The Analytic Hierarchy Process (AHP) is a procedure, which can only consider relative priorities as estimated by decision-makers. A Data Envelopment Analysis (DEA) model is a data-oriented approach for evaluating the relative efficiency of a group of entities referred to as Decision Making Units (DMUs). This research work integrates and combines positive aspects of AHP\u27s estimated qualitative data and DEA\u27s quantitative data. This combination is accomplished by specifying two variants of the DEA methodology for selection of the best DMU. Initially the priority weights of AHP are integrated with the DEA methodology to provide results that are logic based. Next, a method is developed to work backwards through the DEA model to provide values that would be the required results from an AHP formulation to give the same result in DEA. The objective of the research is to propose variants of DEA that would possibly improve the results and also integrate subjective data. Through the application of the methods developed in this research, it is believed that the acceptability of the results obtained from DEA analysis can be improved
