Investigating the discriminating power and efficiency of Data Envelopment Analysis (DEA) model based on Stochastic Data Envelopment Analysis

The problem associated with data envelopment analysis (DEA) is the lack of discrimination power among efficient decision-making units (DMUs), hence yielding many DMUs to be efficient. The issue is highlighted when the number of DMUs evaluated is significantly less than the number of inputs and outputs used in the evaluation. Therefore, other DEA models were proposed in the literature to overcome the discriminant power problems. However, the existing DEA models suffer from some other difficulties such as infeasible solutions for efficient DMUs, the non- uniqueness of the input-output weights, etc. The focus of this article is to propose a stochastic data envelopment analysis (SDEA) model for improving the discriminating power of the DEA and to overcome those drawbacks. In this article, the data set was applied to demonstrate the efficiency of the proposed model. It can be concluded that the proposed SDEA model perform better than other methods in the aspect of discrimination power and efficiency.