Artificial intelligence is increasingly hailed as driving a “new Industrial Revolution,” reshaping how industries operate at every level. But what does that mean in practical terms? One powerful example lies in a field that people don’t often consider: supply chain management.
During the height of the COVID-19 pandemic, supply chains were thrust suddenly into the spotlight. When shelves emptied and shipping delays spiraled, a question previously reserved for boardrooms became everyday: how are goods getting to consumers?
Mark Fagan, a lecturer in public policy at the Harvard Kennedy School, was one of the people who became unexpectedly in demand. Fagan found himself frequently engaging in conversations about global shortages and system failures. But as pandemic fears faded, so too did public interest…until recently. Amid today’s trade tensions and tariff battles, supply chains and their management are once again center stage in public discourse. Simultaneously, artificial intelligence is beginning to reshape supply chain management, helping to reduce shocks.
As Fagan explains, a supply chain network includes not only the materials required to produce a good—say, flour, butter, and packaging for croissants—but also the labor, equipment, transportation, and systems that bring those goods to market. This applies equally to services. The department of motor vehicles, for instance, relies both on physical products (license plates, ID cards) and nonphysical ones, alongside human capital (vision-testing software, trained staff). Chains are a set of “nodes” (factories, warehouses, stores) and “links” (the transportation and information flows that connect them). A failure in any one node or link can bring the whole chain down.
To understand how fragile these systems can be, Fagan suggests an analogy: the family tree. Most people know their immediate relatives well, but few can map out their third cousins or great-great-grandparents. Likewise, companies typically understand their direct suppliers and customers, but the further one moves from the center, the murkier the relationships become. Those remote actors, however, can be the very points where collapse originates. A forgotten subcontractor in Vietnam, an overburdened port in Los Angeles, or a strike in a transit hub can derail an entire operation. This is the paradox of modern complexity. The more global and interconnected our systems become, the more difficult weaknesses are to notice.
Artificial Intelligence in Supply Chain Management
Supply chains are about not just the movement of goods, but also the flow of information. As products and services travel downstream, data and feedback must travel upstream. The health of the chain depends on how well information channels are maintained. A critical concept is what Fagan terms “survival time”: the amount of time an operation can continue functioning after a link in the supply chain fails. Shorter survival times mean tighter dependencies, demanding more perfect coordination. This is where artificial intelligence enters the picture in reshaping how supply chains are built, managed, and repaired.
“Two early examples of AI applications are in robotics,” says Fagan, pointing to automated manufacturing and autonomous vehicles in warehouses. But the transformative uses of AI, he says, will come in prediction, optimization, and system design. He points to four domains.
The first of these is forecasting. AI can now scan thousands of failure events to identify early warning signs of disruption, something no human analyst could do at scale. Fagan gives the example of a collaboration between the health information technology firm GE Healthcare and the Mass General Brigham hospital system, which created a project together to predict “missed care opportunities” (MCOs), or appointments where patients are late or absent. “MCOs lead to inefficiencies in providing care,” says Fagan, “so knowing when they are likely allows hospitals to better align doctor time with demand for urgent, inpatient, or walk-in appointments.” With over 95 percent accuracy, the AI tool enables preemptive outreach, preserving resources and improving care. Applied to supply chains, this approach offers the possibility of predicting and preventing delays before they are compounded.
The second is design and management. Traditional supply chain planning 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. 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.
The third is resilience and agility. Through the creation of what Fagan terms “digital twins,” or virtual models of supply chains, organizations 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.
Finally, AI is optimizing the less visible parts of the chain: the hiring, training, and redeployment of people. Logistics giant Kuehne + Nagel International AG uses AI to identify internal candidates for open roles, shortening training times and improving job satisfaction. The result is a more flexible and capable workforce.
“As we look forward,” Fagan says, “I think what you’re going to see is AI enhancing the quality, the timeliness, and the cost of research and analysis.” Indeed, supply chain management is “critical to economic prosperity,” he adds, “both when it is headline news and not.”
