Abstract
An original data envelopment analysis (DEA) model is to evaluate each decision-making unit (DMU) with a set of most favorable weights of performance indices. The efficient DMUs obtained from the original DEA construct an efficient (best-practice) frontier. The original DEA can be considered to identify good (efficient) performers in the most favorable scenario. For the purpose of identifying bad performers such as bankrupt firms in the most unfavorable (worst-case) scenario, radial worst-practice frontier DEA (WPF–DEA) model in which the “worst efficient” DMUs construct a worst-practice frontier has been proposed. To identify bad performers together with the slack values we formulate another model called WPF–SBM. Then we develop the HypoSBM model to distinguish the worst performers from the bad ones. Finally, a solution approach is suggested to fully rank worst efficiencies in the worst-case scenario.
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