During Chris’s years at Coca-Cola, one recurring question was how long it took for new information to make its way into a supply chain decision. At one point, the answer was measured in weeks. A lot could happen between learning that conditions had changed and doing something about it.
That delay is one reason we are interested in what AI might do for supply chains. Digital tools can shorten the time between sensing a change and deciding how to respond. Robotics and other physical technologies can shorten the time between a decision and its execution. But a faster decision is only valuable if the organization has decided what a good decision looks like.
In the first conversation of our new SCL webinar series, we asked a deliberately provocative question: If AI runs more of the supply chain, what is left to manage? Our answer begins with a familiar idea. The purpose of the supply chain has not changed. The way we organize people, processes, and decisions around that purpose may change considerably.
As AI takes on more sensing, analysis, and routine decision-making, management does not disappear. It moves up a level, from making individual decisions to designing the objectives, boundaries, and decision systems within which those decisions are made.
Set AI aside for a moment. What would we want from an excellent supply chain? It would sense demand and changes in the environment early, anticipate what might happen next, coordinate supply with demand, make decisions across functions, execute reliably, adapt when conditions change, and learn from the results. We have been working toward that kind of system for decades.
The measures are familiar too: service, cost, cash, growth, resilience, and responsibility. Better technology may help us improve several of these at once, but it will not make every tradeoff disappear. A machine still has finite capacity. Inventory still uses cash. A product moving from Atlanta to Dallas still takes time. Suppliers and customers remain people and organizations with their own constraints.
This is the point of starting with the supply chain rather than with an AI tool. We need to know what we are trying to improve before we choose which decisions to automate. A better forecast has limited value if a supplier cannot respond, a plant cannot add capacity, or a customer allocation rule has never been agreed upon.
Supply chain technology has advanced in waves. Enterprise systems connected information. Sensors improved our ability to see what was happening. Forecasting, simulation, and optimization improved our ability to anticipate and evaluate choices. Generative AI has made some of that information and analysis easier to access. Agents are beginning to coordinate steps in a workflow, while robotics and computer vision extend automation into physical operations.
Each wave removes some friction, but adoption is uneven. I remember when RFID was going to change everything quickly. It took much longer to reach useful applications than many expected. Even today, some companies have not fully realized the value of earlier investments in data, planning, and integration. That matters when we talk about what is possible with AI and what a company can operate reliably at scale. Those are different questions.
Integrated business planning offers a practical example. For years, companies organized decisions around a monthly calendar: collect forecasts, reconcile supply and demand, meet, approve a plan, then act. Some can now pull in a new promotion or supply disruption, recalculate options, and circulate an updated plan much faster. The opportunity is to respond while the information is still useful. Whether the resulting action can happen immediately depends on capacity, partners, and clearly assigned decision rights.
If an allocation decision is needed today, who decides which customer receives the limited product? Who tells the other customer? What happens to margins, future demand, and the supplier’s schedule? Faster analysis makes those questions more urgent. It does not answer them on its own.
The framework we used in the webinar separates the physical system, the management system, and the intelligence system. The physical system includes suppliers, plants, inventory, warehouses, transportation, and customers. The management system includes policies, planning routines, metrics, roles, and decision rights. The intelligence system includes people, data, models, and increasingly AI agents that help sense, interpret, recommend, and act.
AI is moving fastest into the intelligence system. That puts pressure on the management system. A company cannot take a much faster, more capable intelligence layer and assume that a meeting calendar, escalation path, and organizational structure designed years ago will still be the best way to use it. It may also discover that some physical design choices, such as the amount or location of inventory, deserve a fresh look.
The physical system can change, but it does not vanish. A decision that looks sound in a model still has to work in a plant, on a truck, and with partners whose systems may be less mature. This is why management design and technology adoption have to advance together.
Consider a replenishment decision. Today, a planner often reviews a forecast, judges the inventory position, and approves an order. With better demand sensing and connected systems, routine decisions within defined limits may happen much faster. The planner’s work then shifts toward setting policy with colleagues, checking assumptions, deciding when to intervene, and learning from results. “Exception management” alone is too narrow a description; AI will also get better at handling some exceptions.
Now consider a supplier disruption. A system might identify the problem earlier, compare alternative sources, revise production and transportation plans, and show the effect on customers. The cheapest alternative might carry a labor practice concern, or a preferred supplier may be unable to move as quickly as the model assumes. We must decide which outcomes and obligations matter before delegating the choice. The system needs those values expressed clearly, and someone must remain accountable for the outcome.
A fulfillment center creates a different test. Sensors, computer vision, and mobile robots could help a team detect congestion, adjust work releases, and route equipment around a problem. Here, a digital decision can put a person in the path of a physical machine or push a shift past a safe workload. Autonomy should be bounded by operational and safety rules, with people able to intervene. These are scenarios to design and test, not claims that most facilities already run this way.
The center of management work moves from touching every routine choice toward designing and governing how choices get made. Leaders have to establish objectives, define which decisions a system may take on its own, decide where approval is required, and monitor whether the results match the intent. They also have to look beyond the first result. A quick allocation might protect this week’s service level while weakening a customer relationship or creating a bottleneck somewhere else.
This work still requires deep supply chain knowledge. As AI takes on more of the analysis, and eventually more of the decisions, leaders may touch fewer individual decisions, but they will carry greater responsibility for the system making them. They need to understand why a recommendation was made, when its assumptions no longer hold, and whether the system is producing the outcomes the organization intended. You cannot govern a decision you do not understand.
It also raises a development question. Many experienced supply chain leaders built judgment by making thousands of everyday decisions and living with the results. If software takes over more of those decisions, how will the next generation develop that judgment? Companies will need to make learning part of the operating model: give people access to the reasoning behind recommendations, let them test scenarios, and review outcomes with those who know the operation.
The anxiety around AI is reasonable. At industry conferences, professionals hear what technology may do but often hear little about how their work will change. Leaders may not have a complete answer, and some jobs will change more than others. Silence leaves employees to draw their own conclusions.
A leadership team can say what it knows now. Is the current goal to help people do their jobs better, to automate particular tasks, or both? What work is under review? What training and opportunities will be available? Those answers can evolve. An honest account of the work underway is more useful than a confident promise no one can support.
Practitioners have a role as well. Use the tools available within your organization’s rules, ask for training, and bring your knowledge of the process into the design conversation. The people who understand why a decision works in the real world will be essential to building systems that work there too.
This first episode focused on the system. AI may help us make better decisions sooner and act on them faster, while the supply chain’s purpose and physical limits remain. Managing that future means setting the objectives and boundaries, developing the people, and learning from what the system actually does.
Our next conversation turns to strategy. If competitors can buy access to similar AI tools, where will supply chain advantage come from? We think the answer will have much to do with design, judgment, and the ability to make these technologies work in a specific business. We would welcome the examples and questions you want us to examine.
This is a companion article to Part 1 of The AI-Managed Supply Chain: What Changes, What Doesn't, and What Leaders Must Do. View the related webinar on our YouTube channel.
]]>The annual U.S. News & World Report Best Colleges rankings are based on assessments from academic leaders at peer institutions nationwide.
The recognition comes at a time of significant momentum for ISyE. This fall, the school moved into George Tower in Tech Square, a new home designed to support learning, collaboration, and innovation while creating new opportunities for engagement with students, alumni, industry partners, and the broader Georgia Tech community.
Demand for ISyE's undergraduate program continues to grow, with a record 1,536 students enrolled for the fall 2026 semester, a 10% increase from the previous year. ISyE graduates continue to find success after graduation, with a median starting salary of $90,000 for 2025-26 bachelor's degree recipients and 84% of job-seeking graduates receiving at least one job offer before graduation. Alumni pursue careers across consulting, logistics, finance, technology, manufacturing, and startups, with top employers including The Home Depot, Delta Air Lines, McKinsey & Company, Capital One, and PwC.
The College of Engineering also achieved a historic milestone in the latest U.S. News rankings, rising to No. 2 nationally. All 10 undergraduate engineering programs were ranked among the nation's top five.
]]>A hospital decides how many operating rooms to staff on a specific day. A vaccine that works in a trial fails to reach the population that needs it. A single manufacturer’s plant shuts down due to a hurricane and a routine medical supply becomes unavailable nationwide. A public health agency has to decide whether limited resources will have greater impact through a screening program or a housing intervention.
These are the types of real-world challenges students in the Health Systems minor will learn to address. They are not purely medical questions. They are systems questions, and they are the kinds of challenges industrial and systems engineers are equipped to solve.
"Many of today’s health challenges are fundamentally systems challenges,” said Pinar Keskinocak, the H. Milton and Carolyn J. Stewart School Chair and professor in ISyE. “Improving health requires not only advances in medicine, but also better systems for prevention, access, care delivery, and decision-making."
The minor was developed by Professors Turgay Ayer, ISyE’s Virginia C. and Joseph C. Mello Chair and professor, and Nicoleta Serban, an ISyE professor, working with faculty across ISyE's health systems area.
“Health systems problems are complex because decisions made in one part of the system often have consequences elsewhere,” said Ayer. “We developed this minor to give students a systems-level understanding of healthcare systems, while equipping them with practical analytical and decision-making tools. Our goal is for students to leave the program prepared to translate data into better decisions and, ultimately, better health outcomes.”
“This minor grew out of years of health systems research and teaching across ISyE,” added Serban. “Through my work in health analytics, Medicaid policy, healthcare access, and health system complexity, and through teaching Public Health Systems, I saw both the breadth of faculty expertise in this area and strong student interest in engaging with it. The minor brings those strengths together in one pathway that enables students from across Georgia Tech to understand health systems and develop the analytical tools to improve them.”
The minor is 15 credit hours: a statistics foundation, two required health systems courses, and at least two electives.
Students begin with a statistics course, choosing from seven options across campus: ISYE 3770, ISYE 3030, BME 2400, ECE 3077, ECON 2250, MGT 2250, or PUBP 3120. This flexibility allows students from a variety of academic disciplines to enter the health systems core with a shared quantitative foundation.
Two ISyE courses form the core. Public Health Systems (ISYE 4510), designed by Serban and Keskinocak, works at the population scale. Many health challenges are shaped long before patients enter a hospital. The course examines factors that influence access to care, prevention, and population health, and explores how engineering and data-driven approaches can improve outcomes.
Students study healthcare access: how to measure it, how geography shapes it, and what epidemiology and spatial analysis reveal about who a system reaches.
The course also spends three weeks on prevention and wellbeing across infectious and non-infectious disease. The organizing theme is what Serban calls the three E's: equity, efficiency, and effectiveness, and the trade-offs among them when resources are finite. Students leave able to identify where quantitative methods can improve public health decision-making.
Intelligent Healthcare Delivery Systems (ISYE 4515) launches alongside the minor. The course was originally designed by Ayer and is being adapted for this minor by Dima Nazzal, ISyE’s associate chair for academic administration, and Lauren Steimle, the Harold R. and Mary Anne Nash Early Career Professor and assistant professor in ISyE. The course focuses on healthcare delivery: how patients move through clinics and hospitals, where care transitions break, and how to map a process well enough to identify where to change it. Students learn to evaluate a health system's performance and model improvements through process design and resource allocation, such as bed allocation, operating room block scheduling, and nurse staffing.
“Just as doctors diagnose and treat disease when patients present with symptoms, students in this class will learn engineering tools to diagnose and solve process problems that lead to symptoms such as delays in care, shortages of medical supplies, and provider burnout,” said Steimle. “With these skills, students will be prepared to help healthcare systems deliver the right care to the right people, in the right place, at the right time.”
The remaining six credit hours come from a slate of more than 25 electives in five categories: health systems analytics and modeling; health policy, economics, and management; environmental, public health, and bio-inspired systems; health systems modeling and optimization; and machine learning and data science for health. The courses come from Biomedical Engineering, the College of Computing, the Scheller College of Business, the Ivan Allen College of Liberal Arts, the College of Design, Civil and Environmental Engineering, and Electrical and Computer Engineering, as well as ISyE. Students are encouraged to choose from more than one category.
"A student can come into this from computing, from policy, from business, from design," said Nazzal. "We built the electives so that whatever quantitative preparation a student already has counts for something, and so that nobody has to leave their major behind to work on health. Health systems problems don't get solved by one discipline, and the curriculum shouldn't pretend otherwise."
Students may also complete an optional experiential component through a health-focused Senior Design capstone, undergraduate research, a Vertically Integrated Project, or a Serve-Learn-Sustain project. Students whose primary project isn't health-related complete a structured addendum connecting the work to health systems challenges.
For most students, courses count toward both major and minor requirements. Prerequisites and full requirements are in the catalog, and students should check them against their own program before declaring.
Georgia Tech is well known for health research. Less well known is the role industrial and systems engineering has played in advancing health systems research for decades.
ISyE's health systems footprint spans different kinds of problems. In healthcare operations, faculty and students work on patient flow, scheduling, capacity, and care coordination inside hospitals and clinics. In health systems and supply chains, they study how care, drugs, vaccines, and medical equipment move across networks of providers and suppliers. In AI and analytics for health, they address medical decision-making, disease modeling, and the role of algorithms in healthcare. In public health systems, they examine access, equity, and population-level resource allocation.
That work runs through a Master of Science in Health Systems, the Center for Health and Humanitarian Systems, and Senior Design projects that have been going into Georgia’s health systems for years. Research collaborators include the Centers for Disease Control and Prevention, Children's Healthcare of Atlanta, Emory University, and the Georgia Department of Public Health among many others. Senior Design sponsors have included Emory Healthcare, Wellstar, Grady Health System, Piedmont Healthcare, and Northside Hospital. These projects are not case studies: student teams work on real challenges brought by sponsors and deliver decision-support tools and other design solutions.
More than twenty ISyE faculty work in health and humanitarian systems, spanning patient-centered decision support, clinician guidance, healthcare operations, health network design, and policy evaluation.
"ISyE has been a leader in health systems research, education, and practice for decades,” said Keskinocak. “This minor creates a clear pathway for undergraduate students from across Georgia Tech to apply analytics, AI, optimization, and systems thinking to some of society's most important challenges in healthcare and public health."
Full requirements are in the Georgia Tech Catalog and on the ISyE Health Systems minor page.
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