Efficiency Security Margin of Decision Making Units in Data Envelopment Analysis Model – Case Study: Departments in University of Science and Culture

Document Type : Research Paper

Authors

Abstract

One of the most applicable methods to measure efficiency is using Data Envelopment Analysis (DEA) models. DEA measures efficiency for some homogenous units (units with the same inputs and outputs) and determines efficient and non-efficient units. Since the evaluated efficiency for each unit is a relative value, it is clear that each unit tries to improve its performance and preserve (or even improve) its ranking in comparison with others. The distance between efficiency values of the units makes a security margin for them. This concept is first introduced by the authors and named Efficiency Security Margin (ESM). In this paper, in addition to illustrating the concept and the motivation for it, an algorithm is proposed to measure the ESM of departments in University of Science and Culture (USC).

Keywords

References

Agasisti, T. & Pérez-Esparrells, C. (2010). Comparing efficiency in a cross-country perspective: the case of Italian and Spanish state universities. Higher Education, 59(1): 85-103.
Alam Tabriz, A. & Rajabi Purmeybodi, A., Zareeian, M. (2009). Survey in Application of Fuzzy TOPSIS technique in improving efficiency evaluation of Bank Branches using DEA. Journal of Industrial Management, 1(3): 99-118. (in Persian)
Azadeh, A. & Keramati, A. & Jafary Songhori, M. (2009). An integrated Delphi/VAHP/DEA framework for evaluation of information technology/ information system (IT/IS) investments. The International Journal of Advanced Manufacturing Technology, 45(11-12): 1233-1251.
Camanho, A.S. & Dyson, R.G. (2005). Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments. European Journal of Operational Research, 161(2): 432-446.
Chen, Y. & Iqbal Ali, A. (2004). DEA Malmquist productivity measure: New insights with an application to computer industry. European Journal of Operational Research, 159(1): 239-249.
Colbert, A., Levary, R., and Shaner, M. (2000). Determining the Relative Efficiency of MBA Programs using DEA. European Journal Operational Research, 125(3): 656-660.
Cook, W.D. & Zhu, J. (2007). Within-group common weights in DEA: An analysis of power plant efficiency. European Journal of Operational Research, 178(1): 207-216.
Cooper, W.W. & Ruiz, J.L. & Sirvent, I. (2009). Selecting non-zero weights to evaluate effectiveness of basketball players with DEA. European Journal of Operational Research, 195(2): 563-574.
Deetz, M. & Poddig, T. & Sidorovitch, I. & Varmaz, A. (2009). An evaluation of conditional multi-factor models in active asset allocation strategies: an empirical study for the German stock market. Financial Markets and Portfolio Management, 23(3): 285-313.
Farrell, M.J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, 120(3): 253-281.
Ghalayini, A.M. & Noble, J.S. & Crowe, T.J. (1997). An integrated dynamic performance measurement system for improving manufacturing competitiveness. International Journal of Production Economics, 48(3): 207-225.
Isakhani, A. (2002). Designing Mathematical Model for Efficiency Evaluation of Human Sciences Departments in TMU using DEA. MSc. Thesis.
(in Persian)
Kuah, C.T. & Wong, K.Y. & Behrouzi, F. (2010). A Review on Data Envelopment Analysis (DEA). Fourth Asia International Conference on Mathematical / Analytical Modeling and Computer Simulation, Kota Kinabalu, Malaysia.
Mehregan, M.R. (2008a). Operation Research. University Book Publications.
(in Persian)
Mehregan, M.R. (2008b). Quantitative Models in Organizations Performance Evaluation. Faculty of Management of University of Tehran Publications.
(in Persian)
Sinuany, Z. & Mehrez, A. & Barboy, A. (1994). Academic Departments Efficiency Via DEA. Computer and Operations Research, 21(5): 543-556.
Yu, M.M. & Chen, P.C. (2011). Measuring air routes performance using a fractional network data envelopment analysis model. Central European Journal of Operations Research, 19(1): 81-98.
Industrial Management Journal
Volume 6, Issue 3
Autumn
October 2014
Pages 453-470

Files

Share

How to cite

Statistics