In many ways, the modern automotive industry is only possible thanks to automation. Amazingly, automation technology—which, by some measures, has been around for more than a century, starting with the introduction of the production assembly line—continues to evolve today. Artificial intelligence, or AI, is one of the key technologies behind this evolution, providing benefits on a much greater scale in many of the same areas that that first assembly line did:
- Increased efficiency
- Higher quality and accuracy
- Safety improvements
- Higher-value tasks for workers
Of course, there are also some areas where AI is making a positive impact that those earliest phases of automation couldn’t have touched, such as the supply chain, remote collaboration and more. AI also addresses emerging challenges in EV production, including battery manufacturing, thermal management and safety validation. For the auto industry, the question is no longer whether or not to use AI, but rather where to apply it, so it delivers the most value in terms of uptime, quality and production efficiency. This means understanding what AI can do as well as the manufacturer’s specific needs when it comes to bottlenecks, equipment criticality, workforce capabilities, data quality, and failure risk.
The evolution of AI in auto manufacturing
Because automation is such an inextricable part of auto manufacturing, the industry was quick to adopt even the earliest implementations of AI technology, at its most rudimentary level. As digital transformation in the automotive industry began to gain a foothold, AI started providing benefits like simple sorting of parts and pieces with vision systems, basic advances in data and analytics for equipment performance assessments, and enhancements for maintenance scheduling and implementation.
Like many digital technologies, AI has made exponential advances and is now used for an increasing number of applications, with the ways in which it can support automotive manufacturing continuing to grow. AI in automotive manufacturing is now an integral part of everyday operations, boosting productivity and effectiveness in production, maintenance, R&D and other areas. Now more than isolated automation support, AI is providing real-time decision support thanks to its growing connections to equipment, sensor data, machine vision, robotics, and predictive maintenance. Here’s a quick overview of how auto manufacturing has transformed through various technological advancements:
Stage | Automotive technology focus | Operational impact |
Early automation | Assembly lines and repetitive tasks | Higher production speed |
Digital transformation | Sensors, data and connected systems | Better visibility |
AI-enabled manufacturing | Analytics, prediction and adaptive systems | Smarter decisions and improved uptime |
Ways AI is changing auto manufacturing
With the continued expansion and development of AI, car manufacturing as a whole is changing, becoming more productive and efficient, while workers make increasing use of technology to complete high-value tasks. Artificial intelligence in automotive manufacturing is making a difference in:
- Equipment monitoring: As industrial sensors become more affordable and accessible, manufacturers now have access to vast stores of data that can help make significant improvements in maintenance effectiveness. AI can help to make sense of this data, supporting the benchmarking and decision-making that drive highly efficient maintenance practices such as predictive maintenance. By introducing artificial intelligence into equipment monitoring, automotive companies can vastly reduce or even eliminate unplanned downtime, while also enabling more targeted maintenance planning and scheduling.
- Robot and human collaboration: Robot automation has long played a role in automotive manufacturing, performing the most undesirable tasks—those identified as dull, dirty and dangerous. While that early form of automation provided raw speed and efficiency benefits, it also introduced new caveats and roadblocks into the process: Automated and manual processes needed to be completely separated, workers could not safely work near automation equipment and complex processes continued to be performed completely by hand. AI advances have facilitated the development of collaborative robots, or cobots—automation equipment that is designed to work directly alongside human workers, creating efficiencies in the highest value, most complex tasks, improving safety, and fostering increased integration between automated and manual processes.
- Improved production quality: From the production line to the QC staging area, artificial intelligence is helping to improve the output quality of automotive production equipment. AI can help to guide and adjust production equipment in real-time to maintain close tolerances and high accuracy standards. It also can drive machine vision systems that detect surface defects, missing components, alignment issues, and tolerance problems that could indicate maintenance needs. It also uses historical QC data to detect potential production anomalies.
- Increased productivity: AI has the ability to create efficiency improvements in every area of manufacturing, even those that previously had not been able to benefit from automation. By taking advantage of incremental efficiency opportunities, AI can drive increased productivity and reduced costs at a large scale.
- Optimized supply chains: Applying AI to supply chain and inventory processes can help take major steps towards optimal efficiency in these areas. AI can incorporate macro data such as global economic trends, weather forecasting and geopolitical factors with more granular data such as inventory needs, spare part usage and maintenance trends to drive more targeted and effective supply chain decision-making.
AI use cases in automotive manufacturing
To use a hypothetical example of how AI fits into the automotive manufacturing workflow, consider a supplier experiencing repeated downtime on a robotic welding cell. Using the data collected from vibration and current sensors, AI analyzes the information to detect any abnormal patterns. Comparing that to historical failures, the system can trigger alerts whenever it detects the emergence of fault conditions. This gives teams a chance to schedule necessary maintenance before failure can occur. Here are some other examples of where AI can deliver value for automakers:
AI use case | Best for | Operational impact |
Predictive maintenance | Critical production assets | Less unplanned downtime |
Machine vision | Quality inspection | Fewer defects and less rework |
Cobots | Repetitive or ergonomic tasks | Better safety and efficiency |
Process optimization | High-volume production lines | Higher throughput |
Supply chain analytics | Inventory and supplier planning | Better resilience |
Digital twins | Simulation and testing | Lower-risk decision-making |
Energy optimization | High-energy production environments | Reduced operating costs |
AI in EV manufacturing
Electric vehicles and hybrids are growing in market share, and their complex nature means automakers are leaning on AI to help them maintain high levels of production. AI systems can support aspects of EV manufacturing such as:
- Battery cell quality inspection
- Thermal management validation
- Automated assembly
- Robotics optimization
- Safety testing
- Predictive maintenance on battery production equipment
This is especially helpful for maintenance teams, who face steep learning curves due to new equipment required for EV manufacturing. These high-value assets depend on predictive maintenance to keep them online for as long as possible, something AI-driven analytics makes possible.
How automotive facilities are investing in AI
Automotive manufacturing facilities continue to be at the forefront of automation by investing in AI for areas like:
- Using AI for the most important projects: In an economic landscape where productivity and ROI are of paramount importance, it makes sense to focus AI technology on the highest value, most critical needs, not those that are only of incidental importance. Taking this vanguard approach can lead to unprecedented advances in innovation, productivity and efficiency, ultimately helping to prove the value of AI investment.
- Working with AI development partners: AI in manufacturing demands a host of new skill sets, including many that may not have previously been prominent in the automotive sector. Working with development partners can help manufacturers ease the burden of hiring and training while ensuring a high level of quality in AI implementations.
- Utilizing industry knowledge to “feed” AI: While in-house industrial sensors and monitoring devices are able to provide a wealth of information for AI to “learn” from and act upon, this technology can become even more effective with the support of industry expertise. This is an area in which the synergy of people and technology can illustrate its full potential, combining the power of data analytics with the irreplaceable benefits of lived experience in the field.
- Storing data and digital twins: As automotive AI modules collect more data, they become increasingly efficient and effective, eventually facilitating the use of technology such as digital twins: virtual representations of real-world products and processes. Digital twins are making a positive impact in R&D, automotive industrial maintenance, collaboration, innovation, training and more, enabling engineers and technicians to accurately gauge the effectiveness of new concepts and techniques with little to no risk.
How ATS supports AI-enabled automotive manufacturing
AI can improve manufacturing processes in the automotive industry by supporting predictive maintenance, quality control, robotics, productivity, and supply chain decision-making. AI brings the most value when the insights it delivers are connected to maintenance execution and continuous improvement.
As leaders in industrial reliability, ATS can evaluate your automotive manufacturing operation by identifying where downtime, quality issues, labor constraints, or production variability are creating the greatest business impact. We can then recommend strategies that will drive measurable improvements in uptime, quality and efficiency. Get in touch with us today to learn more.
