Predictive maintenance for automotive smart manufacturing 

Automotive manufacturing is evolving at a rapid pace. The push for electrification, autonomous technologies and more personalized vehicles is reshaping not only the cars we drive but how they’re made. In this era of transformation, smart manufacturing has emerged as the blueprint for meeting new demands — combining automation, connectivity and data-driven processes to drive efficiency and resilience. 

One key pillar of smart manufacturing is predictive maintenance. Unlike traditional maintenance strategies that rely on scheduled servicing or reactive repairs, predictive maintenance anticipates issues before they happen. It shifts from a fix-it-later mindset to a proactive, insight-driven approach — reducing downtime, increasing asset lifespan and optimizing production without compromising on quality. 

In the high-speed, high-precision world of automotive production, even minor disruptions can have significant ripple effects — delaying vehicle builds, disrupting just-in-time supply chains and impacting product quality. The ability to foresee equipment issues and take targeted action ahead of time is quickly becoming a competitive advantage. 

Closing the gap between maintenance and intelligence 

Today’s automotive plants operate with thousands of connected assets. from stamping machines and robotic welders to final inspection systems. Each of these assets generates an immense amount of operational data — on vibration, torque, temperature, cycle times and more. But without a robust strategy in place, much of this data goes unused. 

Predictive maintenance solutions, like Siemens’ Senseye Predictive Maintenance, help manufacturers make sense of this data by applying advanced analytics and machine learning to identify patterns and anomalies. These insights allow teams to intervene when it matters most — before a fault leads to an unplanned stop or defective part. 

In one real-world case, an automotive OEM partnered with Siemens to deploy predictive maintenance across a global network of manufacturing sites. The OEM wanted to avoid the delays and inefficiencies caused by unexpected equipment failures during peak production periods. By using Senseye’s solution, they were able to connect to over 10,000 assets across four continents and monitor performance data in near real-time. Within just 12 weeks of deployment, the system began delivering tangible results, including a 12% reduction in unplanned downtime and early warnings for several high-impact failures. 

Predictive maintenance is a critical enabler of quality and consistency. For example, in electric vehicle battery production — where manufacturing tolerances are especially tight — being able to maintain equipment within optimal ranges directly impacts safety and performance. Similarly, in body-in-white or paint shops, detecting subtle performance degradation in robotics or conveyor systems can prevent quality issues long before they appear at final inspection. 

Where AI and human expertise meet 

It’s important to note that predictive maintenance doesn’t replace human expertise, it enhances it. While AI algorithms can flag potential issues, experienced technicians and engineers still play a central role in diagnosis and resolution. The goal is to equip maintenance teams with the right data, at the right time, to make faster and more accurate decisions. 

To do that, solutions like Senseye integrate with existing plant systems — connecting data from SCADA, MES, ERP and CMMS platforms to provide a unified, contextual view of asset health. Maintenance teams can prioritize actions based on risk levels, production schedules and historical patterns. Dashboards and alerts are tailored by role, whether for plant managers, reliability engineers or service technicians. 

Many automotive OEMs and suppliers have also found success by combining predictive maintenance with condition monitoring and performance analytics, creating a broader asset performance management (APM) strategy. This layered approach allows companies to not only detect problems but also optimize equipment usage and extend the lifespan of high-value assets. 

Getting started with predictive maintenance 

For manufacturers just beginning their predictive maintenance journey, the path forward doesn’t require a rip-and-replace of existing infrastructure. Siemens provides a stepwise implementation approach: 

• Start with a pilot on critical machines or lines, using existing sensors and PLC data 

• Establish data connectivity and explore initial insights with automated fault detection 

• Scale gradually by connecting more assets, refining alerts and integrating with CMMS for automated work orders 

This modular, scalable deployment model allows manufacturers to see quick wins while building toward a long-term vision of data-driven maintenance across the enterprise. 

Looking ahead: a smarter, more resilient production line 

Predictive maintenance is a cornerstone of smart automotive manufacturing — not just for minimizing downtime, but for delivering consistent quality, meeting sustainability targets and improving overall equipment effectiveness (OEE). As automotive production continues to evolve — embracing new propulsion systems, more flexible production lines, and greater software-defined functionality — the ability to keep assets running at peak performance will be essential. 

By investing in predictive maintenance technologies and building the digital infrastructure to support them, automotive manufacturers can stay ahead of unplanned failures, reduce maintenance overhead and drive smarter, more agile operations.