Another view of efficiency improvement in data envelopment analysis

Data envelopment analysis (DEA) is a mathematical programming approach for measuring the relative efficiencies within a group of decision making units. An important outcome of such an analysis is a set of values for dual variables which indicate how the associated factors should be adjusted so that input wastages and/or output shortfalls can be eliminated. The traditional DEA model gives a dual variable to associate with the normalizing equation. This setting implies that the adjustment proportions of all input or output factors are the same for efficiency improvement. This study modifies the original DEA model by decomposing the normalizing equation in order to for it to be associated with different dual variables. As a consequence, to improve efficiency the adjustment proportion of each input or output factor can be different. In essence, the proposed approach can not only set targets of factors for inefficient decision making units to eliminate inefficiency, but can also deal with the exogenous variables in a DEA context.