Siemens, the global industrial manufacturing and technology conglomerate, faced mounting pressure to improve manufacturing efficiency across its diverse portfolio spanning medical equipment, industrial automation, and energy systems. Traditional manufacturing optimisation relied on periodic process audits and reactive maintenance, resulting in unplanned downtime, quality variations, and long product development cycles. The company's industrial customers operate facilities encompassing thousands of interconnected machines whose interactions create emergent behaviours that defy simplistic modelling.
Creating accurate digital representations of these complex systems required reconciling CAD geometry, physics-based simulations, and real-time operational telemetry into coherent virtual replicas. The diversity of industrial protocols — from PROFINET to OPC-UA — complicated data ingestion from brownfield installations, and customers expected digital twins to deliver measurable ROI within months rather than years. Maintaining digital-twin fidelity across equipment lifecycles spanning twenty-five years, during which physical assets underwent component replacements and firmware upgrades, added further complexity to any comprehensive digital twin approach.
Siemens deployed its Xcelerator platform with comprehensive AI-powered digital twin systems that create virtual replicas of physical manufacturing processes. The platform integrates data from thousands of sensors across production lines, using machine learning to identify patterns, predict equipment failures, and recommend process adjustments. Reduced-order modelling techniques distil high-fidelity physics simulations into computationally efficient surrogate models capable of real-time execution on standard industrial computing hardware.
Automated data connectors supporting over fifty industrial protocols streamline ingestion from heterogeneous brownfield equipment, reducing integration timelines from months to weeks. Machine-learning calibration layers continuously reconcile digital-twin predictions with actual sensor measurements. Engineers can use virtual environments to test production changes, optimise workflows, and design new products entirely in simulation before physical implementation — with early simulation adoption reducing development time by 50 to 80 percent.
At CES 2026, CEO Roland Busch introduced Digital Twin Composer, a new way for companies to design, simulate, and optimise factories digitally before breaking ground. Siemens' Senseye Predictive Maintenance uses Armv9-based AI-powered sensors to continuously monitor vibration patterns, temperature fluctuations, and energy draw, automatically adjusting machine parameters when anomalies are detected.
This is an industry case study based on publicly available information. Siemens is not a Pertama Partners client.
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