| Data Envelopment Analysis (DEA) assigns a score to each production unit (DMU) considered in the analysis. Such score indicates whether the unit is efficient or not. For inefficient units, it also identifies a hypothetical unit as the target and thus suggests improvements to their efficiency. However, for efficient units no further improvement can be indicated based on a DEA analysis. Nevertheless, it is important for management to indicate targets for their efficient units if the organization is to improve as a whole. If the inputs and outputs of efficient units can be varied within a specified range, then it is possible to find other combinations of inputs and outputs from which new, "artificial", DMUs can be created. These DMUs are constrained to be more efficient than the DEA efficient unit from which they were created.; This thesis presents a linear programming model and a methodology for improving the efficiency of empirically efficient units by defining a new "Practical Frontier" and utilizing management input. This new frontier allows the analyst to identify adjusted efficiency scores for DMUs which were on the frontier when only real DMUs were considered. The new frontier, formed mostly from the new, artificial DMUs, thus ranks the efficient units which will now have scores less than 1.0. Available bank branch data was used to illustrate the applicability of this theoretical development. |
