Establishing the "practical frontier" in data envelopment analysis

Data envelopment analysis (DEA) assigns a score to each production unit (decision making 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. Based on possible variations in the input and output levels of efficient DMUs, new units which are more efficient than DEA efficient units can be created to form a new improved frontier. This paper presents a linear programming model, P-DEA, and a methodology for improving the efficiency of empirically efficient units by defining a new "practical frontier" and utilizing management input. Available bank branch data was used to illustrate the applicability of this theoretical development. The sensitivity of the results to the parameters defined by management in the P-DEA model was also examined, which proved the robustness of the proposed model.