Abstract
Benchmarking requires an effective methodology for finding the best performer, which entails an evaluation of the relative efficiencies of competitors in terms of multiple input and output factors. To identify the best performer, Data Envelopment Analysis (DEA) has been popularly used. However, the conventional DEA has some deficiencies with respect to its use for benchmarking. First, the reference set of an inefficient DMU often has multiple efficient DMUs. Second, it might be quite impossible for an inefficient DMU to achieve its target’s efficiency in a single step, especially when the target is far removed from the DMU. To overcome these deficiencies of conventional DEA, we propose a new stepwise benchmarking method using DEA, which enables inefficient DMUs to select the more appropriate benchmarking DMU based on the similarity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alirezaee, M.R., Afsharian, M.: Model improvement for computational difficulties of DEA technique in the presence of special DMUs. Applied mathematics and Computation 186, 1600–1611 (2007)
Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444 (1978)
Cooper, W.W., Lawrence, M.: Seiford and Kaoru T, Introduction to Data Envelopment Analysis and Its uses: with DEA solver software and reference. Interface (2006)
Donthu, N., Hershberger, E.K., Osmonbekov, T.: Benchmarking marketing productivity using data envelopment analysis. Journal of Business Research 58, 1474–1482 (2005)
Gonzales, E., Alvarez, A.: From efficiency measurement to efficiency improvement: The choice of a relevant benchmark. European Journal of Operational Research 133, 512–520 (2001)
Joe, Z.: Quantitative models for performance evaluation and benchmarking-Data Envelopment Analysis with Spreadsheets and DEA Excel Solver. Kluwer Academic Publishers, Dordrecht (2003)
Kohonen, T.: An introduction to neural computing. Neural Networks 1, 3–16 (1988)
Lim, S., Bae, H., Lee, L.H.: A study on the selection of benchmarking paths in DEA. Expert System with Applications 38, 7665–7673 (2011)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Ross, A., Droge, C.: An integrated benchmarking approach to distribution center performance using DEA modeling. Journal of Operations Management 20, 19–32 (2002)
Shaneth, A.E., Hee, S., Young, A., Su, H., Shin, C.: A method of stepwise benchmarking for inefficient DMUs based on the proximity-based target selection. Expert Systems with Applications 36, 11595–11604 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, J., Bae, H., Lim, S. (2011). Method of Benchmarking Route Choice Based on the Input Similarity Using DEA. In: Watada, J., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22194-1_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-22194-1_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22193-4
Online ISBN: 978-3-642-22194-1
eBook Packages: EngineeringEngineering (R0)