Revisión de literatura: optimización del proceso de abastecimiento de una empresa
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Abstract
En el entorno empresarial actual, la optimización del proceso de abastecimiento es esencial para la competitividad organizacional. El objetivo de este estudio fue sistematizar la evidencia científica sobre la optimización del abastecimiento en una empresa de material publicitario, mediante una revisión sistemática cualitativa siguiendo el protocolo PRISMA. La búsqueda se realizó en Scopus, SciELO, ProQuest, Clinical Key y EBSCO, para el periodo 2018-2024, empleando la ecuación: "optimization" AND "Supply optimization". Los resultados identificaron categorías relacionadas con sistemas que optimizan tiempos operativos, destacando modelos de predicción, rutas, automatización, gestión (ABC/EOQ/ERI), diseño de planta y toma de decisiones. La adopción de estos modelos exige inversión en sistemas automatizados y algoritmos que mejoran la planificación, reducen tiempos de respuesta y fortalecen la productividad e imagen corporativa. Se concluye que la integración de tecnologías emergentes, como inteligencia artificial y machine learning, genera reducciones significativas en costos y mejoras sustanciales en la eficiencia de la cadena de suministro.
In today's business environment, the optimization of the sourcing process is essential for organizational competitiveness. The objective of this study was to systematize the scientific evidence on sourcing optimization in an advertising material company, through a qualitative systematic review following the PRISMA protocol. The search was conducted in Scopus, SciELO, ProQuest, Clinical Key and EBSCO, for the period 2018-2024, using the equation: "optimization" AND "Supply optimization". The results identified categories related to systems that optimize operating times, highlighting predictive models, routing, automation, management (ABC/EOQ/ERI), plant design and decision making. The adoption of these models requires investment in automated systems and algorithms that improve planning, reduce response times and strengthen productivity and corporate image. It is concluded that the integration of emerging technologies, such as artificial intelligence and machine learning, generates significant cost reductions and substantial improvements in supply chain efficiency.
In today's business environment, the optimization of the sourcing process is essential for organizational competitiveness. The objective of this study was to systematize the scientific evidence on sourcing optimization in an advertising material company, through a qualitative systematic review following the PRISMA protocol. The search was conducted in Scopus, SciELO, ProQuest, Clinical Key and EBSCO, for the period 2018-2024, using the equation: "optimization" AND "Supply optimization". The results identified categories related to systems that optimize operating times, highlighting predictive models, routing, automation, management (ABC/EOQ/ERI), plant design and decision making. The adoption of these models requires investment in automated systems and algorithms that improve planning, reduce response times and strengthen productivity and corporate image. It is concluded that the integration of emerging technologies, such as artificial intelligence and machine learning, generates significant cost reductions and substantial improvements in supply chain efficiency.
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Vol. 5, No. 11