Efficiency improvement of city administration management by means of distance friction minimization in data envelopment analysis

Data Envelopment Analysis (DEA) has become an established approach in the analysis of efficiency problems in both public and private sectors. The aim of this paper is to present a newly developed Distance Friction Minimization (DFM) approach based on the BCC (Banker-Charnes-Cooper) model in order to provide an appropriate projection model to improve efficiency in DEA. In this approach generalized distance friction is developed to assist a Decision Making Unit (DMU) to improve efficiency by a proper movement towards the efficiency frontier surface. Standard DEA models have always used a uniform input reduction or a uniform output augmentation for improvement projections. This DFM approach aims to generate a new contribution to efficiency enhancement strategies by deploying a weighted projection function, while addressing both input reduction and output augmentation. A suitable form of multidimensional projection functions to improve efficiency is then given by a Multiple Objective Quadratic Programming (MOQP) model. The above-mentioned extended DEA model is empirically illustrated by using a data set on government ordinance designated cities in Japan. The aim is to increase the efficiency of administration management in these cities based on various input and output performance characteristics of these cities.