An application of multi‐component ranking in banks by context‐dependent DEA for non‐extreme efficient DMUs

In most applications of data envelopment analysis (DEA) presented in the literature, the models are designed to acquire a single measure of efficiency. However in many instances, the decision making units (DMUs) can be separated into different components. This paper deals with ranking in DEA analysis of multi‐component decision making units (DMUs). Using DEA technology, we first show how efficiency of each unit and its components can be obtained then from a practical viewpoint the issue of ranking multi‐component decision making units will be discussed. The significant feature of this method is that it does not have much to do with theoretical concepts and it is has been mostly taught over for applicational issues.