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dc.creatorArdila Gamboa, César David
dc.creatorBallesteros Riveros, Frank Alexander
dc.date.accessioned2019-02-12T00:53:12Z
dc.date.available2019-02-12T00:53:12Z
dc.date.issued2018-12-20
dc.identifier.citationArdila Gamboa, C., & Ballesteros Riveros, F. (2018). Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa. INGE CUC, 14(2), 137-146. https://doi.org/10.17981/ingecuc.14.2.2018.13spa
dc.identifier.issn0122-6517, 2382-4700 electrónico
dc.identifier.urihttp://hdl.handle.net/11323/2395
dc.description.abstractIntroduction− 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 perfor-mance 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 net-work; 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 are analyzed to find options for improving the system.Conclusions−Reverse logistics, brings numerous ad-vantages for companies. The analysis of the indicators allows logistics managers involved to make relevant deci-sions for higher performance. The DEA model identifies which DCs have a relative superior and inferior perfor-mance, 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.eng
dc.description.abstractIntroducción− 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 ven-tajas para las empresas. El análisis de los indicadores permite a los gerentes de logística tomar decisiones rel-evantes para mejorar el desempeño del sistema. El mod-elo 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.spa
dc.format.mimetypeapplication/pdf
dc.language.isoengeng
dc.publisherCorporación Universidad de la Costaspa
dc.relation.ispartofseries2;
dc.sourceINGE CUCspa
dc.subjectData envelopment analysisspa
dc.subjectRelative perfor-mancespa
dc.subjectReverse logisticsspa
dc.subjectReturnable packagesspa
dc.subjectWare-housingspa
dc.titleData Envelopment Analysis to measure relative performance based on key indicators from a supply network with reverse logisticsspa
dc.typeArticlespa
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dc.source.urlhttps://revistascientificas.cuc.edu.co/ingecuc/article/view/1783
dc.identifier.doihttps://doi.org/10.17981/ingecuc.14.2.2018.13
dc.identifier.eissn2382-4700
dc.identifier.pissn0122-6517


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