Deepening real-world scenarios to create replicable industrial value
Siemens' approach is to build AI on industrial knowledge, digital twins and real business processes, starting from specific pain points and then developing capabilities that can be reused across various scenarios. In the dairy sector, China Feihe uses Siemens gPROMS to build a digital twin of its spray-drying tower, predicting milk powder moisture in near real time and automatically optimizing operating parameters. In automotive new-material R&D, Kingfa Sci. & Tech. uses Siemens Simcenter Simsolid meshless simulation and PhysicsAI to turn historical simulation data into predictive capabilities that can be reused quickly, enabling new designs to be assessed in seconds. In consumer goods, Siemens and Procter & Gamble are advancing the deployment of an AI visual inspection solution across P&G's global production network, enabling real-time, full-coverage quality inspections while lines run at full speed.
From single-point breakthroughs to ecosystem co-creation: Opening up space for industrial AI at scale
Industrial AI cannot achieve large-scale application through the efforts of a single company, product or technology alone. Sustainable innovation comes from open collaboration. China's rich and diverse industrial scenarios provide distinctive ground for ecosystem co-creation. Through Xcelerator, its open digital business platform, Siemens connects AI infrastructure, models, industrial data and vertical industry partners, making proven industrial AI capabilities easier to develop, deploy and scale. At the same time, Siemens is opening up data and scenarios to provide a real-world testing ground for ecosystem innovation.
