Measuring Performance in Transportation Companies in Developing Countries: A Novel Rough ARAS Model

• Main Authors: Dunja Radović, Željko Stević, Dragan Pamučar, Edmundas Kazimieras Zavadskas, Ibrahim Badi, Jurgita Antuchevičiene, Zenonas Turskis
• Format: Article
• Language: English
• Published: MDPI AG 2018-09-01
• Series: Symmetry
• Subjects: rough ARAS; transport; performance; logistics; MCDM
• Online Access: http://www.mdpi.com/2073-8994/10/10/434

The success of any business depends fundamentally on the possibility of balancing (symmetry) needs and their satisfaction, that is, the ability to properly define a set of success indicators. It is necessary to continuously monitor and measure the indicators that have the greatest impact on the achievement of previously set goals. Regarding transportation companies, the rationalization of transportation activities and processes plays an important role in ensuring business efficiency. Therefore, in this paper, a model for evaluating performance indicators has been developed and implemented in three different countries: Bosnia and Herzegovina, Libya and Serbia. The model consists of five phases, of which the greatest contribution is the development of a novel rough additive ratio assessment (ARAS) approach for evaluating measured performance indicators in transportation companies. The evaluation was carried out in the territories of the aforementioned countries in a total of nine companies that were evaluated on the basis of 20 performance indicators. The results obtained were verified throughout a three-phase procedure of a sensitivity analysis. The significance of the performance indicators was simulated throughout the formation of 10 scenarios in the sensitivity analysis. In addition, the following approaches were applied: rough WASPAS (weighted aggregated sum product assessment), rough SAW (simple additive weighting), rough MABAC (multi-attributive border approximation area comparison) and rough EDAS (evaluation based on distance from average solution), which showed high correlation of ranks by applying Spearman's correlation coefficient (SCC).