General Motors has harnessed advanced AI technology to predict and mitigate supply chain disruptions, ensuring continuous vehicle production amidst weather events and geopolitical tensions. This strategic shift marks a significant step in future-proofing manufacturing operations.
When Hurricane Helene struck North Carolina in September 2024, General Motors (GM) demonstrated how far it has come in mitigating supply chain risks through cutting-edge artificial intelligence. The st...
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orm severely affected Auria Solutions, a key GM supplier responsible for carpeting full-size SUVs like the Chevrolet Tahoe, GMC Yukon, and Cadillac Escalade. Despite the plant enduring power and water outages that would typically halt production, GM’s AI system had anticipated the disruption and facilitated rapid interventions, such as drilling a water well to keep operations running. This proactive approach exemplifies GM’s broader strategy of using AI to predict and prevent costly supply chain stoppages — a system credited with avoiding at least 75 factory shutdowns in 2025 alone.
The backbone of GM’s AI-driven supply chain resilience is a sophisticated four-part system. It first digitises an extensive supply network map, tracking relationships far beyond tier-one suppliers to incorporate second, third, and even further sub-tier partners. This complex mapping feeds into machine-learning tools that continually monitor dynamic supply conditions. Centralised risk analysts in a communication hub in Michigan receive alerts whenever the AI detects disruptions, triggering thousands of investigations. A key component is GM’s AI news scanner, which combs through thousands of daily articles to detect potential risks—from geopolitical upheavals to supplier delays. The comprehensive dashboard consolidates tracking data on shipping timelines, overdue parts, and production schedules, offering real-time insights unseen by human monitoring alone.
This shift towards AI followed hard lessons from the COVID-19 pandemic. The pandemic-era semiconductor chip shortage illustrated the vulnerabilities in GM’s supply chain, causing production halts at eight US facilities in 2021 and further interruptions in 2022. These shortages stemmed from factory lockdowns worldwide, severely delaying delivery of electronic components critical for vehicle manufacturing. In response, GM’s supply chain monitoring expanded tenfold in scope, leveraging AI to crawl beyond immediate suppliers and identify risks throughout the supply network. According to Jeff Morrison, GM’s senior vice president of global purchasing and supply chain, “Data management and analytics are the unlock to improving performance, efficiency, and creating value. AI for us has been a transformative tool.”
GM’s AI system has not only allowed the company to foresee risks like China’s restrictions on rare earth magnets but also enabled constructive collaboration with its suppliers. Sean Gaskin, GM’s director of systems engineering and a key architect of the AI programme, explained how the system detects threats to suppliers earlier than the suppliers themselves, giving them precious time to respond and avoid production delays. This symbiotic relationship benefits the entire supply chain, enhancing profitability and maintaining a steady flow of components.
The implementation of AI in supply chain monitoring is part of GM’s broader AI strategy. Earlier in 2025, GM appointed Barak Turovsky, formerly of Cisco and Google, as its first Chief Artificial Intelligence Officer, reflecting the company’s commitment to embedding AI not only in supply chain operations but across electric, internal combustion, and autonomous vehicle development. This appointment aligns with GM’s concurrent partnership with Nvidia to harness advanced AI chips and software for autonomous driving technologies and factory process automation.
Nonetheless, GM faces ongoing challenges such as the impact of tariffs on its globally diversified supply chain. The company anticipates paying between $4 billion and $5 billion in tariffs by the end of 2025, a considerable burden that necessitates agile adjustments in sourcing strategies. GM’s AI infrastructure equips it to efficiently map supplier alternatives to mitigate these fiscal pressures.
In contrast to its technological advances in AI and supply chain management, GM recently announced a strategic withdrawal from its Cruise robotaxi business, citing the high costs and prolonged timeframe required for scaling the operation in an increasingly competitive field. This pivot underscores GM’s focus on leveraging AI in areas where it anticipates stronger returns and efficiencies.
GM’s journey towards a resilient, AI-backed supply chain exemplifies how the automotive industry is evolving in response to global disruptions. By predicting risks from weather events to geopolitical factors and supplier inconsistencies, GM’s AI system ensures factories remain operational and vehicles continue rolling off the assembly lines. As Gaskin noted, the technology supplements, rather than replaces, the expertise of GM’s workforce, aiding in risk detection that humans alone could not manage efficiently. This forward-looking approach positions GM not only to weather immediate supply shocks but also to compete effectively in the future of automotive manufacturing.
Source: Noah Wire Services
