Enhancing U.S. Economic and Supply Chain Resilience Through Ai-Powered Erp and Scm System Integration

Abstract

The U.S. companies and government suppliers witness the ongoing shocks, including geopolitical conflicts and the natural disasters, which indicate weak interdependencies in the areas of planning, procurement, production, and logistics. This article focuses on the degree to which the deep integration of the enterprise resource planning (ERP) and supply-chain management (SCM) systems with the artificial intelligence (AI) solutions can significantly improve economic and supply-chain resilience. It is suggested that a reference framework can be developed integrating master data, transactional signals and external risk indicators into common, real-time pipelines which enable demand sensing, probabilistic forecasting, dynamically tuned safety-stock, supplier-risk scoring and adaptive production and transportation planning. The aspects of selection of models (gradient-boosting, long short-term memory networks, transformers, graph-based learning), human-in-the-loop exception management and multi-layered decision processes that maximize service levels under disruption and control working capital are discussed. A resilience-adjusted ratio of return-on-investment (ROI) is proposed to measure benefits that go beyond the traditional cost savings and include improvements in lead-time variance, backorders, expediting costs, and on-time-in-full (OTIF) performance and recovery of the revenue at risk. Introduction directions include interoperability standards (EDI/APIs), data management, change management and cybersecurity control that best suit regulated U.S. settings. Examples of mini-scenarios that use semiconductors, pharmaceuticals, and food/agriculture involve the AI-mediated ERP-SCM coordination that reduces the time-to-decision sensing, and balances the throughput under the threat of an upstream or logistics shock. It is shown in the analysis that organizations with high-quality data bases and modular AI services, strong machine-learn operations, and cross-functional governance are capable of achieving long-term resilience benefits which result in macro-level stability by means of increasing employment, service reliability, and faster recovery to disruption.