An integrated ANN and Fuzzy C-mean clustering algorithm for performance assessment of telecommunication sectors and auto industries

This study proposes a non-parametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed computational method is able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Also for increasing DMUs´ homogeneousness, Fuzzy C-means method is used to cluster DMUs. An example using real data is presented for illustrative purposes. In the application to the wireless telecommunication and auto industries, we find that the neural network provide more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.