Data envelopment analysis (DEA) introduced in 1978 has been widely applied to evaluating the relative efficiency and providing efficient target for decision making units (DMUs). The conventional range adjusted measure (RAM) in DEA acts as a well-defined measure satisfying a set of desirable properties, especially the strong monotonicity. However, because of the practicality of the closest efficient target, we focus on formulating the least-distance range adjusted measure (LRAM) and proposing the use of an efficient mixed integer programming (MIP) approach to compute it. Our formulated LRAM: (1) satisfies the desirable properties as the conventional RAM; (2) provides the least-distance benchmarking information for inefficient DMUs, which will make the efficiency improvement easy, and (3) can be computed easily by using the proposed MIP approach. Here, we apply the LRAM to a Japanese banking data set corresponding to the period 2017-2019. Based on the results: the LRAM generates higher efficiency scores and allows inefficient banks to improve their efficiency with a smaller extent of input-output modification than that required by the RAM, which indicates that the LRAM can provide more easy-to-achieve benchmarking information for inefficient banks. Therefore, from the perspective of the managers of DMUs, we provide a valuable LRAM for efficiency evaluation and benchmarking analysis.