DIGITAL TWINS AND ENTERPRISE ARCHITECTURE: A FRAMEWORK FOR REAL-TIME MANUFACTURING DECISION SUPPORT

Abstract

Digital twin technology represents a transformative approach to manufacturing process optimization, yet its integration with enterprise architecture for real-time decision support remains a significant challenge. This article presents a comprehensive framework for implementing digital twins in smart manufacturing environments, with particular emphasis on real-time data processing and enterprise system integration. This article implements systems at major automotive and aerospace manufacturers; this article demonstrates how digital twins can effectively process massive IoT sensor streams while maintaining synchronization with physical processes. This article establishes a scalable architecture that achieves sub-second latency in predictive analytics while seamlessly integrating with existing ERP and MES systems. This article proposes a framework that results in a reduction in maintenance costs and an improvement in product quality across case study implementations. This article outlines key architectural patterns for handling sensor data streams, real-time analytics processing, and enterprise system integration, providing a blueprint for organizations transitioning toward data-driven manufacturing optimization. It also suggests that successful digital twin implementations require a carefully orchestrated approach to data architecture, system integration, and process synchronization.