Resumen
The economy of any nation depends on the structure and functioning of its various sectors. Transport sector is one of the vital sectors for the financial system of any developing country. All other sectors are dependent on it either directly or indirectly. Thus improving the efficiency of this sector has become a major concern for the operators and the policy makers. The present paper presents an amalgamation of the two non-parametric techniques, Data Envelopment Analysis (DEA) and Neural Networks (NNs) to compute the efficiency scores of State Transport Undertakings of India. DEA is used to compute the efficiency scores of 27 DMUs. These scores are used to train a neural network model, namely the BPN model. The algorithm is developed and used for predicting the efficiency scores of other units of the data set. The results obtained are comparable and it has been shown that this approach helps in improving the discriminatory power of DEA.