Data envelopment analysis in performance measurement: a critical analysis of the literature

This study examines the benefits of data envelopment analysis (DEA) in evaluating the performance of decision making units (DMUs). DEA is a mathematical programming tool applied in performance measurement. The problem identified is establishing business support units as value adding business units. A case is made for applying DEA when evaluating the performance of such business support units. To this end, a literature review of the results of applications of DEA to the evaluation of information technology and purchasing supply chain management functions was conducted. The findings indicate the benefits of DEA are that the method identifies efficient performers in a given population and, therefore, allows for benchmarking against the ’best in class’ performer. This as opposed to more commonly used parametric methods, such as regression analysis, which result in a comparator that represents the average performance for a given population, therefore, allowing only for measurement against the average. In addition, the findings indicate that in respect of business support units, the DEA methodology allows for the incorporation of intermediate outcomes, which facilitates the measurement of the contribution of these units to overall company performance. Although the DEA methodology has been widely applied, it is still not as well known or generally applied as the more common approaches. The recommendations made in this paper will be beneficial in bringing DEA to the attention of decision-makers. The recommendations will also raise awareness of the potential benefits to be realised when applying the method in developing performance measurement frameworks for business support units.

[1]  Lisa M. Ellram,et al.  The Impact of Purchasing and Supply Management Activities on Corporate Success , 2002 .

[2]  Madjid Tavana,et al.  A three-stage Data Envelopment Analysis model with application to banking industry , 2014 .

[3]  Dimitris K. Despotis,et al.  Composition versus decomposition in two-stage network DEA: a reverse approach , 2016 .

[4]  Ram Narasimhan,et al.  Purchasing Competence and Its Relationship with Manufacturing Performance , 2000 .

[5]  Sarah Powell,et al.  The challenges of performance measurement , 2004 .

[6]  Chin-Wei Huang,et al.  Evaluating the optimal occupancy rate, operational efficiency, and profitability efficiency of Taiwan's international tourist hotels , 2011 .

[7]  Joe Zhu,et al.  Decomposition weights and overall efficiency in two-stage additive network DEA , 2017, Eur. J. Oper. Res..

[8]  George Halkos,et al.  Efficiency measurement of the Greek commercial banks with the use of financial ratios: a data envelopment analysis approach , 2004 .

[9]  David J. Murphy,et al.  Purchasing performance evaluation: with data envelopment analysis , 2002 .

[10]  R. Narasimhan,et al.  AN EMPIRICAL EXAMINATION OF THE UNDERLYING DIMENSIONS OF PURCHASING COMPETENCE , 2001 .

[11]  Chiang Kao,et al.  Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan , 2008, Eur. J. Oper. Res..

[12]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[13]  Joe Zhu,et al.  Super-efficiency and DEA sensitivity analysis , 2001, Eur. J. Oper. Res..

[14]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[15]  Chin-Wei Huang,et al.  Assessment of China transit and economic efficiencies in a modified value-chains DEA model , 2011, Eur. J. Oper. Res..

[16]  Chiang Kao,et al.  Network data envelopment analysis: A review , 2014, Eur. J. Oper. Res..

[17]  John S. Liu,et al.  Network-based method for ranking of efficient units in two-stage DEA models , 2012, J. Oper. Res. Soc..

[18]  Joe Zhu,et al.  Additive efficiency decomposition in two-stage DEA , 2009, Eur. J. Oper. Res..

[19]  Stephen C. H. Leung,et al.  Facility Management Benchmarking: an Application of Data envelopment Analysis in Hong Kong , 2013, Asia Pac. J. Oper. Res..

[20]  Joe Zhu,et al.  Measuring Information Technology's Indirect Impact on Firm Performance , 2004, Inf. Technol. Manag..

[21]  Sheng Ang,et al.  Pitfalls of decomposition weights in the additive multi-stage DEA model ☆ , 2016 .

[22]  R. Banker Maximum likelihood, consistency and data envelopment analysis: a statistical foundation , 1993 .

[23]  Haritha Saranga,et al.  Performance evaluation of purchasing and supply management using value chain DEA approach , 2010, Eur. J. Oper. Res..

[24]  Stanley Zionts,et al.  Use of Data Envelopment Analysis in assessing Information Technology impact on firm performance , 1997, Ann. Oper. Res..

[25]  Yaakov Roll,et al.  An application procedure for DEA , 1989 .

[26]  Shih-Tong Lu,et al.  Information technology and risk factors for evaluating the banking industry in the Taiwan: an application of a Value Chain DEA , 2015 .