A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies

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Maximizing shareholders’ value has always been an indispensable goal for publicly traded companies. Shareholders value is highly dependent on the operating expenses, profit margin, return on investment and the overall performance in public companies. We propose a hybrid fuzzy multi-criteria decision making method for measuring the performance of publicly held companies in the Pharmaceutical industry. The proposed method helps investors choose a proper portfolio of stocks in the presence of environmental turbulence and uncertainties. The proposed method is composed of three distinct but inter-related phases. In the pre-screening phase, a set of financial and non-financial evaluation criteria are selected based on the balanced scorecard (BSC) approach. In the efficiency measurement phase, the DEMATEL method is used first to determine the inter-relationships among the BSC perspectives. A fuzzy ANP method is used next to determine the relative importance of the criteria based on the resulting DEMATEL interactive network. In the third step, two different fuzzy data envelopment analysis (DEA) methods are used to evaluate the relative efficiency of the decision making units (DMUs). The fuzzy DEA models are modified by using the relative importance of the criteria and the precedence relations among the input and output weights as additional constraints. Finally, the modified fuzzy DEA models are used to calculate the relative efficiency scores of the DMUs. In the ranking phase, an integration method grounded in the Shannon’s entropy concept is used to combine different efficiency scores and calculate the final ranking of the DMUs. The method proposed in this study is used to evaluate the performance of publicly held pharmaceutical companies actively trading on the Swiss Stock Exchange (SSE).




Tavana, M., Khalili-Damghani, K. and Rahmatian, R. (2015) ‘A Hybrid Fuzzy MCDM Method for Measuring the Performance of Publicly Held Pharmaceutical Companies,’ Annals of Operations Research, Vol. 226, No. 1, pp. 589-621.