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Research on Enhancing ERP System Efficiency Through AI in Cross-border Supply Chain Environments
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Abstract
This study investigates how artificial intelligence (AI) technologies can significantly enhance the operational efficiency of Enterprise Resource Planning (ERP) systems within cross-border supply chain environments. By addressing systemic challenges such as fragmented workflows, heterogeneous data structures, and latency in decision execution, the research applies a multi-layered approach that integrates sequence prediction algorithms, intelligent analytics pipelines, and rule-based decision frameworks. These AI components are embedded as modular services to support real-time order management, inventory optimization, and logistics coordination. A case implementation in a multinational e-commerce platform demonstrates measurable improvements in key performance metrics, including processing latency, inventory response times, and decision automation coverage. Controlled trials over a 12-week period yielded statistically significant results across multiple regions. The findings suggest that incorporating pluggable AI modules—combined with robust master data governance—enables ERP systems to adapt dynamically to complex and volatile global operations. This study also discusses the technical prerequisites and architectural principles necessary for scalable, AI-enhanced ERP deployments.
Keywords
Artificial Intelligence; Enterprise Resource Planning; Cross-Border Supply Chain; System Efficiency; Intelligent Optimization
References
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How to cite this paper
Research on Enhancing ERP System Efficiency Through AI in Cross-border Supply Chain Environments
How to cite this paper: Yanchun Wang. (2025) Research on Enhancing ERP System Efficiency Through AI in Cross-border Supply Chain Environments. Advances in Computer and Communication, 6(5), 268-273.
DOI: http://dx.doi.org/10.26855/acc.2025.12.002
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