Longitudinal targets and persistent inefficiency in data envelopment analysis
by James C. Benneyan; Mehmet E. Ceyhan
International Journal of Engineering Management and Economics (IJEME), Vol. 6, No. 2/3, 2018

Abstract: A typical interpretation of data envelopment analysis (DEA) targets in manufacturing and other contexts is to identify performance goals that if achieved in the future will move an inefficient decision-making unit to the best practice efficiency frontier. Less discussed, however, is that this only will occur with certainty if all other units maintain their same input and output levels, an implicit assumption that is rarely the case in practice. Since this phenomenon can be important in many practical applications, this paper explores its manifestation and approaches for interpreting and setting future performance targets in this context, including forward and backward-looking analysis, forecasting, and Monte Carlo approaches. Each method is illustrated and compared using two empirical data sets.

Online publication date: Tue, 16-Oct-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Engineering Management and Economics (IJEME):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com