centric business. In Data-Centric Business and Applications: Evolvements in Business Information Processing and Management. Springer International Publishing 3, 73-91. https://doi.org/10.1007/978-3-030-35649-1_4
Luna Altamirano, K. A., Sarmiento Espinoza, W. H., y Andrade Cordero, C. F. (2019). Matriz de efectos olvidados: Caso sector industrial de Cuenca-Ecuador. Revista de Ciencias Sociales, 25(2), 96-111. https://doi.org/10.31876/rcs.v25i2.27339
Luna Altamirano, K. A., Sarmiento Espinoza, W. H., & Andrade Pesantez, D. J. (2022). Financial ratios with fuzzy logic approach. New estimation perspective. Revista Venezolana De Gerencia, 27(99), 959-972. https://doi.org/10.52080/ rvgluz.27.99.7
Macwan, N., & Sajja, P. S. (2014). Fuzzy logic: an effective user interface tool for decision support system. International Journal of Engineering Science and Innovative Technology (IJESIT), 3(3), 278-283.
Maes, J., & Sels, L. (2014). SMEs’ radical product innovation: The role of internally and externally oriented knowledge capabilities. Journal of Small Business Management, 52(1), 141-163. https://doi.org/10.1111/jsbm.12037
Mählmeyer, V., Rampeltshammer, L., & Hertwig, M. (2017). European Works Councils during the financial and economic crisis: Activation, stagnation, or disintegration? European Journal of Industrial Relations, 23(3), 225–242. doi:10.1177/0959680116685951
Manjarres, D., Landa-Torres, I., & Andonegui, I. (2017). An Intelligent Decision Support System for Assessing the Default Risk in Small and Medium-Sized Enterprises. In Artificial Intelligence and Soft Computing: 16th International Conference, ICAISC 2017, Zakopane, Poland, June 11-15, 2017, Proceedings, Part II 16 (533-542). Springer International Publishing.
Medina, S. (2006). Estado de la cuestión acerca del uso de la lógica difusa en problemas financieros. Cuadernos de Administración, 32 (19). 195-223.
Meier, J., Andor, M. A., Doebbe, F., Haddaway, N., & Reisch, L. A. (2021). Can green defaults reduce meat consumption?. Food Policy, 110, 102298. http://dx.doi.org/10.2139/ssrn.3903160
Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital signal processing, 73, 1-15. https://doi.org/10.1016/j.dsp.2017.10.011
Muhamediyeva, D., & Abdul-Azalova, M. (2022). Application of the theory of fuzzy logic for analysis of management systems of business processes of an enterprise. Scientific Collection InterConf, 22 (113), 467-471. https://doi.org/10.51582/interconf.19-20.06.2022.049
Niemeyer, J., Rudolf, S., Kvaratskhelia,L. Mennenga, M., & Herrmann, C. (2022). A creativity-driven Case-Based Reasoning Approach for the systematic Engineering of Sustainable Business Models. Procedia CIRP, 105, 470-475, https://doi.org/10.1016/j.procir.2022.02.078.
Olarte, J. (2006). Incertidumbre y evaluación de riesgos financieros. Scientia Et Technica, 12(32), 347- 350. https://www.redalyc.org/articulo.oa?id=84911652061
Pérez, F. O., & Fernández, H. (2007). Las redes neuronales y la evaluación del riesgo de crédito. Revista Ingenierías Universidad de Medellín, 6(10), 77-91. https://revistas.udem.edu.co/index.php/ingenierias/article/view/225
Pérez, I., y Melero, R. (2006). Evaluación de aspirantes a docentes en la Facultad de Ingeniería de la Universidad del Valle del Momboy mediante un modelo difuso de soporte de decisiones. Revista Telos, 8(3), 454- 474. https://ojs.urbe.edu/index.php/telos/article/view/2703
Piñeiro-Chousa, J., López-Cabarcos, M., Romero-Castro, N., & Pérez-Pico, A. (2020). Innovation, entrepreneurship and knowledge in the business scientific field: Mapping the research front. Journal of Business Research, 115, 475-485. https://doi.org/10.1016/j.jbusres.2019.11.045
Reig, J., y González, J. (2002). Modelo borroso de control de gestión de materiales. Revista Española de Financiación y Contabilidad, 31(12). 431-459.
Rhee, J., Park, T., & Lee, D. H. (2010). Drivers of innovativeness and performance for innovative SMEs in South Korea: Mediation of learning orientation. Technovation, 30(1), 65-75. https://doi.org/10.1016/j.technovation.2009.04.008
Rico, M., y Tinto, J. (2008). Matemática borrosa: Algunas aplicaciones en las ciencias económicas, administrativas y contables. Contaduría Universidad de Antioquia, (52) 199-214. https://doi.org/10.17533/udea.rc.2169
Rosenbusch, N., Brinckmann, J., & Bausch, A. (2011). Is innovation always beneficial? A meta-analysis of the relationship between innovation and performance in SMEs. Journal of business Venturing, 26(4), 441-457. https://doi.org/10.1016/j.jbusvent.2009.12.002
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Müller, K. R. (2016). Evaluating the visualization of what a deep neural network has learned. IEEE transactions on neural networks and learning systems, 28(11), 2660-2673. https://doi: 10.1109/TNNLS.2016.2599820.
Sarvamangala, D. R., & Kulkarni, R. V. (2022). Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence, 15(1), 1-22. https://doi.org/10.1007/s12065-020-00540-3
Shanahan, J. G. (2000). Fuzzy Logic. En Soft Computing for Knowledge Discovery (pp. 67–91). Springer US.
Singh, S., Olugu, E. U., & Fallahpour, A. (2014). Fuzzy-based sustainable manufacturing assessment model for SMEs. Clean Technologies and Environmental Policy, 16, 847-860. https://doi.org/10.1007/s10098-013-0676-5
Szalacha-Jarmużek, J., Polasik, M., & Jakubowska, M. (2022). The Institutionalization of “Cashless Poland.” Values, Norms, Sanctions and Grand Narrations in the SMEs’ Perspective on the Adoption of Cashless Payments. Polish Sociological Review, 217(1), 115-138. http://dx.doi.org/10.2139/ssrn.3802643
Uhlaner, L. M., van Stel, A., Duplat, V., & Zhou, H. (2013). Disentangling the effects of organizational capabilities, innovation and firm size on SME sales growth. Small Business Economics, 41, 581-607. https://doi.org/10.1007/s11187-012-9455-7
Uzun, B., Uzun Ozsahin, D., & Duwa, B. (2021). Fuzzy logic and fuzzy based multi criteria decision analysis. Application of multi-criteria decision analysis in environmental and civil engineering, 47-56.
Vidyadhar, R., Sudeep Kumar, R., Vinodh, S., & Antony, J. (2016). Application of fuzzy logic for leanness assessment in SMEs: a case study. Journal of Engineering, Design and Technology, 14(1), 78-103. https://doi.org/10.1108/JEDT-05-2014-0029
Yadav, H. B., Kumar, S., Kumar, Y., & Yadav, D. K. (2018). A fuzzy logic based approach for decision making. Journal of Intelligent & Fuzzy Systems, 35(2), 1531-1539.
Yang, C. W., Fang, S. C., & Lin, J. L. (2010). Organizational knowledge creation strategies: A conceptual framework. International Journal of Information Management, 30(3), 231-238. https://doi.org/10.1016/j.ijinfomgt.2009.08.005
Zadeh, L. (1965). “Fuzzy Sets”. Information and Control. 8 (4). Cambridge, Inglaterra, 338-353.
Zou, J., Han, Y., & So, S. S. (2009). Overview of artificial neural networks. Artificial neural networks: methods and applications, 14-22. https://doi.org/10.1007/978-1-60327-101-1_2
Publicado
2024-07-15
Cómo citar
Coronel Balderramo, C. R., Luna Altamirano, K. A., & Erazo Álvarez, J. C. (2024). Gestión de impagos bajo incertidumbre en pequeñas y medianas empresas: aplicaciones con redes neuronales. Revista Venezolana De Gerencia, 29(11), 222-242. https://doi.org/10.52080/rvgluz.29.e11.13
Sección
Artículos
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0.
.png)
