Core Viewpoint - The article discusses the evolution of collaboration between departments within the company, emphasizing the transition from isolated data handling to a shared data language and co-creation, ultimately leading to a more efficient and integrated approach to problem-solving and product development [4][5][10]. Group 1: Challenges of Departmental Silos - Departmental silos create barriers that hinder effective communication and collaboration, leading to conflicts in objectives and a lack of a unified approach to problem-solving [3]. - The division of responsibilities among departments, while enhancing specialization, results in a fragmented view of issues, making it difficult to establish a cross-departmental mechanism for addressing problems [3]. Group 2: Initial Collaboration and Data Sharing - The initial collaboration between the Ideal Lianshan team and the thermal management team began with addressing poor cloud signal data quality, leading to the development of a common analytical framework [4]. - The shift from a "data request-result" model to a shared data language allowed both teams to engage in meaningful dialogue using the same data and metrics [4][5]. Group 3: Evolution of Collaborative Methods - The collaboration evolved from merely sharing data to co-creating solutions, focusing on common goals and fostering trust through transparency [5][6]. - The implementation of automated testing processes helped alleviate the burdens faced by engineers during extreme conditions, showcasing the practical benefits of this collaborative approach [5]. Group 4: Productization of Collaboration - Over three years, the company expanded its collaborative model to include supply chain and production line processes, developing AI-driven solutions to intercept quality issues at the source [9]. - The establishment of a standardized, replicable methodology for data science projects has transformed the collaboration into a sustainable and scalable productized approach [10]. Group 5: Achievements and Future Aspirations - The company has accumulated significant achievements, including 83 data science projects, 3545 warning models, and extensive monitoring capabilities across production lines and suppliers [10]. - The goal is to promote this collaborative model further, enabling seamless cooperation among individuals, AI, and across departments to address real business challenges [11].

Core Viewpoint - The article discusses the development and implementation of AI-Brain, an intelligent hardware solution by the Li Auto Lianshan team, aimed at enhancing production line efficiency and decision-making through AI integration [2][6]. Group 1: AI-Brain Overview - AI-Brain is a compact and cost-effective intelligent hardware that enables AI algorithms to perform real-time inference directly on the production line, enhancing the intelligence of manufacturing processes [2]. - The solution ensures data privacy and security by operating independently in a closed network environment [2]. - The platform has demonstrated significant performance in various business sectors of Li Auto, achieving over 90% accuracy and recall rates in after-sales service and 99% accuracy in core manufacturing processes [3][4]. Group 2: Challenges and Solutions - The Li Auto Lianshan team identified common pain points between component suppliers and vehicle manufacturers, leading to the decision to share their intelligent solutions with a broader range of enterprises [6]. - The team aims to create a collaborative AI product that allows engineers, regardless of their coding skills, to effectively utilize AI tools for problem-solving [7]. Group 3: AI-Brain Capabilities - AI-Brain addresses several industry challenges, including low efficiency and high costs of manual inspections, difficulty in knowledge transfer from experienced engineers, and lengthy decision-making processes due to inadequate data [8]. - It supports over 10 industrial communication protocols for seamless integration with production equipment, ensuring real-time data collection and analysis [9]. - The hardware boasts a processing power of up to 275 TOPS, enabling real-time execution of complex AI models for quality issue prediction [11]. Group 4: Market Engagement and Future Prospects - Currently, nearly 500 AI-Brain units are operational in Li Auto and its suppliers' production lines, with significant commercial partnerships established with 49 suppliers, including 5 in negotiation stages [15]. - The Li Auto Lianshan team aims to collaborate with more quality enterprises in the automotive industry to further explore AI technology applications in industrial settings [15].

Core Insights - The article highlights the rapid development of the smart electric vehicle (SEV) industry in Wujin District, Jiangsu Province, emphasizing its significance in the integration of automotive, artificial intelligence, and new energy technologies [1][2] Industry Development - Wujin District has been recognized as one of the first pilot areas for vehicle networking and smart connected vehicles in Jiangsu Province, with its new energy vehicle (NEV) parts industry cluster receiving provincial recognition [1] - The district has established a comprehensive industrial chain covering electric drive research and development, motor production, and vehicle manufacturing, with Li Auto as a leading player [2] Collaborative Ecosystem - The manufacturing facility of Li Auto in Wujin features advanced automation with thousands of robotic arms and a highly efficient assembly line, which is crucial for driving growth in upstream raw materials and downstream applications [3] - Wujin District has attracted 48 suppliers related to the SEV industry, with Li Auto achieving nearly 60% localization in its L series production [3] Innovation and Technology - Li Auto collaborates closely with suppliers to foster innovation, exemplified by the development of a five-in-one drive assembly for range-extended electric vehicles and advancements in autonomous driving technology [4] - The "Lianshan System" developed by Li Auto integrates digital and AI technologies to enhance manufacturing precision and quality control, ensuring real-time monitoring of production processes [6] Future Outlook - The "Lianshan Data Science Collaboration Platform" is set to empower suppliers by providing tools for industrial algorithm development, aiming to elevate the automotive industry in Jiangsu through data-driven manufacturing [7]