Evaluation of Logistics Performances of G20 Countries Using SD-Based COPRAS and SAW Methods

Abstract

One of the important issues in the economic development of countries is their effectiveness in logistics activities. Countries gain competitive advantage by maintaining effective and efficient logistics processes. Therefore, determining logistics performance is important for both businesses and countries. The main aim of this study is to examine the logistics performances of countries in the context of G20 countries and to determine how they change over time. Within the framework of this aim, the Logistics Performance Index published by the World Bank has been used to determine the logistics performance of countries (LPI (2018) and LPI (2023)). Standard Deviation (SD) method has been used in weighting the criteria “customs, infrastructure, international shipments, logistics competence and quality, timeliness, tracing and tracking” included in the LPI and in determining the performance of G20 countries. Data for 2018 and 2023 have been examined using the methods COPRAS (Complex Proportional Assessment) and SAW (Simple Additive Weight). The results obtained from the methods have been compared with LPI (2018) and LPI (2023). As a result of the analysis, according to the COPRAS method, Germany, Japan, and the United Kingdom rank first in 2018, while the Russian Federation, Argentina and Brazil rank last, respectively. According to 2023 data, Germany ranks first according to both methods, while Canada and Japan follow Germany in line with the COPRAS method. According to the SAW method, Japan and Canada follow Germany. Russia and Argentina rank in last place in both methods, similar to the current index.

Keywords

• Logistics Performance
• SD Method
• COPRAS Method
• SAW Method

References

1. Acar, M. F. (2021). Lojistik performans indeks: Türkiye-Avrupa Birliği karşılaştırması. International Journal of Advances in Engineering and Pure Sciences, 33(3), 422-428. https://doi.org/10.7240/jeps.845982
2. Adiguzel Mercangöz, B., Yildirim, B. F., & Kuzu Yildirim, S. (2020). Time period based COPRAS-G method: application on the logistics performance index. LogForum, 16(2), 239-250. http://doi.org/10.17270/J.LOG.2020.432
3. Afshari, A., Mojahed, M., & Yusuff, R. M. (2010). Simple additive weighting approach to personnel selection problem. International Journal of Innovation, Management and Technology, 1(5), 511-515. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bec304ffd086871cdc9dd84cbee6e610da5030 d8
4. Akbulut, O. Y., & Şenol Z. (2021). Bütünleşik SD ve PROMETHEE ÇKKV yöntemleri ile portföy optimizasyonu: BIST gıda, içecek ve tütün sektöründe ampirik bir uygulama. Muhasebe ve Finansman Dergisi, (92), 161-182. https://doi.org/10.25095/mufad.935545
5. Aksoy, E., Ömürbek, N., & Karaatlı, M. (2015). AHP Temelli MULTIMOORA ve COPRAS yöntemi ile Türkiye Kömür İşletmeleri’nin performans değerlendirmesi. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33(4), 1-28. https://doi.org/10.17065/huiibf.10920
6. Alma, J. (2023). MCDM methods for selection of handling equipment in logistics: a brief review. Spectrum of Engineering and Management Sciences, 1(1), 13-24. https://doi.org/10.31181/sems1120232j
7. Altın, F. G., Tunca, M. Z., & Ömürbek, N. (2020). Entropi temelli SAW ve ARAS yöntemleri ile NATO ülkeleri askeri güçlerinin sıralanması. Alanya Akademik Bakış, 4(3), 731-753. https://doi.org/10.29023/alanyaakademik.646385
8. Altıntaş F.F. (2021). Avrupa Birliği ülkelerinin lojistik performanslarının CRITIC tabanlı WASPAS ve COPRAS teknikleri ile analizi. Türkiye Sosyal Araştırmalar Dergisi, 25(1), 117-146. https://dergipark.org.tr/tr/download/article-file/1106399

9. Arıkan Kargı, V. S. (2022). Evaluation of logistics performance of the OECD Member countries with integrated Entropy and Waspas method. Yönetim ve Ekonomi Dergisi, 29(4), 801-811. https://doi.org/10.18657/yonveek.1067480
10. Beysenbaev, R., & Dus, Y. (2020). Proposals for improving the logistics performance index. The Asian Journal of Shipping and Logistics, 36(1), 34-42. https://doi.org/10.1016/j.ajsl.2019.10.001
11. Bozkurt, C., & Mermertaş, F. (2019). Türkiye ve G8 ülkelerinin lojistik performans endeksine göre karşılaştırılması. İşletme ve İktisat Çalışmaları Dergisi, 7(2), 107-117. https://dergipark.org.tr/tr/download/articlefile/ 840193
12. Çakır, S., & Perçin, S. (2013). Çok kriterli karar verme teknikleriyle lojistik firmalarında performans ölçümü. Ege Akademik Bakış, 13(4), 449-459. https://dergipark.org.tr/en/download/article-file/559959
13. Çemberci, M., Civelek, M. E., & Canbolat, N. (2015). The moderator effect of global competitiveness index on dimensions of logistics performance index. Procedia-Social and Behavioral Sciences, 195, 1514-1524. https://doi.org/10.1016/j.sbspro.2015.06.453
14. Chatterjee, P., Athawale, V. M., & Chakraborty, S. (2011). Materials selection using complex proportional assessment and evaluation of mixed data methods. Materials & Design, 32(2), 851-860. https://doi.org/10.1016/j.matdes.2010.07.010 Christopher, M. (2016). Logistics and Supply Chain Management. Pearson UK.
15. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: the CRITIC method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305- 0548(94)00059-H
16. Gök Kısa, A. C., & Açin, E. (2019). OECD ülkelerinin lojistik performanslarının SWARA tabanlı EDAS yöntemi ile değerlendirilmesi. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(1), 301-325. https://doi.org/10.18074/ckuiibfd.500320
17. Hezer, S., Gelmez, E., & Özceylan, E. (2021). Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 regional safety assessment. Journal of Infection and Public Health, 14(6), 775-786. https://doi.org/10.1016/j.jiph.2021.03.003 https://bigpara.hurriyet.com.tr/haberler/ekonomi-haberleri/ingiltere-lojistik-sorunlarla-karsikarsiya_ ID1467599/, Date of access: 04/03/2024. https://disiliskiler.ktb.gov.tr/TR-333524/g- 20.html#:~:text=%22G20%2C%20uluslararas%C4%B1%20sistemde%20ba%C5%9Fl%C4%B1ca%20geli%C5 %9Fmi%C5%9F,amac%C4%B1yla%20kurulmu%C5%9F%20bir%20uluslararas%C4%B1%20platformdur., Date of access:16.03.2024. https://lpi.worldbank.org/report, Date of access: 03/01/.2024. https://www.mfa.gov.tr/g-20-tr.tr.mfa, Date of access: 03/14/2024.
18. İnce, Ö., Çetiner, B., & Ecer, F. (2023). G20 ülkelerinin COVID-19 öncesi ve COVID-19 dönemi lojistik performanslarının kıyaslanması: MEREC ve CODAS entegre yaklaşımı. Journal of Transportation and Logistics, 8(2), 112-147. https://doi.org/10.26650/JTL.2023.1317958
19. 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), 549-559. https://doi.org/10.17270/J.LOG.2020.504
20. Kabak, Ö., Ekici, Ş. Ö., & Ülengin, F. (2020). Analyzing two-way interaction between the competitiveness and logistics performance of countries. Transport Policy, 98, 238-246. https://doi.org/10.1016/j.tranpol.2019.10.007
21. Karaköy, Ç., & Ölmez, U. (2019). Balkan ülkelerinde lojistik performans endeksi değerlendirilmesi. Uluslararası Sosyal, Beşeri ve İdari Bilimlerde Yenilikçi Yaklaşımlar Sempozyumu, 178-180. https://doi.org/10.36287/setsci.4.8.031
22. Koç Ustalı, N., & Tosun, Ö. (2020). Investigation of logistic performance of G-20 countries using data envelopment analysis and malmquist total factor productivity analysis. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 7(3), 755-781. https://doi.org/10.30798/makuiibf.792066
23. Liu, J., Yuan, C., Hafeez, M., & Yuan, Q. (2018). The relationship between environment and logistics performance: Evidence from Asian countries. Journal of Cleaner Production, 204, 282-291. https://doi.org/10.1016/j.jclepro.2018.08.310 LPI (2023). https://lpi.worldbank.org/, Date of access: 01/10/2024.
24. Martí, L., Puertas, R., & García, L. (2014). The importance of the logistics performance index in international trade. Applied Economics, 46(24), 2982-2992. https://doi.org/10.1080/00036846.2014.916394 Mešić, A., Miškić, S., Stević, Ž., & Mastilo, Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13-34. https://doi.org/10.2478/eoik-2022- 0004 Miškić, S., Stević, Ž., Tadić, S., Alkhayyat, A., & Krstić, M. (2023). Assessment of the LPI of the EU countries using MCDM model with an emphasis on the importance of criteria. World Review of Intermodal Transportation Research, 11(3), 258-279. https://doi.org/10.1504/WRITR.2023.132501
25. Oguz, S. (2023). 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. Journal of Management, Marketing and Logistics (JMML), 10(4), 167-178. http://doi.org/10.17261/Pressacademia.2023.1837
26. Pehlivan, P., Aslan, A. I., David, S., & Bacalum, S. (2024). Determination of logistics performance of G20 countries using quantitative decision-making techniques. Sustainability, 16(5), 1852. https://doi.org/10.3390/su16051852
27. Pelit, İ. (2023). Türkiye’nin lojistik performans endeksinin incelenmesi. Uluslararası Ekonomi ve Yenilik Dergisi, 9(1), 37-49. https://doi.org/10.20979/ueyd.1185216
28. Senir, G. (2021). Comparison of domestic logistics performances of Turkey and European Union countries in 2018 with an integrated model. LogForum, 17(2), 193-204. https://doi.org/10.17270/J.LOG.2021.576
29. Sezer, S., & Abasiz, T. (2017). The impact of logistics industry on economic growth: An application in OECD countries. Eurasian Journal of Social Sciences, 5(1), 11-23. https://doi.org/10.15604/ejss.2017.05.01.002
30. Stojanov, A., & Ugrinov, D. (2013). Multicriterial analisys of selection of coal with SAW and COPRAS methods. Zaštita Materijala, 54(4), 419-422. https://idk.org.rs/wp-content/uploads/2013/12/17ASTOJANOV.pdf
31. Tongzon, J. (2011). Liberalisation of logistics services: the case of ASEAN. International Journal of Logistics Research and Applications, 14(1), 11-34. https://doi.org/10.1080/13675567.2010.550871
32. Türkoğlu, M., & Duran, G. (2023). G20 ülkelerinin lojistik performanslarının CRITIC tabanlı GIA ve WASPAS uygulaması ile değerlendirilmesi. Hukuk ve İktisat Araştırmaları Dergisi, 15(1), 50-72. https://doi.org/10.53881/hiad.1247196
33. Ulutaş, A., & Karaköy, Ç. (2019a). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5(4), 49-69. https://doi.org/10.18559/ebr.2019.4.3
34. Ulutaş, A., & Karaköy, Ç. (2019b). G-20 ülkelerinin lojistik performans endeksinin çok kriterli karar verme modeli ile ölçümü. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 71-84. http://esjournal.cumhuriyet.edu.tr/tr/download/article-file/866939
35. Yaşar Dinçer, F. C. (2021). 2007-2018 lojistik performans endekslerinde başat aktör olan Almanya’nın lojistik potansiyeli ve stratejilerinin incelenmesi. Third Sector Social Economic Review, 56(2), 1190-1209. https://doi.org/10.15659/3.sektor-sosyal-ekonomi.21.06.1444
36. Yu, M. M., & Rakshit, I. (2025). An alternative assessment approach to global logistics performance evaluation: Common weight H‐DEA approach. International Transactions in Operational Research, 2, 839-862. https://doi.org/10.1111/itor.13360
37. Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Tamošaitiene, J. (2008). Selection of the effective dwelling house walls by applying attributes values determined at intervals. Journal of Civil Engineering and Management, 14(2), 85-93. https://doi.org/10.3846/1392-3730.2008.14.3
38. Zavadskas, E.K., Kaklauskas, A., & Vilutiene, T. (2009). Multicriteria evaluation of apartment blocks maintenance contractors: Lithuanian case study. International Journal of Strategic Property Management, 13(4), 319-338. https://doi.org/10.3846/1648-715X.2009.13.319-338