Winning in a World of Complexity
Why Digital Twins are a Must Have for Machine Builders

Increasing system complexity and fierce global competition are forcing industrial machine builders to rethink how they design, build, and deliver machines. Fragmented engineering processes, outdated tools, limited design validation, and data quality issues can raise development costs and slow time to market.

This whitepaper from ABI Research examines how digital twins can help machine builders address these challenges through end-to-end lifecycle support, high-fidelity machine modeling, virtual testing, and cross-disciplinary integration. It also explores the growing role of AI, the capabilities that differentiate digital twin solutions, and the implementation practices needed to maximize ROI.

Topics Covered

• Industry Challenges: Fragmented engineering workflows, outdated tools, limited validation capabilities, and competitive pressure
• Digital Twin Capabilities: Lifecycle support, high-fidelity modeling, virtual testing, faster delivery, and cross-disciplinary integration
• AI-Enhanced Digital Twins: Synthetic data, what-if scenario modeling, Sim2Real validation, and continuous machine improvement
• Integrated Engineering Ecosystems: Connecting design, simulation, lifecycle data, automation, and operational systems
• Real-World Outcomes: A machine builder case study covering improvements in engineering changes, information consistency, BOM synchronization, and product structure management
• Implementation and ROI: Data standardization, commercial models, frontline involvement, unified toolchains, and strategic AI integration

Download the whitepaper to explore how digital twins can help industrial machine builders manage complexity, improve engineering and validation processes, and build a foundation for faster, more scalable machine development.