Skip to main content
Log in

Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry

  • Original Paper
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In today’s competitive business environment, service providers have a strong objective to satisfy the customers with low cost to ensure a patronage/loyalty. Performance measurement defines the information or feedback on actions to meeting strategic objectives and client satisfaction. Generally, performance evaluation of the service provider is a time consuming complicated process, depends customer satisfaction. Over the past two decades several researchers have proposed methods to measure service and quality performance in order to improve the performance efficiency of the organization, since there is a considerable room exists. Hence, in this paper, we analyse efficient and inefficient levels of service performance using data envelopment analysis (DEA) and balance scorecard (BSC) techniques, to bridge the exist gap. The DEA approach has been used to measure the performance of automobile dealers from different areas to know their service levels and also treats the quality of service by making use of different cross-efficiency data envelopment analysis models to discriminate the units. Then, a BSC approach analyzes which aspects of decision making units are inefficient, grounded on four perspectives like as; customers, financial, internal business process and learning and growth, based on the study carried out on ten automobile dealers from various areas. The results identify that dealers are inefficient in learning about customer’s growth, which help the dealers to transform from inefficient into efficient. In addition, this study also focused on various insights related to performance evaluation and provide some useful recommendations which can be practiced in future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Acar, Y., Kadipasaoglu, S., & Schipperijn, P. (2010). A decision support framework for global supply chain modelling: An assessment of the impact of demand, supply and lead-time uncertainties on performance. International Journal of Production Research, 48, 3245–3268.

    Article  Google Scholar 

  • Alves, M. E. D., & Portela, M. C. S. (2015) Performance evaluation of PARFOIS retailing stores. In: Operational research (pp. 1–17). Berlin: Springer.

  • Amado, C. A. F., Santos, S. P., & Marques, P. M. (2012). Integrating the data envelopment analysis and the balanced scorecard approaches for enhanced performance assessment. Omega, 40, 390–403.

    Article  Google Scholar 

  • Amaratunga, D., & Baldry, D. (2002). Moving from performance measurement to performance management. Facilities, 20(5/6), 217–223.

    Article  Google Scholar 

  • Andes, S. (2002). Measuring efficiency of physician practices using data envelopment analysis. Managed Care, 11(11), 48–56.

    Google Scholar 

  • Asosheh, A., Nalchigar, S., & Jamporazmey, M. (2010). Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach. Expert Systems with Applications, 37, 5931–5938.

    Article  Google Scholar 

  • Avkiran, N. K. (2015). An illustration of dynamic network DEA in commercial banking including robustness tests. Omega, 55, 141–150.

    Article  Google Scholar 

  • Azadeh, A., Zarrin, M., & Salehi, N. (2016). Supplier selection in closed loop supply chain by an integrated simulation-Taguchi-DEA approach. Journal of Enterprise Information Management, 29(3), 302–326.

    Article  Google Scholar 

  • Azadi, M., Jafarian, M., Saen, R. F., & Mirhedayatian, S. M. (2015). A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context. Computers & Operations Research, 54, 274–285.

    Article  Google Scholar 

  • Balios, D., Eriotis, N., Fragoudaki, A., & Giokas, D. (2015). Economic efficiency of Greek retail SMEs in a period of high fluctuations in economic activity: A DEA approach. Applied Economics, 47(33), 3577–3593.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Somemodelsforestimatingtechnical and scale inefficiencies indataenvelopmentanalysis. Management Science, 30(9), 1078–1092.

    Article  Google Scholar 

  • Beamon, B. (1999). Measuring supply chain performance. International Journal of Operations and Production Management, 19(3), 275–292.

    Article  Google Scholar 

  • Beechey, J., & Garlick, D. (1999). Using the balanced scorecard in banking. Journal of the Australian Institute of Bankers, 113(1), 28–31.

    Google Scholar 

  • Bhagwat, R., & Sharma, M. K. (2007). Performance measurement of supply chain management: A balanced scorecard approach. Computers and Industrial Engineering, 53, 43–62.

    Article  Google Scholar 

  • Bourne, M., Mills, J., Wilcox, M., Neely, A., & Platts, K. (2000). Designing, implementing and updating performance measurement systems. International Journal of Operations and Production Management, 20(7), 754–771.

    Article  Google Scholar 

  • Brewer, P. C., & Speh, T. W. (2000). Using the balanced scorecard to measure supply chain performance. Journal of Business Logistics, 21(1), 75–94.

    Google Scholar 

  • 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, 432–446.

    Article  Google Scholar 

  • Chan, F. T. S. (2003). Performance measurement in a supply chain. International Journal of Advanced Manufacturing Technology, 21, 534–548.

    Article  Google Scholar 

  • Chan, F. T. S., & Qi, H. J. (2003). An innovative performance measurement method for supply chain management. Supply Chain Management: An International Journal, 8, 209–223.

    Article  Google Scholar 

  • Chand, D., Hachey, J. H., Owhoso, V., & Vasudevan, S. (2005). A balanced scorecard based framework for assessing the strategic impacts of ERP systems. Computers in Industry, 56, 558–572.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., Lewin, A., & Seiford, L. M. (1994). Data envelopment analysis: Theory, methodology and applications. Massachusetts: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of the Operational Research, 2, 429–444.

    Article  Google Scholar 

  • Chauhan, N. S., Mohapatra, P. K. J., & Pandey, K. P. (2006). Improving energy productivity in paddy production through benchmarking: An application of data envelopment analysis. Energy Conversion and Management, 47(9–10), 1063–1085.

    Article  Google Scholar 

  • Chen, C. C. (2008). An objective-oriented and product-line-based manufacturing performance measurement. International Journal of Production Economics, 112(1), 380–390.

    Article  Google Scholar 

  • Chen, M. J., Chiu, Y. H., Jan, C., Chen, Y. C., & Liu, H. H. (2015). Efficiency and risk in commercial banks-hybrid DEA estimation. Global Economic Review, 44(3), 335–352.

    Article  Google Scholar 

  • Chiou, Y. C., & Chen, Y. H. (2006). Route-based performance evaluation of Taiwanese domestic airlines using data envelopment analysis. Transportation Research Part E: Logistics and Transportation Review, 42(2), 116–127.

    Article  Google Scholar 

  • Cho, D. W., Lee, Y. H., Ahn, S. H., & Hwang, M. K. (2012). A framework for measuring the performance of service supply chain management. Computers & Industrial Engineering, 62, 801–818.

    Article  Google Scholar 

  • Chytas, P., Glykas, M., & Valiris, G. (2011). A proactive balanced scorecard. International Journal of Information Management, 31, 460–468.

    Article  Google Scholar 

  • Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1–4.

    Article  Google Scholar 

  • Denton, G. A., & White, B. (2000). Implementing a balanced-scorecard approach to managing hotel operations: The case of white lodging services. The Cornell Hotel and Restaurant Administration Quarterly, 41(1), 94–107.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Edirisinghe, N. C. P., & Zhang, X. (2007). Generalized DEA model of fundamental analysis and its application to portfolio optimization. Journal of Banking and Finance, 31, 3311–3335.

    Article  Google Scholar 

  • Eilat, H., Golany, B., & Shtub, A. (2006). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega. doi:10.1016/j.omega.2006.05.002.

  • Eilat, H., Golany, B., & Shtub, A. (2008). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega, 36(5), 895–912.

    Article  Google Scholar 

  • Emrouznejad, A. (2014). Advances in data envelopment analysis. Annals of Operations Research, 214(1), 1.

    Article  Google Scholar 

  • Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42, 151–157.

    Article  Google Scholar 

  • Farrell, M.J., (1957). The measurement of productive efficiency. Journal of the Royal Statistical Association Series A, CXX, 253–281.

  • Fitzgerald, L., Johnston, R., Brignall, T. J., Silvestro, R., & Voss, C. (1991). Performance measurement in service businesses. London: CIMA.

    Google Scholar 

  • Folan, P., Browne, J., & Jagdev, H. (2007). Performance: Its meaning and content for today’s business research. Computers in Industry, 58(7), 605–620.

    Article  Google Scholar 

  • Franco-Santos, M., Kennerley, M. P., Micheli, P., Martinez, V., Mason, S., Marr, B., et al. (2007). Towards a definition of a business performance measurement system. International Journal of Operations and Production Management, 27(8), 784–801.

    Article  Google Scholar 

  • Fu, C., & Yang, S. (2012). The combination of dependence-based interval-valued evidential reasoning approach with balanced scorecard for performance assessment. Expert Systems with Applications, 39, 3717–3730.

    Article  Google Scholar 

  • Gaiardelli, P., Saccani, N., & Songini, L. (2006). Performance measurement systems in the after sales service: An integrated framework. International Journal of Business Performance Measurement, 9(2), 147–171.

    Google Scholar 

  • Garcia-Valderrama, T., Mulero-Mendigorri, E., & Revuelta-Bordoy, D. (2009). Relating the perspectives of the balanced scorecard for R&D by means of DEA. European Journal of Operational Research, 196, 1177–1189.

    Article  Google Scholar 

  • Giannakis, M. (2011). Management of service supply chains with a service oriented reference model: The case of management consulting source. Supply Chain Management: An International Journal, 16(5), 346–361.

    Article  Google Scholar 

  • Globerson, S. (1985). Issues in developing a performance criteria system for an organization. International Journal of Production Research, 23(4), 639–646.

    Article  Google Scholar 

  • Gomes, C. F., Yasin, M. M., & Lisboa, J. V. (2004). A literature review of manufacturing performance measures and measurement in an organizational context: a framework and direction for future research. Journal of Manufacturing Technology Management, 15(6), 511–530.

    Article  Google Scholar 

  • Gouveia, M. C., Dias, L. C., Antunes, C. H., Mota, M. A., Duarte, E. M., & Tenreiro, E. M. (2015). An application of value-based DEA to identify the best practices in primary health care. OR Spectrum (pp.1–25).

  • Green, R., Doyle, J., & Cook, W. D. (1996). Preference voting and project ranking using DEA and cross evaluation. European Journal of Operational Research, 90, 461–472.

    Article  Google Scholar 

  • Grigoroudisn, E., Orfanoudaki, E., & Zopounidis, C. (2012). Strategic performance measurement in a healthcare organization: A multiple criteria approach based on balanced scorecard. Omega, 40(2012), 104–119.

    Article  Google Scholar 

  • Gumbus, A. (2005). Introducing the balanced scorecard: Creating metrics to measure performance. Journal of Management Education, 29(4), 617–630.

    Article  Google Scholar 

  • Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87, 333–347.

    Article  Google Scholar 

  • Hong, S., Yuedong, Z., & Gang, W. (2015). Efficiency evaluation of low-carbon agriculture development supported by public finance based on DEA—taking Heilongjiang province as an example. Chinese Agricultural Science Bulletin, 23, 046.

    Google Scholar 

  • Huang, S. H., Sheoran, S. K., & Keskar, H. (2005). Computer assisted supply chain configuration based on supply chain operations reference (SCOR) model. Computers and Industrial Engineering, 48, 377–394.

    Article  Google Scholar 

  • Jalali Naini, S. G., Aliahmadi, A. R., & Jafari-Eskandari, M. (2011). Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain. Resources, Conservation and Recycling, 55, 593–603.

    Article  Google Scholar 

  • Ji, X., Wu, J., & Zhu, Q. (2015). Eco-design of transportation in sustainable supply chain management: A DEA-like method. Transportation Research Part D: Transport and Environment (in press).

  • Johnes, J. (2006). Measuring teaching efficiency in higher education: An application of data envelopment analysis to economics graduates from UK Universities 1993. European Journal of Operational Research, 174, 443–456.

    Article  Google Scholar 

  • Kaplan, R. S., & Norton, D. P. (1992a). The balanced scorecard as a strategic management system. Harvard Business Review, 6, 1–66.

    Google Scholar 

  • Kaplan, R. S., & Norton, D. P. (1992b). The balanced scorecard: Measures that drive performance. Harvard Business Review (January–February) (pp. 71–79).

  • Kaplan, R. S., & Norton, D. P. (1996a). Using the balanced scorecard as a strategic management system. January–February. Harvard Business Review.

  • Kaplan, R. S., & Norton, D. P. (1996b). The balanced scorecard—Translating strategy into action. Boston, MA: Harvard Business School Press.

    Google Scholar 

  • Kaplan, R. S. (1998). Innovation action research: Creating new management theory and practice. Journal of Management Accounting Research, 10(89–1), 18.

    Google Scholar 

  • Kaplan, R. S., & Norton, D. P. (2006). Alignment: Using the balanced scorecard to create corporate synergies. Boston: Harvard Business Press. 302.

    Google Scholar 

  • Khodabakhshi, M., & Aryavash, K. (2014). The fair allocation of common fixed cost or revenue using DEA concept. Annals of Operations Research, 214(1), 187–194.

    Article  Google Scholar 

  • Kim, D., Cavusgil, S. T., & Calantone, R. J. (2006). Information system innovations and supply chain management: Channel relationships and firm performance. Journal of the Academy of Marketing Science, 34(1), 40–54.

    Article  Google Scholar 

  • Koning, G. M. J. (2004). Making the balanced scorecard work (part 1). Gallup Management Journal. http://gmj.gallup.com/content/12208/making-balancedscorecard-work-part.aspx.

  • Kroes, J. R., & Ghosh, S. (2010). Outsourcing congruence with competitive priorities: Impact on supply chain and firm performance. Journal of Operations Management, 28, 124–143.

    Article  Google Scholar 

  • Kwon, H. B., Lee, J., & Roh, J. J. (2016). Best performance modeling using complementary DEA-ANN approach: Application to Japanese electronics manufacturing firms. Benchmarking: An International Journal, 23(3), 704–721.

    Article  Google Scholar 

  • Lee, A. H. I., Chen, W. C., & Chang, C. J. (2008). A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Systems with Applications, 34, 96–107.

    Article  Google Scholar 

  • Lee, K. H., & Farzipoor Saen, R. (2012). Measuring corporate sustainability management: A data envelopment analysis approach. International Journal of Production Economics, 104(1), 219–226.

    Article  Google Scholar 

  • Leung, L. C., Lam, K. C., & Cao, D. (2006). Implementing the balanced scorecard using the analytic hierarchy process and the analytic network process. Journal of the Operational Research Society, 57, 682–691.

    Article  Google Scholar 

  • Li, K., & Lin, B. (2016). Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model. Applied Energy, 168, 351–363.

    Article  Google Scholar 

  • Liang, L., Wu, J., Cook, D. D., & Zhu, J. (2008). Alternative secondary goals in DEA cross-efficiency evaluation. International Journal of Production Economics, 113, 1025–1030.

    Article  Google Scholar 

  • Liu, F. H. F., & Hai, H. L. (2005). The voting analytic hierarchy process method for selecting supplier. International Journal of Production Economics, 97, 308–317.

    Article  Google Scholar 

  • Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). Data envelopment analysis (1978–2010): A citation-based literature survey. Omega, 41(1), 3–15.

    Article  Google Scholar 

  • Lockamy, A., & McCormack, K. (2004). Linking SCOR planning practices to supply chain performance: An exploratory study. International Journal of Operations and Production Management, 24, 1192–1218.

    Article  Google Scholar 

  • Lohman, C., Fortuin, L., & Wouters, M. (2004). Designing a performance measurement system design: A case study. European Journal of Operational Research, 156(2), 267–286.

    Article  Google Scholar 

  • Mannino, M., Hong, S. N., & Choi, I. J. (2008). Efficiency evaluation of data warehouse operations. Decision Support Systems, 44, 883–898.

    Article  Google Scholar 

  • Milis, K., & Mercken, R. (2004). The use of the balanced scorecard for the evaluation of information and communication technology projects. International Journal of Project Management, 22, 87–97.

    Article  Google Scholar 

  • Neely, A., Adams, C., & Kennerley, M. (2002). The performance prism: The scorecard for measuring and managing business success. London: FT Prentice-Hall.

    Google Scholar 

  • Neely, A. D., Gregory, M., & Platts, K. (1995). Performance measurement system design: A literature review and research agenda. International Journal of Operations and Production Management, 15(4), 80–116.

    Article  Google Scholar 

  • Neely, A. D., Mills, J., Platts, K., Gregory, M., & Richards, H. (1996). Performance measurement system design: Should process based approaches be adopted? International Journal of Production Economics, 46–47, 423–431.

    Article  Google Scholar 

  • Neely, A. D., Richards, H., Mills, J., Platts, K., & Bourne, M. (1997). Designing performance measures: A structured approach. International Journal of Operations and Production Management, 17(11), 1131–1152.

    Article  Google Scholar 

  • Oral, M., Kettani, O., & Lang, P. (1991). A methodology for collective evaluation and selection of industrial R&D projects. Management Science, 37(7), 871–883.

    Article  Google Scholar 

  • Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61–79.

    Article  Google Scholar 

  • Parasuraman, A., Zeithaml, V., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 13–40.

    Google Scholar 

  • Park, S., & Kim, J. (2016). Energy efficiency in Korea: Analysis using a hybrid DEA model. Geosystem Engineering, 19(3), 143–150.

    Article  Google Scholar 

  • Phusavat, K., Anussornnitisarn, P., Helo, P., & Dwight, R. (2009). Performance measurement: Roles and challenges. Industrial Management & Data Systems, 109(5), 646–664.

    Article  Google Scholar 

  • Qi, Z. (2015). Empirical research on the efficiency of resource allocation of compulsory education based on DEA—Case study of primary schools in an eastern city. Educational Research, 3, 012.

    Google Scholar 

  • Rajesh, R., Pugazhendhi, S., Ganesh, K., Ducq, Y., & LennyKohe, S. C. (2012). Generic balanced scorecard framework for third party logistics service provider. International Journal of Production Economics, 140(1), 269–282.

    Article  Google Scholar 

  • Rickards, R. C. (2007). BSC and benchmark development for an e-commerce SME. Benchmarking: An International Journal, 14, 222–250.

    Article  Google Scholar 

  • Seiford, L. M. (1996). Data envelopment analysis: The evolution of the state of the art (1978–1995). Journal of Productivity Analysis, 7(2–3), 99–137.

    Article  Google Scholar 

  • Sevkli, Mehmet, Koh, S. C. Lenny, Zaim, Selim, Demirbag, Mehmet, & Tatoglu, Ekrem. (2007). An application of data envelopment analytic hierarchy process for supplier selection: A case study of BEKO in Turkey. International Journal of Production Research, 45(9), 1973–2003.

    Article  Google Scholar 

  • Sexton, T. R., Silkman, R. H., & Hogon, A. J. (1986). Data envelopment analysis. Critique and extensions. In R. H. Silkman (Ed.), Measuring efficiency: An assessment of DEA (pp. 73–105). San Francisco, CA: Jossey-Boss.

    Google Scholar 

  • Shepherd, C., & Gunter, H. (2006). Measuring supply chain performance: Current research and future directions. International Journal of Productivity and Performance Management, 55, 242–258.

    Article  Google Scholar 

  • Shwartz, M., Burgess, J. F., & Zhu, J. (2016). A DEA based composite measure of quality and its associated data uncertainty interval for health care provider profiling and pay-for-performance. European Journal of Operational Research, 253(2), 489–502.

    Article  Google Scholar 

  • Smith, J. S., Karwan, K. R., & Markland, R. E. (2007). A note on the growth of research in service operations management. Production and Operations Management, 16(6), 780–790.

    Article  Google Scholar 

  • Srdjevic, B., Medeiros, Y. D. P., & Porto, R. L. L. (2005). Data envelopment analysis of reservoir system performance. Computers and Operations Research, 32(12), 3209–3226.

    Article  Google Scholar 

  • Stoica, O., Mehdian, S., & Sargu, A. (2015). The impact of internet banking on the performance of romanian banks: DEA and PCA approach. Procedia Economics and Finance, 20, 610–622.

    Article  Google Scholar 

  • Tan, K. H., & Platts, K. W. (2009). Linking operations objectives to actions: A plug and play approach. International Journal of Production Economics, 121(2), 610–619.

    Article  Google Scholar 

  • Thanassoulis, E., De Witte, K., Johnes, J., Johnes, G., Karagiannis, G., & Portela, M. (2016). Applications of DEA in education.

  • Tseng, M. L. (2010). Implementation and performance evaluation using the fuzzy network balanced scorecard. Computers and Education, 55, 188–201.

    Article  Google Scholar 

  • Vachon, S., & Klassen, R. D. (2008). Environmental management and manufacturing performance: The role of collaboration in the supply chain. International Journal of Production Economics, 111, 299–3.

    Article  Google Scholar 

  • Wang, Rong-Tsu, Ho, Chien-Ta Bruce, & Oh, K. (2008). Measuring production and marketing efficiency using grey relation analysis and data envelopment analysis. International Journal of Production Research, 48(1), 183–199.

    Article  Google Scholar 

  • Weber, C. A., Current, J. R., & Desai, A. (1998). Non-cooperative negotiation strategies for vendor selection. European Journal of Operational Research, 108, 208–223.

    Article  Google Scholar 

  • Wiersma, E. (2009). For which purposes do managers use balanced scorecard? An empirical study. Management Accounting Research, 20(4), 239–251.

    Article  Google Scholar 

  • Wu, I. L., & Chang, C. H. (2012). Using the balanced scorecard in assessing the performance of e-SCM diffusion: A multi-stage perspective. Decision Support Systems, 52, 474–485.

    Article  Google Scholar 

  • Wu, T. H., Chen, M. S., & Yeh, J. H. (2010). Measuring the performance of police forces in Taiwan using data envelopment analysis. Evaluation and Program Planning, 33(3), 246–254.

    Article  Google Scholar 

  • Yasin, M. M., & Gomes, C. F. (2010). Performance management in service operational settings: A selective literature examination. Benchmarking: An International Journal, 17(2), 214–231.

    Article  Google Scholar 

  • Yuksel, I., & Dagdeviren, M. (2010). Using the fuzzy analytic network process (ANP) for balanced scorecard (BSC): A case study for a manufacturing firm. Expert Systems with Applications, 37, 1270–1278.

    Article  Google Scholar 

  • Zervopoulos, P. D., Brisimi, T. S., Emrouznejad, A., & Cheng, G. (2016). Performance measurement with multiple interrelated variables and threshold target levels: Evidence from retail firms in the US. European Journal of Operational Research, 250(1), 262–272.

    Article  Google Scholar 

  • Zeydan, M., & Çolpan, C. (2009). A new decision support system for performance measurement using combined fuzzy TOPSIS/DEA approach. International Journal of Production Research, 47(15), 4327–4349.

    Article  Google Scholar 

  • Zhang, W. (2015). The analysis of the agriculture input and output efficiency based on DEA model. Agricultural Science & Technology, 16(2), 414.

    Google Scholar 

Download references

Acknowledgments

This research was supported in part by the National Natural Science Foundation of China (Grant Nos. 71302005, 71402084), the major Program of the National Social Science Fund of China (Grant No. 13&ZD147).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yang Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tan, Y., Zhang, Y. & Khodaverdi, R. Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry. Ann Oper Res 248, 449–470 (2017). https://doi.org/10.1007/s10479-016-2196-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-016-2196-2

Keywords

Navigation