, Volume 56, Issue 4, pp 1213–1239 | Cite as

A comparative study on performance measurement of Indian public sector banks using AHP-TOPSIS and AHP-grey relational analysis

  • Shivani GuruEmail author
  • D. K. Mahalik
Application Article


Banks are the financial intermediaries and important means for the advancement of economies. In the cutthroat competitions, the increase in market shares is a matter of concern for all. Banks are expected to increase their efficiency to boost competitive capacity, which also helps the Decision-maker to know about grey areas for development. Therefore, performance measurements of efficiency calculation, by using different methods are the concern for research across the world. This paper tries to use the combination of AHP, TOPSIS, and Grey Relational Analysis for efficiency calculation of different public sector banks in India and finally, results were compared. AHP is used to determine the weight criteria and Grey Relational Analysis and TOPSIS are used to rank the bank performances. The proposed method of this study used various inputs and outputs criteria which were taken from various banks annual reports. Descriptive statistics and correlation matrix were used to test the validity of the criteria. The findings reveal that banks which are considered as efficient are close to relative closeness to the ideal solution, expose an alternative ranking of the banks, present research also provides better insight to focus on the area of improvement in comparison to others banks. The Comparative result shows both models have the almost same interpretation. Little deviation in their ranks is due to methodological differences. The proposed research will provide a framework for further applications and both approaches will help decision maker of Indian Public sector banks to find optimal solutions to the complex problems by assessing various alternatives.


Banking Efficiency Grey relational analysis TOPSIS AHP Performance ranking 



  1. 1.
    Ertug, Z.K., Girginer, N.: Evaluation of Banks’ commercial credit applications using the analytic hierarchy process and grey relational analysis: a comparison between public and private banks. South Afr. J. Econ. Manag. Sci. 18(3), 308–324 (2015)CrossRefGoogle Scholar
  2. 2.
    Sari, T.: Quantitative techniques in bank efficiency measurement. In: Econ World. Lisbon, Portugal, pp. 1–11 (2018)Google Scholar
  3. 3.
    Sakinc, S.O.: Comparison of Turkish state banks’ performances via multi-criteria performance measurement method. Int. J. Sci. Res. Manag. 4(1), 4857–4871 (2016)Google Scholar
  4. 4.
    Ranjan, S.S., Reddy, K.L.N., Pandit V.N.: Working Paper 199 (2011)Google Scholar
  5. 5.
    Neupane, B.: Efficiency and productivity of commercial banks in Nepal: a Malmquist Index approach. Asian J. Finance Account. 5(2), 220–243 (2013)CrossRefGoogle Scholar
  6. 6.
    Ferreira, A.F, Radrigues, P.M.M., Santos, S.P., Spahr, R.W.: How to create indices for bank branch financial performance measurement using MCDA techniques: an illustrative example. Banco de Portugal Working Paper 13, pp. 1–25 (2012)Google Scholar
  7. 7.
    Zimmermann, H.J.: Fuzzy Set Theory and its Application, 2nd edn. Kluwer Academic Publisher, Dordrecht (1991)CrossRefGoogle Scholar
  8. 8.
    Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)Google Scholar
  9. 9.
    Yoon, K., Hwang, C.L.: Manufacturing plant location analysis by multiple attribute decision making: part II Multi-plant strategy and plant relocation. Int. J. Prod. Res. 23(2), 361–370 (1985)CrossRefGoogle Scholar
  10. 10.
    Sari, T., Baynal, K., Ergul, O.: Supplier selection with grey relational analysis. Int. J. Emerg. Res. Manag. Technol. 5(4), 61–70 (2016)Google Scholar
  11. 11.
    Triantaphyllou, E., Mann, S.H.: Using the analytic Hierarchy process for decision making in engineering application: some challenges. Int. J. Ind. Eng. Appl. Pract. 2(1), 35–44 (1995)Google Scholar
  12. 12.
    Ozbek, A.: Performance analysis of public banks in Turkey. Int. J. Bus. Manag. Econ. Res. 6(13), 21–30 (2015)Google Scholar
  13. 13.
    Galankashi, M.R., Helmi, S.A., Hashemzahi, P.: Supplier selection in automobile industry: a mixed balanced scorecard-fuzzy AHP approach. Alex. Eng. J. 55(1), 93–100 (2016)CrossRefGoogle Scholar
  14. 14.
    Islam, R., Selim, A., Tarique, K.M.: Prioritisation of service quality dimensions for healthcare sector. Int. J. Med. Eng. Inform. 8(2), 108–123 (2016)CrossRefGoogle Scholar
  15. 15.
    Gurcan, O.F., Yazici, I., Beyca, D.F., Arslan, C.Y., Eldemir, F.: Third party logistics (3PL) provider selection with AHP application. Proc. Soc. Behav. Sci. 235, 226–234 (2016)CrossRefGoogle Scholar
  16. 16.
    Luthra, S., Mangla, S.K., Kumar, S., Garg, D.: Identify and prioritise the critical factors in implementing the reverse logistics practices: a case of Indian auto component manufacturer. Int. J. Bus. Syst. Res. 11(1–2), 42–61 (2017)CrossRefGoogle Scholar
  17. 17.
    Wang, S., Sheng, Z., Xi, Y., Ma, X., Zhang, H., Kang, M., Ren, F., Du, Q., Ke, H., Han, Z.: The application of the analytic hierarchy process and a new correlation algorithm to urban construction and supervision using multi-source government data in Tianjin. Int. J. Geo-Inf. 7(50), 3–14 (2017)Google Scholar
  18. 18.
    Ratna, S., Subham, A., Saiuddin, M.: Assessment of lean performance of manufacturing cells in an SME using AHP. Int. J. Mech. Prod. Eng. Res. Dev. (IJMPERD) 8(3), 435–440 (2018)Google Scholar
  19. 19.
    Petrovic, I.B., Kankaras, M.: DEMATEL-AHP multi-criteria decision making model for the determination and evaluation of criteria for selecting an air traffic protection aircraft. Decis. Mak. Appl. Manag. Eng. 1(2), 93–110 (2018)CrossRefGoogle Scholar
  20. 20.
    Popovic, M., Kuzmanovic, M., Savic, G.: A comparative empirical study of analytic hierarchy process and conjoint analysis: literature review. Decis. Mak. Appl. Manag. Eng. 1(2), 153–163 (2018)CrossRefGoogle Scholar
  21. 21.
    Pamucar, D., Stevic, Z., Zavadskas, E.K.: Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages. Appl. Soft Comput. 67, 141–163 (2018)CrossRefGoogle Scholar
  22. 22.
    Stankovic, M., Gladovic, P., Popovic, V.: Determining the importance of the criteria of traffic accessibility using fuzzy AHP and rough AHP method. Decis. Mak. Appl. Manag. Eng. 2(1), 86–104 (2019)CrossRefGoogle Scholar
  23. 23.
    Suraraksa, J., Shin, K.S.: Comparative analysis of factors for supplier selection and monitoring: the case of the automotive industry in Thailand. Sustainability 11, 981 (2019)CrossRefGoogle Scholar
  24. 24.
    Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making Methods and Applications. Springer, Berlin (1981)CrossRefGoogle Scholar
  25. 25.
    Gundogdu, A.: Measurement of financial performance using TOPSIS method for foreign banks of established in Turkey between 2003–2013 years. Int. J. Bus. Soc. Sci. 6(1), 139–151 (2015)Google Scholar
  26. 26.
    Rezaei, M., Ketabi, S.: Ranking the banks through performance evaluation by integrating fuzzy AHP and TOPSIS methods: a study of Iranian private banks. Int. J. Acad. Res. Account. Finance Manag. Sci. 6(3), 19–30 (2016)Google Scholar
  27. 27.
    Dash, M.: Banking performance measurement for Indian banks using AHP and TOPSIS. Int. J. Bank. Finance 12(2), 63–76 (2016)Google Scholar
  28. 28.
    Hancerliogullari, G., Hancerliogullari, K.O., Koksalmis, E.: The use of multi-criteria decision making models in evaluating anaesthesia method options in circumcision surgery. BMC Med. Inform. Decis. Mak. 17(1), 14 (2017)CrossRefGoogle Scholar
  29. 29.
    Cho, J., Chun, J., Kim, I., Choi, J.: Preference evaluation system for construction products using QFD-TOPSIS logic by considering trade-off technical characteristics. In: Hidawi Mathematical Problems in Engineering, No. 3, pp. 1–15 (2017)CrossRefGoogle Scholar
  30. 30.
    You, P., Guo, S., Zhao, H., Zhao, H.: Operation performance evaluation of power grid enterprise using a hybrid BWM-TOPSIS method. Sustainability 9, 2329 (2017)CrossRefGoogle Scholar
  31. 31.
    Yılmaz, G., Inel, M.N.: Assessment of sustainability performances of banks by TOPSIS method and balanced scorecard approach. Int. J. Bus. Appl. Soc. Sci. (IJBASS) 4(1), 62–75 (2018)Google Scholar
  32. 32.
    Lakshmipathy, N., et al.: A fuzzy TOPSIS method to analyze the housemaids in Chennai City. Int. J. Pure Appl. Math. 118(10), 377–388 (2018)Google Scholar
  33. 33.
    Soto, C.M.A., Liern, V., Perez-Gladish, B.: Multiple criteria performance evaluation of YouTube mathematical educational videos by IS-TOPSIS. Operat. Res. Int. J. 18, 1–23 (2018)CrossRefGoogle Scholar
  34. 34.
    Anitha, J., Das, R.: Multi-attribute decision making of electric discharge machining on AISI-D2 steel using TOPSIS method. Int. J. Pharm. Technol. 10(1), 31188–31201 (2018)Google Scholar
  35. 35.
    Nnaji, C.C., Banigo, A.: Multi criteria evaluation of sources for self-help domestic water Supply. J. Appl. Water Sci. 8(12), 2–13 (2018)Google Scholar
  36. 36.
    Hou, Q., Xie, L.: Research on supplier evaluation in a green supply chain. Discret Dyn. Nat Soc. (Hindawi) 2019, 1–14 (2019)CrossRefGoogle Scholar
  37. 37.
    Tong, L., Pu, Z., Ma, J.: Maintenance supplier evaluation and selection for safe and sustainable production in the chemical industry: a case study. Sustainability 11(6), 1533 (2019)CrossRefGoogle Scholar
  38. 38.
    Ertug, Z.K., Girginer, N.: Evaluation of Banks’ commercial credit applications using the analytic hierarchy process and grey relational analysis: a comparison between public and private banks. South Afr. J. Econ. Manag. Sci. 18(3), 308–324 (2015)CrossRefGoogle Scholar
  39. 39.
    Krishna, T.H.: Evaluation of performance of indian banks by using CAMEL AND GRA techniques. Iran. J. Optim. 8(1), 29–36 (2016)Google Scholar
  40. 40.
    Garcia, P.A.A., Duim, F.: A grey relational analysis based approach to the evaluation of Brazilian postgraduate programs in master of business administration. Electron. J. Manag. Syst. 12(4), 391–400 (2017)Google Scholar
  41. 41.
    Skrinjaric, T., Sego, B.: Using grey incidence analysis approach in portfolio selection. Int. J. Financial Stud. 7(1), 1 (2018)CrossRefGoogle Scholar
  42. 42.
    Chen, C., Esangbedo, M.O.: Evaluating university reputation based on integral linear programming with grey possibility. Mathematical Problems in Engineering (Hindawi), Article ID 5484326, pp 1–17 (2018)Google Scholar
  43. 43.
    Sakinc, S.O.: Comparison of Turkish State Banks’ performances via multi-criteria performance measurement method. Int. J. Sci. Res. Manag. 4(1), 4857–4871 (2016)Google Scholar
  44. 44.
    Nyoga, R., Maguta, P., Wang, M.: Application of grey-TOPSIS approach to evaluate value chain performance of tea processing chains. Decis. Sci. Lett. 5, 431–446 (2016)CrossRefGoogle Scholar
  45. 45.
    Venkateswarlu, R., Rao, G.S.S.B.: Profitability evaluation and ranking of indian non-life insurance firms using GRA and TOPSIS. Eur. J. Bus. Manag. 8(22), 153–170 (2016)Google Scholar
  46. 46.
    Pamuar, D., Mihajlovi, M., Obradovi, R., Atanaskovi, P.: Novel approach to group multi-criteria decision making based on interval rough numbers. Exp. Syst. Appl. Int. J. 88(C), 58–80 (2017)CrossRefGoogle Scholar
  47. 47.
    Chatterjee, K., Pamucar, D., Zavadskas, E.K.: Evaluating the performance of suppliers based on using the R’AMATEL-MAIRCA method for green supply chain implementation in electronics industry. J. Clean. Prod. 184, 101–129 (2018)CrossRefGoogle Scholar
  48. 48.
    Ramanathan, R.: A data envelopment analysis of comparative performance of schools in Netherland. Opsearch 38(2), 160–182 (2001)CrossRefGoogle Scholar

Copyright information

© Operational Research Society of India 2019

Authors and Affiliations

  1. 1.Department of Business AdministrationSambalpur UniversityBurlaIndia
  2. 2.JharsugudaIndia

Personalised recommendations