jmml

Journal of Management Marketing and Logistics

2148-6670

PressAcademia

Business Administration

İşletme

EVALUATION OF CUSTOMS, INFRASTRUCTURE AND LOGISTICS SERVICES WITH MULTI-CRITERIA DECISION-MAKING METHODS: A COMPARATIVE ANALYSIS FOR THE TOP 10 COUNTRIES IN THE LOGISTICS PERFORMANCE INDEX

https://orcid.org/0000-0003-4876-3173

Oğuz

Suzan

Independent Researcher

12 31 2023

10 4 167 178 10 11 2023 12 25 2023

2014

Journal of Management Marketing and Logistics

Purpose- The aim of this study is to examine the performance of customs, infrastructure and logistics services of the countries ranked in the top 10 in the Logistics Performance Index (LPI) ranking in 2023 with Multi-Criteria Decision Making Methods (MCDM) and to rank and compare them. The World Bank's LPI criteria and weights were taken into consideration while determining the criteria considered within the scope of the study.Methodology- When employing the MCDM methods, various elements are taken into account during the decision-making process, and options are graded in accordance with these criteria. By taking into account the importance of many criteria and the performance of alternatives, MCDM techniques provide researchers with an organized and objective method for making difficult judgments. In this study Topsis and Edas methods, which are among the MCDM methods, were used to rank the countries.Findings- When the results of the analysis are summarized, it is observed that the first three ranks differ from the results of the LPI report. According to the Topsis method, Finland ranks first, Singapore ranks second and Austria ranks third. According to the Edas method, Singapore and Finland ranked in the first two places as in the LPI 2023 report, while Switzerland ranked third. The findings of the study show that the rankings may be different even when the criteria and criteria weights used within the scope of the analysis are the same in MCDM methods. Conclusion- The findings demonstrate that, even though the criteria and weights employed within the parameters of the study are the same, the rankings in MCDM approaches may differ. This is due to the possibility of various computation strategies or theoretical underpinnings for the procedures being used. The results of this study can therefore be applied to assist decision-makers in this field and improve a country's logistics performance. By illustrating the utility of MCDM techniques in making such decisions, it also makes an important contribution to ongoing research in this field.

Logistics performance LPI Multi-Criteria Decision Making MCDM Edas Topsis

Arvis, J. F., Ojala, L., Shepherd, B., Ulybina, D. and Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in an Uncertain Global Economy-The Logistics Performance Index and Its Indicators. The World Bank, Washington, DC.

Asante, D., He, Z., Adjei, N. O. and Asante, B. (2020). Exploring the barriers to renewable energy adoption utilising Multimoora-Edas method. Energy Policy, 142, 111479.

Banister, D. and Berechman, Y. (2001). Transport investment and the promotion of economic growth. Journal of Transport Geography, 9(3), 209-218.

Bayraktutan, Y. and Özbilgin, M. (2015). Lojistik maliyetler ve lojistik performans ölçütleri. Maliye Araştırmaları Dergisi, 1(2), 95-112.

Bensassi, S., Márquez-Ramos, L., Martínez-Zarzoso, I. and Suárez-Burguet, C. (2015). Relationship between logistics infrastructure and trade: Evidence from Spanish regional exports. Transportation Research Part A: Policy and Practice, 72, 47-61.

Bozkurt, C. and Mermertaş, F. (2019). Türkiye ve G8 ülkelerinin lojistik performans endeksine göre karşılaştırılması. İşletme ve İktisat Çalışmaları Dergisi, 7(2), 107-117.

Chejarla, K. C., Vaidya, O. S. and Kumar, S. (2022). MCDM applications in logistics performance evaluation: A literature review. Journal of Multi‐Criteria Decision Analysis, 29(3-4), 274-297.

Çakir, S. and Perçin, S. (2013). Çok kriterli karar verme teknikleriyle lojistik firmalarinda performans ölçümü. Ege Akademik Bakis, 13(4), 449-461.

Dhurkari, R.K. (2022). MCDM methods: practical difficulties and future directions for improvement. RAIRO-Operations Research, 56(4), 2221 - 2233.

Gotzamani, K., Longinidis, P. and Vouzas, F. (2010). The logistics services outsourcing dilemma: Quality management and financial performance perspectives. Supply Chain Management, 15(6), 438-453.

Huang, Y., Lin, R. and Chen, X. (2021). An enhancement Edas method based on prospect theory. Technological and Economic Development of Economy, 27(5), 1019-1038.

Jahanshahloo, G. R., Lotfi, F. H. and Izadikhah, M. (2006). An algorithmic method to extend Topsis for decision-making problems with interval data. Applied Mathematics and Computation, 175(2), 1375-1384.

Kara, K. (2022). Relationship between domestic logistics opportunity efficiency and international logistics opportunity efficiency based on market potential: Empirical research on developing countries. Journal of Management, Marketing and Logistics (JMML), 9(2), 79-89.

Karabasevic, D., Zavadskas, E. K., Stanujkic, D., Popovic, G. and Brzakovic, M. (2018). An approach to personnel selection in the IT industry based on the Edas method. Transformations in Business & Economics, 17, 54-65.

Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L. and Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (Edas). Informatica, 26(3), 435-451.

Khan, S. A. R., Jian, C., Zhang, Y., Golpîra, H., Kumar, A. and Sharif, A. (2019). Environmental, social and economic growth indicators spur logistics performance: from the perspective of South Asian Association for Regional Cooperation Countries. Journal of Cleaner Production, 214, 1011-1023.

Langley Jr, C. J. and Holcomb, M. C. (1992). Creating logistics customer value. Journal of Business Logistics, 13(2), 1.

Lei, F., Wei, G., Shen, W. and Guo, Y. (2022). Pdhl-Edas method for multiple attribute group decision making and its application to 3D printer selection. Technological and Economic Development of Economy, 28(1), 179-200.

Li, Z., Wei, G., Wang, R., Wu, J., Wei, C. and Wei, Y. (2020). Edas method for multiple attribute group decision making under Q-rung orthopair fuzzy environment. Technological and Economic Development of Economy, 26(1), 86-102.

Mentzer, J. T. and Konrad, B. P. (1991). An efficiency/effectiveness approach to logistics performance analysis. Journal of Business Logistics, 12(1), 33-51.

Moldabekova, A., Philipp, R., Reimers, H. E. and Alikozhayev, B. (2021). Digital technologies for improving logistics performance of countries. Transport and Telecommunication, 22(2), 207-216.

Oğuz, S., Alkan, G. and Yılmaz B. (2019). Seçilmiş Asya ülkelerinin lojistik performanslarının Topsis yöntemi ile değerlendirilmesi. IBAD Sosyal Bilimler Dergisi, 497-507.

Özbek, A. (2017). Çok Kriterli Karar Verme Yöntemleri ve Excel Ile Problem Çözümü. Ankara: Seçkin Yayıncılık.

Özceylan, E., Çetinkaya, C., Erbaş, M. and Kabak, M. (2016). Logistic performance evaluation of provinces in Turkey: A GIS-based multi-criteria decision analysis. Transportation Research Part A: Policy and Practice, 94, 323-337.

Ren, L., Zhang, Y., Wang, Y. and Sun, Z. (2007). Comparative analysis of a novel M-Topsis method and Topsis. Applied Mathematics Research eXpress, 7, 5-17..

Rezaei, J., van Roekel, W. S. and Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using best worst method. Transport Policy, 68, 158-169.

Sergi, B. S., D’Aleo, V., Konecka, S., Szopik-Depczyńska, K., Dembińska, I. and Ioppolo, G. (2021). Competitiveness and the logistics performance index: The Anova method application for Africa, Asia, and the EU regions. Sustainable Cities and Society, 69, 1-9.

Song, M. J. and Lee, H. Y. (2022). The relationship between international trade and logistics performance: A focus on the South Korean industrial sector. Research in Transportation Business & Management, 44, 100786.

Stanujkic, D., Zavadskas, E. K., Ghorabaee, M. K. and Turskis, Z. (2017). An extension of the Edas method based on the use of interval grey numbers. Studies in Informatics and Control, 26(1), 5-12.

Tang, C. F. and Abosedra, S. (2019). Logistics performance, exports, and growth: Evidence from Asian economies. Research in Transportation Economics, 78, 100743.

Türkoğlu, M. and Duran, G. (2023). Çok kriterli karar verme yöntemleri ile Bölgesel Kapsamlı Ekonomik Ortaklık (RCEP) ülkelerinin lojistik performanslarının değerlendirilmesi. Ekonomi Bilimleri Dergisi, 15(1), 45-69.

Ulutaş, A. (2018). Entropi tabanlı edas yöntemi ile lojistik firmalarının performans analizi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 23, 53-66.

Ulutaş, A. and Karaköy, Ç. (2019). G-20 ülkelerinin lojistik performans endeksinin çok kriterli karar verme modeli ile ölçümü. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 71-84.

Winkelhaus, S. and Grosse, E. H. (2020). Logistics 4.0: A systematic review towards a new logistics system. International Journal of Production Research, 58(1), 18-43.

World Bank (2023). International LPI. https://lpi.worldbank.org/international/global. (05.08.2023)

Yingfei, Y., Mengze, Z., Zeyu, L., Ki-Hyung, B., Avotra, A. A. R. N. and Nawaz, A. (2022). Green logistics performance and infrastructure on service trade and environment-measuring firm’s performance and service quality. Journal of King Saud University-Science, 34(1), 101683.

Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A. and Nor, K. M. (2016). Development of topsis method to solve complicated decision-making problems—an overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making, 15(03), 645-682.

Zulqarnain, R. M., Saeed, M., Ahmad, N., Dayan, F. and Ahmad, B. (2020). Application of Topsis method for decision making. International Journal of Scientific Research in Mathematical and Statistical Sciences, 7(2), 76-81