Determining the practical frontier for decision-making units by developing a new additive model in the DEA

Data envelopment analysis (DEA) assigns a score to each unit of the decision-making units being analyzed indicating the efficiency or inefficiency of that unit over the other units. However, in the early DEA models, there is no strategy to improve the efficiency of the efficient units. Therefore, in Paradi & Solati's (2004) practical boundary theory, they tried to expand these models to increase the efficiency for the efficient decision-making units. They had a basis for improving performance to a certain extent, thus, they presented the P-DEA linear programming model to extend the efficiency of the efficient units. Because of the staff management in organizations, it is important to increase the efficiency units in order to improve the organization based on the possible changes in the level of input and output of decision-making units. This is done to produce new advanced based on the efficiency of these new units. In this research, after studying the P-DEA model thoroughly, we identified its drawbacks and proposed a new method for determining the practical boundary by developing an additive model using an example.