Knowledge Graph-Augmented Top-k Pattern Discovery for Intelligent Supply Chain Anomaly Detection and Decision Support Systems
Keywords:
supply chain management, knowledge graphs, top-k pattern mining, anomaly detection, decision support systems, data qualityAbstract
Contemporary supply chains generate vast streams of heterogeneous data from logistics, procurement, production, and market demand signals. Detecting anomalous patterns within these interconnected flows is essential for maintaining operational continuity, mitigating disruptions, and enabling resilient decision-making. Traditional anomaly detection systems often rely on statistical thresholds or isolated feature spaces that fail to capture the rich relational semantics across multi-tier supplier networks, material flows, and geospatial constraints. This paper presents a system-level framework that integrates knowledge graphs with top-k pattern discovery to enhance anomaly detection and decision support in intelligent supply chains. The architecture constructs a domain-specific knowledge graph embodying entity relationships, provenance metadata, and temporal dynamics, then applies top-k pattern mining algorithms guided by data quality-aware rule discovery to extract interpretable, high-utility patterns indicative of emerging anomalies. The integration of knowledge graphs allows contextual enrichment that transcends purely transactional data, linking disruptions to upstream causal factors such as weather events, geopolitical indicators, or supplier financial distress. We examine structural trade-offs between centralized and federated graph architectures, discuss governance mechanisms for ensuring fairness and transparency, and analyze sustainability implications of large-scale knowledge graph augmentations. Through a system-level lens, the paper offers forward-looking perspectives on how knowledge graph-augmented pattern discovery can transform supply chain intelligence from reactive alerting to proactive risk orchestration.
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