These slides were presented at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

Digital Twins (DTs), empowered by simulation, and Artificial Intelligence (AI), revolutionize product life cycle management by predicting system behavior, leveraging operational data, and informing strategic decision-making. This paper explores how these technologies synergize to deliver transformative benefits across all life cycle stages, ensuring substantial returns on technological investments. In product development, AI-augmented digital twins enable comprehensive design exploration, evaluating system and sub-system interactions to accelerate optimization and ensure technical requirements are met efficiently. For operational systems, physics- and AI-driven DTs monitor real-world conditions, offering actionable intelligence to enhance overall equipment effectiveness, minimize maintenance costs, and improve operational efficiency. Through practical examples and customer-driven case studies, this work highlights how leading organizations from different industry sectors deploy DTs to inform strategy and push the boundaries of product design and innovation. We will share when and why companies in Heavy Equipment, Industrial Machinery, and Automotive do invest in Digital Twins, offer insights into the challenges faced during implementation and how they were overcome. The specific use cases presented will answer the following questions: ? How to increase efficiency of an excavator by +20% thanks to holistically optimizing the bucket shape within two working days? ? How to reduce production waste by -15% through improved process capabilities in sheet metal forming? ? How an operational Digital Twin for damage evaluation processing sensor data in real-time helps to switch from time-based to load dependent maintenance of a vehicle fleet leading to savings of in a seven digit range? A focus is placed on the core technologies'?simulation, AI, and data analytics'?that underpin the realization of valuable DTs. Thereby we'll address the role of different fidelity levels of physics-based simulations, the need for reduced-order modeling to ensure real-time capable models and how AI is helping to close the feedback loop between product design and operation. By leveraging these convergent technologies, DTs provide robust frameworks for design optimization, real-time monitoring, and effective control, driving unparalleled improvements in efficiency and sustainability. This paper serves as a roadmap for leveraging DTs to maximize product performance and operational success, showcasing how digital innovation redefines product and systems management in competitive industries.