Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies
In this research, a method was developed to evaluate and predict the efficiency of small exporting companies taking as input or asset variables the total assets, equity, total liabilities, operating expenses, sales costs and as output or result variables. net sales, net income and operating income....
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8727
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8727
- Palabra clave:
- Artificial neural networks
Data envelopment analysis
Technical efficiency
Data envelopment analysis
Discriminant analysis
Forecasting
Neural networks
Classification prediction
Evaluation and predictions
Exporting companies
Net incomes
Net sales
Operating expense
Operating Income
Technical efficiency
Efficiency
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies |
| dc.title.alternative.none.fl_str_mv |
Método análisis envolvente de datos y redes neuronales en la evaluación y predicción de la eficiencia técnica de pequeñas empresas exportadoras |
| title |
Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies |
| spellingShingle |
Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies Artificial neural networks Data envelopment analysis Technical efficiency Data envelopment analysis Discriminant analysis Forecasting Neural networks Classification prediction Evaluation and predictions Exporting companies Net incomes Net sales Operating expense Operating Income Technical efficiency Efficiency |
| title_short |
Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies |
| title_full |
Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies |
| title_fullStr |
Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies |
| title_full_unstemmed |
Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies |
| title_sort |
Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companies |
| dc.subject.keywords.none.fl_str_mv |
Artificial neural networks Data envelopment analysis Technical efficiency Data envelopment analysis Discriminant analysis Forecasting Neural networks Classification prediction Evaluation and predictions Exporting companies Net incomes Net sales Operating expense Operating Income Technical efficiency Efficiency |
| topic |
Artificial neural networks Data envelopment analysis Technical efficiency Data envelopment analysis Discriminant analysis Forecasting Neural networks Classification prediction Evaluation and predictions Exporting companies Net incomes Net sales Operating expense Operating Income Technical efficiency Efficiency |
| description |
In this research, a method was developed to evaluate and predict the efficiency of small exporting companies taking as input or asset variables the total assets, equity, total liabilities, operating expenses, sales costs and as output or result variables. net sales, net income and operating income. For this, the envelopment data analysis was used in the evaluation of the efficiency, the discriminant analysis in the evaluation of the classification of efficient and inefficient companies and the artificial neural networks to evaluate its capacity of classification prediction in 90 companies of the sector of the city of Barranquilla-Colombia. The results allowed to classify the companies according to level of efficiency showing an average technical efficiency of 41.38% of the sector with 11 representative companies of efficiency. The results show the relevance of the proposed methodology to correctly classify and forecast technical efficiency in small exporting companies. © Centro de Informacion Tecnologica. All rights reserved. |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018 |
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2019-11-06T19:05:11Z |
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2019-11-06T19:05:11Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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Artículo |
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publishedVersion |
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Informacion Tecnologica; Vol. 29, Núm. 6; pp. 267-276 |
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0716-8756 |
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https://hdl.handle.net/20.500.12585/8727 |
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10.4067/S0718-07642018000600267 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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Informacion Tecnologica; Vol. 29, Núm. 6; pp. 267-276 0716-8756 10.4067/S0718-07642018000600267 Universidad Tecnológica de Bolívar Repositorio UTB |
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https://hdl.handle.net/20.500.12585/8727 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional |
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Centro de Informacion Tecnologica |
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Centro de Informacion Tecnologica |
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2019-11-06T19:05:11Z2019-11-06T19:05:11Z2018Informacion Tecnologica; Vol. 29, Núm. 6; pp. 267-2760716-8756https://hdl.handle.net/20.500.12585/872710.4067/S0718-07642018000600267Universidad Tecnológica de BolívarRepositorio UTBIn this research, a method was developed to evaluate and predict the efficiency of small exporting companies taking as input or asset variables the total assets, equity, total liabilities, operating expenses, sales costs and as output or result variables. net sales, net income and operating income. For this, the envelopment data analysis was used in the evaluation of the efficiency, the discriminant analysis in the evaluation of the classification of efficient and inefficient companies and the artificial neural networks to evaluate its capacity of classification prediction in 90 companies of the sector of the city of Barranquilla-Colombia. The results allowed to classify the companies according to level of efficiency showing an average technical efficiency of 41.38% of the sector with 11 representative companies of efficiency. The results show the relevance of the proposed methodology to correctly classify and forecast technical efficiency in small exporting companies. © Centro de Informacion Tecnologica. All rights reserved.Recurso electrónicoapplication/pdfengCentro de Informacion Tecnologicahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85061498080&doi=10.4067%2fS0718-07642018000600267&partnerID=40&md5=f35d2330398b42b4bf0accf56c8587deScopus 57200633636Scopus 57070183000Scopus 26031339600Data envelopment analysis method and neural networks in the evaluation and prediction of the technical efficiency of small exporting companiesMétodo análisis envolvente de datos y redes neuronales en la evaluación y predicción de la eficiencia técnica de pequeñas empresas exportadorasinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Artificial neural networksData envelopment analysisTechnical efficiencyData envelopment analysisDiscriminant analysisForecastingNeural networksClassification predictionEvaluation and predictionsExporting companiesNet incomesNet salesOperating expenseOperating IncomeTechnical efficiencyEfficiencyFontalvo Herrera, Tomás JoséDe la Hoz Domínguez, Enrique JoséAhn, H., Neumann, L., Novoa, N., Measuring the relative balance of DMUs (2012) European Journal of Operational Research, 221 (2), pp. 417-423.Alaka, H., Oyedele, L., Systematic review of bankruptcy prediction models: Towards a framework for tool selection (2017) Expert Systems with Applications, 94, pp. 164-184. , y otros cuatro autoresCaicedo, E., López, J., (2009) Una Aproximación Práctica A Las Redes Neuronales Artificiales, , y 1a Ed., Programa Editorial Univeridad del Valle, Cali, ColombiaCano, J., Campo, E., Baena, J., Application of DEA in international market selection for the export of goods (2017) Revista Dyna, 84 (200), pp. 376-382.De Bock, K., The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles (2017) Expert Systems with Applications, 90, pp. 23-39Delfín, O., Melo, A., Eficiencia del Transporte Público en La Ciudad de Morelia, Michoacán (México) en el año 2015: Un análisis de la envolvente de datos (2017) Revista Facultad De Ciencias Económicas: Investigación Y Reflexión, 25 (2), pp. 7-23.Du Jardin, P., Dynamics of firm financial evolution and bankruptcy prediction (2017) Expert Systems with Applications, 75, pp. 25-43Fontalvo, T., Evaluación de la Gestión Financiera: Empresas del Sector Automotriz y Actividades Conexas En El Atlántico (2012) Dimensión Empresarial, 10 (2), pp. 11-20Fontalvo, T., Morelos, J., De la Hoz, E., Aplicación del Análisis Discriminante para Evaluar el Mejoramiento de los Indicadores Financieros en las Empresas del Sector Extracción de Petróleo Crudo y Gas Natural en Colombia (2011) Revista Soluciones De Postgrado EIA, 1 (7), pp. 11-26. , yFontalvo, T., Mendoza, A., Vishal, D., Comparative analysis of financial efficiency: A case study of BASC sector in Barranquilla (2015) Prospectiva, 2 (13), pp. 16-24.Fontalvo, T., Aplicación de análisis discriminante para evaluar la productividad como resultado de la certificación BASC en las empresas de la ciudad de Cartagena (2014) Contaduría Y Administración, 59 (1), pp. 43-62García, A., El Análisis Envolvente de Datos, Herramienta para a Medición de la Eficiencia en Instituciones Sanitarias, Potencialidades y Limitaciones (2009) Revista Cubana De Higiene Y Epidemiología, 47 (2)Guzmán, I., Predicción de resultados empresariales versus medidas no paramétricas de eficiencia técnica: Evidencia para pymes de la Región de Murcia (2005) Presentado En VII Reunión De Economía Mundial, , Madrid, AbrilHanafizadeh, P., Reza, H., Emrouznejad, A., Derakhshan, M., Neural network DEA for measuring the efficiency of mutual funds (2014) International Journal of Applied Decision Sciences, 7 (3), pp. 255-269.Heidari, M., Omid, M., Akram, A., Using nonparametric analysis (DEA) for measuring technical efficiency in poultry farms (2011) Revista Brasileira De Ciência Avícola, 13 (4), pp. 271-277.Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators (1989) Neural Netw, 2, pp. 359-366.Jiahe, A., Jiang, X., Artificial neural network prediction of the microstructure of rod based on its controlled rolling and cooling process parameters (2003) Materials Science and Engineering, 344 (1), pp. 318-322. , y otros tres autoresKhalili-Damghani, K., Mohammad-Taghavifard, M., Un enfoque difuso DEA de tres etapas para medir el rendimiento de un proceso en serie que incluye prácticas de JAT, índices de agilidad y objetivos en las cadenas de suministro (2012) En T. J. of Services and Operations Management, 13 (2), pp. 147-188.Kovacova, M., Kliestik, T., Logit and probit application for the prediction of bankruptcy in Slovak companies (2017) Equilibrium-Quarterly Journal of Economics and Economic Policy, 12 (4), pp. 775-791.Lachenbruch, P., (1975) Discriminant Analysis, pp. 1-128. , 1a Ed, Editorial Macmillan Pub Co., New York, EE.UULins, M., Lobo, M., Uso da Análise Envoltória de Dados (DEA) para Avaliação de Hospitais Universitários Brasileiros (2007) Ciência Y Saúde Coletiva, 12 (4), pp. 985-998. , y otros tres autoresMartín, O., Lopez, M., Martín, F., Artificial neural networks for quality control by ultrasonic testing in resistance spot welding (2007) Process. Techno, 183, pp. 226-233.McMillan, G., (2013) Process/Industrial Instruments and Controls Handbook, , Mc.Graw Hill, México D.F., MéxicoMoreno, J., López, O., Díaz, J., Productividad, Eficiencia y sus Factores Explicativos en el Sector de la Construcción en Colombia 2005-2010 (2014) Cuadernos De Economía, 33 (63), pp. 569-588.Mojtaba, G., Efficiency improvement and resource estimation: A tradeoff analysis (2018) International Journal of Productivity and Quality Management, 25 (2). , https://doi.org/10.1504/IJPQM.2018.094758Peres, C., Antao, M., The use of multivariate discriminant analysis to predict corporate bankruptcy: A review (2017) Aestimatio-The Leb International Journal of Finance, 14, pp. 108-131.Quesada, V., Estimación de la Eficiencia Mediante el Análisis Envolvente de Datos (DEA) (2013) Revista Panorama Económico, 11, pp. 7-33Reddy, N., Rao, A., Chakraborty, M., Murty, B., Prediction of grain size of Al-7Si alloy by neural networks (2005) Engineering Science, 391, pp. 131-140.Rodrigres, L., Rodrigues, L., Economic-financial performance of the Brazilian sugarcane energy industry: An empirical evaluation using financial ratio, cluster and discriminant analysis (2017) Biomass and Bioenergy, 108, pp. 289-296.Rodríguez, J., Moreno, A., (2011) Fragilidad Financiera De Las Firmas En Colombia, 2000 – 2006: Un Análisis Discriminante De Un Modelo Minskiano, 8 (2), pp. 1-42. , y Documentos FCE ISSN: 2011-6322Senra, L., Nancil, L., Mello, J., Meza, L., Estudo sobre Métodos de Seleção de Variáveis em DEA (2007) Pesquisa Operacional, 27 (2), pp. 191-207.Soares, A., Pereira, A., Milagre, S., A model for multidimensional efficiency analysis of public hospital management (2017) Research on Biomedical Engineering, 33 (4), pp. 352-361.Tabachnick, B., Fidell, L., (2013) Using Multivariate Statistics, pp. 34-48. , y 6a Ed, Editorial Pearson, California, EE.UUTsay, H., Liu, H., Wu, J., Performance assessment of Hong Kong hotels (2017) Journal of China Tourism Research, 13 (2), pp. 123-140.Velásquez, J., Franco, C., García, H., Un Modelo no Lineal para la Predicción de la Demanda Mensual de Electricidad en Colombia (2009) Revista Estudios Gerenciales, 25 (112), pp. 37-54.Visbal-Cadavid, D., Mendoza-Mendoza, A., Corredor-Carrascal, K., Evaluación del Desempeño Docente Mediante Análisis Envolvente de Datos: Un Estudio De Caso (2015) Revista Entramado, 11 (2), pp. 218-225.Zhou, Y., Yan, Z., Li, N., Cloud-data envelopment analysis method used for assessment of restoration building block schemes (2015) CSEE Journal of Power and Energy Systems, 1 (2), pp. 43-52.http://purl.org/coar/resource_type/c_6501ORIGINALDOI10_4067S0718-07642018000600267.pdfapplication/pdf569755https://repositorio.utb.edu.co/bitstream/20.500.12585/8727/1/DOI10_4067S0718-07642018000600267.pdfb01dd7f0c731f13025073a7689206c96MD51TEXTDOI10_4067S0718-07642018000600267.pdf.txtDOI10_4067S0718-07642018000600267.pdf.txtExtracted texttext/plain38472https://repositorio.utb.edu.co/bitstream/20.500.12585/8727/4/DOI10_4067S0718-07642018000600267.pdf.txtb686933754d02fa972f944be453dada1MD54THUMBNAILDOI10_4067S0718-07642018000600267.pdf.jpgDOI10_4067S0718-07642018000600267.pdf.jpgGenerated Thumbnailimage/jpeg91249https://repositorio.utb.edu.co/bitstream/20.500.12585/8727/5/DOI10_4067S0718-07642018000600267.pdf.jpg1c8f7914ebb1e3054affe3c03855b3bfMD5520.500.12585/8727oai:repositorio.utb.edu.co:20.500.12585/87272023-05-26 09:36:33.935Repositorio Institucional UTBrepositorioutb@utb.edu.co |