Digital logistics anomaly management systems (DLAMS): A bibliometric analysis and research framework

Abstract

Digital Logistics Anomaly Management Systems (DLAMS) have become a critical infrastructure in modern supply chain operations, enabling the real-time monitoring and management of anomalies through the integration of technologies such as the Internet of Things (IoT), machine learning, and big data analytics. This paper presents a structured and systematic bibliometric analysis to trace the evolution of DLAMS research by examining 89 Scopus-indexed articles published between 2009 and 2023. Utilizing VOSviewer and Publish or Perish tools, this study identified eight major thematic clusters, 142 topic interconnections, and the growing importance of intelligent systems in anomaly detection. The findings reveal a significant shift from traditional logistics monitoring to data-driven and technologically advanced frameworks, with recent attention to intrusion detection, medical logistics applications, and cross-disciplinary integration. The analysis also uncovered key research gaps, particularly in real-world implementation and sector-specific applications, while proposing a research framework that connects technical progress with broader academic and policy implications. This study contributes to mapping the intellectual landscape of DLAMS and shapes future research directions in digital logistics anomaly management.