Efficiency Measurement With A Three-Stage Hybrid Method

Year 2018, Volume: 5 Issue: 2, 370 - 388, 19.05.2018

İrfan Ertuğrul , Tayfun Öztaş

https://doi.org/10.21449/ijate.423602

Cited By: 1

Abstract

Data Envelopment Analysis (DEA) is one of the most widely used efficiency measurement techniques in the literature. In the method developed by Charnes, Cooper, and Rhodes, the relation between input(s) and output(s) is examined and relative efficiency values are obtained for many decision-making units. In order to be able to accurately measure the efficiency with Data Envelopment Analysis, the selection of input and output variables needs to be done carefully otherwise, the results may be misleading. For this purpose, it is aimed to make an objective selection process by using Grey Relational Analysis (GRA) in the identification of variables in the study. Via this method 17 financial ratios of 20 firms in the BIST Food Index for the period of 2013-2015 categorized into 4 groups, then each category clustered and the ratios which have the highest correlation within each cluster selected as representative indicator. Thus, 3 inputs and 2 output variables were selected so that the number of variables was reduced from 17 to 5. An input-oriented BCC model was established with selected variables to determine the efficiencies of firms in each period. The Malmquist Total Factor Productivity Index was used to analyze the productivity changes between periods. It was concluded that 7 firms were efficient in each year and the productivity of the sector increased between the periods as a result of the analysis.

Keywords

Efficiency measurement, Grey Relational Analysis, Data Envelopment Analysis, Malmquist index, Clustering

Efficiency Measurement With A Three-Stage Hybrid Method

Abstract

Data
Envelopment Analysis (DEA) is one of the most widely used efficiency
measurement techniques in the literature. In the method developed by Charnes, Cooper, and Rhodes, the relation
between input(s) and output(s) is examined and relative efficiency values are
obtained for many decision-making units. In order to be able to accurately
measure the efficiency with Data
Envelopment Analysis, the selection of input and output variables needs to be
done carefully otherwise, the results may be misleading. For this purpose, it
is aimed to make an objective selection process by using Grey Relational
Analysis (GRA) in the identification of variables in the study. Via this method
17 financial ratios of 20 firms in the BIST Food Index for the period of
2013-2015 categorized into 4 groups, then each category clustered and the
ratios which have the highest correlation within each cluster selected as representative indicator. Thus, 3 inputs and 2
output variables were selected so that the number of variables was reduced from
17 to 5.  An input-oriented BCC model was
established with selected variables to determine the efficiencies of firms in
each period. The Malmquist Total Factor Productivity Index was used to analyze
the productivity changes between periods. It was concluded that 7 firms were
efficient in each year and the productivity of the sector increased between the
periods as a result of the analysis.

Keywords

Efficiency measurement, Grey Relational Analysis, Data Envelopment Analysis, Malmquist index, Clustering

References

• Ahn, T., Charnes, A., & Cooper, W. W. (1988). Efficiency characterizations in different DEA models. Socio-Economic Planning Sciences, 22(6), 253-257.
• Atrill, P. (2012). Financial Management for Decision Makers (6th ed.). Essex: Pearson Education.
• Banker, R. D., & Thrall, R. M. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research, 62(1), 74-84.
• Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
• Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980-89. The Scandinavian Journal of Economics, 94, Supplement. Proceedings of a Symposium on Productivity Concepts and Measurement Problems: Welfare, Quality and Productivity in the Service Industries, S211-S228.
• Bloomberg. Financial Analysis Reports. Retrieved from Bloomberg Terminal.
• Bowlin, W. F. (1998). Measuring performance: An introduction to data envelopment analysis (DEA). The Journal of Cost Analysis, 15(2), 3-27. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
• Chavas, J. P., & Aliber, M. (1993). An analysis of economic efficiency in agriculture: A nonparametric approach. Journal of Agricultural and Resource Economics, 18(1), 1-16.
• Chiang-Ku, F., Shu-Wen, C., & Cheng-Ru, W. (2009). Using GRA and DEA to compare efficiency of bancassurance sales with an insurer’s own team. The Journal of Grey System, 21(4), 395-406.
• Chorafas, D. N. (2015). Business Efficiency and Ethics: Values and Strategic Decision Making. New York, NY: Palgrave Macmillan. Coelli, T. J., & Rao, D. S. P. (2005). Total factor productivity growth in agriculture: A Malmquist index analysis of 93 countries, 1980-2000. Agricultural Economics, 32(s1), 115-134. Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York, NY: Springer Science & Business Media. Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-Thirty years on. European Journal of Operational Research, 192(1), 1-17. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2nd ed.). New York, NY: Springer Science & Business Media.
• Daraio, C., & Simar, L. (2007). Advanced robust and nonparametric methods in efficiency analysis: Methodology and applications. New York, NY: Springer Science & Business Media.
• Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288-294.
• Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1-24.
• Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society, 45(5), 567-578.
• Durga Prasad, K. G., Venkata Subbaiah, K., & Prasad M. V. (2017). Supplier evaluation and selection through DEA-AHP-GRA integrated approach-A case study. Uncertain Supply Chain Management, 5(4), 369-382.
• Dyson, J. R. (2010). Accounting for non-accounting students (8th ed.). Essex: Pearson Education.
• Elyasiani, E., & Mehdian, S. M. (1990). A nonparametric approach to measurement of efficiency and technological change: The case of large U.S. commercial banks. Journal of Financial Services Research, 4(2), 157-168.
• Ertugrul, I., Oztas, T., Ozcil, A., & Oztas, G. Z. (2016). Grey relational analysis approach in academic performance comparison of university: A case study of Turkish universities. European Scientific Journal, June 2016 Special Edition, 128-139.
• Färe, R., Grosskopf, S., Lindgren, B., & Roos, P. (1992). Productivity changes in Swedish pharamacies 1980-1989: A non-parametric Malmquist approach. Journal of Productivity Analysis, 3(1-2), 85-101.
• Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. The American Economic Review, 84(1), 66-83.
• Feng, C. M., & Wang, R. T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142.
• Fried, H. O., Lovell, C. A. K., & Schmidt, S. S. (2008). Efficiency and productivity. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The Measurement of Productive Efficiency and Productivity Growth (pp. 3-91). New York, NY: Oxford University Press.
• Girginer, N., Köse, T., & Uçkun, N. (2015). Efficiency analysis of surgical services by combined use of data envelopment analysis and gray relational analysis. Journal of Medical Systems,39: 56. DOI: 10.1007/s10916-015-0238-y
• Ho, C. T. B. (2011). Measuring dot com efficiency using a combined DEA and GRA approach. Journal of the Operational Research Society, 62(4), 776-783.
• Ho, C. T., & Zhu, D. S. (2004). Performance measurement of Taiwan’s commercial banks. International Journal of Productivity and Performance Management, 53(5), 425-434.
• Hsu, L. C. (2015). Using a decision-making process to evaluate efficiency and operating performance for listed semiconductor companies. Technological and Economic Development of Economy, 21(2), 301-331.
• Huang, C., Dai, C., & Guo M. (2015). A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection. Applied Mathematics and Computation, 251, 431-441.
• Hürriyet. (2014). Türk salça devi Tukaş satıldı işte alıcısı. Retrieved from http://www.hurriyet.com.tr/ekonomi/turk-salca-devi-tukas-satildi-iste-alicisi-26962638
• İç, Y. T., Tekin, M., Pamukoğlu, F. Z., & Yıldırım, S. E. (2015). Development of a financial performance benchmarking model for corporate firms. Journal of the Faculty of Engineering and Architecture of Gazi University, 30(1), 71-85.
• Isberg, S., & Pitta, D. (2013). Using financial analysis to assess brand equity. Journal of Product & Brand Management, 22(1), 65-78.
• Kaygısız Ertuğ, Z., & Girginer, N. (2015). Bütünleşik VZA ve GİA yöntemleriyle büyükşehir belediyelerinin mali etkinlik analizi: Türkiye örneği [Financial efficiency analysis of metropolitan municipalities with integrated DEA and GRA: The case of Turkey]. International Journal of Economic and Administrative Studies, 8(15), 411-428.
• Koopmans, T. C. (1951). Analysis of production as an efficient combination of activities. In T. C. Koopmans (Ed.), Activity Analysis of Production and Allocation: Proceedings of a Conference (pp. 33-97). New York, NY: John Wiley & Sons.
• Kung, C. Y., & Wen, K. L. (2007). Applying grey relational analysis and grey decision-making to evaluate the relationship between company attributes and its financial performance—A case study of venture capital enterprises in Taiwan. Decision Support Systems, 43(3), 842-852.
• Kuo, Y., Yang, T., & Huang, G. W. (2008). The use of grey relational analysis in solving multiple attribute decision-making problems. Computers & Industrial Engineering, 55(1), 80-93.
• Lovell, C. A. K., & Pastor, J. T. (1995). Units invariant and translation invariant DEA models. Operations Research Letters, 18(3), 147-151.
• Murillo-Zamorano, L. R. (2004). Economic efficiency and frontier techniques. Journal of Economic Surveys, 18(1), 33-77.
• Ng, D. K. W. (1994). Grey system and grey relational model. ACM SIGICE Bulletin, 20(2), 2-9.
• Önem, H. B., & Demir, Y. (2015). Mülkiyet yapısının firma performansına etkisi: BİST imalat sektörü üzerine bir uygulama [A survey of ownership structure on the performance of the firms: An application on the production sector at BIST]. Suleyman Demirel University the Journal of Visionary, 6(13), 31-43.
• Pakkar, M. S. (2017). Hierarchy grey relational analysis using DEA and AHP. PSU Research Review, 1(2), 150-163.
• Pakkar, M. S. (2018). Fuzzy multi-attribute grey relational analysis Using DEA and AHP. In J. Xu, M. Gen, A. Hajiyev, & F. L. Cooke (Eds.), Proceedings of the Eleventh International Conference on Management Science and Engineering Management. ICMSEM 2017. Lecture Notes on Multidisciplinary Industrial Engineering (pp. 695-707). Cham, CH: Springer International Publishing.
• Pastor, J. T. (1996). Translation invariance in data envelopment analysis: A generalization. Annals of Operations Research, 66(2), 91-102.
• Sheth, J. N., & Sisodia, R. S. (2002). Marketing productivity: Issues and analysis. Journal of Business Research, 55(5), 349-362.
• Tayyar, N., Akcanlı, F., Genç, E., & Erem, I. (2014). BİST’e kayıtlı bilişim ve teknoloji alanında faaliyet gösteren işletmelerin finansal performanslarının analitik hiyerarşi prosesi (AHP) ve gri ilişkisel analiz (GİA) yöntemiyle değerlendirilmesi [Financial performance evaluation of technology companies quoted in BIST with Analytic Hierarchy Process (AHP) and Grey Relational Analysis]. Journal of Accounting and Finance, Ocak/2014(61), 19-40.
• Tsaur, R. C., Chen, I. F., & Chan, Y. S. (2017). TFT-LCD industry performance analysis and evaluation using GRA and DEA models. International Journal of Production Research, 55(15), 4378-4391.
• Tzeng, C. J., Lin, Y. H., Yang, Y. K., & Jeng, M. C. (2009). Optimization of turning operations with multiple performance characteristics using the Taguchi method and grey relational analysis. Journal of Materials Processing Technology, 209(6), 2753-2759.
• Van Horne, J. C., & Wachowicz, J. M. (2008). Fundamentals of Financial Management (13th ed.). Essex: Pearson Education.
• Wahlen, J. M., Baginski, S. P., & Bradshaw, M. T. (2011) Financial reporting, financial statement analysis, and valuation: A strategic perspective (7th ed.). Mason, OH: South Western, Cengage Learning.
• Wang, R. T. (2007). Performance evaluation of Taiwan’s TFT-LCD industry. International Journal of Value Chain Management, 1(4), 372-386.
• Wang, S., Ma, Q., & Guan, Z. (2007). Measuring hospital efficiency in China using grey relational analysis and data envelopment analysis. In Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, 18-20 November 2007, Nanjing, China (pp. 135-139), IEEE.
• Wang, Y. J. (2008). Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan. Expert Systems with Applications, 34(3), 1837-1845.
• Wang, Y. J. (2014). The evaluation of financial performance for Taiwan container shipping companies by fuzzy TOPSIS. Applied Soft Computing, 22, 28-35.
• Yu, K., Luo, B. N., Feng, X., & Liu J. (2018). Supply chain information integration, flexibility, and operational performance: An archival search and content analysis. The International Journal of Logistics Management, 29(1), 340-364.