Strengths and Limitations of DEA

As the earlier list of applications suggests, DEA can be a powerful tool when used wisely. A few of the characteristics that make it powerful are:

• 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 significant problems.
• 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 intensive.