The automotive industry is living through one of the most decisive moments in its recent history.
The Center for Automotive Research, known as CAR, and Rockwell Automation just released a joint report that lays out exactly what is already happening inside factories and what is still to come. The document, titled Smart Manufacturing in the Automotive Industry: Deployment and Impact, brings concrete data on how artificial intelligence and advanced automation are changing the way cars, tires, and batteries are produced, and why this transformation is picking up speed right now. 🚗⚙️
It is no exaggeration to say the sector is entering a new phase. The pressures are real: increasingly complex production, persistent warranty-related challenges, rising costs, intense global competition, and a growing difficulty finding skilled labor in tight job markets. And it is precisely in this scenario that technologies like machine learning and AI have gone from being a promise to becoming part of everyday operations. In the sections below, you will understand what the report reveals, where automation has already landed, where it still needs to advance, and which numbers show the real impact on the factory floor. 👇
What the CAR and Rockwell Automation report reveals
The study was developed based on proprietary data drawn from Rockwell Automation’s 11th Annual State of Smart Manufacturing Report. The result is a detailed overview of the current stage of smart manufacturing within the automotive supply chain, including the vehicle, tire, and battery sectors, with implications that extend across the global market. The research shows that industrial automation, machine learning, and AI are reshaping operations, enabling companies to manage increasingly complex production environments.
One of the central takeaways from the document is that the pressure for efficiency has never been higher. Manufacturers are simultaneously dealing with the transition to electric vehicles, persistent warranty-related pressures, rising costs, and global competition. In this context, artificial intelligence appears not as an optional resource but as a structural response to problems that the traditional production model simply cannot solve with the same speed and precision.
Receive the best innovation content in your email.
All the news, tips, trends, and resources you're looking for, delivered to your inbox.
By subscribing to the newsletter, you agree to receive communications from Método Viral. We are committed to always protecting and respecting your privacy.
The report also highlights that these technologies are supporting reshoring efforts, meaning the return of production closer to consumer markets. This is happening because automation and AI make operations more cost-competitive, even in labor markets where finding qualified professionals is a constant challenge. Companies that move faster along this journey are beginning to see measurable advantages, according to the study. And this difference goes beyond numbers: it translates into the ability to fulfill orders, maintain contracts with automakers, and survive in a market that does not wait around anymore.
As Edgar Faler, senior mobility analyst and strategy lead at CAR, puts it, what is changing now is how manufacturers use AI and data to manage growing complexity, improve decision-making, and create competitive advantage. Faler adds that companies moving faster are already reaping concrete benefits. 🤖
Where automation has already arrived and where it still needs to advance
Inside the most modern automotive factories, automation is already a well-established reality in stages like welding, painting, and body assembly. According to the report, automakers and suppliers already operate with highly automated processes in these areas, which historically were the first to receive heavy investments in industrial robotics. This does not mean workers have been replaced, but rather that their role has shifted, moving toward supervision, adjustments, and decisions that require contextual judgment.
On the other hand, the industry’s focus is now shifting to areas that have historically been harder to automate. These include electronic component assembly, validation processes, production coordination, and logistics. These are precisely the points where major opportunities for improvement still exist and where data integration can make all the difference. Solving these bottlenecks is essential for smart manufacturing to stop being partial and become truly systemic.
Within existing operations, AI and machine learning are being applied to optimize predictive maintenance, improve inspection accuracy, and increase system processing capacity, known as throughput. This layer of intelligence on top of processes that were already automated is what allows even more value to be extracted from production lines without necessarily replacing all existing machinery.
Battery production for electric vehicles is a chapter of its own in this story. Because it is a relatively new area within the traditional automotive supply chain, facilities are being built with a digital-first mindset from the start. This creates an interesting advantage: these factories tend to be born with sensors at critical points, real-time data analytics, and architectures that allow rapid adjustments to manufacturing processes. The result is an environment where artificial intelligence was not adapted to an existing line but integrated from the design stage. 🔋
The numbers that show the real impact on operational performance
When it comes to concrete impact, the report leaves no room for doubt. The data points to quantifiable results in selected applications, including reductions of up to 50% in unplanned equipment downtime. This might sound like a distant technical number, but in practice it means less time with lines stopped, lower emergency repair costs, and fewer delivery delays. Every hour of downtime on an automotive assembly line can represent hundreds of thousands of dollars in lost production, so this reduction has a direct and significant financial impact on the operational performance of plants.
Another relevant finding from the study is the improvement in overall equipment effectiveness, a metric known by the acronym OEE. According to the report, the analyzed applications recorded gains of approximately 5% in this indicator. It might seem small at first glance, but in large-scale production environments, every percentage point of efficiency represents significant volumes of additional products over the course of a year, without any extra investment in physical infrastructure.
The report also highlights throughput gains between 5% and 7% through real-time production analytics. In other words, by looking at data while production is happening, teams can identify bottlenecks and adjust parameters on the spot, increasing output without compromising quality. These numbers reinforce that technology, when implemented well, simultaneously improves cost, speed, and consistency.
The report also addresses the workforce topic with a perspective that goes beyond the simplistic debate about job replacement. The challenge is not just technological but human: the speed of tool adoption depends directly on how well companies can prepare their teams to work with them. And this is perhaps one of the biggest challenges the automotive industry faces going forward. 🤖
Why this transformation is accelerating now
The combination of factors the market faces today is unique. The shortage of skilled labor, which has intensified in recent years, created an urgency that forced many companies to move forward with automation projects that had been on the back burner. As the report points out, manufacturers are being pressured to do more with less while managing ever-increasing complexity. The combination of automation and AI is helping teams identify problems earlier and improve performance across multiple plants.
Another factor the report highlights is competitive pressure within the supply chain itself. As more agile companies reap measurable advantages, it creates a cascading effect where transformation is not an isolated choice for each company but a condition for continuing to be part of the ecosystem. Suppliers that fail to keep up with this pace face a real risk of losing contracts and relevance in the market.
James Glasson, global vice president of Industry for automotive, tire, and advanced mobility at Rockwell Automation, sums up the current moment well. According to him, the combination of automation and AI is helping teams identify problems sooner, reduce downtime, and improve performance across plants. Glasson emphasizes that the big difference now is how effectively companies can scale these capabilities.
Finally, the advancement of machine learning models applied to industrial data has opened possibilities that just a few years ago were not technically viable at scale. Today, it is possible to train models with real production data, identify failure patterns before they occur, optimize logistics processes, and even improve coordination between different stages of production. This ability to anticipate problems and make better decisions is at the core of what the CAR and Rockwell report defines as the near future of the automotive industry. 🚀
