An illustrative application of IDEA (imprecise data envelopment analysis) to a Korean mobile telecommunication company

Data Envelopment Analysis (DEA) models, as ordinarily employed, assume that the data for all inputs and outputs are known exactly. In some applications, however, a number of factors may involve imprecise data, which take forms such as ordinal rankings and knowledge only of bounds. Here we provide an example involving a Korean mobile telecommunication company. The Imprecise Data Envelopment Analysis (IDEA) method we use permits us to deal not only with imprecise data and exact data but also with weight restrictions as in the (now) widely used ‘Assurance Region’ (AR) and ‘cone-ratio envelopment’ approaches to DEA. We also show how to transform AR bounds on the variables, obtained from managerial assessments, for instance, into data adjustments. This involves an extended IDEA model, which we refer to as AR-IDEA. All these uses are illustrated by an example application directed to evaluate efficiencies of branch offices of a telecommunication company in Korea.