Eficiencia de los sistemas productivos de las pequeñas y medianas empresas
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Palabras clave

Eficiencia
Pymes
Análisis envolvente de Datos
productividad productivity
Data Envelopment Analysis
Smes
Efficiency

Cómo citar

Morelos Gomez, J., Gomez Fernandez, R. ., & Acevedo Chedid, J. . (2022). Eficiencia de los sistemas productivos de las pequeñas y medianas empresas. Saber, Ciencia Y Libertad, 17(2), 369–398. https://doi.org/10.18041/2382-3240/saber.2022v17n2.9298

Resumen

Esta investigación tiene como objetivo evaluar la eficiencia en los sistemas productivos de bienes y servicios de las pequeñas y medianas empresas (Pymes) en el Departamento de Bolívar-Colombia. Para este propósito se utilizó la técnica de Análisis Envolvente de Datos (DEA), en la cual se determinó las eficiencias técnicas de las 120 Pymes formalmente registradas en la Cámara de Comercio de Cartagena para los años 2017 a 2020. Se contrasta con otros estudios cuya técnica no paramétrica fue aplicada en sectores productivos similares que, el grupo de pequeñas y medianas empresas evaluadas mostraron resultados análogos en sus procesos operacionales. Se concluye que las Pymes evaluadas presentaron un desempeño productivo exiguo en sus actividades operacionales debido a factores relacionados con el bajo aplacamiento financiero y deficiente gestión de la innovación.

https://doi.org/10.18041/2382-3240/saber.2022v17n2.9298
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Amin, G. R., Emrouznejad, A., & Gattoufi, S. (2017). Minor and major consolidations in inverse DEA: Definition and determination. Computers & Industrial Engineering, 103, 193-200. https://doi.org/10.1016/j.cie.2016.11.029

Andersen, A. L., Brunoe, T. D., Nielsen, K., & Rösiö, C. (2017). Towards a generic design method for reconfigurable manufacturing systems: Analysis and synthesis of current design methods and evaluation of supportive tools. Journal of Manufacturing Systems, 42, 179-195. https://doi.org/10.1016/j.jmsy.2016.11.006

Awan, U., Khattak, A., & Kraslawski, A. (2019). Corporate Social Responsibility (CSR) Priorities in the Small and Medium Enterprises (SMEs) of the Industrial Sector of Sialkot, Pakistan. In Corporate Social Responsibility in the Manufacturing and Services Sectors 1(1) ,267-278. https://doi.org/10.1007/978-3-642-33851-9_15

Brunswicker, S., & Vanhaverbeke, W. (2015). Open innovation in small and medium‐sized enterprises (SMEs): External knowledge sourcing strategies and internal organizational facilitators. Journal of Small Business Management, 53(4), 1241-1263.https://doi.org/10.1111/jsbm.12120

Caldas, P., Ferreira, D., Dollery, B., & Marques, R. (2019). Are there scale economies in urban waste and wastewater municipal services? A non-radial input-oriented model applied to the Portuguese local government. Journal of Cleaner Production, 219, 531-539. https://doi.org/10.1016/j.jclepro.2019.02.076

Cerchione, R., Esposito, E., & Spadaro, M. R. (2016). A literature review on knowledge management in SMEs. Knowledge Management Research & Practice, 14(2), 169-177. https://doi.org/10.1057/kmrp.2015.12

Costa, E., Soares, A. L., & De Sousa, J. P. (2016). Information, knowledge and collaboration management in the internationalisation of SMEs: A systematic literature review. International Journal of Information Management, 36(4), 557-569. https://doi.org/10.1016/j.ijinfomgt.2016.03.007

Chen, J. C., Chen, T. L., & Harianto, H. (2017). Capacity planning for packaging industry. Journal of manufacturing systems, 42, 153-169. https://doi.org/10.1016/j.jmsy.2016.12.007

Chen, X., Fu, T. T., Juo, J. C., & Yu, M. M. (2019). A comparative analysis of profit inefficiency and productivity convergence between Taiwanese and Chinese banks. BRQ Business Research Quarterly. https://doi.org/10.1016/j.brq.2019.02.001

Departamento Administrativo Nacional de Estadística (2019), Boletín de Prensa: Micro-establecimientos Evolución 2018. Consultado 29/052020 en: https://www.dane.gov.co/index.php/estadisticas-por-tema/comercio-interno/microestablecimientos

Feenstra, R. C. (2018). Restoring the product variety and pro-competitive gains from trade with heterogeneous firms and bounded productivity. Journal of International Economics, 110, 16-27. https://doi.org/10.1016/j.jinteco.2017.10.003

Fontalvo, T. J., De La Hoz, E. J. y Morelos, J. (2018). La productividad y sus factores: incidencia en el mejoramiento organizacional. Dimensión empresarial, 16(1), 47-60. Consultado 15/02/2019 en: https://dialnet.unirioja.es/servlet/articulo?codigo=6233008

Fuentes, R., Molinos-Senante, M., Hernández-Sancho, F., & Sala-Garrido, R. (2020). Analysing the efficiency of wastewater treatment plants: The problem of the definition of desirable outputs and its solution. Journal of Cleaner Production, 267(10) https://doi.org/10.1016/j.jclepro.2020.121989

Garone, L. F., Villalba, P. A. L., Maffioli, A., & Ruzzier, C. A. (2020). Firm-level productivity in Latin America and the Caribbean. Research in Economics. https://doi.org/10.1016/j.rie.2020.04.004

Goel, V., Agrawal, R., & Sharma, V. (2017). Factors affecting labour productivity: an integrative synthesis and productivity modelling. Global Business and Economics Review, 19(3), 299-322. https://doi.org/10.1504/GBER.2017.083964

Gonnermann, C., & Reinhart, G. (2019). Automatized setup of process monitoring in cyber-physical systems. Procedia CIRP, 81, 636-640. https://doi.org/10.1016/j.procir.2019.03.168

Hariharan, S., Liu, T., & Shen, Z. J. M. (2020). Role of resource flexibility and responsive pricing in mitigating the uncertainties in production systems. European Journal of Operational Research284(2), 498-513. https://doi.org/10.1016/j.ejor.2019.12.040

Herman, E. (2020). Labour Productivity and Wages in the Romanian Manufacturing Sector. Procedia Manufacturing, 46, 313-321. https://doi.org/10.1016/j.promfg.2020.03.046

Hossain, M. and Kauranen, I. (2016) Open innovation in SMEs: a systematic literature review, Journal of Strategy and Management, 9(1), pp. 58-73. https://doi.org/10.1108/JSMA-08-2014-0072

Hu, W., Guo, Y., Tian, J., & Chen, L. (2019). Eco-efficiency of centralized wastewater treatment plants in industrial parks: A slack-based data envelopment analysis. Resources, Conservation and Recycling, 141, 176-186. https://doi.org/10.1016/j.resconrec.2018.10.020

Javid, N., Khalili-Damghani, K., Makui, A., & Abdi, F. (2020). Multi-objective flexibility-complexity trade-off problem in batch production systems using fuzzy goal programming. Expert Systems with Applications, 148(1). https://doi.org/10.1016/j.eswa.2020.113266

Jodlbauer, H., & Strasser, S. (2019). Capacity-driven production planning. Computers in Industry, 113, 103-126. https://doi.org/10.1016/j.compind.2019.103126

Joppen, R., von Enzberg, S., Kühn, I. A., & Dumitrescu, I. R. (2019). A practical framework for the optimization of production management processes. Procedia Manufacturing, 33, 406-413. https://doi.org/10.1016/j.promfg.2019.04.050

Kamble, R., & Wankhade, L. (2017). Perspectives on productivity: identifying attributes influencing productivity in various industrial sectors. International Journal of Productivity and Quality Management, 22(4), 536-566. https://doi.org/10.1504/IJPQM.2017.087868

Karanikas, N., Melis, D. J., & Kourousis, K. I. (2018). The balance between safety and productivity and its relationship with human factors and safety awareness and communication in aircraft manufacturing. Safety and health at work, 9(3), 257-264. https://doi.org/10.1016/j.shaw.2017.09.001

Krüger, J., Wang, L., Verl, A., Bauernhansl, T., Carpanzano, E., Makris, S., ... & Pellegrinelli, S. (2017). Innovative control of assembly systems and lines. CIRP annals, 66(2), 707-730. https://doi.org/10.1016/j.cirp.2017.05.010

Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing letters, 3, 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001

Li, T., Yang, W., Zhang, H., & Cao, X. (2016). Evaluating the impact of transport investment on the efficiency of regional integrated transport systems in China. Transport Policy, 45, 66-76. https://doi.org/10.1016/j.tranpol.2015.09.005

Ministerio de Comercio, Industria y Turismo ( 2016) ¿Cuáles son los programas y proyectos que se desarrollan para fortalecer a las Mipymes colombianas? MinCIT. Consultado 23/11/2016 en : http://www.mincit.gov.co/servicio-al-ciudadano/preguntas-frecuentes/mipymes

Mohammadian, I., & Rezaee, M. J. (2018). A new decomposition and interpretation of Hicks-Moorsteen productivity index for analysis of stock exchange companies: Case study on pharmaceutical industry. Socio-Economic Planning Sciences, 100674. https://doi.org/10.1016/j.seps.2018.12.001

Morelos, J. , Fontalvo, T. J., & Vergara, J. C. (2013). Incidencia de la certificación ISO 9001 en los indicadores de productividad y utilidad financiera de empresas de la zona industrial de Mamonal en Cartagena. Estudios Gerenciales, 29(126), 99-109. Consultado 4/0672017 en: https://www.redalyc.org/pdf/212/21228397012.pdf

Morelos, J. (2016). Análisis de la variación de la eficiencia en la producción de biocombustibles en América Latina. Estudios gerenciales, 32(139), 120-126. https://doi.org/10.1016/j.estger.2016.01.001

Morelos, J., & Nuñez, M. Á. (2017). Productividad de las empresas de la zona extractiva minera-energética y su incidencia en el desempeño financiero en Colombia. Estudios gerenciales, 33(145), 330-340. https://doi.org/10.1016/j.estger.2017.11.002

Nedaei, H., Naini, S. G. J., & Makui, A. (2020). A dynamic DEA model to measure the learning rates of efficient frontier and DMUs: An application to oil and gas wells drilling. Computers & Industrial Engineering, 144(1) 106434. https://doi.org/10.1016/j.cie.2020.106434

Nikolakis, N., Senington, R., Sipsas, K., Syberfeldt, A., & Makris, S. (2020). On a containerized approach for the dynamic planning and control of a cyber-physical production system. Robotics and Computer-Integrated Manufacturing, 64(1). https://doi.org/10.1016/j.rcim.2019.101919

Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2017). Human resource management: Gaining a competitive advantage. New York, NY: McGraw-Hill Education.

OECD. (2015). The future of productivity. Joint Economics Department and the Directorate for Science, Technology and Innovation Policy Note.

Onkelinx, J., Manolova, T. S., & Edelman, L. F. (2016). The human factor: Investments in employee human capital, productivity, and SME internationalization. Journal of International Management, 22(4), 351-364. https://doi.org/10.1016/j.intman.2016.05.002

Ouyang, W., & Yang, J. B. (2020). The network energy and environment efficiency analysis of 27 OECD countries: A multiplicative network DEA model. Energy, 197(1) 117161. https://doi.org/10.1016/j.energy.2020.117161

Palominos, P., Quezada, L. E., & Gonzalez, M. A. (2019). Incorporating the voice of the client in establishing the flexibility requirement in a production system. International Journal of Production Economics, 211, 34-43. https://doi.org/10.1016/j.ijpe.2019.01.029

Pipitone, V., & Colloca, F. (2018). Recent trends in the productivity of the Italian trawl fishery: The importance of the socio-economic context and overexploitation. Marine Policy, 87, 135-140. https://doi.org/10.1016/j.marpol.2017.10.017

Putra, P. O. H., & Santoso, H. B. (2020). Contextual factors and performance impact of e-business use in Indonesian small and medium enterprises (SMEs). Heliyon, 6(3), e03568. https://doi.org/10.1016/j.heliyon.2020.e03568

Polotski, V., Kenné, J. P., & Gharbi, A. (2019). Production control of hybrid manufacturing–remanufacturing systems under demand and return variations. International Journal of Production Research, 57(1), 100-123. https://doi.org/10.1080/00207543.2018.1461272

Simon, P., Diehl, D., Glasschroeder, J., & Reinhart, G. (2019). Approach for the identification of influencing factors and their effects on energy flexible production systems. Procedia CIRP, 79, 239-244. https://doi.org/10.1016/j.procir.2019.02.057

Shao, X. F. (2019). What is the right production strategy for horizontally differentiated product: Standardization or mass customization?. International Journal of Production Economics, 223(1) https://doi.org/10.1016/j.ijpe.2019.107527

Shuai, S., & Fan, Z. (2020). Modeling the role of environmental regulations in regional green economy efficiency of China: Empirical evidence from super efficiency DEA-Tobit model. Journal of Environmental Management, 261, 110227. https://doi.org/10.1016/j.jenvman.2020.110227

Tamberi, M. (2020). Productivity differentials along the development process: A “MESO” approach. Structural Change and Economic Dynamics, 53, 99-107. https://doi.org/10.1016/j.strueco.2020.01.006

Tiammee, S., & Likasiri, C. (2020). Sustainability in corn production management: A multi-objective approach. Journal of Cleaner Production, 257(1). https://doi.org/10.1016/j.jclepro.2020.120855

Tsutsumi, D., Gyulai, D., Kovács, A., Tipary, B., Ueno, Y., Nonaka, Y., & Fujita, K. (2020). Joint optimization of product tolerance design, process plan, and production plan in high-precision multi-product assembly. Journal of Manufacturing Systems, 54, 336-347. https://doi.org/10.1016/j.jmsy.2020.01.004

Valdez, L. E., Solano-Rodríguez, O. J., & Martin, D. P. (2018). Modes of learning and profitability in Colombian and Mexican SMEs. The Journal of High Technology Management Research, 29(2), 193-203. https://doi.org/10.1016/j.hitech.2018.09.007

Wu, G., Hong, J., Li, D., & Wu, Z. (2019). Efficiency assessment of pollutants discharged in urban wastewater treatment: Evidence from 68 key cities in China. Journal of cleaner production, 233, 1437-1450. https://doi.org/10.1016/j.jclepro.2019.06.012

Yadav, A., & Jayswal, S. C. (2018). Modelling of flexible manufacturing system: a review. International Journal of Production Research, 56(7), 2464-2487. https://doi.org/10.1080/00207543.2017.1387302

Yan, Y., Liu, X., Wen, Y., & Ou, J. (2019). Quantitative analysis of the contributions of climatic and human factors to grassland productivity in northern China. Ecological indicators, 103, 542-553. https://doi.org/10.1016/j.ecolind.2019.04.020

Yan, J. (2019). Spatiotemporal analysis for investment efficiency of China’s rural water conservancy based on DEA model and Malmquist productivity index model. Sustainable Computing: Informatics and Systems, 21, 56-71. https://doi.org/10.1016/j.suscom.2018.11.004

Yu, D., & He, X. (2020). A bibliometric study for DEA applied to energy efficiency: Trends and future challenges. Applied Energy, 268. https://doi.org/10.1016/j.apenergy.2020.115048

Yu, F., Shi, Y., & Wang, T. (2020). R&D Investment and Chinese Manufacturing SMEs Corporate Social Responsibility: The Moderating Role of Regional Innovative Milieu. Journal of Cleaner Production, 258(1). https://doi.org/10.1016/j.jclepro.2020.120840

Zhang, R., Lu, C. C., Lee, J. H., Feng, Y., & Chiu, Y. H. (2019). Dynamic environmental efficiency assessment of industrial water pollution. Sustainability, 11(11), 1-12. https://doi.org/10.3390/su11113053

Zhang, G., & Cui, J. (2020). A general inverse DEA model for non-radial DEA. Computers & Industrial Engineering, 142 (1), 106368. https://doi.org/10.1016/j.cie.2020.106368

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