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.

[1]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[2]  Joe Zhu,et al.  Sensitivity analysis of DEA models for simultaneous changes in all the data , 1998, J. Oper. Res. Soc..

[3]  P. W. Wilson Detecting Outliers in Deterministic Nonparametric Frontier Models with Multiple Outputs , 1993 .

[4]  Lawrence M. Seiford,et al.  Stability regions for maintaining efficiency in data envelopment analysis , 1998, Eur. J. Oper. Res..

[5]  P. Andersen,et al.  A procedure for ranking efficient units in data envelopment analysis , 1993 .

[6]  F. Førsund,et al.  Slack-adjusted efficiency measures and ranking of efficient units , 1996 .

[7]  Emmanuel Thanassoulis,et al.  Weights restrictions and value judgements in Data Envelopment Analysis: Evolution, development and future directions , 1997, Ann. Oper. Res..

[8]  Lawrence M. Seiford,et al.  Prioritization models for frontier decision making units in DEA , 1992 .

[9]  Abraham Charnes,et al.  Sensitivity and stability analysis in dea , 1984, Ann. Oper. Res..

[10]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[11]  J. Paradi,et al.  Best practice analysis of bank branches: An application of DEA in a large Canadian bank , 1997 .

[12]  Emmanuel Thanassoulis,et al.  Simulating Weights Restrictions in Data Envelopment Analysis by Means of Unobserved Dmus , 1998 .

[13]  W. Cooper,et al.  Idea and Ar-Idea: Models for Dealing with Imprecise Data in Dea , 1999 .

[14]  Celik Parkan Measuring the efficiency of service operations: An application to bank branches , 1987 .

[15]  篠原 正明,et al.  William W.Cooper,Lawrence M.Seiford,Kaoru Tone 著, DATA ENVELOPMENT ANALYSIS : A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers, 2000年, 318頁 , 2002 .

[16]  Chiang Kao,et al.  Efficiency improvement in data envelopment analysis , 1994 .

[17]  A. Charnes,et al.  Chance-Constrained Programming , 1959 .

[18]  Joe Zhu Robustness of the efficient DMUs in data envelopment analysis , 1996 .

[19]  B. Golany,et al.  Controlling Factor Weights in Data Envelopment Analysis , 1991 .

[20]  Joe Zhu,et al.  Imprecise data envelopment analysis (IDEA): A review and improvement with an application , 2003, Eur. J. Oper. Res..

[21]  Abraham Charnes,et al.  Programming with linear fractional functionals , 1962 .

[22]  W. Cooper,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 1999 .

[23]  F Pedraja-Chaparro,et al.  On the quality of the data envelopment analysis model , 1999, J. Oper. Res. Soc..

[24]  Zilla Sinuany-Stern,et al.  DEA and the discriminant analysis of ratios for ranking units , 1998, Eur. J. Oper. Res..

[25]  Jay Liebowitz,et al.  The Handbook of Applied Expert Systems , 1997 .

[26]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[27]  Mette Asmild,et al.  Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry , 2004 .

[28]  Abraham Charnes,et al.  Sensitivity analysis of the additive model in data envelopment analysis , 1990 .

[29]  Zilla Sinuany-Stern,et al.  Combining ranking scales and selecting variables in the DEA context: the case of industrial branches , 1998, Comput. Oper. Res..