Recently, the DeepSeek craze has swept the globe, sparking a new wave of AI enthusiasm among automotive industry enterprises. It has broken the traditional reliance on large models requiring massive computing power, accelerating the popularization and accessibility of AI technology. AI empowering the entire automotive value chain has become the core engine and a new strategic pivot driving the intelligent transformation of automobiles, helping car companies seize the initiative in this transformation. As we welcome the arrival of the AI-defined automotive era, we must also acknowledge the many real and potential challenges faced by AI applications.
In 2023, when ChatGPT reached 100 million users in just two months, the global industrial sector suddenly realized that AI (artificial intelligence) is no longer just a laboratory concept but a strategic lever to drive the real economy. Subsequently, in the vast arena of the automotive manufacturing industry, a transformation led by AI has quietly unfolded. From the Seres super factory in Chongqing to BMW's future mobility center in Sokolov, Czech Republic, and from Changan Automobile's "dark factory" to Tesla's global logistics network, AI technology is currently reshaping the underlying logic of the century-old automotive industry at an unexpected pace.
AI-Powered Manufacturing
In the Seres smart factory in Chongqing Liangjiang New Area, AGV transport vehicles shuttle along paths planned by the digital twin system, and robotic arms complete assembly tasks with millimeter precision guided by AI vision. This super factory, with an annual production capacity of 450,000 vehicles, has fully upgraded the four major processes of traditional automobile manufacturing—stamping, welding, painting, and final assembly—into a fully digitalized production system. Digital twin technology is the key underlying technology that enables the super factory. Its core control system utilizes AI technology to simulate over 2,000 production variables in real-time, rehearsing production processes in virtual space and significantly reducing equipment downtime.Taking stamping technology as an example, in the stamping workshop, AI robots can monitor parameters such as mold temperature and pressure in real time. They analyze and predict production data through deep learning algorithms, thus achieving precise control over stamped parts. Thanks to this, the precision of stamped parts on the production line has reached the micron level, significantly enhancing the quality and safety of the entire vehicle.
Changan Automobile's Nanjing factory showcases another possibility. By deploying an AI-driven flexible manufacturing system, the factory can switch production of different electric vehicle chassis models within 5 minutes. Additionally, the factory employs leading technological architecture to build a unified production digital platform and an "AI + digital twin" operating system. It can utilize AI to analyze parameters such as historical order data, supply chain status, and equipment wear curves to autonomously generate optimal production plans.
Behind the evolution of physical factories is the qualitative upgrade of the digital nervous system. The "smart brain" of Fast's new intelligent factory processes 12TB of production data daily. It uses time-series prediction models to forecast tool wear trends 72 hours in advance, maintaining the machining precision of transmission gears within ±3μm. More notably, its quality inspection system employs Generative Adversarial Networks (GAN), capable of simulating 2800 types of defect patterns, enhancing the generalization ability of the inspection model by 40%, reducing the defect rate of finished products to 0.12 ppm (parts per million).
"If industrial robots are the hands, AGV carts are the legs, and automated warehouses and transport tracks are the blood vessels, then the intelligent management system is the digital heart and brain of the factory. It can autonomously identify, judge, control, and command the entire factory's full-process scheduling." According to a relevant official, amidst the wave of digital and physical integration, Fast's "intelligent manufacturing" gears are accelerating: production efficiency has increased by 72%, energy consumption has decreased by 14%, and product delivery cycles have been shortened by 20%. The high-intelligence new factory has been selected as one of the first "Digital Pilot" enterprises by the state, and its annual production and sales of heavy-duty vehicle transmissions have ranked first in the world for 19 consecutive years.
It is worth mentioning that the aforementioned factories can all achieve "dark factory" production. As the name implies, dark factory production is primarily carried out by intelligent robots or automated equipment following system instructions to handle production, storage, transportation, testing, and other processes, with almost no human intervention, and can operate as usual even in the dark. In such factories, automated production lines can be described as "perpetual motion machines," running tirelessly year-round...
These cases also reveal a profound transformation in the automotive manufacturing industry: automotive production lines are evolving from physical entities to "digital twins." The "Mega" platform released by NVIDIA at the 2025 CES conference is a microcosm of this transformation. This platform integrates NVIDIA's accelerated computing, artificial intelligence, Isaac robot platform, and Omniverse virtual world technology. By creating digital twins, enterprises can simulate the behavior of robots and equipment in a virtual environment, not only optimizing operations but also enabling real-time monitoring of robots' performance in complex environments.This digital solution not only improves production efficiency but also significantly enhances the flexibility and safety of facility operations. With the gradual rollout of the "Mega" platform, more and more enterprises will be able to leverage digital twin technology to simulate and optimize the operation of their physical facilities.
AI Logistics System Upgrade
Not only in the production and manufacturing stages, but with the widespread adoption of AI technology, the logistics systems in the automotive industry chain are also undergoing digital twin evolution.
This evolution presents three significant characteristics: firstly, "predictive logistics," where Tesla's AI system at the Shanghai factory can forecast North American battery supply fluctuations 8 weeks in advance and automatically adjust global procurement strategies; secondly, "dynamic routing optimization," where BYD's logistics network reduces transportation costs by 18% through real-time analysis of over 300 parameters including weather, traffic, and energy prices; finally, "unmanned closed-loop," where the Xiaopeng Zhaoqing base has achieved full-process unmanned delivery from parts warehousing to complete vehicle shipping, with the AGV cluster's path planning efficiency improving by 5% each quarter through autonomous evolution algorithms. In this regard, JD Logistics can be considered an industry model. As introduced, as early as 2019, JD established the first 5G intelligent logistics demonstration park in China and proposed the "Supply Chain Industry Platform (OPDS)," providing integrated supply chain services for enterprises based on industries with different attributes. In its solutions, through the integration of millimeter-wave radar and visual SLAM for positioning, resource optimization and scheduling can be conducted promptly, enhancing the AGV cluster's coordination efficiency to three times that of traditional systems. Additionally, it can intelligently identify vehicles and guide trucks to the system-recommended docks for operations, helping automotive companies further reduce material turnover time.
In terms of warehouse management, AI technology also plays a crucial role. By applying computer vision and machine learning technologies, AI systems can automatically identify the type, quantity, and location of goods, and achieve automated storage and inventory management.This not only significantly enhances the efficiency and accuracy of warehouse management but also effectively reduces labor costs and error rates.
Take the Dongfeng Lantu Wuhan Smart Logistics Center as an example. Through AI algorithms, the turnover rate of parts inventory can be increased by 40%, warehouse space utilization can be improved by 35%, and logistics costs can be reduced by more than 2 billion yuan annually. In addition to Lantu Auto, globally renowned Tier 1 enterprises such as Bosch and Continental are also actively exploring the application of AI technology in the supply chain.
For instance, at the industrial collaboration level, the AI supply chain hub established by CATL can connect in real-time with production data from 76 global raw material bases and 228 core suppliers. Using digital twin technology, it simulates supply strategies under different geopolitical scenarios. This intelligent supply chain network enabled CATL to successfully control battery pack cost fluctuations within ±3% during the severe lithium price volatility in 2024.
On the other side of the ocean, Continental Group uses AI to generate digital twins to test the collaborative capabilities of new suppliers in a virtual environment. This "digital-first" model can shorten the traditional six-month supplier introduction cycle to 45 days, greatly enhancing the competitiveness and responsiveness of the entire supply chain.
Industry experts indicate that the application prospects of AI technology in the automotive industry chain are broad. These companies have achieved real-time monitoring and optimization of transportation and warehousing through the establishment of intelligent logistics management systems. As technology continues to develop and mature, AI technology will play an increasingly important role in the automotive field, driving the automotive industry chain towards more efficient, smarter, and greener development.
Reconstructing the Entire Automotive Value Chain
Simultaneously, this AI-driven transformation is reshaping value creation in the automotive industry. In research and development, Geely Research Institute's AI fluid dynamics platform has developed a self-evolving simulation model using reinforcement learning algorithms. During the development of the Galaxy E8 model, this system completed wind tunnel experiment iterations in just 72 hours, a process that typically takes three months, ultimately reducing the vehicle's drag coefficient to 0.199 Cd, setting a new world record for mass-produced electric sedans. Meanwhile, Toyota Research Institute has applied design tools to the development of the latest generation of hydrogen fuel cell vehicles. It is reported that through parametric modeling, AI can generate topology-optimized structures unimaginable by traditional methods, ultimately increasing the volumetric energy density of the fuel cell stack by 42% while reducing weight by 15%.
At the same time, AI is also fostering a new model of "manufacturing as a service"—currently, the Changan UNI series models allow users to participate in design through an AI interface, with the system automatically generating manufacturable solutions. This model has reduced the delivery cycle of personalized orders from 45 days to 72 hours, significantly shortening the R&D cycle.
In the product validation phase, this transformation is equally profound. Traditionally, automotive simulation testing required a significant amount of time and resources and struggled to cover all possible scenarios. However, with the aid of multi-modal AI technology, automotive companies can establish more accurate and efficient simulation models, helping them quickly identify "corner cases" and predict and analyze vehicle performance under various conditions. This approach not only greatly shortens the simulation testing time but also further enhances the accuracy and reliability of autonomous driving system test results.Taking XPeng Motors as an example, its XNGP system employs a multimodal fusion simulation engine for validation, capable of automatically generating test scenarios that include extreme weather and complex road conditions. More disruptively, AI has subtly influenced the profit model of automotive companies—each intelligent connected vehicle is both a data producer and a participant in algorithm evolution. Tesla's Dojo supercomputer cluster processes over 160 million miles of real driving data daily, and through deep neural network training, the neural network of the Autopilot system undergoes a full update every 72 hours. This continuous evolution capability breaks the traditional fixed model of automotive products being set upon delivery. As of now, Autopilot has become a significant profit source for Tesla. These innovations are also reconstructing the value chain of "product lifecycle services," transforming traditional one-time transactions into ongoing value creation processes.
When Karl Friedrich Benz obtained the world's first automobile invention patent at the end of the 19th century, he might not have imagined that over a century later, AI technology would make car manufacturing as precise as weaving code. The uniqueness of the current transformation lies in the fact that it is not a simple automation upgrade, but a qualitative change in "manufacturing intelligence" achieved through AI.
Post time: Jun-04-2025