Up: Data Envelopment Analysis
As the earlier list of applications suggests, DEA can be a powerful tool when used wisely. A few of the characteristics that make it
DEA can handle multiple input and multiple output models.
It doesn't require an assumption of a functional form relating inputs to outputs.
DMUs are directly compared against a peer or combination of peers.
Inputs and outputs can have very different units. For example, X1 could be in units of lives saved and X2 could be in units of
dollars without requiring an a priori tradeoff between the two.
The same characteristics that make DEA a powerful tool can also create problems. An analyst should keep these limitations in mind
when choosing whether or not to use DEA.
Since DEA is an extreme point technique, noise (even symmetrical noise with zero mean) such as measurement error can cause
DEA is good at estimating "relative" efficiency of a DMU but it converges very slowly to "absolute" efficiency. In other
words, it can tell you how well you are doing compared to your peers but not compared to a "theoretical maximum."
Since DEA is a nonparametric technique, statistical hypothesis tests are difficult and are the focus of ongoing research.
Since a standard formulation of DEA creates a separate linear program for each DMU, large problems can be computationally
Michael A. Trick
Mon Aug 24 16:19:33 EDT 1998