At Toyota Motor North America, a monthly supply-chain planning process can involve more than 75 spreadsheets. By the time employees finish, it is nearly time to begin again.
Toyota is developing tools to reduce that burden, part of a digital transformation that has returned $3 to the company for every $1 invested over roughly five or six years, according to Jason Ballard, vice president of digital innovations at Toyota Motor North America. The figure covers several operational and technology initiatives including machine learning, rather than measuring returns on generative AI alone.
Ballard discussed the work as part of the Seeking Value video series presented by Cognizant. He was joined by Nishanth Vallabhu, strategic business unit head for manufacturing and automotive at Cognizant. Gabriel Snyder, Newsweek’s executive editor, enterprise, moderated the conversation.
Two crises prompted Toyota to reconsider how its supply chain worked: the COVID-19 pandemic and the semiconductor shortage. Ballard said experienced employees repeatedly kept operations moving under extraordinary pressure, revealing how dependent the company had become on individual judgment.
“We can’t continue to count on that,” Ballard told Newsweek.
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Dealer inventory fell from more than 50 days’ supply to levels in the teens during those disruptions, he said. The experience raised questions about whether Toyota understood which vehicles customers wanted and whether the right combinations were reaching dealerships.
A question Ballard frequently heard from friends and family capture the problem in more familiar terms: “How come I can track a Domino’s pizza, but I can’t track where my $50,000-or-more vehicle is?” he recalled them asking.
Toyota began examining how to improve visibility into vehicle orders and make better planning decisions. Teams tested individual applications to establish which approaches worked and could be used more widely.
Those early projects “pulled the platform into existence,” Ballard said.
Toyota’s design supports different applications through shared components. The company started with machine learning and optimization to improve demand forecasting and supply planning across North America. It has since begun incorporating generative AI and developing plans for agents alongside those earlier tools.
Ballard described a modular architecture: reliable data as the foundation, services built on it, an intelligence layer being developed to support agents and products that provide employees with a common interface. Toyota also wants the freedom to choose among AI models rather than build its systems around a single provider. He noted that leading models regularly overtake one another as new versions are released.
He wanted to avoid repeating the technology decisions that had made earlier generations of enterprise software difficult to maintain.
“Anytime you wanted to touch it, it was going to cost a lot of money,” he said.
The financial case was uncertain at the outset. Ballard said Toyota presented finance and accounting teams with a wide range of possible outcomes rather than a narrow forecast. The earliest projects delivered returns near the upper end of that range, strengthening the case for continued investment.
“It’s a bit scientific. It’s a bit gut feel,” he said of the decisions Toyota faced when work began.
Financial gains came from several areas. The company had not always optimized vehicle configurations for profitability, Ballard said. Better information about the features customers requested helped Toyota match those configurations more closely to demand and generate additional revenue.
During the semiconductor shortage, the company also built a digital twin using machine learning to assess which components were available and which vehicles could still be assembled. Ballard said the technology helped the automaker make production decisions and keep assembly lines moving despite supply constraints.
The technology is also changing how Toyota’s planning teams work.
“It’s elevating the game for our team members, so they don’t spend a lot of time on the routine work,” Ballard said. “Now they’re spending a lot of the time thinking and acting on the information that’s been given to them.”
Determining what those improvements are worth is a separate challenge, according to Vallabhu. In manufacturing, improvements in quality or reductions in inventory and rework can often be measured directly. However, savings claimed from freeing up an office worker’s time are harder to translate into financial performance.
An employee might use those hours for more valuable work, but the economic benefit depends on what happens next. Vallabhu offered another example from the factory floor: Increasing production capacity creates value only if there is enough demand for the additional output.
“You’ve got to be bold to say, ‘hey, I’m going to actually take the entire path to value and not assume it,’” he told Newsweek.
He did not dismiss self-reported productivity improvements. They can establish the basis for future changes, but companies still need to determine whether the benefits eventually produce savings or additional revenue. Vallabhu said the challenge becomes complicated when AI affects how businesses serve customers, because several intermediate changes may occur before sales improve.
Toyota’s reported gains, Vallabhu said, had been quantified and validated by its finance team.
Cognizant’s longstanding relationship with Toyota brings another consideration into the transformation. Vallabhu said understanding the automaker’s business requires familiarity with its working culture, including practices such as examining problems firsthand and building consensus before making decisions.
But an established technology partner must also introduce ideas and capabilities developed outside the company. He described the challenge of balancing Toyota’s established practices with advances that could change how its employees work.
Cognizant has worked with Toyota for roughly two decades. Ballard described the company as a strategic partner whose employees work closely with Toyota teams and participate in decisions affecting the business.
“We want them to challenge us. We want them to bring fresh ideas,” Ballard said.
Employee involvement has been equally important to Toyota. Its culture emphasizes respect for people and continuous improvement, Ballard said, and the company brought experienced workers directly into the effort to redesign its operations.
Some had spent decades dealing with the same operational difficulties and knew enough about emerging technologies to imagine alternatives. Ballard said bringing those perspectives together helped the company identify opportunities that a technology team working independently might have missed.
“It starts with your people, and then the process, and then you follow up with the right technology,” he said.
The goal, he said, extended beyond updating individual tasks: “We wanted radical transformation. We wanted to reimagine the entire operation.”
Toyota also needed employees to understand what the changes would mean for their jobs. Ballard emphasized the importance of explaining the purpose of the work repeatedly and making clear how new tools could benefit the people expected to use them.
“You don’t want it happening to them. You want everyone to feel like this is happening for us,” he said.