  The models that set the closest targets have made an important contribution
to DEA as tool for the best-practice benchmarking of decision making units
(DMUs). These models may help defining plans for improvement that require less
effort from the DMUs. However, in practice we often find cases of poor
performance, for which closest targets are still unattainable. For those DMUs,
we propose a two-step benchmarking approach within the spirit of
context-dependent DEA and that of the models that minimize the distance to the
DEA efficient frontier. This approach allows to setting more realistically
achievable targets in the short term. In addition, it may offer different
alternatives for planning improvements directed towards DEA efficient targets,
which can be seen as representing improvements in a long term perspective. To
illustrate, we examine an example which is concerned with the research
performance of public Spanish universities.
