Transactions on Transport Sciences 2026, 17(2)
From Performance to Policy: A Cluster Analysis Using Logistics Performance Index Data
- Department of Industrial Engineering, Diponegoro University, Semarang 50275, Indonesia
Logistics performance plays a vital role in facilitating international trade and strengthening economic competitiveness by enabling efficient supply chain operations. The Logistics Performance Index (LPI), developed by the World Bank, evaluates countries based on six dimensions—customs, infrastructure, international shipments, logistics competence, tracking and tracing, and timeliness. Despite its extensive use, prior studies have focused primarily on ranking countries rather than identifying performance patterns and relationships within the dataset. With the increasing complexity of global trade, there is a need for advanced analytical approaches to explore insights beyond rankings. This study addresses this gap by applying clustering analysis techniques to the recent 2023 LPI dataset to classify countries based on logistics performance. The aim is to evaluate logistics performance clusters, highlight disparities, and provide policy recommendations. Three clustering algorithms (i.e., k-means, k-medoids, and CLARA) were applied to group countries based on LPI dimensions. Silhouette Index, Dunn Index, and Davies-Bouldin Index validated cluster quality. Countries were then classified into three clusters: high, moderate, and low performers. High performers exhibited advanced logistics systems, while low performers faced challenges in infrastructure and customs efficiency. The analysis effectively reveals logistics performance patterns, emphasizing the need for targeted policies to address inefficiencies in low-performing countries through investments in infrastructure, digitalization, and customs reforms.
Keywords: Clustering; data mining; k-means; logistics performance index
Published: November 15, 2026 Show citation
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