An Integrated Artificial Neural Network Fuzzy C-Means-Normalization Algorithm for performance assessment of decision-making units: The cases of auto industry and power plant

Efficiency frontier analysis has been an important approach of evaluating firms’ performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes two non-parametric efficiency frontier analysis sub-algorithms based on (1) Artificial Neural Network (ANN) technique and (2) ANN and Fuzzy C-Means for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. Normal probability plot is used to find the outliers and select from these two methods. The proposed computational algorithms are able to find a stochastic frontier based on a set of input–output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. In these algorithms, 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 in the second algorithm, for increasing DMUs’ homogeneousness, Fuzzy C-Means method is used to cluster DMUs. Two examples using real data are presented for illustrative purposes. First example which deals with power generation sector shows the superiority of Algorithm 2 while the second example dealing auto industries of various developed countries shows the superiority of Algorithm 1. Overall, we find that the proposed integrated algorithm based on ANN, Fuzzy C-Means and Normalization approach provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.