Publication: Desempeño logístico en entidades turísticas cubanas de la cadena de suministro: Estudio comparativo mediante machine learning
Authors
Guerra Castellón, Emilio Enrique ; Vázquez Alfonso, Yasser ; Núñez Torres, Edgar
item.page.secondaryauthor
item.page.director
Publisher
Universidad de Murcia: Escuela Universitaria Turismo de Murcia
publication.page.editor
publication.page.department
Description
Abstract
Esta investigación evalúa el desempeño logístico de trece entidades turísticas cubanas de la cadena de suministro, identificando patrones y áreas de mejora mediante técnicas avanzadas de machine learning . Se utilizó el modelo de referencia de la logística de excelencia para la evaluación y se aplicaron técnicas de machine learning , tales como la regresión l ineal múltiple, clustering K-means y Random Forest . Los resultados mostraron un desempeño pr omedio de 3. 25 en una escala de 1 – 5, con ITH Ciego de Ávila l iderando ( 3. 92 ) y ITH Base de Transporte en último lugar ( 2. 46). Los módulos más débiles fueron Tecnologías de la Información, Sistema de Software y Barreras y Riesgos. El análisis identificó cua tro grupos de entidades con perfiles similares y destacó la relevancia del transporte y las tecnologías para mejorar el desempeño. Se concluye que son necesarias intervenciones específicas en tecnología y gestión de riesgos para optimizar la cadena de sumi nistro turística cubana.
This research evaluates the logistics performance of thirteen Cuban tourism entities in the supply chain, identifying patterns and areas for improvement through advanced machine learning techniques. The reference model of logistics of excellence was usedfor the evaluation and machine learning techniques such as multiple l inear regression, K-means clustering and Random Forest were applied. The results showed an average performance of 3. 25 on a scale of 1 -5 , with ITH Ciego de Avila leading ( 3 . 92) and ITH Bas e de Transporte in last place ( 2. 46). The weakest modules were Information Technologies, Software System and Barriers and Risks. The analysis identified four groups of entities with similar profiles and highlighted the relevance of transportation and techn ologies to improve performance. It is concluded that specific interventions in technology and risk management are necessary to optimize the Cuban tourism supply chain.
This research evaluates the logistics performance of thirteen Cuban tourism entities in the supply chain, identifying patterns and areas for improvement through advanced machine learning techniques. The reference model of logistics of excellence was usedfor the evaluation and machine learning techniques such as multiple l inear regression, K-means clustering and Random Forest were applied. The results showed an average performance of 3. 25 on a scale of 1 -5 , with ITH Ciego de Avila leading ( 3 . 92) and ITH Bas e de Transporte in last place ( 2. 46). The weakest modules were Information Technologies, Software System and Barriers and Risks. The analysis identified four groups of entities with similar profiles and highlighted the relevance of transportation and techn ologies to improve performance. It is concluded that specific interventions in technology and risk management are necessary to optimize the Cuban tourism supply chain.
publication.page.subject
Citation
item.page.embargo
Collections
Ir a Estadísticas
Este ítem está sujeto a una licencia Creative Commons. Licencia Creative Commons