Abstract
In performance evaluation, it is important to identify both the efficient frontier and the critical measures. Data envelopment analysis (DEA) has been proven an effective tool for estimating the efficient frontiers, and the optimized DEA weights may be used to identify the critical measures. However, due to multiple DEA optimal weights, a unique set of critical measures may not be obtained for each decision making unit (DMU). Based upon a set of modified DEA models, this paper develops an approach to identify the critical measures for each DMU. Using a set of four Fortune's standard performance measures, capital market value, profit, revenue and number of employees, we perform a performance comparison between the Fortune's e-corporations and 1000 traditional companies. Profit is identified as the critical measure to the performance of e-corporations while revenue the critical measure to the Fortune's 1000 companies. This finding confirms that high revenue does not necessarily mean profit for e-corporations while revenue means a stable proportion of profit for the Fortune's 1000 companies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ali, A.I. and L.M. Seiford. (1990). “Translation Invariance in Data Envelopment Analysis.” Operations Research Letters 9, 403–405.
Camp, R.C. (1995). Business Process Benchmarking, Finding and Implementing Best Practices. Milwaukee: ASQC Quality Press.
Charnes, A., W.W. Cooper, A.Y. Lewin, and L.M. Seiford. (1994). Data Envelopment Analysis: Theory, Methodology and Applications. Boston: Kluwer Academic.
Dewan, S., S.C. Michael, and C. Min. (1998). “Firm Characteristics and Investments in Information Technology: Scale and Scope Effects.” Information Systems Research 9, 219–232.
Eccles, R.G. and N. Nohria. (1992). Beyond the Hype. Harvard Business School Press.
Schmenner, R.W. and M.L. Swink. (1998). “On Theory in Operations Management.” Journal of Operations Management 17, 97–113.
Seiford, L.M. and J. Zhu. (1998). “Stability Regions for Maintaining Efficiency in Data Envelopment Analysis.” European Journal of Operational Research 108, 127–139.
Seiford, L.M. and J. Zhu. (1999a). “An Investigation of Returns to Scale under Data Envelopment Analysis.” OMEGA 27, 1–11.
Seiford, L.M. and J. Zhu. (1999b). “Profitability and Marketability of the Top 55 US Commercial Banks.” Management Science 45(9), 1270–1288.
Thanassoulis, E. and R.G. Dyson. (1992). “Estimating Preferred Target Input-Output Levels Using Data Envelopment Analysis.” European Journal of Operational Research 56, 80–97.
Zhu, J. (1996). “Data Envelopment Analysis with Preference Structure.” Journal of Operational Research Society 47, 136–150.
Zhu, J. (2000). “Multi-Factor Performance MeasureModel with an Application to Fortune 500 Companies.” European Journal of Operational Research 123(1), 105–124.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Chen, Y., Zhu, J. DEA Models for Identifying Critical Performance Measures. Annals of Operations Research 124, 225–244 (2003). https://doi.org/10.1023/B:ANOR.0000004771.11875.9f
Issue Date:
DOI: https://doi.org/10.1023/B:ANOR.0000004771.11875.9f