To celebrate its new position as No. 1 in J.D. Power’s preliminary quality rankings among mainstream automakers, Ford is talking openly about the challenges it has faced in recent years, particularly regarding its reliance on automated systems in production and design. It turned out that those automated systems were not as robust as first thought, causing Ford to hire experienced technicians – sometimes bringing back former employees – to fix errors made by the company’s robots.
In Ford’s view, AI is powerful and also prone to harm. Its effectiveness entirely depends on the quality of the data used to train the AI model. Furthermore, the automaker underestimated the value of institutional knowledge accumulated by its more experienced engineers who had worked through multiple vehicle-development cycles. And this combination of events led to a decline in the quality of Ford’s vehicles.
“Mistakenly, we thought that by simply introducing artificial intelligence and adjusting the design requirements we had, it would create a higher quality product,” Charles Poon, vice president of vehicle hardware engineering, said in a briefing with reporters this week.
“Mistakenly, we thought that just by introducing artificial intelligence and adjusting to the design requirements we had, that would produce a high quality product.”
-Charles Poon, Ford’s vice president of vehicle hardware engineering
According to Poon, some of the company’s most experienced personnel left all their accumulated knowledge before it was fully transferred to Ford’s automated systems. This required bringing back some of those employees to retrain those systems, or in some cases, mentor young engineers who were currently struggling to maintain Ford’s vehicle quality. Poon said Ford hired, promoted or brought back more than 350 experienced engineers to rebuild that layer of expertise. In addition to mentoring young engineers, he is also tasked with improving the data collection and AI training that underpin Ford’s automated systems.
“That’s where some of our most experienced engineers have the experience to solve and identify problems before they get into the system,” Poon said.
Ford currently leads the industry in terms of the number of recalls, and its quality ratings have declined over the past several years. Those challenges became more pronounced recently, with difficulties associated with the launch of the Explorer and Aviator, supply-chain disruptions during the Covid pandemic and a significant increase in the number of its vehicle recalls.
According to Ford COO Kumar Galhotra, the automaker ultimately concluded that its approach to quality had become too fragmented. Different departments operated in silos, and the company relied heavily on a “find and fix” philosophy, which focused on identifying defects after they appeared and fixing them as quickly as possible. While that approach might solve immediate problems, it didn’t prevent those problems from occurring in the first place.
“We’re moving forward with a mindset of finding and fixing problems before they happen,” Galhotra said. “We’re focused on outputs versus enablers and early indicators. Stop admiring the problem and start solving it.”
The changes extend beyond vehicle hardware. Software and digital teams now work more closely with vehicle engineering, manufacturing and supply-chain teams, officials said. And Ford is now attempting to combine the speed and flexibility associated with software development with the rigor and validation requirements of automotive-grade engineering.
Historically, this was not always the case. Poon said Ford was discovering software bugs only late in the process because it was not taking full advantage of the rapid iteration cycles available. That said, the automaker couldn’t push software updates like consumer electronics companies with the mentality that it could “move quickly and fix later,” Poon said. Unlike smartphones, vehicles operate in safety-critical environments, where customers depend on the software working correctly from the moment the vehicle is delivered. To fix this, Ford created a dedicated 40-person software quality assurance team whose sole responsibility was to prevent problems before they occurred.
But don’t think Ford isn’t dedicated to integrating AI into more of its processes. The automaker says it has dramatically expanded its automated testing capabilities, including more than 100,000 new AI-powered tests designed to identify edge cases and stress software systems under a variety of conditions. Because the testing framework is highly automated, software changes can be rapidly re-validated even late in development, ensuring that modifications do not introduce new defects.
“Because these tests are highly automated, even if we make late changes to the software, we can quickly complete the entire validation process to guarantee that it works perfectly well before it reaches the customer,” Poon said. “We have established software reliability as our own rigorous disciplines with strict metrics.”
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