An analysis of the logistics performance index of EU countries with an integrated MCDM model

Abstract

Countries can check the performance of their logistics’ activities to determine their competitiveness in trade logistics. One way to check these performances is to analyze the country’s LPI value in detail which is released by the WB every two years. When calculating the LPI, six indicators (criteria) are taken into account. The weights (importance level) of these criteria are important for countries which would like to focus more on the most important criteria and move their ranking up in the LPI list. However the WB takes into account indicators (criteria) weights equally when calculating LPI values. In order to overcome this problem some studies have used subjective weighting methods and others have used objective weighting methods. Both methods have advantages and disadvantages. The aim of this study is to integrate two weighting methods (subjective (SWARA) and objective (CRITIC)) in determining the weights of criteria in order to balance the two weighting methods. Unlike other studies in the literature this study combines two weighting methods. Additionally the PIV method, which is seldom used to address any MCDM problem, is used in this study and a new integrated MCDM model is introduced to literature. In this respect this study contributes to the literature.

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  • Adalı, E. A., & Işık, A. T. (2017). Bir Tedarikçi Seçim Problemi Için SWARA ve WASPAS Yöntemlerine Dayanan Karar Verme Yaklaşımı. International Review of Economics and Management, 5(4), 56-77. Retrieved from https://doi.org/10.18825/iremjournal.335408

  • Alimardani, M., Hashemkhani Zolfani, S., Aghdaie, M. H., & Tamošaitiene, J. (2013). A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technological and Economic Development of Economy, 19(3), 533-548. Retrieved from https://doi.org/10.3846/20294913.2013.814606

  • Brauers, W. K. M., & Zavadskas, E. K. (2011). Multimoora optimization used to decide on a bank loan to buy property. Technological and Economic Development of Economy, 17(1), 174-188. Retrieved from https://doi.org/10.3846/13928619.2011.560632

  • Çakır, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi-Criteria Decision Analysis, 24(3-4), 177-186. Retrieved from https://doi.org/10.1002/mcda.1601

  • Çakır, S., & Perçin, S. (2013). Performance measurement of logistics firms with multi-criteria decision making methods. Ege Akademik Bakış Dergisi, 13(4), 449-459.

  • Chen, W., Goh, M., & Zou, Y. (2018). Logistics provider selection for omni-channel environment with fuzzy axiomatic design and extended regret theory. Applied Soft Computing Journal, 71, 353-363. Retrieved from https://doi.org/10.1016/j.asoc.2018.07.019

  • D’Aleo, V. (2015). The mediator role of Logistic Performance Index: A comparative study. Journal of International Trade, Logistics and Law, 1(1), 1-7.

  • Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers and Operations Research, 22(7), 763-770. Retrieved from https://doi.org/10.1016/0305-0548(94)00059-H

  • Ecer, F. (2018). Third-party logistics (3PLs) provider selection via fuzzy AHP and EDAS integrated model. Technological and Economic Development of Economy, 24(2), 615-634. Retrieved from https://doi.org/10.3846/20294913.2016.1213207

  • Heidary Dahooie, J., Beheshti Jazan Abadi, E., Vanaki, A. S., & Firoozfar, H. R. (2018). Competency-based IT personnel selection using a hybrid SWARA and ARAS-G methodology. Human Factors and Ergonomics in Manufacturing & Service Industries, 28(1), 5-16. Retrieved from https://doi.org/10.1002/hfm.20713

  • Jahan, A., Mustapha, F., Sapuan, S. M., Ismail, M. Y., & Bahraminasab, M. (2012). A framework for weighting of criteria in ranking stage of material selection process. International Journal of Advanced Manufacturing Technology, 58(1-4), 411-420. Retrieved from https://doi.org/10.1007/s00170-011-3366-7

  • Karbassi Yazdi, A., Hanne, T., Osorio Gómez, J. C., & García Alcaraz, J. L. (2018). Finding the best third-party logistics in the automobile industry: A hybrid approach. Mathematical Problems in Engineering, 2018. Retrieved from https://doi.org/10.1155/2018/5251261

  • Kawa, A., & Anholcer, M. (2019). Intangible assets as a source of competitive advantage for logistics service providers. Transport Economics and Logistics, 78, 29-41. Retrieved from https://doi.org/10.26881/etil.2018.78.03

  • Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243-258. Retrieved from https://doi.org/10.3846/jbem.2010.12

  • Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., & Antuchevičienė, J. (2017). Assessment of third-party logistics providers using a CRITIC–WASPAS approach with interval type-2 fuzzy sets. Transport, 32(1), 66-78. Retrieved from https://doi.org/10.3846/16484142.2017.1282381

  • Khan, N. Z., Ansari, T. S. A., Siddiquee, A. N., & Khan, Z. A. (2019). Selection of E-learning websites using a novel Proximity Indexed Value (PIV) MCDM method. Journal of Computers in Education, 6(2), 241-256. Retrieved from https://doi.org/10.1007/s40692-019-00135-7

  • Madić, M., & Radovanović, M. (2015). Ranking of some most commonly used non-traditional machining processes using ROV and CRITIC methods. U.P.B. Sci. Bull., Series D, 77(2), 193-204.

  • Martí, L., Martín, J. C., & Puertas, R. (2017). A DEA-logistics performance index. Journal of Applied Economics, 20(1), 169-192. Retrieved from https://doi.org/10.1016/S1514-0326(17)30008-9

  • Martí, L., Puertas, R., & García, L. (2014). The importance of the Logistics Performance Index in international trade. Applied Economics, 46(24), 2982-2992. Retrieved from https://doi.org/10.1080/00036846.2014.916394

  • Mufazzal, S., & Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers and Industrial Engineering, 119, 427-438. Retrieved from https://doi.org/10.1016/j.cie.2018.03.045

  • Pamucar, D., Chatterjee, K., & Zavadskas, E. K. (2019). Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers and Industrial Engineering, 127, 383-407. https://doi.org/10.1016/j.cie.2018.10.023

  • Perçin, S. (2019). An integrated fuzzy SWARA and fuzzy AD approach for outsourcing provider selection. Journal of Manufacturing Technology Management, 30(2), 531-552. Retrieved from https://doi.org/10.1108/JMTM-08-2018-0247

  • Rezaei, J., Van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169. Retrieved from https://doi.org/10.1016/j.tranpol.2018.05.007

  • Singh, R. K., Gunasekaran, A., & Kumar, P. (2018). Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach. Annals of Operations Research, 267(1-2), 531-553. Retrieved from https://doi.org/10.1007/s10479-017-2591-3

  • Sremac, S., Stević, Ž., Pamučar, D., Arsić, M., & Matić, B. (2018). Evaluation of a Third-Party Logistics (3PL) provider using a rough SWARA–WASPAS model based on a New Rough Dombi Agregator. Symmetry, 10(8), 305. Retrieved from https://doi.org/10.3390/sym10080305

  • Stanujkic, D., Karabasevic, D., & Zavadskas, E. K. (2015). A framework for the selection of a packaging design based on the SWARA method. Engineering Economics, 26(2), 181-187. Retrieved from https://doi.org/10.5755/j01.ee.26.2.8820

  • Ulutaş, A. (2019). Entropi Tabanlı EDAS Yöntemi İle Lojistik Firmalarının Performans Analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, (23), 53-66. Retrieved from https://doi.org/10.18092/ulikidince.458754

  • Ulutaş, A., & Karaköy, Ç. (2019). CRITIC ve ROV Yöntemleri İle Bir Kargo Firmasının 2011-2017 Yılları Sırasındaki Performansının Analiz Edilmesi. MANAS Sosyal Araştırmalar Dergisi, 8(1/1), 223-230. Retrieved from https://doi.org/10.33206/mjss.458643

  • Vujicic, M., Papic, M., & Blagojevic, M. (2017). Comparative analysis of objective techniques for criteria weighing in two MCDM methods on example of an air conditioner selection. Tehnika, 72(3), 422-429. Retrieved from https://doi.org/10.5937/tehnika1703422v

  • World Bank. (2019). Retrieved May 10, 2019 from https://lpi.worldbank.org/

  • Yahya, S. M., Asjad, M., & Khan, Z. A. (2019). Multi-response optimization of TiO2/EG-water nano-coolant using entropy based preference indexed value (PIV) method. Materials Research Express, 6(8). Retrieved from https://doi.org/10.1088/2053-1591/ab23bb

  • Yildirim, B. F., & Mercangoz, B. A. (2019). Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G. Eurasian Economic Review. Retrieved from https://doi.org/10.1007/s40822-019-00131-3

  • Zavadskas, E. K., & Podvezko, V. (2016). Integrated determination of objective criteria weights in MCDM. International Journal of Information Technology and Decision Making, 15(2), 267-283. Retrieved from https://doi.org/10.1142/S0219622016500036

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