Benchmarking in Data Envelopment Analysis: balanced efforts to achieve realistic targets

N. Ramon, H. P. Guevel, J. Aparicio Baeza

The minimum distance models have undoubtedly represented a significant advance for the establishment of targets in Data Envelopment Analysis (DEA). These models may help in defining improvement plans that require the least overall effort from the inefficient Decision Making Units (DMUs). Despite the advantages that come with closest targets, in some cases unsatisfactory results may be given, since improvement plans, even in that context, differ considerably from the actual performances. In the absence of information, the most plausible and conservative solution would be the one where an equitable redistribution of efforts is possible. We propose different approaches with the aim of reaching an impartial distribution of efforts to achieve optimal operating levels without neglecting the overall effort required. Moreover, and as something new in the benchmarking DEA context, we will study which properties satisfy the targets generated by the different models proposed.

Keywords: Data Envelopment Analysis – Benchmarking – Targets – Balanced efforts

Data Envelopment Analysis I
November 10, 2023  12:00 PM
CC3: Room 1

M. D. Guillén, J. Aparicio Baeza, M. Esteve

R. Moragues, J. Aparicio Baeza, M. Esteve

V. J. España Roch, J. Aparicio Baeza, J. X. Barber Vallés