Rita Markovits-Somogyi

Measuring efficiency in transport: the state of the art of applying data envelopment analysis

Data Envelopment Analysis (DEA) is a non-parametric linear programming method used for determining the efficiency of a set of companies as compared to the best practice frontier. It can be employed to analyze private, public or non-profit organizations. The application of the method in the transport sector is wide-spread, especially in the evaluation of airports, ports, railways and urban transport companies. The present paper is aimed at giving a review of how DEA is applied in the transport sector with a special emphasis on the inputs and outputs selected in the DEA models employed in different fields. For this reason, the author has compiled data from 69 DEA applications found or reported in literature, investigated their characteristics and the field of applying them along with the inputs and outputs used.

Assessing the logistics efficiency of European countries by using the DEA-PC methodology

Data envelopment analysis is a non-parametric linear programming method widely used for the efficiency evaluation of decision making units active in the transport sector. However, it is seldom applied for the efficiency assessment of logistics efficiency at a macro level. The article presents such an example which is at the same time the very first application of a lately developed methodology where data envelopment analysis is combined with analytic hierarchy process to yield an appropriate tool for efficiency evaluation with full ranking. The logistics efficiency of 29 European countries is tested with the new DEA-PC (pairwise comparison) methodology while it is also compared with the results gained with the original DEA method. Furthermore, the outcomes are also evaluated in light of the ‘Logistics quality and competence’ index of the Logistics Performance Indicator (LPI), a major international survey into the logistics competence of countries. Thus, the results of traditional DEA and DEA-PC are both weighted against survey data which is also a novelty in the logistics sector.