Propuesta de optimización del inventario en restaurante "Exclusive People" mediante pronósticos de demanda con Machine Learning
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Date
2025-10-01
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Abstract
El objetivo de esta investigación fue examinar y sugerir tácticas para la optimización de la
administración de inventarios y la merma de las pérdidas operativas en el restaurante Exclusive
People, a través de la implementación de modelos de predicción de demanda basados en
inteligencia artificial. La finalidad del estudio consistió en concebir una solución tecnológica que
facilitara la anticipación de la demanda, optimizara la planificación de adquisiciones y la toma de
decisiones estratégicas. El enfoque metodológico adoptado comprendió la implementación de
encuestas al personal, entrevistas con la dirección, análisis de registros internos, técnicas de
minería de datos y la simulación de un modelo predictivo en Python utilizando Prophet. Los
hallazgos demostraron que la inteligencia artificial tiene la capacidad de incrementar la precisión
de las proyecciones, disminuir los costos asociados al sobre inventario y optimizar los niveles de
rotación. Además, se identificó la exigencia de uniformizar procedimientos, otorgar formación al
personal y establecer indicadores de rendimiento. Para concluir, se estableció que la incorporación
de instrumentos predictivos, complementada con un robustecimiento tecnológico y análisis de
datos, constituye la opción más eficaz para incrementar la eficiencia operativa del restaurante.
The objective of this research was to examine and propose strategies for optimizing inventory management and minimizing operational losses at the restaurant Exclusive People through the implementation of demand forecasting models based on artificial intelligence. The purpose of the study was to design a technological solution that would facilitate demand anticipation, optimize procurement planning, and strengthen strategic decision-making. The methodological approach included the implementation of staff surveys, interviews with management, analysis of internal records, data mining techniques, and the simulation of a predictive model in Python using Prophet. The findings demonstrated that artificial intelligence has the capacity to significantly increase forecasting accuracy, reduce costs associated with overstocking, and optimize inventory turnover levels. In addition, the study identified the need to standardize procedures, provide staff training, and establish performance indicators. In conclusion, it was determined that the progressive integration of predictive tools, complemented by enhanced technological capabilities and data analysis, constitutes the most effective option for improving the restaurant’s operational efficiency.
The objective of this research was to examine and propose strategies for optimizing inventory management and minimizing operational losses at the restaurant Exclusive People through the implementation of demand forecasting models based on artificial intelligence. The purpose of the study was to design a technological solution that would facilitate demand anticipation, optimize procurement planning, and strengthen strategic decision-making. The methodological approach included the implementation of staff surveys, interviews with management, analysis of internal records, data mining techniques, and the simulation of a predictive model in Python using Prophet. The findings demonstrated that artificial intelligence has the capacity to significantly increase forecasting accuracy, reduce costs associated with overstocking, and optimize inventory turnover levels. In addition, the study identified the need to standardize procedures, provide staff training, and establish performance indicators. In conclusion, it was determined that the progressive integration of predictive tools, complemented by enhanced technological capabilities and data analysis, constitutes the most effective option for improving the restaurant’s operational efficiency.
Keywords
Análisis predictivo, Inteligencia artificial, Inventarios, Predicción de la demanda, Rotación