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espanolSe desarrollo un metodo que permitio establecer criterios de valoracion, clasificacion y prediccion para evaluar la eficiencia productiva y de innovacion de las empresas del sector quimico de Barranquilla. Se recolecto informacion asociada a variables de clima laboral, gestion de la informacion, gestion del conocimiento, gestion de la productividad y la innovacion. Seguidamente se valido los resultados con el analisis discriminante y se modelaron procesos de pronostico y prediccion 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 tecnicas de analisis discriminante multivariado, se pudo determinar la calidad de clasificacion lograndose un 92,6 % de clasificacion correcta. Asi mismo el modelo de redes neuronales seleccionado genero una precision de clasificacion de 98,82%, 95,78% y 94,28% para las muestras de entrenamiento, prueba y reserva, lo que evidencia la relevancia del modelo de clasificacion. Se concluye que las variables analizadas son significativas para discriminar la eficiencia productiva y de innovacion. EnglishA 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.

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Análisis Envolvente de Datos y Cálculo Multivariado para Valorar, Clasificar y Predecir la Eficiencia Productiva y de Innovación de las Empresas del Sector Químico