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

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.

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Publication Details

Journal
Journal of Turkish Operations Management
Published
2024-12-31
DOI
https://doi.org/10.56554/jtom.1471209
Citations
3
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
1.47
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article

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

Hasan Kürşat Güleş, Emel Gelmez, Muammer Zerenler
3 citations
Journal of Turkish Operations Management
Multi-Criteria Decision Making
1.47
article

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

Hasan Kürşat Güleş, Emel Gelmez, Muammer Zerenler
article en
3 citations

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.

Journal of Turkish Operations ManagementVol. 8(2)
Selçuk University (TR)
Openalex Percentile: Top 15%
Multi-Criteria Decision Making
1.47
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