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Balanced performance assessment under uncertainty: an integrated CSW-DEA and balanced scorecard (BSC)


Data Envelopment Analysis (DEA) is a mathematical programming model that calculates the relative efficiency of homogenous Decision Making Units (DMUs). The conventional DEA models used to calculate the efficiency require the exact amount of inputs and outputs; in real business situations, however, it is often impossible to determine the exact numeral value of some inputs and outputs. At the same time the Common Set of Weights (CSW) overcomes the weakness of DEA models for assessment under same conditions. On the other hands, it is important to considering the balance in evaluation and calculation of indicators. This study develops a new model to calculate the CSW in fuzzy environments, considering the balanced environment using the Balanced Scorecard (BSC). Our proposed model is linear for fairly and equitably evaluating the DMUs on the same scale, also enables us to deal with fuzzy environment and greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in fuzzy DEA models. From a managerial point of view, this paper aims to provide an integrated framework to form a better strategic decision-making process about organization performance, which ultimately leads to the competitive advantages and success of the organization in the long run. Finally, in the field of performance management, the proposed model was applied to evaluate the performances of ten manufacturing enterprises in to confirm the validity and applicability of the proposed approach.

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  • Ayvaz, E., Kaplan, K., & Kuncan, M. (2020). An integrated lstm neural networks approach to sustainable balanced scorecard-based early warning system. IEEE Access, 8, 37958–37966.

    Article  Google Scholar 

  • Azar, A., Zarei Mahmoudabadi, M., & Emrouznejad, A. (2016). A new fuzzy additive model for determining the common set of weights in data envelopment analysis. Journal of Intelligent & Fuzzy Systems, 30(1), 61–69.

    Article  Google Scholar 

  • Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9, 67–88.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chu, J., & Jiang, H. (2019). Fixed cost allocation based on the utility: A DEA common-weight approach. IEEE Access, 7, 72613–72621.

  • Cook, W., Roll, Y., & Kazakov, A. (1990). A DEA model for measuring the relative efficiencies of highway maintenance portals. Infor, 28(2), 113–124.

    Google Scholar 

  • Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA) – thirty years on. European Journal of Operational Research, 192, 1–17.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Emrouznejad, A., & Tavana, M. (2014). Performance measurement with fuzzy data envelopment analysis. Berlin and Heidelberg: Springer.

    Book  Google Scholar 

  • Guo, P., & Tanaka, H. (2001). Fuzzy DEA: a perceptual evaluation method. Fuzzy Sets and Systems, 119(1), 149–160.

    Article  Google Scholar 

  • Hatami-Marbini, A., Emrouznejad, A., & Tavana, M. (2011). A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making. European Journal of Operational Research, 214, 472–457.

    Article  Google Scholar 

  • Hatami-Marbini, A., Saati, S., & Tavana, M. (2010). An ideal-seeking fuzzy data envelopment analysis framework. Applied Soft Computing, 10(4), 1062–1070.

    Article  Google Scholar 

  • Jahanshahloo, G. R., Memariani, A., Lotfi, F. H., & Rezai, H. Z. (2005). A note on some of DEA models and finding efficiency and complete ranking using common set of weights. Applied Mathematics and Computation, 166(2), 265–281.

    Article  Google Scholar 

  • Kao, C., & Hung, H. T. (2005). Data envelopment analysis with common weights: the compromise solution approach. Journal of the Operational Research Society, 56, 1196–1203.

    Article  Google Scholar 

  • Kao, C., & Liu, S. T. (2003). A mathematical programming approach to fuzzy efficiency ranking. International Journal of Production Economics, 86(2), 145–154.

    Article  Google Scholar 

  • Khalili-Damghani, K., Tavana, M., & Santos-Arteaga, F. J. (2016). A comprehensive fuzzy DEA model for emerging market assessment and selection decisions. Applied Soft Computing, 38, 676–702.

    Article  Google Scholar 

  • Lertworasirikul, S., Fang, S. C., Joines, J. A., & Nuttle, H. L. (2003). Fuzzy data envelopment analysis (DEA): a possibility approach. Fuzzy Sets and Systems, 139(2), 379–394.

    Article  Google Scholar 

  • Makui, A., Alinezhad, A., Kiani Mavi, R., & Zohrebandian, M. (2008). A goal programming method for finding common weights in dea with an improved discriminating power for efficiency. Journal of Industrial and Systems Engineering, 1(14), 293–303.

    Google Scholar 

  • Mavi, R. K., Saen, R. F., & Goh, M. (2018). Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach‏. Technological Forecasting and Social Change.

  • Nojavan, M., Heidary, A., & Mohammaditabar, D. (2020). A fuzzy service quality based approach for performance evaluation of educational units. Socio-Economic Planning Sciences, 100816.

  • Omrani, H., Valipour, M., & Mamakani, S. J. (2018). Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran‏. Socio-Economic Planning Sciences.

  • Roll, Y., Cook, W. D., & Golany, B. (1991). Controlling factor weights in data envelopment analysis. IIE Transactions, 23(1), 2–9.

    Article  Google Scholar 

  • Saati, S., Memariani, A., & Jahanshahloo, G. R. (2002). Efficiency analysis and ranking of DMUs with fuzzy data. Fuzzy Optimization and Decision Making, 1(3), 255–267.

    Article  Google Scholar 

  • Sengupta, J. K. (1992a). A fuzzy systems approach in data envelopment analysis. Computers and Mathematics with Applications, 24(8–9), 259–266.

    Article  Google Scholar 

  • Sengupta, J. K. (1992b). Measuring efficiency by a fuzzy statistical approach. Fuzzy Sets and Systems, 46(1), 73–80.

    Article  Google Scholar 

  • Sirin, O., Gunduz, M., & Moussa, A. (2020). Application of tools of quality function deployment and modified balanced scorecard for optimal allocation of pavement management resources. IEEE Access, 8, 76399–76410.

  • Siriopoulos, C., & Tziogkidis, P. (2010). How do Greek banking in stitutions react after significant events? a DEA approach. Omega, 38, 294–308.

    Article  Google Scholar 

  • Su, W., Wang, D., Xu, L., Zeng, S., & Zhang, C. (2020). A nonradial super efficiency DEA framework using a MCDM to measure the research efficiency of disciplines at chinese universities. IEEE Access, 8, 86388–86399.

  • Tubis, A., & Werbińska-Wojciechowska, S. (2017). Balanced scorecard use in passenger transport companies performing at polish market. Procedia Engineering, 187, 538–547.

    Article  Google Scholar 

  • Wang, A. Y., Luo, Y., & Liang, L. (2009). Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises. Expert Systems with Applications, 36, 5205–5211.

    Article  Google Scholar 

  • Wanke, P., Barros, C. P., & Nwaogbe, O. R. (2016). Assessing productive efficiency in Nigerian airports using Fuzzy-DEA. Transport Policy, 49, 9–19.

    Article  Google Scholar 

  • Xu, M., Liu, S., Xu, Z., & Zhou, W. (2019). DEA evaluation method based on interval intuitionistic Bayesian network and its application in enterprise logistics. IEEE Access, 7, 98277–98289.

  • Zarei Mahmoudabadi, M., Azar, A., & Emrouznejad, A. (2018). A novel multilevel network slacks-based measure with an application in electric utility companies. Energy, 158, 1120–1129.

    Article  Google Scholar 

  • Zarei Mahmoudabadi, M., & Emrouznejad, A. (2019). Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis. International Review of Economics & Finance, 64, 359–376.

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The article was prepared within the framework of the Basic Research Program at HSE University.

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Correspondence to Ali Emrouznejad.

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Zarei Mahmoudabadi, M., Emrouznejad, A. Balanced performance assessment under uncertainty: an integrated CSW-DEA and balanced scorecard (BSC). Ann Oper Res (2022).

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  • Decision analytics
  • Data envelopment analysis (DEA)
  • Fuzzy DEA
  • Common set of weights (CSW)
  • Balanced scorecard (BSC)