La Influencia de Big Data en la definición de Indicadores Clave de Rendimiento Logístico para la Satisfacción del Cliente: Una revisión sistemática de la literatura

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The present study conducted a Systematic Literature Review (SLR) on the influence of Big Data in the definition of Key Performance Indicators (KPIs) in logistics and their impact on customer satisfaction. The reviewed literature showed numerous studies on the use of Big Data in logistics; however, t...

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Detalles Bibliográficos
Autores: Melgarejo Zelaya, Luis Alfonso, Santisteban, Jose
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad Nacional Mayor de San Marcos
Repositorio:Revistas - Universidad Nacional Mayor de San Marcos
Lenguaje:español
OAI Identifier:oai:revistasinvestigacion.unmsm.edu.pe:article/28533
Enlace del recurso:https://revistasinvestigacion.unmsm.edu.pe/index.php/rpcsis/article/view/28533
Nivel de acceso:acceso abierto
Materia:Key Performance Indicators (KPIs)
Big Data
Customer Satisfaction
Supply Chain
Indicadores Clave de Rendimiento (KPIs)
Satisfacción del cliente
Cadena de suministro
Descripción
Sumario:The present study conducted a Systematic Literature Review (SLR) on the influence of Big Data in the definition of Key Performance Indicators (KPIs) in logistics and their impact on customer satisfaction. The reviewed literature showed numerous studies on the use of Big Data in logistics; however, there were few efforts that synthesized aspects such as defined KPIs, developed methods, and implemented tools. The objective was to explore and analyze how Big Data supported the definition of logistics KPIs and its influence on customer satisfaction. The methodology encompassed three stages: planning, implementation, and results, following Kitchenham's approach. A review protocol was established with key research questions and inclusion and exclusion criteria. Systematic searches were conducted in relevant databases, and pertinent studies were selected. The results revealed that the definition of KPIs varied according to context and industry; 23 KPIs, 9 Big Data methods, and 7 IT tools were identified. Finally, the conclusions emphasized the importance of adapting logistics KPIs to business priorities and the relevance of Big Data in their definition to improve both decision-making and customer satisfaction.
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