Ford had to rehire former engineers to fix errors made by its automated systems

To celebrate its new status as No. 1 in J.D. Power’s initial quality ranking among major automakers, Ford announced the challenges it has faced in recent years, particularly related to its reliance on automated systems in production and design. Those automated systems turned out to not be as robust as previously assumed, requiring Ford to hire experienced technicians — and sometimes bring back former employees — to correct mistakes made by the company’s robots.

In Ford’s view, AI is both powerful and vulnerable to pitfalls. Its effectiveness depends entirely on the quality of the data used to train the AI ​​models. In addition, the automaker underestimated the value of the institutional knowledge accumulated by its most experienced engineers who had worked through multiple vehicle development cycles. This combination of phenomena led to a decline in the quality of Ford cars.

“We mistakenly believed that just by introducing AI and adjusting the design requirements that we had, that it would produce a higher-quality product,” Charles Boone, vice president of vehicle hardware engineering, said in a briefing this week with reporters.

According to Boone, some of the company’s most experienced employees left before all their accumulated knowledge could be fully transferred to Ford’s automated systems. This necessitated bringing back some of those employees to retrain those systems, or in some cases, mentor young engineers who were currently struggling to maintain Ford quality. Ford has hired, promoted or brought back more than 350 experienced engineers to rebuild that layer of expertise, Boone said. In addition to mentoring the young engineers, they are also tasked with improving data collection and training on the artificial intelligence powered by Ford’s robotic systems.

“This is where some of our most experienced engineers gain experience in solving and identifying those problems before they creep into the system,” Boone said.

Ford currently It leads the industry in the number of recallsand their quality ratings It has declined over the past few years. These challenges have become more apparent recently, with difficulties associated with the launch of the Explorer and Aviator, supply chain disruptions during the Covid pandemic, and notable growth in the number of recalls of its vehicles.

According to Ford COO Kumar Galhotra, the automaker eventually concluded that its approach to quality had become too fragmented. Different departments worked in silos, and the company relied heavily on a “find and fix” philosophy that focused on identifying defects after they appeared and correcting them as quickly as possible. While this approach can address immediate problems, it does not prevent those problems from occurring in the first place.

“We are moving from a find-and-fix mentality to preventing problems before they happen,” Galhotra said. “We focus on enablers and early indicators versus deliverables. Stop liking the problem and start solving it.”

The transformation extends beyond the car’s hardware. Software and digital teams now work more closely with vehicle engineering, manufacturing and supply chain teams, executives said. Ford is now trying to combine the speed and flexibility associated with software development with the precision and verification requirements of automotive engineering.

Historically, this has not always been the case. Boone said Ford did not discover the bugs until late in the process because it was not taking full advantage of the fast iteration cycles available. However, Boone said the automaker has not been able to roll out software updates as quickly as consumer electronics companies with the mindset that they can “move quickly and fix later.” Vehicles, unlike smartphones, operate in a safety-sensitive environment where customers rely on software that works properly from the moment the vehicle is delivered. To solve this problem, Ford created a dedicated software quality assurance team of 40 people with sole responsibility for preventing problems before they occur.

But don’t think Ford isn’t committed to integrating AI into more of its operations. The automaker says it has significantly expanded its automated testing capabilities, adding more than 100,000 new AI-powered tests designed to identify edge cases and stress software systems under a wide range of conditions. Because the testing framework is highly automated, software changes can be quickly re-verified even late in development, ensuring that modifications do not lead to new defects.

“Because these tests are so automated, even if we have a late change in the software, we can quickly go back through the entire validation process to ensure it works well before it gets to the customer,” Boone said. “We established software reliability as its own rigorous systems with stringent metrics.”