Evaluating Decision Making Units with Multi-Periodic Data Using DEA-R Models

In measuring the efficiency of a set of units over a period of time covering multiple periods, models based on the standard DEA technique ignore the status of each unit in each period that causes misleading results. This paper develops DEA-R models in the presence of multi-periodic data in such a way that the proposed method can evaluate the overall efficiency with respect to the overall and periodic efficiencies of all units. By providing a lower bound on the weights derived from periods, the proposed method prioritizes the units for yielding valuable insights that aid decision makers to better understand the findings from a performance evaluation process. The contribution of this paper is fourfold: (1) the overall efficiency calculated from the proposed method depends on the unit performance of all units in all periods, (2) the proposed method determines the overall efficiency by imposing a lower bound obtained from all periods on the weights, (3) this method endowed with a high discriminatory power in differentiating the units which are evaluated as efficient in the existing multi-period models, (4) To elucidate the details of the proposed method, a comparison is made between the existing models in the literature and the proposed multi-period DEA-R method to measure the efficiency of 22 Taiwanese commercial banks for the period of 2009–2011.