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Artificial intelligence (AI) is touching nearly every corner of the business world right now, and supply chains are no exception. What began as small experiments in forecasting and robotics has evolved into practical tools for improving visibility and response. Companies are using AI to identify risks earlier, reroute shipments when needed and manage data across increasingly complex networks.
Supply chain organizations are now using AI in practical ways to solve routine problems. In “How AI Is Reshaping Supply Chains,” Olivia Farrar discusses how the technology can “scan thousands of failure events” to pinpoint early warning signs of looming disruption. “[This is] something no human analyst can do at scale,” she writes.
Traditional supply chain planning also relies on models built around costs and transport times. “AI enhances these models by integrating real-time data, allowing organizations to dynamically reconfigure routes, locations, and inventory,” Farrar explains. “The shipping and delivery firm UPS, for instance, now uses AI to make real-time decisions about last-mile delivery—for example, rerouting drivers based on traffic, weather, or shifting priorities.”
Companies are also using AI to increase organizational resilience and agility. Using virtual models of supply chains (aka digital twins), they can simulate disruptions and test responses. “The U.S. Department of Defense has deployed such systems to prepare for everything from natural disasters to adversarial threats,” Farrar writes.
Finally, AI is optimizing the less visible parts of the chain, including the hiring, training and redeployment of human labor. For example, logistics provider Kuehne + Nagel International AG uses it to identify internal candidates for open roles, shorten training times and improve job satisfaction. “The result is a more flexible and capable workforce,” Farrar notes.
AI in Action in the Supply Chain
Walmart is one high-profile company that’s already using AI to move products faster and keep costs low across its global supply chain. In “Walmart leverages AI to enhance supply chain efficiency and reduce costs,” Serenah McKay explains how the retailer is using AI to analyze demand patterns, inventory levels and shipping data. It uses that data to predict what customers will need and when they’ll need it. The system adjusts orders, replenishment schedules and delivery routes in real time to prevent shortages and reduce waste.
“By analyzing demand patterns, inventory levels and global and local conditions, our network is becoming faster, smarter and more resilient, delivering a seamless omnichannel experience for customers shopping online or in stores,” Walmart’s Indira Uppuluri told the Arkansas Democrat-Gazette.
The role of artificial intelligence primarily revolves around the ordering, tracking, management and delivery of inventory. McKay explains that AI can forecast trends and analyze factors that may affect consumer demand for products that Walmart buys and imports. “Walmart has created a network of interconnected units called neurons,” she writes, “much like those in the human brain, that can learn from data and recognize patterns.
It Starts with Good Data
Getting more value from AI in supply chains starts with clean, connected data. In “Gartner Says Chief Supply Chain Officers Can Scale AI With Data Fabric Architecture,” the firm says organizations can build a stronger foundation for analytics and automation with a data fabric. By definition, a data fabric connects information across different systems so information can flow freely across the business. This helps supply chain teams see what’s really happening across their systems instead of having to piece data together after the fact.
“For CSCOs, data fabrics offer reduced costs and time for data integration efforts, as well as improved decision making through the ability to operationalize AI across supply chain activities,” said Vas Plessas, director analyst in Gartner's supply chain practice. “As supply chains face continued uncertainty and complexity in their operating environments, data fabrics enable integrated, scalable intelligence across expanding data models.”
Plessas also cautions that the approach is still developing. “Vendor solutions are still maturing, and buyers must understand both the current solutions’ limitations and have a strong grasp on their own supply chain architecture,” he said. “Laying the right data foundation today is what turns the vision of AI-enabled supply chains into reality tomorrow.”
