Gradual efficiency improvement through a sequence of targets

Abstract

The goal in efficiency analysis is not only to evaluate a Decision Making Unit (DMU) performance, but also to find an efficient target which provides information on inputs reduction and outputs increment values that are necessary to remove inefficiencies for each inefficient DMU. In Data Envelopment Analysis (DEA) the target unit is located on the efficient frontier and possibly far from the unit under assessment. Therefore, in practice performance improvement seems to be disappointing or even impossible to achieve in only one step for some inefficient DMUs. In this regard, finding intermediate targets is of great importance in benchmarking literature. In this paper we find a sequence of targets instead of a single target for each inefficient unit. In our method, the intermediate target at each step has three properties:(I) the intermediate targets and the unit under evaluation are all similar in size; (II) efficiency scores are ascending through the sequence of targets; (III) the target unit at each step is close to the special part of the efficient frontier as much as possible. These properties lead to finding a target that is more achievable in real applications.

A compromise programming approach for target setting in DEA

“…CP-DEA targets and efficiency scores for units E and F Table 5. Sequence of targets and efficiency scores for units E and F computed by Dehnokhalaji and Soltani (2019) Table 6. Ideal points and normalizing coefficients (efficient DMUs in bold) Table 7.…”

“…Visualization of the CP-DEA targets for unit F Figure 3. Visualization of the sequence of targets computed by Dehnokhalaji and Soltani (2019) for units E and F Kao and Hung (2005) Hosseinzadeh Lofti et al Non-linear Equivalent to Roll and Golani (1993) and 1 p  model in Kao and Hung (2005) Davoodi and Rezai 2012 Table 4. CP-DEA targets and efficiency scores for units E and F…”

“…Visualization of the CP-DEA targets for unit F Figure 3. Visualization of the sequence of targets computed by Dehnokhalaji and Soltani (2019) for units E and F p  CP-DEA 2 p  CP-DEA p  SBM, Hampf and Krüger (2015), PBM RDM MEA Figure 4. Parallel coordinates representation of the computed targets for USA…”

Identifying suitable benchmarks in the way toward achieving targets using data envelopment analysis

“…(2018), Nasrabadi et al. (2019), Dehnokhalaji and Soltani (2019), An et al. (2021) and Lozano and Soltani (2020a), who propose stepwise benchmarking approaches.…”

“…A large number of models and approaches have been developed within DEA for purposes of benchmarking and target setting, often in combination with other methodologies. Some recent papers dealing with these issues include Korhonen et al (2018), who use a lexicographic approach to reach the efficient frontier, Lozano and Soltani (2020c), who address target setting with the hyperbolic distance function, Camanho et al (2021), who use a pseudo-Malmquist index, Lozano et al (2020), who use compromise programming, Silva et al (2020), Stumbriene et al (2020), andVan Puyenbroeck et al (2021), who use composite indicators, Chen and Wang (2019), who propose a target setting approach within the framework of cross efficiency, Lim (2018), who deal with forecasting targets in presence of infeasibility, Moreno and Lozano (2018), who combine DEA and network DEA, Wu et al (2020), who combine DEA and game theory, An et al (2020), who use agency theory also in combination with games, and Park and Lee (2018), Lozano and Calzada-Infante (2018), Ramón et al (2018), Nasrabadi et al (2019), Dehnokhalaji and Soltani (2019), An et al (2021) and Lozano and Soltani (2020a), who propose stepwise benchmarking approaches.…”