Manufacturers are increasingly turning to AI in supply chain management to navigate the complex challenges of global tariffs and economic volatility. With AI-driven tools providing real-time insights and decision-making support, companies can maintain lean inventories while adapting to fluctuating conditions.
AI’s Role in Managing Tariff Fluctuations
The U.S. lawnmower manufacturer The Toro Company is among the manufacturers embracing AI to help mitigate the impact of tariffs. Despite the disruptions caused by events like the COVID-19 pandemic and ongoing trade wars, Toro has avoided overstocking its warehouses. Instead, it has turned to AI in supply chain management to make smarter, data-driven decisions that reduce risk and optimize efficiency.
Kevin Carpenter, Toro’s chief supply-chain manager, notes that the company is currently operating with inventory levels similar to pre-pandemic times. AI helps manage this delicate balance by analyzing an ongoing stream of data—everything from tariff news to commodity prices—allowing for rapid, informed decision-making.
Generative AI: A Booming Industry for Supply Chains
Generative AI, a cutting-edge form of AI capable of performing tasks and making autonomous decisions, is transforming supply chain operations. Gartner projects that spending on generative AI for supply chains could skyrocket to $55 billion by 2029, up from just $2.7 billion in 2023. The technology can predict supply chain demands, automate procurement, and offer real-time recommendations for optimizing operations.
AI tools, such as those used by Toro, digest vast amounts of data and present clear, actionable insights. For example, AI can suggest optimal amounts of products to order and even recommend which supplier to source from, based on real-time data. This boosts efficiency while minimizing risks associated with tariff fluctuations and trade barriers.
Challenges and Limitations of AI in Supply Chains
While the potential of AI in supply chain management is undeniable, experts caution against overhyping its capabilities. AI is a powerful enabler for supply chain resilience, but it’s not a panacea. As Minna Aila from Konecranes states, “AI is really a powerful enabler for supply chain resilience, but it’s not a silver bullet.”
AI is still in its early stages, and most firms are in the pilot phase. While AI tools can help automate routine tasks like inventory ordering and maintenance scheduling, they still require human oversight for strategic decisions. Industry experts warn that companies may waste resources if they invest too heavily in AI without fully understanding its current limitations.
The Shift Toward Leaner Inventories
In light of rising global costs and the pressure to maintain profitability, many manufacturers are shifting back to “just-in-time” inventory management. This approach, which minimizes inventory to reduce waste, is particularly attractive in a climate of economic uncertainty.
AI tools make this lean inventory model possible by helping companies stay agile and responsive to sudden changes in tariffs, export bans, or other trade restrictions. Without AI, companies would be slower to react and more likely to build up excessive inventories as a buffer against uncertainty.
AI is especially valuable in the face of rising costs. For example, McKinsey’s surveys show that reliance on larger inventories to cushion supply chain disruptions has decreased significantly since the pandemic. In 2022, 60% of supply chain managers relied on bigger inventories, but that figure fell to just 34% in 2023. Early responses from McKinsey’s 2025 survey suggest this trend will continue.
AI’s Limitations in Predicting Major Events
Despite its capabilities, AI in supply chain management is not without its shortcomings. AI tools can analyze vast amounts of data, but they cannot predict certain unforeseen events, such as terrorist attacks or natural disasters, which can still disrupt supply chains. As Aila points out, “I’m still looking forward to the day when AI can predict terrorist attacks that are at sea, for instance.”
In the meantime, manufacturers continue to use AI for more routine tasks, such as optimizing shipping routes and managing production schedules. Konecranes, for example, combines weather forecasts with other data points like bridge heights to determine the best shipping routes for its massive port cranes.
Conclusion
AI in supply chain management is transforming how manufacturers approach inventory management and logistics. With the ability to analyze data in real-time, AI helps companies navigate tariff volatility, rising costs, and supply chain disruptions. While AI is a powerful tool, it is not a cure-all. Manufacturers must balance AI adoption with strategic decision-making to achieve optimal results. As the industry continues to evolve, AI’s role in supply chains will likely expand, offering more solutions to complex challenges.
