THE IMPACT OF SENSITIVITY ANALYSIS ON THE EVALUATION OF THE LOGISTICS PERFORMANCE INDEX

University of East Sarajevo, Faculty of Transport and Traffic Engineering, Doboj, Republic of Srpska, Bosnia and Herzegovina
Bosnia and Herzegovina

University of East Sarajevo, Faculty of Transport and Traffic Engineering, Doboj, Republic of Srpska, Bosnia and Herzegovina
Bosnia and Herzegovina

University of East Sarajevo, Faculty of Business Economics Bijeljina, Republic of Srpska, Bosnia and Herzegovina
Bosnia and Herzegovina


Abstract

The logistics performance index (LPI) represents an important indicator of the state of logistics and its development in countries. The LPI is directly linked to the level of economic system development, and as such provides an adequate basis for the improvement of economy, through logistics and trade. The aim of this paper is to determine the impact of sensitivity analysis on the evaluation and ranking of the LPI in the Balkan countries, according to the report of the World Bank. Sensitivity analysis implies the change of the importance of six criteria based on which the LPI ranking is done. The multi-criteria decision-making model (MCDM), which consists of CRITIC and MARCOS methods for determining the LPI rank in the Balkan countries, was previously used. Criteria weights are simulated through 36 scenarios, whereby the weights of the observed criteria change in the range of 15% - 90%. The final results show that criteria values play very important role in the ranking of the Balkan countries, when it comes to the LPI.

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References


Bouraima, M. B., Stević, Ž., Tanackov, I., & Qiu, Y. (2021). Assessing the performance of Sub-Saharan African (SSA) railways based on an integrated Entropy-MARCOS approach. Operational Research in Engineering Sciences: Theory and Applications, 4(2), 13-35.

Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.

Erceg, Ž., Starčević, V., Pamučar, D., Mitrović, G., Stević, Ž., & Žikić, S. (2019). A new model for stock management in order to rationalize costs: ABC-FUCOM-interval rough CoCoSo model. Symmetry, 11(12), 1527.

GDP per capita (current US$) | Data (worldbank.org)

International_LPI_from_2007_to_2018.xlsx (live.com)

Isik, O., Aydin, Y., & Kosaroglu, S. M. (2020). The assessment of the logistics Performance Index of CEE Countries with the New Combination of SV and MABAC Methods. LogForum, 16(4).

Martí, L., Martín, J. C., & Puertas, R. (2017). A DEA-logistics performance index. Journal of applied economics, 20(1), 169-192.

Melo, I. C., Péra, T. G., Júnior, P. N. A., do Nascimento Rebelatto, D. A., & Caixeta-Filho, J. V. (2020). Framework for logistics performance index construction using DEA: an application for soybean haulage in Brazil. Transportation Research Procedia, 48, 3090-3106.

Mitrović Simić, J., Stević, Ž., Zavadskas, E. K., Bogdanović, V., Subotić, M., & Mardani, A. (2020). A novel CRITIC-Fuzzy FUCOM-DEA-Fuzzy MARCOS model for safety evaluation of road sections based on geometric parameters of road. Symmetry, 12(12), 2006.

Stević Ž., Pamučar. D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to Compromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231.

Stević, Ž., & Brković, N. (2020). A novel integrated FUCOM-MARCOS model for evaluation of human resources in a transport company. Logistics, 4(1),

Ulutaş, A., & Karaköy, Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model 1. Economics and Business Review, 5(4), 49-69.




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