The increase in international integration along with technological developments in the world is effective in increasing the diversity of communication and transportation means between countries. In addition, production and export volumes are also increasing thanks to the removal of obstacles to international trade and facilitating legislative practices. The dynamic structure of production and export growth causes the increase and intensification of logistics activities, further increasing the importance of logistics activities. From this point of view, this study analyzes the relationship between the logistics performance indicators of eight developed countries and their export rates. In the analysis, the decision tree method, which is one of the machine learning methods that has been developed differently from other data analysis methods and gives more stable and qualified results in the established models, has been used. In the decision tree analysis, the logistics performance index and export rate data obtained from eight developed countries between the years 2007 and 2022 were used. As a result of the analysis, it is seen that the efficiency of logistics processes significantly affects export performance. However, depending on the differentiation of the country’s trade and legislation structures, it is seen that the logistics performance indicators that affect exports differ in the countries that are the subject of the analysis.
Export Logistics Performance Decision Trees Machine Learning
Birincil Dil | İngilizce |
---|---|
Konular | Lojistik |
Bölüm | Araştırma Makalesi |
Yazarlar | |
Erken Görünüm Tarihi | 22 Mart 2024 |
Yayımlanma Tarihi | 17 Mayıs 2024 |
Gönderilme Tarihi | 1 Eylül 2023 |
Kabul Tarihi | 25 Aralık 2023 |
Yayımlandığı Sayı | Yıl 2024 |
The JTL is being published twice (in April and October of) a year, as an official international peer-reviewed journal of the School of Transportation and Logistics at Istanbul University.