Leveraging Generative AI to Build Custom Engineering Apps in PLM Ecosystems

Abstract

PLM (Product Lifecycle Management) systems customarily represent a highly rigid, monolithic Systems-of-Record for engineering and product-related data. The emergence of Generative AI-powered Low-Code/No-Code development platforms presents a transformative opportunity to evolve these legacy architectures into adaptive Platforms of Apps specifically designed for Digital Twin implementations. This architectural transition enables domain experts—including mechanical engineers, quality specialists, and production managers—to create purpose-built micro-applications such as geometric dimensioning and tolerancing validation tools, first article inspection apps, corrective action tracking systems, Overall Equipment Effectiveness dashboards, and predictive maintenance solutions without requiring formal programming expertise. The Platform of Apps model separates core data management functions from application-layer innovations, establishing PLM as a foundational services infrastructure upon which diverse engineering applications can be rapidly developed and deployed. By integrating real-time data synchronization, machine learning-based predictive analytics, and prescriptive maintenance recommendations, organizations can transform passive data repositories into active optimization engines. This paradigm shift addresses critical integration challenges in traditional PLM deployments while establishing technical foundations for comprehensive Digital Twin architectures in regulated manufacturing environments.