Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa
Introducción: El análisis envolvente de datos (DEA), se usa para medir el desempeño relativo de una serie de centros de distribución (DCs), utilizando indicadores clave basados en logística inversa para una empresa que produce suministros eléctricos y electrónicos en Colombia. Objetivo: Medir el ren...
- Autores:
-
Ardila Gamboa, César David
Ballesteros Riveros, Frank Alexander
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/12193
- Palabra clave:
- Data envelopment analysis
Relative performance
Reverse Logistics
Returnable packages
Warehousing
Análisis Envolvente de Datos
Eficiencia relativa
Logística Inversa
Empaques Retornables
Almacenamiento
- Rights
- openAccess
- License
- INGE CUC - 2018
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Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa |
| dc.title.translated.eng.fl_str_mv |
Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics |
| title |
Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa |
| spellingShingle |
Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa Data envelopment analysis Relative performance Reverse Logistics Returnable packages Warehousing Análisis Envolvente de Datos Eficiencia relativa Logística Inversa Empaques Retornables Almacenamiento |
| title_short |
Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa |
| title_full |
Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa |
| title_fullStr |
Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa |
| title_full_unstemmed |
Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa |
| title_sort |
Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa |
| dc.creator.fl_str_mv |
Ardila Gamboa, César David Ballesteros Riveros, Frank Alexander |
| dc.contributor.author.spa.fl_str_mv |
Ardila Gamboa, César David Ballesteros Riveros, Frank Alexander |
| dc.subject.eng.fl_str_mv |
Data envelopment analysis Relative performance Reverse Logistics Returnable packages Warehousing |
| topic |
Data envelopment analysis Relative performance Reverse Logistics Returnable packages Warehousing Análisis Envolvente de Datos Eficiencia relativa Logística Inversa Empaques Retornables Almacenamiento |
| dc.subject.spa.fl_str_mv |
Análisis Envolvente de Datos Eficiencia relativa Logística Inversa Empaques Retornables Almacenamiento |
| description |
Introducción: El análisis envolvente de datos (DEA), se usa para medir el desempeño relativo de una serie de centros de distribución (DCs), utilizando indicadores clave basados en logística inversa para una empresa que produce suministros eléctricos y electrónicos en Colombia. Objetivo: Medir el rendimiento relativo de los centros de distribución en función de indicadores clave (KPI) de una red de abastecimiento con logística inversa. Metodología: Se aplica un modelo DEA a través de 5 pasos: Selección de KPIs; Recopilación de datos para los 18 DCs en la red de distribución; Se construye y ejecuta el modelo DEA; Identificar los DCs que serán el foco de la mejora; Analizar los DCs que restringen o disminuyen el rendimiento total del sistema. Resultados: Inicialmente se definen KPI, a partir de los datos recolectados y se presentan los KPI para cada DCs. Se ejecuta el modelo DEA y se determinan las eficiencias relativas para cada DCs. Posteriormente, se realiza un análisis de la frontera y se analizan los DCs que limitan o reducen el rendimiento del sistema en busca de opciones para mejorar el sistema. Conclusiones: La logística inversa, trae numerosas ventajas para las empresas. El análisis de los indicadores permite a los gerentes de logística tomar decisiones relevantes para mejorar el desempeño del sistema. El modelo DEA identifica a los DCs que presentan rendimientos relativamente superiores e inferiores; lo cual facilita la toma de decisiones informadas para cambiar, aumentar o disminuir los recursos y las actividades, o aplicar las mejores prácticas que optimicen el rendimiento de la red. |
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2018 |
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2018-07-02 00:00:00 2024-04-09T20:14:56Z |
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2018-07-02 00:00:00 2024-04-09T20:14:56Z |
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2018-07-02 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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Journal article |
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http://purl.org/redcol/resource_type/ART |
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https://doi.org/10.17981/ingecuc.14.2.2018.13 |
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2382-4700 |
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eng |
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Inge Cuc |
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S. Agrawal, R. Singh and Q. Murtaza, “A literature review and perspectives in reverse logistics”, Resources, Conservation and Recycling, vol. 97, pp. 76-92, 2015. https://doi.org/10.1016/j.resconrec.2015.02.009 N. Bahiraei, H. Panjehfouladgaran and R. Yusuff, “Ranking of critical success factors in reverse logistics by TOPSIS”, Presented at IEOM International Conference, pp. 1-5, 2015. http://dx.doi.org/10.1109/IEOM.2015.7093787 E. Bayraktar, E. Tatoglu and S. Zaim, “Measuring the relative efficiency of quality management practices in Turkish public and private universities”, Journal of the Operational Research Society, vol. 64, no. 12, pp. 1810- 1830, 2013. https://doi.org/10.1057/jors.2013.2 S. Çakir, S. Perçin and H. Min, “Evaluating the comparative efficiency of the postal services in OECD countries using context-dependent and measure-specific data envelopment analysis” Benchmarking: An International Journal, vol. 22, no. 5, pp. 839-856, 2015. https://doi.org/10.1108/BIJ-10-2013-0098 Q.Q. Chang and H.Z. Zheng, “An effective strategy for non-defective reverse logistics”. Presented at ICIA International Conference, pp. 1273-1277, 2014. https://doi.org/10.1109/ICInfA.2014.6932844 W. Cook and J. Zhu, Data Envelopment Analysis: A Handbook on the Modelling of Internal Structures and Networks. New York, USA: Springer, 2014. https://doi.org/10.1007/978-1-4899-8068-7 Council of Supply Chain Management Professionals, CSCMP Glossary [Online], 2013. Available: http://cscmp.org/ K. Das, “Integrating reverse logistics into the strategic planning of a supply chain”, International Journal of Production Research, vol. 50, no. 5, pp. 1438–1456, 2012. https://doi.org/10.1080/00207543.2011.571944 A. Davoodi, H. Rezai and R. Fallahnejad, “Congestion analysis in DEA inputs under weight restrictions”, Journal of the Operational Research Society, vol. 63, no. 8, pp. 1089-1097, 2012. https://doi.org/10.1057/jors.2011.104 J. Ding, W. Dong, G. Bi and L. Liang, “A decision model for supplier selection in the presence of dual-role factors”, Journal of the Operational Research Society, vol. 66, no. 5, pp. 737-746, 2015. https://doi.org/10.1057/jors.2014.53 R. Dyson and E. Shale, “Data Envelopment Analysis, Operational Research and Uncertainty, Journal of the Operational Research Society, vol. 61, no. 1, pp. 25-34, 2010. http://www.jstor.org/stable/40540225 A. Faed, O.K. Hussain and E. Chang, “A methodology to map customer complaints and measure customer satisfaction and loyalty”, Service Oriented Computing and Applications, vol. 8, no. 1, pp. 33-53, 2014. https://doi.org/10.1007/s11761-013-0142-6 M. J. Farrell, “The measurement of productive efficiency”. Journal of the Royal Statistical Society. Series A (General), vol. 120, no. 3, pp. 253-290, 1957. https://doi.org/10.2307/2343100 P. Guarnieri, V. Sobreiro, M. Nagano and A. Marques, “The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: A Brazilian case”, Journal of Cleaner Production, vol. 96, pp. 209-219, 2015. https://doi.org/10.1016/j.jclepro.2014.05.040 S. Haghighi, S. Torabi and R. Ghasemi, “An integrated approach for performance evaluation in sustainable supply chain networks (with a case study)” Journal of Cleaner Production, vol. 137, pp. 579-597, 2016. http://dx.doi.org/10.1016/j.jclepro.2016.07.119 J. R. Huscroft, B. T. Hazen, D. J. Hall and J. B. Hanna, Task-technology fit for reverse logistics performance. International Journal of Logistics Management, 24(2), 230-246, 2013. https://doi.org/10.1108/IJLM-02-2012-0011 M. Izadikhah and R. F. Saen, “A new preference voting method for sustainable location planning using geographic information system and data envelopment analysis”, Journal of Cleaner Production, vol. 137, pp. 1347-1367, 2016. https://doi.org/10.1016/j.jclepro.2016.08.021 Y. Jiang and H. Zheng, “A construction method of Enterprise reverse logistics based on bilateral resource integration”, Presented at ICIA International Conference, pp. 1268-1272, 2014. https://doi.org/10.1109/ICInfA.2014.6932843 C. Kao, “Efficiency decomposition and aggregation in network data envelopment analysis”, European Journal of Operational Research, vol. 255, no. 3, pp. 778-786, 2016. https://doi.org/10.1016/j.ejor.2016.05.019 V. Lall, R. Lumb and A. Moreno, “Selection and Prioritization of Projects: A Data Envelopment Analysis (DEA) approach” Indian Journal of Economics and Business, vol. 11, no. 2, pp. 359-372, 2012. K. H. Lau, “Distribution network rationalisation through bench-marking with DEA”, Benchmarking: An International Journal, vol. 19, no. 6, pp. 668-689, 2012. https://doi.org/10.1108/14635771211284260 K. Lieckens and N. Vandaele, “Multi-level reverse logistics network design under uncertainty”. International Journal of Production Research, vol. 50, no. 1, pp. 23-40, 2012. https://doi.org/10.1080/00207543.2011.571442 S. Lim, “Context-dependent data envelopment analysis with cross-efficiency evaluation”. Journal of the Operational Research Society, vol. 63, no. 1, pp. 38-46, 2012. https://doi.org/10.1057/jors.2011.29 J. S. Liu and W. M. Lu, “Network-based method for ranking of efficient units in two-stage DEA models”, Journal of the Operational Research Society, vol. 63, no. 8, pp. 1153-1164, 2012. https://doi.org/10.1057/jors.2011.132 S. M. Mirhedayatian, M. Azadi and R. F. Saen, “A novel network data envelopment analysis model for evaluating green supply chain management”, International Journal of Production Economics, vol. 147(B). pp. 544-554, 2014. https://doi.org/10.1016/j.ijpe.2013.02.009 A. Mostafaee, “An equitable method for allocating fixed costs by using data envelopment analysis”, Journal of the Operational Research Society, vol. 64, no. 3, pp. 326- 335. https://doi.org/10.1057/jors.2012.56 A. Shabani and R.F. Saen, “Developing a novel data envelopment analysis model to determine prospective benchmarks of green supply chain in the presence of dual-role factor”, Benchmarking: An International Journal, vol. 22, no. 4, pp. 711-730, 2015. https://doi.org/10.1108/BIJ-12-2012-0087 X. Shi, L. Li, L. Yang, Z. Li and J. Choi, “Information flow in reverse logistics: an industrial information integration study”, Information Technology and Management, vol. 13, no. 4, pp. 217-232, 2012. https://doi.org/10.1007/s10799-012-0116-y B. Şimşek and F. Tüysüz, “An application of Network Data Envelopment Analysis with fuzzy data for the performance evaluation in cargo sector”, Journal of Enterprise Information Management, (just-accepted), pp. 00-00, 2018. https://doi.org/10.1108/JEIM-01-2017-0026 R. Skapa and A. Klapalová, “Reverse logistics in Czech companies: increasing interest in performance measurement”, Management Research Review, vol. 35, no. 8, pp. 676- 692, 2012. https://doi.org/10.1108/01409171211247686 C. C. Tu, S. H. Chang, C. J. Tu and A. C. Lee, “Study of the performance of reverse logistics for supply chain management”, Presented at IEEM International Conference, pp. 2323-2327, 2010. https://doi.org/10.1109/IEEM.2010.5674146 C. Ya-Ping, “Cost and Benefit Analysis of Reverse Logistics”, Presented at International Conference on BCGIN, pp. 75-77, 2012. https://doi.org/10.1109/BCGIN.2012.26 M. Zerafat, A. Emrouznejad and A. Mustafa, “Fuzzy data envelopment analysis: A discrete approach”, Expert Systems with Applications, vol. 39, no. 3, pp. 2263-2269, 2012. https://doi.org/10.1016/j.eswa.2011.07.118 Y. Zou, “Study on logistics operation cost control based on the DEA model”, Presented at MSIE International Conference, pp. 1025-1028, 2011. https://doi.org/10.1109/MSIE.2011.5707590 |
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Ardila Gamboa, César DavidBallesteros Riveros, Frank Alexander2018-07-02 00:00:002024-04-09T20:14:56Z2018-07-02 00:00:002024-04-09T20:14:56Z2018-07-020122-6517https://hdl.handle.net/11323/12193https://doi.org/10.17981/ingecuc.14.2.2018.1310.17981/ingecuc.14.2.2018.132382-4700Introducción: El análisis envolvente de datos (DEA), se usa para medir el desempeño relativo de una serie de centros de distribución (DCs), utilizando indicadores clave basados en logística inversa para una empresa que produce suministros eléctricos y electrónicos en Colombia. Objetivo: Medir el rendimiento relativo de los centros de distribución en función de indicadores clave (KPI) de una red de abastecimiento con logística inversa. Metodología: Se aplica un modelo DEA a través de 5 pasos: Selección de KPIs; Recopilación de datos para los 18 DCs en la red de distribución; Se construye y ejecuta el modelo DEA; Identificar los DCs que serán el foco de la mejora; Analizar los DCs que restringen o disminuyen el rendimiento total del sistema. Resultados: Inicialmente se definen KPI, a partir de los datos recolectados y se presentan los KPI para cada DCs. Se ejecuta el modelo DEA y se determinan las eficiencias relativas para cada DCs. Posteriormente, se realiza un análisis de la frontera y se analizan los DCs que limitan o reducen el rendimiento del sistema en busca de opciones para mejorar el sistema. Conclusiones: La logística inversa, trae numerosas ventajas para las empresas. El análisis de los indicadores permite a los gerentes de logística tomar decisiones relevantes para mejorar el desempeño del sistema. El modelo DEA identifica a los DCs que presentan rendimientos relativamente superiores e inferiores; lo cual facilita la toma de decisiones informadas para cambiar, aumentar o disminuir los recursos y las actividades, o aplicar las mejores prácticas que optimicen el rendimiento de la red.Introduction: Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia. Objective: The aim is to measure the relative performance of distribution centers based on Key Performance Indicators (KPI) from a supply network with reverse logistics. Methodology: A DEA model is applied through 5 steps: KPIs selection; Data collection for all 18 DCs in the network; Build and run the DEA model; Identify the DCs that will be the focus of improvement; Analyze the DCs that restrict or diminish the total performance of the system. Results− KPIs are defined, data is collected and KPI’s for each DCs are presented. The DEA model is run and the relative efficiencies for each DCs are determined. A frontier analysis is made and DCs that limit or reduce the performance of the system were analyzed to find options for improving the system. Conclusions: Reverse logistics, brings numerous advantages for companies. The analysis of the indicators allows logistics managers involved to make relevant decisions for higher performance. The DEA model identifies which DCs have a relative superior and inferior performance, making it easier to make informed decisions to change, increase or decrease resources, and activities or apply best practices that optimize the performance of the network.application/pdfengUniversidad de la CostaINGE CUC - 2018https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/1783Data envelopment analysisRelative performanceReverse LogisticsReturnable packagesWarehousingAnálisis Envolvente de DatosEficiencia relativaLogística InversaEmpaques RetornablesAlmacenamientoAnálisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística InversaData Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse LogisticsArtículo 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 CucS. Agrawal, R. Singh and Q. Murtaza, “A literature review and perspectives in reverse logistics”, Resources, Conservation and Recycling, vol. 97, pp. 76-92, 2015. https://doi.org/10.1016/j.resconrec.2015.02.009N. Bahiraei, H. Panjehfouladgaran and R. Yusuff, “Ranking of critical success factors in reverse logistics by TOPSIS”, Presented at IEOM International Conference, pp. 1-5, 2015. http://dx.doi.org/10.1109/IEOM.2015.7093787E. Bayraktar, E. Tatoglu and S. Zaim, “Measuring the relative efficiency of quality management practices in Turkish public and private universities”, Journal of the Operational Research Society, vol. 64, no. 12, pp. 1810- 1830, 2013. https://doi.org/10.1057/jors.2013.2S. Çakir, S. Perçin and H. Min, “Evaluating the comparative efficiency of the postal services in OECD countries using context-dependent and measure-specific data envelopment analysis” Benchmarking: An International Journal, vol. 22, no. 5, pp. 839-856, 2015. https://doi.org/10.1108/BIJ-10-2013-0098Q.Q. Chang and H.Z. Zheng, “An effective strategy for non-defective reverse logistics”. Presented at ICIA International Conference, pp. 1273-1277, 2014. https://doi.org/10.1109/ICInfA.2014.6932844W. Cook and J. Zhu, Data Envelopment Analysis: A Handbook on the Modelling of Internal Structures and Networks. New York, USA: Springer, 2014. https://doi.org/10.1007/978-1-4899-8068-7Council of Supply Chain Management Professionals, CSCMP Glossary [Online], 2013. Available: http://cscmp.org/K. Das, “Integrating reverse logistics into the strategic planning of a supply chain”, International Journal of Production Research, vol. 50, no. 5, pp. 1438–1456, 2012. https://doi.org/10.1080/00207543.2011.571944A. Davoodi, H. Rezai and R. Fallahnejad, “Congestion analysis in DEA inputs under weight restrictions”, Journal of the Operational Research Society, vol. 63, no. 8, pp. 1089-1097, 2012. https://doi.org/10.1057/jors.2011.104J. Ding, W. Dong, G. Bi and L. Liang, “A decision model for supplier selection in the presence of dual-role factors”, Journal of the Operational Research Society, vol. 66, no. 5, pp. 737-746, 2015. https://doi.org/10.1057/jors.2014.53R. Dyson and E. Shale, “Data Envelopment Analysis, Operational Research and Uncertainty, Journal of the Operational Research Society, vol. 61, no. 1, pp. 25-34, 2010. http://www.jstor.org/stable/40540225A. Faed, O.K. Hussain and E. Chang, “A methodology to map customer complaints and measure customer satisfaction and loyalty”, Service Oriented Computing and Applications, vol. 8, no. 1, pp. 33-53, 2014. https://doi.org/10.1007/s11761-013-0142-6M. J. Farrell, “The measurement of productive efficiency”. Journal of the Royal Statistical Society. Series A (General), vol. 120, no. 3, pp. 253-290, 1957. https://doi.org/10.2307/2343100P. Guarnieri, V. Sobreiro, M. Nagano and A. Marques, “The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: A Brazilian case”, Journal of Cleaner Production, vol. 96, pp. 209-219, 2015. https://doi.org/10.1016/j.jclepro.2014.05.040S. Haghighi, S. Torabi and R. Ghasemi, “An integrated approach for performance evaluation in sustainable supply chain networks (with a case study)” Journal of Cleaner Production, vol. 137, pp. 579-597, 2016. http://dx.doi.org/10.1016/j.jclepro.2016.07.119J. R. Huscroft, B. T. Hazen, D. J. Hall and J. B. Hanna, Task-technology fit for reverse logistics performance. International Journal of Logistics Management, 24(2), 230-246, 2013. https://doi.org/10.1108/IJLM-02-2012-0011M. Izadikhah and R. F. Saen, “A new preference voting method for sustainable location planning using geographic information system and data envelopment analysis”, Journal of Cleaner Production, vol. 137, pp. 1347-1367, 2016. https://doi.org/10.1016/j.jclepro.2016.08.021Y. Jiang and H. Zheng, “A construction method of Enterprise reverse logistics based on bilateral resource integration”, Presented at ICIA International Conference, pp. 1268-1272, 2014. https://doi.org/10.1109/ICInfA.2014.6932843C. Kao, “Efficiency decomposition and aggregation in network data envelopment analysis”, European Journal of Operational Research, vol. 255, no. 3, pp. 778-786, 2016. https://doi.org/10.1016/j.ejor.2016.05.019V. Lall, R. Lumb and A. Moreno, “Selection and Prioritization of Projects: A Data Envelopment Analysis (DEA) approach” Indian Journal of Economics and Business, vol. 11, no. 2, pp. 359-372, 2012.K. H. 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