Smart manufacturing gains speed in automotive plants

Rockwell Automation and the Centre for Automotive Research have released a white paper on smart manufacturing in the automotive sector. The report examines how artificial intelligence, machine learning and automation are used across automotive, tyre and battery manufacturing.

Titled Smart Manufacturing in Automotive: Deployment and Impact, the paper was authored by the Centre for Automotive Research using data supplied by Rockwell Automation. It argues that manufacturers have moved beyond early adoption and are now deciding where to deploy digital tools most quickly as production systems grow more complex.

The findings show that carmakers and suppliers already rely on advanced automation in body, paint and welding operations. The next phase is extending into areas that have proved harder to automate, including electronics assembly, validation, production co-ordination and logistics.

AI and machine learning are also being applied to established factory operations. These tools are improving predictive maintenance, inspection accuracy and overall system performance, according to the report.

One section of the research focuses on the commercial pressures behind this shift. More complex production environments, warranty costs, rising operating expenses and stronger global competition are cited as key factors pushing manufacturers to expand automation programmes.

The study also links automation to onshoring efforts, noting that manufacturers are using it to support cost-competitive production in labour markets where staffing remains tight.

Measured gains

Rockwell data cited in the paper points to measurable improvements in plant operations. These include reductions of up to 50% in unplanned downtime in selected applications, roughly 5% gains in overall equipment effectiveness and throughput improvements of 5% to 7% from real-time production analytics.

The figures suggest that investment decisions are increasingly tied to operational returns rather than experimentation alone. This is especially relevant as automotive groups face pressure to improve output, maintain quality and manage more varied product mixes.

Edgar Faler, Principal Mobility Analyst and Strategy Lead at CAR, said the industry is now building on an established base of factory automation.

"The industry has built a strong automation foundation. What is changing now is how manufacturers are using AI and data to manage growing complexity, improve decision-making, and create competitive advantage," Faler said.

"Those that move faster are starting to see measurable advantages," he added.

Adoption gap

The paper says differences in adoption are beginning to create wider performance gaps across the sector. It points to disparities in quality, uptime and productivity that could affect supplier performance and long-term competitiveness.

This divide matters because the automotive supply chain depends on tightly managed production targets and quality standards. Companies that are slower to adopt more advanced production tools may find it harder to match the consistency and efficiency of faster-moving rivals.

The research draws on proprietary data from Rockwell's 11th annual State of Smart Manufacturing report alongside analysis by CAR. It covers not only vehicle assembly but also tyre and battery production, where manufacturers are dealing with increasingly technical processes and tighter cost controls.

James Glasson, VP, Global Industry - Automotive, Tyre & Advanced Mobility at Rockwell Automation, said manufacturers are under pressure to improve plant performance while operating with fewer resources.

"Manufacturers are being asked to do more with less while managing greater complexity," Glasson said.

"The combination of automation and AI is helping teams identify issues earlier, reduce downtime and improve performance across plants. The difference now is how effectively companies scale these capabilities," he added.