Digital News

　　As technological innovation becomes increasingly integrated with industrial transformation, artificial intelligence (AI) is accelerating the building materials sector toward a more intelligent, greener, and higher-end development trajectory.

　　“Through digital transformation, enterprises in the building materials industry can basically achieve digitalization in research and development, integration of production and operations, and agility in customer service. This enhances decision-making efficiency, coordination capabilities, and service standards, thereby rapidly improving productivity and core competitiveness. Moreover, by deeply empowering the entire production process with AI technologies, transformative applications can be achieved across key stages, such as R&D and design, manufacturing, supply chain management, and sales and services, significantly enhancing the overall operational efficiency of enterprises.” Zhou Yuxian, Party Secretary and Chairman of China National Building Material Group, said at the recent special meeting on the “Collaborative Innovation and Development Strategy of Artificial Intelligence+Manufacturing Industry” under the Great Wall Engineering Conferences that, the Group is steadily refining its AI-integrated innovation system and actively exploring innovative pathways for deep synergy between AI and the real economy.

　　The Ministry of Industry and Information Technology recently launched a dedicated research program to deploy and promote the development of the AI industry and empower new industrialization. It has called for an acceleration in identifying application scenarios and upgrading the full spectrum of manufacturing processes with intelligent technologies, thus transforming production and management models. According to Xue Zhongmin, Standing Committee Member of the CPC Committee and Vice President of China National Building Material Company Limited, the “Xiaomiao” large industry model, developed by the Material Digital Intelligence Research Institute established with investments from affiliates of China National Building Material Group, has already demonstrated significant results in the cement industry. By integrating data models, mechanism models, business models, domain knowledge bases, large language models, and multimodal agent-based AI architectures, the model effectively supports business decision-making, enabling real-time closed-loop control in manufacturing and end-to-end optimization in operational decision-making. The model has achieved technological breakthroughs in three key areas: fusion of time-series data with industrial mechanisms, coordination across multimodal scenarios, and fault-tolerant decision-making. Notably, the cement formulation model has been deployed across 66 cement companies of the Group and is now expanding into other sectors, including new materials and energy.

　　Digital transformation continues to ignite innovation within the building materials sector. Xue explained that the “Xiaomiao” large industry model, with the cement sector as its “experimental field”, has developed into a mature engineering delivery solution after over two years of application. It has reduced the cost of cement formulation by more than 1% per ton, significantly enhancing cost-efficiency. In practice, the model has proven adaptable to complex conditions across production lines, with highly variable equipment and processes. A standardized, replicable, lightweight, and result-oriented implementation framework has been established. Specifically, the data governance cycle for a single factory can now be compressed to less than 14 days, model construction and deployment can be completed in under 7 days, and the average return on investment cycle is approximately one year. This fast and agile delivery capability, combined with a high cost-performance ratio, signifies that the industry foundation model has successfully bridged the “last mile” of AI application in manufacturing, offering a replicable reference for the digital transformation of the basic building materials industry.

　　The exploratory practice of industry foundation models is expected to profoundly reshape the sector. According to Liu Zhen, Director of the Material Digital Intelligence Research Institute, AI-driven transformation in the building materials industry manifests in three dimensions: first, at the factory level, it will bring about a profound shift in human-machine collaborative execution paradigms; second, at the regional company level, it will drive deep changes in centralized control and collaborative operations; and third, at the corporate group level, it will lead to transformations in operational models and decision-making optimization. The rapid iteration of industry foundation models compels building materials enterprises to continuously refine business processes and decision-making efficiency, while gradually accumulating standardized management systems, business workflows, data assets, and AI assets. Taking the cement industry as an example, the current foundation model encompasses over 200 scenario models, having preliminarily achieved full-chain optimization across procurement, production, and sales.

　　“In the future, we will rely on the industry foundation model to reconstruct the operational and management frameworks of building materials enterprises,” Liu Zhen said. In terms of the value chain for product lifecycle iteration, the focus will be on developing scenario-based applications such as intelligent product design, performance simulation, and process optimization. In the value chain for production operations, digital twin factories, production capacity planning, intelligent equipment control, and multimodal inspections will be key application scenarios. Within the enterprise management value chain, scenarios such as financial digital twins and production line resource coordination will be developed. Meanwhile, in the value chain for business execution, applications will include coordinated procurement and production, supply-demand balancing, and dynamic pricing. Ultimately, the goal is to achieve comprehensive enterprise-wide coverage of large industry model applications.