AI-Driven Predictive Manufacturing and Smart Warehouse Optimization in Semiconductor Supply Chains

Abstract

Semiconductor supply chains carry a structural vulnerability that transactional ERP architectures cannot address on their own: the interval between an emerging operational problem and the moment that problem becomes visible in the execution system is long enough for significant damage to accumulate. A yield-degrading equipment drift event may traverse dozens of wafer lots before inline metrology flags it; a reservation contention crisis may be hours from materialising when the last lot that would have prevented it enters the finished goods queue. The Predictive Semiconductor Operations Framework (PSOF) proposed here resolves this vulnerability through a four-layer architecture that embeds predictive analytics inside the ERP data fabric rather than beside it. Equipment telemetry, lot genealogy, inventory state, and demand signals are consolidated in a unified pipeline that feeds Long Short-Term Memory (LSTM)-based failure prediction, gradient boosting yield correlation, and demand-driven replenishment models. Each predictive output translates directly into an ERP-executable action — a maintenance work order, a yield-risk inspection hold, an anticipatory lot reservation — without leaving the transactional boundary that semiconductor lot traceability requires. Deployment implications for multi-fab Oracle ERP environments are discussed, with attention to phased implementation and data quality prerequisites. The article advances the position that ERP-data primacy, rather than external data replication, is the architectural foundation that makes predictive manufacturing intelligence operationally durable in high-mix fab supply chains.