Exploring Data Value to Enhance Intelligent Manufacturing Efficiency

In the production workshop, 527 robots form an automated production line; in the painting workshop, an intelligent spraying system efficiently applies coatings; the production process is monitored in real-time with an intelligent scheduling system automatically adjusting the production pace; an AI visual inspection system helps improve accuracy and efficiency in detection. At the Beijing Xiangjie factory, technologies such as intelligent manufacturing and digital twins have further refined and enhanced the entire vehicle production process. In recent years, the process of digital transformation has injected new momentum into the high-quality development of the manufacturing industry through technological innovation and ecological reconstruction. In the automotive sector, with the continuous increase in the penetration of new energy vehicles, technologies such as industrial internet, big data, 5G, and artificial intelligence are assisting in restructuring the production system and driving the intelligence leap of products. Zhang Jinhua, president of the China Society of Automotive Engineers, believes the automotive industry is facing a transformation, with data becoming a core element. He emphasizes the need to drive technological innovation and cross-industry integration through data standardization, lightweighting, openness, and sharing. Yue Pengyu, general manager of BAIC Technology Service, points out that traditional automotive enterprises have independent and closed business systems, making it difficult to integrate data, leading to low governance efficiency and insufficient value extraction. Additionally, the supply chain is relatively lengthy and complex, causing information to be easily fragmented between upstream and downstream. To enhance data empowerment efficiency, he suggests building a unified data platform to achieve comprehensive data access, inventorying data asset catalogs, standardizing data management, and continuously mining data gold mines. "We are exploring ways to activate frontline innovation through a software application platform, allowing non-technical personnel to combine templates with custom processes to autonomously build applications, digitizing approval, repair, inspection, and other processes to shorten decision chains. At the same time, as a lightweight data collection entry point, we aim to form a closed loop of 'business digitalization - data feedback into business,' assisting grassroots transformation from 'experience-driven' to 'data-driven,'" Yue described. Currently, artificial intelligence technology is driving the automotive industry's digital transformation into a deeper phase. Practical optimization for reasoning and training scenarios, collaboration between cloud and edge, and rapid empowerment of AI applications are creating new requirements for intelligent computing infrastructure and AI algorithm platforms, with related technologies and models gradually becoming new market drivers. Ju Peiwen, a senior automotive industry consultant at Fanruan, asserts that true digitalization is not about technology piling but about converting industrial knowledge into reusable data intelligence assets. Through AI large models and inference technology, the industry is exploring how to translate production data into decision-making bases, enhancing quality control capabilities and development efficiency. "The application of AI technology requires enterprises to strengthen talent training and growth during the digital transformation process. With technology support, companies need composite talents who 'understand both business and data,'" Yue emphasized.