AI-Infused Enterprise Data Architecture for Predictive Supply Chain Management

Abstract

The AI-Infused Enterprise Data Architecture for Predictive Supply Chain Management presents a unified framework integrating SAP HANA, cloud-native ecosystems, and AI/ML pipelines to enhance supply chain intelligence. The article addresses fragmented data architectures by establishing a three-tier structure embedding machine learning models for demand forecasting, vendor lead-time prediction, and material shortage detection directly within operational flows. A comprehensive implementation methodology details specialized algorithmic approaches across critical supply chain domains, including LSTM networks for demand patterns and reinforcement learning for inventory optimization. Empirical validation through manufacturing and logistics sector implementations demonstrates substantial operational improvements through enhanced forecast accuracy, reduced delivery variability, and optimized inventory positioning. The architectural blueprint provides a foundation for scalable, AI-driven supply chain ecosystems that align enterprise data governance with predictive capabilities to transform reactive supply networks into anticipatory systems capable of continuous adaptation amid global volatility.