Quality management in automotive manufacturing is undergoing a profound transformation.
Automakers and ecosystem players have long touted the rise of software-defined vehicles (SDVs), a trend that promises to transform manufacturing practices and customer experience. But in practical terms, increased adoption of electronic components and infotainment systems has contributed to a staggering increase in quality issues – on the order of nearly 50 problems per 100 vehicles (PP100) over two years.
“Innovation often breeds instability,” writes J.D. Power, “which can be infuriating when it comes to the in-vehicle experience.”
As vehicles become more complex and connected, traditional approaches are struggling to keep pace. But harnessing AI for quality has the potential to fundamentally shift the business across multiple dimensions.
Read on to learn how AI-driven quality management is reshaping the industry, using real-world examples from global automakers.
At a major automaker, quality teams were overwhelmed by data from hundreds of sensors in each vehicle. Identifying potential quality issues took an average of 180 days from first occurrence to flagging for investigation.
After implementing AI-powered quality analytics, the same automaker saw dramatic improvements – with the average time to identify issues reduced from 180 to 39 days. In many cases, advanced analytics based on telematics data enabled the identification of what turned out to be a systemic quality issue even prior to the first warranty claim.
AI detected unusual readings from ambient air sensors in a flagship sedan model, coinciding with front bumper paint refinishing. This issue was caught 172 days earlier than it would have been using traditional methods, affecting 86,000 vehicles and saving $3.83 million in potential warranty costs.
A quality executive noted: "We're now ahead of issues before they become costly problems. This proactive approach has transformed how we allocate resources and manage risk."
An automotive OEM was spending $2 billion annually on warranties, with customer satisfaction scores at 72%.
After adopting AI for quality management, the company saw significant improvements:
AI identified an issue with signal loss in the HMI unit of a luxury model, causing power loss and restarts in cold weather. By addressing this proactively, the automaker prevented 5,473 incidents and saved $3.6 million in projected customer churn.
The Chief Customer Officer remarked: "We're not just fixing problems faster; we're often addressing them before customers even notice. This has significantly boosted our brand loyalty and reputation."
At a heavy-duty vehicle manufacturer, quality investigators often found themselves bogged down by manual, hands-on tasks, such as analyzing failed parts, replacing components on test vehicles, and conducting site visits (scrambles) to intercept problematic vehicles. Despite their deep expertise, these investigators were heavily reliant on data analysts to gather data and perform statistical analysis. This dependency slowed down the process, limiting both the frequency and quality of the investigations. As a result, they spent less time on actual problem-solving and more time waiting for critical data insights.
With AI-driven quality management, the dynamics within the quality team transformed dramatically:
AI flagged an anomaly in battery voltage readings across multiple vehicle models, allowing the quality team to correlate this with a specific batch of parts from a supplier. Instead of waiting weeks for data analysis, the investigation was completed in two days, enabling the team to take immediate corrective action.
The VP of Quality observed: "Our quality investigators have evolved from being primarily hands-on troubleshooters to data-driven decision-makers. The combination of AI insights and their field expertise has not only enhanced productivity but also improved the precision of our quality interventions."
A global automaker ranked 7th out of 15 major automotive brands in quality perception and faced two major recalls in a year.
After adopting AI for quality management:
By consistently catching and addressing issues early, the automaker dramatically reduced customer-reported problems. This led to a surge in positive reviews and word-of-mouth recommendations.
The CEO commented: "Quality has become our key differentiator in a crowded market. The trust we've built with our customers through consistent, high-quality vehicles is invaluable. It's not just about avoiding problems; it's about building a reputation for excellence that drives our entire business forward."
The transformation seen across these global automakers illustrates the power of AI in revolutionizing automotive quality management. By shifting from reactive to proactive approaches, these companies have not only improved their bottom lines but have repositioned themselves as leaders in automotive quality.
As vehicles continue to evolve and become more complex, AI-powered quality management is becoming a necessity. The automotive executives who embrace this technology today are positioning themselves to lead the industry tomorrow.
Learn more about how Viaduct is helping automotive OEMs lead the quality revolution.
.png)
.jpg)
