Özet:

The supply chain performance of countries has a significant impact on the overall performance of countries. These indices primarily emphasized countries' standings, rankings, and improvement areas. Clustering countries based on a single index does not always yield the desired results. Using cluster analysis may help get critical information when many indicators are evaluated. The supply chain-connected indicators were chosen to be included in the research initially. In this study, three global indices were selected. We chose the Logistics Performance Index(LPI) to evaluate the logistics industry, which is essential in supply chain management. Logistics is one of the critical areas that affect and have also been affected by many fundamental indicators used to evaluate a country's performance. One critical indicator that globally measures the processes is the Logistics Performance Index. We included Environmental Performance Index(EPI) in the study to evaluate environmental policies that impact supply chain operations. The final index used in the study is the Global Competitiveness Index(GCI), which examines the competitiveness of countries with a heavy dependence on supply chain management performance. It is one of the crucial indications in evaluating a country's productivity. We used clustering analysis based on supply chain management-related indicators in the following phase. K-Means clustering algorithm was applied to the extracted data set. Python code is written to implement the K-Means clustering algorithm. In the final part of the study, differences between clusters and submitted research proposals ideas were discussed. This research proposes a three-step methodological framework for mining supply chain indicators derived from the LPI, GCI, and EPI indicators. The research aims to conclude from the analyses of the change in centers based on indicators, the variation based on datasets between clusters, and the grouping of countries based on any combination of the LPI, GCI, and EPI indicators .

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