Quarterly Publication

Document Type : Original Article


1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran.

3 Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.

4 Department of Industrial Engineering, Malek Ashtar University, Lavizan, Tehran, Iran.



Measuring the performance of a production system has been an important task in management for control, planning, etc. The Balanced Scorecard (BSC) allows us to do just that. BSC is widely used in government and industry because of the clear representation of the relationship and logic between the Key Performance Indicators (KPIs) of 4 perspectives-financial, customer, internal process, and learning and growth. Conversely, traditional studies in Data Envelopment Analysis (DEA) view systems as a whole when measuring efficiency, ignoring the operation of individual processes within a system. We present and demonstrate a multi-criteria approach for evaluating every project in different stages. Our approach integrates the BSC and DEA and develops an extended DEA model. The input and output measures for the integrated DEA-BSC model are grouped in “cards,” which are associated with "BSC". With efficiency decomposition, the process that causes the inefficient operation of the system can be identified for future improvement. Finally, we illustrate the proposed approach with a case study involving six banking branches.


[1]     Braam, G. J. M., & Nijssen, E. J. (2004). Performance effects of using the balanced scorecard: a note on the Dutch experience. Long range planning, 37(4), 335–349.
[2]     Prajogo, D. I. (2016). The strategic fit between innovation strategies and business environment in delivering business performance. International journal of production economics, 171, 241–249. DOI:10.1016/j.ijpe.2015.07.037
[3]     Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: measures that drive performance (Vol. 83). Harvard Business Review.
[4]     Kaplan, R. S., & Norton, D. P. (1996). Translating strategy introduction the balanced scorecard. Harvard Business School.
[5]     Banker, R. D., Chang, H., Janakiraman, S. N., & Konstans, C. (2004). A balanced scorecard analysis of performance metrics. European journal of operational research, 154(2), 423–436. DOI:10.1016/S0377-2217(03)00179-6
[6]     Eilat, H., Golany, B., & Shtub, A. (2008). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega, 36(5), 895–912. DOI:10.1016/j.omega.2006.05.002
[7]     Kaplan, R. S., & Norton, D. P. (2007). Using the balanced scorecard as a strategic management system. Harvard business review, 85(7–8), 75-85.
[8]     García-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(3), 1177–1189. DOI:10.1016/j.ejor.2008.05.015
[9]     Kanji, G. K., & Moura E Sá, P. (2001). Kanji’s business scorecard. Total quality management, 12(7), 898–905. DOI:10.1080/09544120120096025
[10]   Malina, M. A., & Selto, F. H. (2001). Communicating and controlling strategy: an empirical study of the effectiveness of the balanced scorecard. Journal of management accounting research, 13(1), 47–90. DOI:10.2308/jmar.2001.13.1.47
[11]   Eilat, H., Golany, B., & Shtub, A. (2006). Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology. European journal of operational research, 172(3), 1018–1039. DOI:10.1016/j.ejor.2004.12.001
[12]   Gosselin, M. (2005). An empirical study of performance measurement in manufacturing firms. International journal of productivity and performance management, 54(5–6), 419–437. DOI:10.1108/17410400510604566
[13]   Roy, J., & Wetter, M. (2000). Performance drivers: a practical guide to using the balanced scorecard. John Wiley & Sons.
[14]   Niven, P. R. (2010). Balanced scorecard step-by-step: Maximizing performance and maintaining results. John Wiley & Sons.
[15]   Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429–444.
[16]   Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1997). Data envelopment analysis theory, methodology and applications. Journal of the operational research society, 48(3), 332–333.
[17]   Charnes, A., Cooper, W. W., Wei, Q. L., & Huang, Z. M. (1989). Cone ratio data envelopment analysis and multi-objective programming. International journal of systems science, 20(7), 1099–1118.
[18]   Charnes, A., Cooper, W. W., Karwan, K. R., & Wallace, W. A. (1979). A chance-constrained goal programming model to evaluate response resources for marine pollution disasters. Journal of environmental economics and management, 6(3), 244–274.
[19]   Charnes, A., & Cooper, W. W. (1984). Preface to topics in data envelopment analysis. Annals of operations research, 2(1), 59–94. DOI:10.1007/BF01874733
[20]   Prieto, A. M., & Zofío, J. L. (2007). Network DEA efficiency in input-output models: With an application to OECD countries. European journal of operational research, 178(1), 292–304. DOI:10.1016/j.ejor.2006.01.015
[21]   Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics letters, 50(1), 65–70.
[22]   Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-economic planning sciences, 34(1), 35–49.
[23]   Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network DEA. Modeling data irregularities and structural complexities in data envelopment analysis, 209–240. DOI:10.1007/978-0-387-71607-7_12
[24]   Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 U.S. commercial banks. Management science, 45(9), 1270–1288. DOI:10.1287/mnsc.45.9.1270
[25]   Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. European journal of operational research, 185(1), 418–429.
[26]   Banker, R. D., & Morey, R. C. (1996). Estimating production frontier shifts: An application of DEA to technology assessment. Annals of operations research, 66, 181–196.