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.
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.
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