Análisis Envolvente de Datos (DEA) para medir el desempeño relativo basado en indicadores de una red de abastecimiento con Logística Inversa
DOI:
https://doi.org/10.17981/ingecuc.14.2.2018.13Palabras clave:
Análisis Envolvente de Datos, Eficiencia relativa, Logística Inversa, Empaques Retornables, AlmacenamientoResumen
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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