Análisis envolvente de datos y cálculo multivariado para valorar, clasificar y predecir la eficiencia productiva y de innovación de las empresas del sector químico
Cargando...
Archivos
Fecha
2019
Autores
De La Hoz, Efraín
Fontalvo, Tomás
López, Ludis
Título de la revista
ISSN de la revista
Título del volumen
Editor
Centro de Información Tecnológica
Resumen
Se desarrolló un método que permitió establecer criterios de valoración, clasificación y predicción para evaluar
la eficiencia productiva y de innovación de las empresas del sector químico de Barranquilla. Se recolectó
información asociada a variables de clima laboral, gestión de la información, gestión del conocimiento, gestión
de la productividad y la innovación. Seguidamente se validó los resultados con el análisis discriminante y se
modelaron procesos de pronóstico y predicción de la eficiencia de las empresas del sector con las redes
neuronales artificiales. Los resultados muestran que la eficiencia promedio en el sector es del 52,9%, con 6
empresas clasificadas como eficientes. Con las técnicas de análisis discriminante multivariado, se pudo
determinar la calidad de clasificación lográndose un 92,6 % de clasificación correcta. Así mismo el modelo de
redes neuronales seleccionado generó una precisión de clasificación de 98,82%, 95,78% y 94,28% para las
muestras de entrenamiento, prueba y reserva, lo que evidencia la relevancia del modelo de clasificación. Se
concluye que las variables analizadas son significativas para discriminar la eficiencia productiva y de
innovación.
A method to establish assessment, classification and prediction criteria to evaluate the productive efficiency and innovation of the companies in the chemical sector of Barranquilla was developed. Concepts of data envelopment analysis, discriminant analysis and artificial neural networks were used. Information associated with variables of labor climate, information management, knowledge management, productivity management and innovation, were collected. The results were then validated with the discriminant analysis and forecasting processes and prediction of the efficiency of the companies in the sector with artificial neural networks were modeled. The results show that the average efficiency in the sector is 52.9%, with 6 companies classified as efficient ones. With the multivariate discriminant analysis techniques, the classification quality could be determined, achieving a 92.6% correct classification. Likewise, the selected neural network model generated a classification accuracy of 98.82%, 95.78% and 94.28% for the training, test and reserve samples, which shows the relevance of the classification model. It is concluded that the analyzed variables are significant to discriminate productive efficiency and innovation.
A method to establish assessment, classification and prediction criteria to evaluate the productive efficiency and innovation of the companies in the chemical sector of Barranquilla was developed. Concepts of data envelopment analysis, discriminant analysis and artificial neural networks were used. Information associated with variables of labor climate, information management, knowledge management, productivity management and innovation, were collected. The results were then validated with the discriminant analysis and forecasting processes and prediction of the efficiency of the companies in the sector with artificial neural networks were modeled. The results show that the average efficiency in the sector is 52.9%, with 6 companies classified as efficient ones. With the multivariate discriminant analysis techniques, the classification quality could be determined, achieving a 92.6% correct classification. Likewise, the selected neural network model generated a classification accuracy of 98.82%, 95.78% and 94.28% for the training, test and reserve samples, which shows the relevance of the classification model. It is concluded that the analyzed variables are significant to discriminate productive efficiency and innovation.
Descripción
Palabras clave
Eficiencia técnica, Análisis envolvente de datos, Redes neuronales artificiales, Technical efficiency, Data envelopment analysis, Artificial neural networks
