As ISyE’s A. Russell Chandler III Professor, Huo’s theoretical research focuses on explaining why modern deep-learning methods preform so well. He also uses statistics, machine learning, and data science to better to better understand the reliability and fairness of learning systems.

Huo has authored more than 15 refereed journal articles since 2023 and currently has 10 papers under review. His career includes 71 journal articles, 41 conference papers, 10 book chapters, and an edited volume.

“What I find most rewarding is that rigorous theory and useful tools are not in tension – the mathematical questions that fascinate me most often turn out to be the ones that help others make sense of their data,” Huo said. “I’m deeply honored to receive this award and especially grateful to my students and collaborators, who have been at the heart of this work from the very beginning.”

Huo’s first algorithm for distance covariance remains a standard tool for testing statistical dependence and is reproduced in widely used statistical software. He co-directs the Georgia Clinical and Translational Science Alliance's Biostatistics, Epidemiology and Research Design program, which is supported by the National Institutes of Health. He also serves as a co-principal investigator on the $20 million NSF AI Institute for Agent-based Cyber Threat Intelligence and Operation.  

Huo said he is proud of the researchers he has trained. In the most recent recruiting cycle, his doctoral graduates earned tenure-track faculty offers from Georgetown University and the University of Florida. Earlier advisees hold faculty positions at the City University of Hong Kong, Korea Advanced Institute of Science and Technology, and Seoul National University of Science and Technology, while other former students are researchers at companies that include Apple, Citadel, and JP Morgan.

His recently published work includes two 2024 papers in the Journal of Machine Learning Research (JMLR). He and his students established learning guarantees for deep neutral networks, including minimax-optimal convergence rates of neural-network classifiers was accepted in 2026 by IEEE Transactions on Information Theory, and his work on the universal consistency of wide and deep networks appeared at the International Conference on Machine Learning.   

Huo’s work on the fairness of learning systems is reflected in a 2025 Journal of the American Statistical Association paper that characterizes the asymptotic behavior of the adversarial-training estimator, complementing his JMLR work on distributionally robust estimation. At the Conference on National Information Processing Systems (NeurIPS) 2025, Huo and collaborators introduced a kernel-based quantification of the accuracy fairness trade-off in representation learning, along with a new diffusion method for imbalanced text-to-image generation, while a 2026 International Conference on Learning Representations paper advanced policy optimization for large-language-model reasoning.

Michael Carson’s roots at Georgia Tech run on both sides of his family tree. His father-in-law, Dean Sutter, and his mother-in-law, Karen Ann Sutter, worked on campus for decades before retiring. His brother-in-law, Dean C. Sutter, has logged more than 24 years on campus, helping manage equipment in the Institute for Matter and System’s cleanroom. 

“It was actually a suggestion from my father-in-law that prompted me to apply for a role at Georgia Tech in 2020. He was coming to the close of his GT career when he suggested what would be the beginning of mine,” said Carson. “Things were going great here at Tech, and since my dad had recently lost his father and was ready for a chance, we decided that it would be a good thing for my dad to come here.”   

At that point, Michael Carson recruited Michael Sr. to apply for a job driving buses for Georgia Tech Parking and Transportation. Before long, Michael Sr. was moving from Compton California to the Atlanta area to join his son on campus.   

Michael Carson basically has his workday down to a set process, making sure all the little and big things are taken care of across the ISyE Main building and the connecting Groseclose building. 

This process starts with what he calls “the morning rounds,” stocking faculty, Ph.D., and staff lounges; preparing conference rooms; and setting up the main atrium, especially for events such as Senior Design presentations or ISyE Advisory Board Meetings. 

He might also be preparing classrooms for students and setting up displays or other furniture for a range of lecture series hosted at ISyE. In 2024, his dedication won him the ISyE Buzz Award for exceptional service. 

Mike Sr. worked as a shuttle bus driver for the Los Angeles International Airport before moving to Georgia. Even after a year driving a Stinger bus, he said there is no such thing as a typical day at the office. 

“We don’t have those. Driving on campus, you must always keep your head on a swivel,” he said. “It took me a while to adjust to campus life, and I would say driving here keeps you more on your toes than driving in California.”  

No matter what their days bring, the Carsons make it a point to meet on campus for lunch when their schedules permit. 

Over a quick bite, they might reflect on the past—rabbit hunting at 4 a.m., fishing, playing one-on-one basketball in Michael Sr.’s father’s driveway, and going to high school basketball games—and talking about what the future may hold in Georgia. 

Long before he was managing the ISyE building, Michael Carson was tearing up basketball courts in Compton, winning three state championships at his father’s alma mater, Manuel Dominguez High School. Basketball was Michael’s ticket to college, but it required moving across the country to Alabama, a somewhat tough pill to swallow for his dad, Mike Sr. 

“Growing up in Compton wasn’t bad in our neighborhood. Mike played basketball from the age of seven until he graduated high school and we didn’t allow Mike to get involved with gangs or other negative influences that may have been around at that time,” Carson Sr., said. “Mike had to move to Alabama to continue playing. Originally, I didn’t like that idea. But it was the best thing for him. And now, we are here in the same place, so it all worked out.”

The younger Carson attributes his work ethic to his father and grandfather, Melvin Carson. 

“I remember watching my grandfather wake up at the crack of dawn, as early as 4 a.m., to go to work on construction sites and at warehouses to provide for his family,” Carson said. “Then my father carried that same work ethic and instilled it into me, so I guess you could say that became a Carson ritual.” 

Beyond their shared work ethic, the Carsons share another timeless bond: a deep love for art, animation, and comedy. It’s a creative thread that now connects three generations, all starting with Carson, Sr.’s sketch pad. 

“Many people don’t know that my father was a really good artist. I remember when I was younger, my dad would carry a bag of drawings with him and add to his collection whenever inspiration struck him along the way,” Carson said. “I’ll never forget growing up watching The Little Rascals, The Three Stooges, and Wile E. Coyote and the Road Runner with my father. He grew up on those shows, and it intrigued me to watch those classics. Now, my two boys, Christopher and Corey, sometimes sit down to watch those same shows with me today.” 

Having those three generations together in Georgia has given new meaning to Father’s Day for Carson Sr. 

“It’s difficult at times celebrating Father’s Day since I lost my dad a few years ago. I didn’t celebrate it as much until I moved here,” the elder Carson said. “My son’s family is always putting something together for us, and for me that is a way to stay connected with my pops in my own way because it reminds me of the times of being at my father’s house after church, my sisters cooking dinner, watching games, and enjoying family.”   

So, what do the two Mikes do when they aren’t working on campus or helping their families? 

“We enjoy bowling, fishing or just watching sports. I’ll admit he is a better bowler than me, but I am the better fisherman,” Carson Sr. said. “I also enjoy watching my son coach his J.V. basketball team. He really has the patience for that sort of thing, although he did not get that from me.” 

In this article:

• Why the relentlessness of change in logistics is a legitimate concern — not a complaint
• Structural shifts reshaping the competitive floor
• Predictions most leaders are still underweighting
• What staying current actually requires of individuals and teams
   

The Weight of Constant Curveballs

A few months ago, I caught up with a former colleague at an industry event. He is a senior leader at a large global company with a well-regarded supply chain organization. His team had been through a lot since we last talked. Port closures. Tariff escalations. Freight volatility. Inventory repositioning. The kinds of disruptions that used to arrive once in a cycle had become an avalanche, and this was before Hormuz!

His people had responded well. They had adapted. But now he was thinking about something harder to solve:  what it would take to keep them in the game longer term. The experience his team had gained came at a cost and he worried some would look for roles that were not on the “firing line”. He also wondered whether he could still attract the best and brightest in the next generation of talent who would be looking at this field and asking whether the complexity was worth it.

That conversation has stayed with me. Supply chain and logistics has always been a field of cycles — demanding, but navigable. What has changed is that the field has moved from cyclical difficulty to something more structural: a persistent state of volatility where the curveballs do not stop coming.

That conversation was in the back of my mind as I developed a recent talk on logistics trends from 2026 to 2030 for GT SCL Industry Partner Manhattan’s annual Momentum conference. The brief was to look ahead and be a bit provocative. What follows builds on that talk, but with a broader point in view: if the curveballs keep coming, leaders need a clearer sense of which shifts matter most and what they should do about them.

The New Operating Environment

Logistics has entered a structurally more volatile era, not a groundbreaking insight given the last four years. Several things changed at roughly the same time, and they have not changed back:

Several shifts hit the industry at once, and none of them have meaningfully reversed. Geopolitics is now a supply chain design variable, not something to catch up on in a podcast. Strategic decoupling between China and the United States, instability in the Middle East, and the long shadow of the Russia-Ukraine conflict have pushed energy, sourcing, and network design into the same conversation. What once sat in the news feed now needs to be in the nominal scenario during business planning.

At the same time, customer expectations have permanently shifted. Amazon reset the standard for visibility, precision, and speed, and that standard now applies even more as Amazon is emerging as an open source 3PL. Labor and energy costs have also changed the economics of physical logistics in ways that will not self-correct. Demographic pressure, wage inflation, and energy volatility have altered the baseline cost structure calling into question existing network locations.

Meanwhile, AI and automation have moved out of the experimental category and into the realm of near-term value creation. The tools are real, and organizations that understand where to apply them are making materially better decisions than those that do not. That matters because networks now have to optimize for two things at once: cost and recovery. Efficiency still matters, but a network that performs well in steady state and fails under disruption no longer meets the standard.

There is also a macro pattern worth calling out: the industry is in a longer-duration rebalancing cycle than many executives expected. We examined the Hormuz disruption and its downstream effects in a recent SCL Spotlight piece. The short version is that energy pass-through effects, freight volatility, and extended planning uncertainty will impact costs and capacity well into 2027. Executives planning around a near-term return to normal are making a strategic error.

The next decade will reward adaptable logistics networks more than simply optimized ones.

The Benchmark Has Changed — For Everyone

Amazon's logistics operation is not just something to amaze us as packages arrive at our doorstep consistently with compressed lead times. It is a capability demonstration that has redefined what customers consider normal — same-day expectations, ETA precision, real-time visibility, low-friction returns. The important implication is not that every organization needs to replicate Amazon's infrastructure. It is that Amazon-shaped expectations are now the standard against which every supply chain is measured, whether or not Amazon is a direct competitor. Amazon’s recent announcement that it is making its capabilities available to all only raises the bar.

Organizations that understand this have shifted their strategic question from "how do we improve our operations" to "where will we compete, where will we leverage others' capabilities, and where will we differentiate on something Amazon cannot replicate." The benchmark is no longer functional excellence alone. It is well oiled end-to-end execution.

The Real Automation Story: Error-Proofing Over Spectacle

There is a version of the automation conversation that focuses on  “wow” demos — autonomous vehicles, lights-out warehouses, robotics showcases. That version makes for compelling conference content. It is also not where most of the real value is being created today.

The highest-value wins tend to be quieter: fewer errors, fewer touches, fewer injuries, fewer claims. Computer vision that catches a loading error before a truck leaves the dock. Sensor verification that eliminates a reconciliation step. An alert from a Machine Learning model that prevents a cascading service failure. These are error-proofing stories, and they are compelling because the ROI is measurable in terms operations leaders understand.

The reason automation is scaling in these areas is not novelty — it is because the math finally works, driven by labor scarcity, safety pressure, and the compounding cost of variability. My own view informed by industry contacts and academic researchers is that computer vision may become one of the most quietly transformative technologies of this decade, not because it is the most advanced, but because it applies to so many high-variability, human-intensive touchpoints across logistics operations.

That said, a high percentage of large-scale automation efforts still fail.  Many of the reasons are well known and tackling this issue is critical for those who do not yet have a model for success.

The next margin pool may come more from consistency and reliability than from flashy robotics demonstrations.

This theme generated significant discussion at the Manhattan Associates' Momentum conference this spring — enough that we are dedicating our July SCL webinar to it directly. If your organization is navigating automation decisions, the session is worth your time.

Autonomy: Watch the Middle Mile Before the Long Haul

Autonomous vehicle technology has generated significant hype and its share of missed timelines. A more realistic view is emerging. Autonomy scales first where variability is lowest, economics are clearest, and environments are most constrained — yard operations, middle-mile freight on repetitive lanes, internal shuttles, port drayage, and warehouse orchestration. This amounts to millions of miles and load counts that are increasing daily.

The organizations watching this most carefully are not asking when full autonomy will arrive. They are asking which specific lanes and operations have the cost structure where autonomy pays out today. One dynamic worth watching: the scaling of urban robotaxi operations is building safety data, insurance frameworks, and regulatory precedent that may indirectly accelerate confidence in middle-mile freight and warehouse applications.

The shift that matters is not from no autonomy to full autonomy. It is from technology demonstrations to lane economics — and that is the transition that creates real operating decisions for logistics leaders.

AI Is Real — But Workflow Discipline Matters More Than Tool Selection

The common reality in most logistics organizations today includes AI copilots, workflow assistance tools, exception management support, improved ETA prediction, and document automation. These are useful. They are also early.
What is still uncommon: autonomous execution, fully integrated AI decisioning across functions, self-optimizing networks, and end-to-end agentic orchestration. Those capabilities exist in pilots and in forward-leaning early adopters. They are not yet standard operating practice in most organizations.

The framing that I keep coming back to is this: start with a broken logistics workflow, then apply the lightest AI capable of clearing a hard ROI threshold. I got a text from a mentee today that showed a picture of a Microsoft Co-pilot Studio agent he built that automates a daily inventory check on a critical SKU. Organizations that start by selecting the most impressive tool and then look for a process to apply it to are making the investment in the wrong order.

There is another structural shift worth highlighting. The industry has moved out of data scarcity and is living in decision overload. The challenge is not access to information — it is building the discipline to convert that information into insight and informed decisions at the right time to impact action.

Logistics first, AI second. Start with the broken workflow. Then apply the lightest tool that clears a hard ROI threshold.

The Rising Value of Human Judgment

Back to the conversation I opened with. The concern was not that my friend’s team lacked technical skills — it was sustaining engagement and attracting talent to a field that had become genuinely exhausting. That challenge is real, and it is connected to something missed in the automation conversation.

As AI automates more routine work — reporting, documentation, tracking, reconciliation — the work that remains becomes more demanding in different ways. The value shifts toward judgment: escalation management, cross-functional orchestration, interpreting second-order consequences, maintaining trust when the data is ambiguous. The organizations that will attract and retain the strongest professionals are not necessarily those with the most advanced tools. They are the ones that create conditions where smart people make consequential decisions and continue to grow.

As AI capabilities become more democratized across the industry, the differentiating capabilities will increasingly be leadership, communication, collaboration, and the kind of critical thinking that no tool can fully replicate.

Predictions Leaders Should Keep an Eye On

These are the shifts I believe deserve more attention than they are getting in most leadership conversations:

1. The Next Major Logistics Disruption May Come From Energy, Not Freight
   Grid strain, electrification demand, AI compute infrastructure buildout, and charging capacity constraints are converging in ways that could reshape logistics economics faster than expected. Power availability is not yet a front-burner strategic issue for most logistics leaders. It should be.
    
2. Amazon, Walmart, and Chinese Platforms May Become Competing Logistics Operating Systems
   Competition is shifting from retailer vs. retailer to ecosystem vs. ecosystem. The organizations that do not think clearly about which ecosystems they are part of, and on what terms, may find themselves structurally disadvantaged.
    
3. Cyber Attacks on Physical Supply Chains Will Become a Defining Executive Risk
   As logistics networks become more connected, more automated, and more AI-dependent, your exposure grows. The distinction between cyber risk and operational risk is collapsing. This belongs on the executive agenda as a strategic issue, not just an IT issue.
    
4. Trusted Operational Data May Become the Most Valuable Logistics Asset
   Organizations with clean, well-governed operational data will be able to move fast on AI adoption. Organizations with fragmented, inconsistent data will face a structural disadvantage that no AI investment can overcome. Data discipline is a strategic investment, not a cleanup project.
    
5. Insurance Companies May Quietly Become Gatekeepers of Automation Adoption
   Scaling autonomy and connected logistics infrastructure depends as much on insurability, liability frameworks, and safety validation as on technical capability. Insurance market dynamics will shape the adoption curve for autonomous operations in ways that are not yet widely discussed in logistics circles.
    
6. The Industry May Shift From "Lowest Cost" to "Fastest Recovery" as the Defining Competitive Dimension
   Pure cost optimization as a primary network design principle may increasingly underperform against resilience and recovery speed as the basis of competition. The organizations that have already internalized this are building different networks than those still optimizing for cost alone.

What Staying Current Actually Requires

I want to close by coming back to my colleague's concern — and to the question he was really asking: how do we help our people process all of this and remain effective?

Here is my honest answer. The field is not going to slow down. What staying current requires is not reading every article or attending every conference. It requires developing a point of view on the shifts that matter most for your specific context, and then actively deciding how you will act and adjust. Passive awareness is not enough. The question is not whether you know what is changing. It is what you have decided to do about it.

For organizations, that means investing in conditions that allow talented people to keep learning. For individuals, it means resisting the temptation to treat busyness as a substitute for development. The professionals who remain most valuable will be those who continue to understand what is changing and develop the judgment to translate that understanding into better decisions.

You cannot sit still. The question is not whether you know what is changing. It is what you have decided to do about it.

The Opportunity on the Other Side

I want to end where I began — with empathy for everyone in this field who is carrying a lot right now. Fatigue is real. The complexity is real. The ongoing intensity is real.

And so is the opportunity.

Logistics is no longer just moving product. It is becoming a resilience system, a customer experience system, a technology system, an energy system, and a real-time decision system simultaneously. The professionals who learn to navigate that complexity — who develop both technical fluency and human judgment — will be among the most valuable people in any organization.

The future arrives not as one dramatic breakthrough, but as a sequence of operational readthroughs: decisions made well, workflows redesigned thoughtfully, capabilities built deliberately. That is hard work. It is also genuinely exciting work. And I believe the best of it is still ahead.

Related Upcoming SCL Webinar 7/2/2026

Why Do So Many Automation Projects Fail?
Automation in logistics is accelerating — but so is the gap between what is promised and what is delivered. Systems get sized on optimistic assumptions. Hidden dependencies become single points of failure. Technology that shines in the demo struggles under real operating conditions.

Leaders from Georgia Tech's Supply Chain and Logistics Institute join industry practitioners to dig into the root causes of automation underperformance — and the design, evaluation, and implementation practices that build more resilient, effective operations.

Register Online to attend via Zoom
Can't attend live? Register anyway, and we'll send you the recording afterward

“The IISE Sustainable Development Teaching Award confirms sustainable development as a strength of industrial engineering,” said Professor Thomas, who also has a joint appointment in Georgia Tech’s Jimmy and Rosalynn Carter School of Public Policy. “Receiving this award brings ISyE’s sustainable development offerings to the attention of the wider industrial engineering community.”  

The award was developed by IISE’s Sustainable Development Division to recognize the contribution of an individual advancing the knowledge and practice of sustainability in the field of industrial engineering. 

Thomas has an extensive history of high-quality teaching and leadership in developing interdisciplinary, sustainability focused courses at both the undergraduate and graduate level. The quality and effectiveness of her sustainability-focused teaching efforts have consistently yielded high evaluations in student feedback. 

The IISE award committee also highlighted Thomas’ teaching impact beyond courses and classrooms, through global learning experiences in Rwanda focused on sustainable development, and her scholarly contributions in sustainability education. 

Thomas has created three sustainability-focused courses at Georgia Tech, one at the undergraduate level and two at the graduate level, and a study abroad program. 

Thomas’ undergraduate course, Energy, Efficiency and Sustainability (ISYE 4501), is offered annually. This course focuses on technology cost analysis and environmental modelling. Students learn how to assess efficiency and impacts of industrial systems such as power generation, transportation, manufacturing, and building operation. 

At the graduate level, Thomas collaborated with Georgia Tech Jimmy and Rosalynn Carter School of Public Policy Professor Marilyn Brown to develop an interdisciplinary course, Energy Technology and Policy (ISYE/PUBP 6701), that draws graduate students from ISyE, Public Policy, College of Engineering, and from throughout Georgia Tech, with both an in-person section and a distance-learning section. The distance-learning section draws students from online master’s programs in Industrial Engineering, Mechanical Engineering and more. To support this course, Brown and Thomas co-wrote a textbook, “Energy Technology and Policy Innovation.”

“I’ve had the privilege of teaching Energy Technology and Policy with Valerie once a year for 20 years. We used to do major marketing with flyers and videos, but soon the course caught on, moved to larger classrooms, and doubled in numbers with an on-line section. Our course focuses on core scientific principles and practical tools to support fact-based energy system decision-making. We never could find a textbook with all the material we wanted students to master. So we created a book from our lecture notes - all 610 pages and 44 chapters - that was just published by Springer-Nature,” said Professor Brown, Regents' and Brook Byers Professor of Sustainable Systems. “Teaching beside Valerie for twenty years has been both a joy and an education in itself.” 

Also at the graduate level, Thomas created a new course on life cycle assessment (LCA); this course draws graduate students from across Georgia Tech. Students gain hands-on experience with a comprehensive set of LCA methods and tools, including the life cycle process-based method, energy and greenhouse gas calculations, cost analysis, allocation and system expansion, economic input–output LCA, hybrid LCA, the life cycle process matrix method, consequential LCA, and life cycle impact assessment. The course culminates in a substantial, project-based life cycle assessment, providing students with conceptual understanding and practical expertise.

In addition to academic courses, Professor Thomas has developed a Rwanda Study Abroad program that powerfully complements classroom learning. In this program, students work on sustainable development projects during the spring semester at Georgia Tech and then travel to Rwanda during spring break.

“They kept losing in the same way, every single time. I thought to myself ‘if coach just had really good data, maybe we’d win some games,’” Mast (IE 2015) jokingly said. “I met Coach Gregory at the front door of that fraternity event, pitched him my idea, and he liked it. I did everything in Excel with ‘Moneyball-Style’ lineup optimization. It worked, they changed their style, we started winning games, and we beat North Carolina that year.” 

Mast’s scrappy beginning in sports analytics eventually led to work with the Atlanta Hawks from 2017-2021 and to his current role as a sports scientist for the Detroit Pistons.

Mast shared his story during a recent Georgia Tech ISyE alumni entrepreneurship panel moderated by Advisory Board Chair Evren Ozkaya, PhD IE 2008, Founder and CEO of Supply Chain Wizard and SCW.AI. 

They were joined by Fred Grimm, IE 2014, co-founder and COO of FIXD; Andy Ibbotson, IE 1998, founder of RatingsMD; and Jeff Anderson IE 1991, co-founder and chief growth officer of Kaizen Analytix. 

Together, the panelists offered a candid look at the grind of building a company and the systems-minded discipline that gives ISyE graduates a distinctive edge.

The Scrappy Era: Learning by Doing

For Grimm and two of his Georgia Tech classmates, the founding idea behind FIXD started with a common frustration: most drivers did not know why their check-engine light was on. And finding out was expensive and inconvenient. Their answer was a plug-in diagnostic device paired with a smartphone app that put that information directly in a driver’s hands. 

What began as a senior project launched on Kickstarter in 2014 would raise $37,000. It was grown into one of the nation’s most widely used automotive apps.

FIXD’s early days were far from glamorous. Grimm remembers working from a windowless “closet” in the basement of a campus building and looking for customers anywhere they could find them. The team would even go to Piedmont Park and stop strangers to pitch their concept. 

“We realized that if we talked to people, we could sell to about 60% of them,” Grimm said. “It wasn't scalable, but it taught us how to talk to customers.”

Ibbotson described a different kind of test during the panel discussion: surviving the “nuclear winter” after the dot-com bubble burst. 

“My business partner and I stopped taking salaries. We both were paid $2,000 a year for two years, while continuing to pay six-figure salaries to our remaining employees,” he said. “We were betting that things would be good on the other end. Our competitors were going out of business, but we just kept persevering.” 

The ISyE Advantage: Systems Thinking in Action

A recurring theme during the discussion was that ISyE graduates are trained not just to solve isolated problems, but to understand how the pieces fit together. Ozkaya noted that many entrepreneurs focus on a single feature or narrow pain point. The stronger opportunity is often broader: designing the operating system around the problem: the data, incentives, workflows, decisions, and people that must work together for a solution to scale.

Mast connected that idea to what he learned at Georgia Tech. 

“A fifth-year student once told me that Tech teaches you how to think. That mindset stayed with me,” Mast said. “I truly don't believe there is a problem I can’t solve. I know how to convert problems into bite-sized chunks and attack them.”

The AI Inflection Point

The panelists agreed that artificial intelligence is changing the economics and pace of entrepreneurship. Ibbotson recalled that when he started his first company, he spent $100,000 on a Dell server and Microsoft Exchange licenses just to provide corporate email. Many of those barriers have since disappeared.

“For example, my 13-year-old son recently used AI to build a minimum viable product in two months for less than $200,” Ibbotson said. “It would have taken my partner and me 12 months and $200,000 many years ago.”

Anderson said AI is also changing the way established teams operate, allowing engineering teams to move dramatically faster. The premium skill, he argued, is no longer simply the ability to code quickly. 

“It’s the ability to define the problem clearly, ask better questions, and make sound decisions,” Anderson said. “In other words, the speed of tools makes the quality of thinking even more important.”

Advice for the next generation of founders

The panel closed with practical advice for students and alumni considering an entrepreneurial path. The message was direct: start before the risk feels comfortable. Financial obligations tend to grow over time, while the ability to experiment is often greatest early in a career.

The panelists urged founders to listen carefully to customers and that a company does not have to be first to win because being a fast follower can be powerful when the problem is already understood, the market has a budget, and the founder can execute with better insight and discipline.  

“Another Georgia Tech grad who had just exited his software company was trying to figure out what to do next. And one of our customers — early customers that he was friends with — said, ‘Hey, Andy just sold his company to NRC. Why don’t you build the same thing and try to sell it to their competitor,’” Ibbotson said. “And so, they we were, second to market, not as good, but NRC’s competitor needed an offering to be competitive with ours. They ended up selling for $70 million more than we sold for, even though they didn’t have a good solution because the bigger company needed something.”   

The panelists encouraged students to use the Georgia Tech and ISyE network aggressively, noting how a single faculty introduction or alumni connection can open doors that would otherwise be difficult to reach.

“Lots of coffee, lots of sleepless nights, lots of hard work. You need to outlive the competition, and connections and networking is critical,” Ozkaya said. “I wrote an article a year into Supply Chain Wizard and said: ‘Eat, pray, love and network.’ You need to love what you do, you need to pray that you survive, and you need to network. Networking is what brings you the opportunities.”  

Meet the Panelists:

·      Fred Grimm (IE 2014): Co-founder & COO, FIXD.

·      Andy Ibbotson (IE 1998): Founder, RatingsMD.

·      Jeff Anderson (IE 1991): Co-founder & Chief Growth Officer, Kaizen Analytix.

·      Chris Mast (IE 2015): Sports Scientist, Detroit Pistons.

·      Evren Ozkaya (Moderator, IE 2005): Founder & CEO, Supply Chain Wizard and SCW.AI

Grajczak currently serves as a Managing Director in Accenture's Supply Chain & Engineering practice. He is a leader in the firm's Private Equity practice, where he advises operating partners on developing and executing high-impact value creation plans across their investment portfolios.

"We are thrilled to welcome Conrad to our Industry Advisory Board," said SCL Managing Director Chris Gaffney. "His extensive background as a management consultant specializing in margin improvement and cost reduction for private equity-backed companies will be invaluable. We look forward to leveraging his insights to drive our strategic initiatives forward."

A Proven Track Record in Value Creation

Throughout his career, Grajczak has partnered closely with Private Equity Operating Partners to deliver bottom-line value through strategic sourcing. He routinely leads pre-purchase and post-acquisition initiatives to establish a value creation roadmap and implement transformative cost programs across Consumer, Industrial, and Healthcare clients. 

Prior to Accenture, Grajczak was part of Insight Sourcing for 11 years and promoted to Partner.  Insight Sourcing was a boutique procurement consulting firm where he focused on Private Equity.  Insight Sourcing was acquired by Accenture in 2024.

Before consulting, Grajczak started his career out at Merck & Co. in Vaccine Manufacturing Science and Commercialization.  

Academic Credentials

A proud Georgia Tech alumnus, Grajczak earned his B.S. in Chemical and Biomolecular Engineering with a minor in Business through the Technology & Management Program. He also holds an MBA from Emory University’s Goizueta Business School.

Academic & Research IT Support Engineer May Li: Exceptional IT Support Professional. This award recognizes a staff member who has demonstrated exceptional job performance and/or service to ISyE above and beyond the call of duty.

Program Support Coordinator Rudy Paratchek: Exceptional Administrative Staff. This award recognizes a staff member who has shown excellence in managing day-to-day administrative processes in ISyE. This can be related to any general administrative activity or to a staff member who has shown a proven track record of accomplishing daily activities at a high level. 

Academic Advisor Caroline Layton: Exceptional Process Improvement & Policy Leader. This award is presented to a staff member who has demonstrated a collaborative leadership style, created a positive work environment using appropriate leadership skills, designed and implemented a process improvement that increases compliance, reduces liability, or saves measurable time.

Academic Program Manager II Harry Sharp: The Buzz Award (Going the Extra Mile Award). This award is presented quarterly and is to be alternated between a staff member who has gone above and beyond to help others in the ISyE community and achieve exceptional levels of performance in one or more areas of strategic focus. 

Events Coordinator II Monike Welch: ISyE Service Award. This award is to be presented to a staff member who has benefitted ISyE, profession, Georgia Tech, or the general public in an exemplary manner.  

Academic Program Manager I Christian Gallie: Outstanding Student Academic Advising Award. This award is presented to staff who have demonstrated excellent student advising qualities. Displays strong, professional, and caring student support. Demonstrates mastery of institute policies and procedures. Provides accessibility, flexibility, leadership in advising/mentoring, and has made a positive impact when it comes to student experience and helps support all-around student success. Their support is essential to student progress and retention.

The ISyE Staff Awards have been established to honor individuals or groups in three categories: administrative, leadership, and service excellence.  

Mukherjee teaches a broad range of courses in applied probability and stochastic processes, including foundational undergraduate courses as well as advanced graduate topics. His teaching is widely recognized for combining mathematical rigor with accessibility, helping students engage deeply with technically challenging material while building confidence and long-term interest in the subject.

A central aspect of Mukherjee’s teaching is careful course design and the development of structured learning materials. For ISYE 2027 (Probability with Applications), one of the foundational courses taken by ISyE students, he developed an extensive set of lecture notes over the course of more than a year. The notes, which are freely available to students, effectively function as a full textbook and are now used by multiple instructors within the school.

“While there are many resources available for introductory probability, I felt that most of them did not strike the right balance between mathematical rigor, detailed explanations, and real-world examples that our students can genuinely relate to,” said Mukherjee. “My goal was to create a self-contained resource that students could continue to use well beyond a single semester.”

Students frequently highlight the clarity and organization of both the lectures and course materials. In course feedback and Thank-a-Teacher notes, students described the lecture notes as “the most informative” part of the course and praised the lectures for making difficult concepts approachable. One student wrote that the course “completely changed” their attitude toward the subject, while another described the lectures as “like a mathematical story.”

Mukherjee’s teaching philosophy emphasizes student-centered learning, interactive classroom engagement, and strong individual support. He actively incorporates discussion into lectures, adapts pacing based on student feedback, and designs assignments that encourage independent thinking rather than purely mechanical problem solving.

“The most exciting part of teaching for me is interacting with students,” Mukherjee said. “Those interactions constantly give me new perspectives and help me evolve my teaching. I enjoy connecting fundamental ideas in probability with modern topics students care about today, while also showing them the timeless principles that continue to drive the field.”

In graduate instruction, Mukherjee has also worked to connect theoretical concepts with modern applications. In ISYE 6762 (Stochastic Processes II), he introduced data-driven projects designed to bridge abstract mathematical ideas with contemporary applications, helping students develop both intuition and technical depth.

Student evaluations across multiple semesters consistently highlight clarity of instruction, enthusiasm, accessibility outside class, and strong learning outcomes. Students frequently praise his ability to translate “complex formulas into understandable real-world examples,” as well as the welcoming classroom environment that encourages questions and discussion.

Mukherjee has also received several Thank-a-Teacher recognitions from students over the years, reflecting the lasting impact of his teaching both inside and outside the classroom.

Working in teams of six to eight, students are responsible for identifying industry clients and spending the semester developing data-driven solutions. This semester, teams collaborated with organizations such as American Airlines and Wellstar Health Systems to address pressing logistical, procedural, and forecasting challenges, delivering analyses and recommendations designed to drive measurable improvements.

While most teams work with external partners, several this semester chose to assist clients across Georgia Tech’s campus. Like many complex organizations, the Institute encompasses dozens of divisions and departments that work to improve their processes in support of its broader educational mission. This semester, three teams focused their capstone work on strengthening operational functions across the Institute to deliver solutions designed to create lasting impact.

Advising Designed with Students in Mind (Team Lean on Me)

Team Lean on Me worked with Academic Success & Advising to improve the current advising system for students across campus. Team members Ansley Nguyen, Josh Raug, Julianne Latimer, Noah Koh, Shivani Murugapiran, Surya Rangaswamy, Thien-An (Amy) Dang, and Wyatt Stephens wanted to make the current advising system more proactive so that advisors can connect with students who have expressed interest in specific advising goals, such as major exploration or pre-graduate advising.

Using anonymized data from the current advising platform, Navigate360, the team implemented various tools they had learned as undergraduate students. They forecast advising demand to help advisors better understand when they need to have advising opportunities available and when they should reach out to students who are not normally involved in the advising program. The team also used simulation and optimization techniques to understand how to schedule and plan advisors’ time to better meet students’ needs.

They also developed an AI chatbot that can respond to basic student inquiries, giving advisors more time to either proactively reach out to students or take more exploratory appointments. They predict that chatbots will save 603 advising hours in basic inquiry appointments per semester.

Their process also included getting feedback from current students about how advising is working for them and learning more about how the Institute operates, a unique lesson that goes beyond what students can learn in a classroom.

“Being on a very student-facing side has allowed us to learn a lot of perspectives. I've gone through four years at Georgia Tech not really knowing that much about the School of Architecture, or Aerospace, …but being on the side of surveying people, tabling, hearing from students themselves, what they want has really informed me about what our school has been like in ways that I would have never been exposed to otherwise,” Dang said.

In all, they expect their innovations to double the percentage of students captured by the advising system from 4.3 percent to about 8.6 percent while only increasing advisor workload by 3.2 percent, giving more students the opportunity to explore their futures with an advisor.

First-Year Registration, Re-imagined (Team FASET Your SEATbelts) 

Team FASET Your SEATbelts worked with Georgia Tech’s Registrar’s Office to improve how first-year students register for classes during FASET — the Institute’s student orientation program. A key focus of the program was reducing the number of students who leave FASET unable to register for a full-time course load of 12 credit hours.

As former incoming students themselves, team members Alexis Almeida, Claire Wu, Irene Chang, Madeline Sanders, Mahathi Manikandan, Shaan Patel, Zach Thomas, and Zarah Khan were keenly aware of the challenges students can face when registering for classes for the first time. Failing to register for enough credit hours or enroll in the correct classes can jeopardize scholarships and, in some cases, delay graduation.

The Registrar’s Office was able to provide them with detailed data about registration, including student schedules immediately after their FASET registration, final schedules after Phase II registration, selected major, and incoming AP credits. The team also has access to the types of students who will attend each FASET session.

Using this data, they created a demand model to predict how many seats students will seek in a given class on a given FASET day, based on the number of different types of students attending that day. This information will assist managers in the Registrar’s Office in deciding how many seats to allocate to which classes for each FASET session and in ensuring that students find the classes they need on the day.

Like any project, the team encountered challenges along the way. Team member Madeline Sanders explained that she didn’t feel they were leveraging each member’s strengths, but a recent shift in approach led to better collaboration and results. After overcoming their challenges and taking on new experiences, she said she gained important lessons from her work this semester.

“I feel that out of all the projects I've done at Georgia Tech, this has taught me the most. I think I’ve learned a lot about working with a team and also working with a client because we have a lot of different stakeholders,” Sanders said.

Their solution incorporated the dynamic release of seats in Freshman courses and improved scheduling around AP test score results. They simulated their new process to estimate how it will impact students if implemented this coming summer. Using the allocations that FASET Your SEATbelts suggested decreased the number of students who left their FASET session without registering for 12 credit hours from 33 percent to 7 percent. The spread between the most successful and least successful FASET sessions in registering for 12 credit hours dropped by 36 percentage points in their simulation, indicating that their allocation would be fairer for students regardless of when their FASET session is scheduled.

Engineering a Better Game Day Experience (Team Linebackers)

At Georgia Tech, innovation on the football field doesn’t stop during the off-season. The Linebackers team — Carson Veal, Harrison Preston, Jedidiah (J.D.) Cheng, Julian Varga, Lauren McDonald, Sophia Hawkins, Wade Chappell, and William Wyatt — worked with the Georgia Tech Athletic Association to improve the function of the Yellow Jackets’ Bobby Dodd Stadium on game day. Their project tackled three critical areas that shape the fan experience, such as stadium ingress and concessions.

When large numbers of fans arrive at the same time, long entry lines can form, which not only diminishes the fan experience and, if left unmanaged, raising safety concerns. To address these ingress challenges, the team analyzed ticket-scanning data to reassess where staffing and resources could be more effectively allocated to keep the lines moving. 

One bottleneck they identified was slowdowns caused by scanning individual mobile tickets. To increase throughput, the team is looking into ways that would allow a single scan of a group of tickets purchased together, streamlining entry while maintaining security. They expect this one change to reduce the time that entrants have to wait from 19 minutes to 9 minutes during peak times.

Concessions are an integral part of the game-day experience, and fans expect to find their favorite items in stock when they look for refreshments or food. Linebackers had access to concession purchase data, which they used to track where guests went when they wanted certain types of refreshments. 

They used this data to determine when stands ran out of items and which stands were most successful, to improve the restocking schedule. Based on simulations, their improved restocking schedule decreased the maximum wait time for concessions by 4 minutes and reduced stockouts by 94 percent.

Throughout the project, they relied on the methods they were taught in class to analyze the current system and suggest improvements. They developed forecasting models to predict concession demand, optimization models to recommend resource allocation for ticketing, and a simulation model during the initial phases of their parking design. 

As football fans themselves, they said they found it rewarding to work on a project that improved the experience for fellow fans, and they also found career growth along the way.

“[We learned] to deal with incomplete data, to figure out how to find recommendations, and how to work with that data or missing data. And how to adapt to change and pivot from one solution that you thought would be great. And then realizing as you get further along that that's just not feasible,” McDonald said. “You don't have everything laid out for you perfectly. And I think those are two of the bigger soft skills we've learned from this project. That we would definitely take to our careers.”

The annual honor recognizes one faculty member for their excellence in expanding access to learning and creating meaningful educational experiences for global audiences. 

Sokol was honored for his sustained and impactful contributions to education at scale, highlighting his development and leadership of the Online Master of Science in Analytics (OMSA) program. 

Since launching in 2017, OMSA has graduated more than 6,900 students and reaches learners from more than 140 countries.

The program serves a wide range of students, from early-career professionals to senior leaders and individuals pursuing new career paths, bringing analytics education to those who would not otherwise have access.     

OSMA combines flexibility with rigor, integrating interdisciplinary content with large-scale, project-based learning. Its capstone experience connects hundreds of students each semester with companies and organizations, enabling them to apply analytics to practical challenges in diverse settings. 

"It's particularly rewarding, because OMSA has been able to impact so many students of all ages and backgrounds around the world, as well as all of the employers they apply their learning for," said Professor Sokol, who has also won the EURO Management Science Strategic Innovation Prize and twice been named to Chief Data Officer Magazine's list of Academic Data Leaders.

OSMA courses have reached tens of thousands of learners and consistently receive strong student feedback, demonstrating that high-quality instruction can be sustained even at very large scale.  

Beyond OMSA, Sokol has extended Georgia Tech’s impact through additional initiatives in professional and online education, as well as new program development across degree and non-degree offerings. His work reflects a broader commitment to making analytics and optimization accessible to learners at all stages of their careers.  

Students began arriving around 11 a.m., filling the ISyE main atrium with conversation and excitement. Teams enjoyed lunch together while catching up with advisors, clients, faculty members, and representatives from industry partners. Project posters lined the space, giving attendees a chance to walk through the semester's work and explore the wide range of challenges tackled by ISyE students. 

The atmosphere felt equal parts celebration and reflection; students exchanged stories about long nights spent debugging models, clients discussed implementation possibilities with teams, and faculty and industry guests stopped to ask questions or to congratulate students.  

The energy was fitting. This semester's cohort was the largest in recent memory, and an exceptional number of outstanding projects made the selection process particularly competitive. ISyE recognized work across three categories: Senior Design Finalists, the KS2 Technologies Design with Impact Award, and Senior Design Honorable Mentions, with a larger-than-usual number of teams honored in each. 

“Across the full cohort of 31 teams, the collective value created this semester is striking. Projects delivered outcomes that include more than $51 million in cost savings, more than $19 million in increased revenue, more than $200K in profit increase, a doubling of the number of teachers served, dramatic improvements to GT first year course registration outcomes, kitting time reductions of 20-45%, doubled output in production environments, and inventory and annual cost reductions of 25% each,” said ISyE Professor Emeritus and Senior Design Examiner Leon McGinnis. “No two projects tackled the same problem, and that range of impact reflects the breadth of what ISyE students can do." 

After lunch, ISyE Chair Dr. Pinar Keskinocak opened the event with warm remarks, welcoming attendees and celebrating the students' accomplishments before turning to Senior Lecturer Laura Li to introduce the finalist presentations. 

Category 1: Senior Design Finalists 

Being selected as a Senior Design finalist represents one of the highest honors in the ISyE capstone program. Finalist teams are evaluated by faculty members and corporate experts, and they are chosen based on a combination of project challenge, technical rigor, presentation quality, innovation, and the measurable impact of their solutions.  

AA Flight Dispatcher Scheduling 

Working with American Airlines, this team developed an optimization model to improve dispatcher scheduling across multiple regions and shifts. Their model balanced millions of variables and constraints simultaneously to reduce understaffing, improve shift coverage, and increase overall operational efficiency—delivering a tool that American Airlines can integrate directly into scheduling workflows. 

Team members: Amanda Ehrenhalt, Colin Fravel, Alexis Frith, Sehaj Munot, Christopher Schulte, Sahana Yerneni, Chaitanya Sri Yetukuri 

Client Sponsor: Analytics Manager, Data Scientist, Samuel Rodriguez-Gonzalez 

Faculty Advisor: Associate Professor Mathieu Dahan 

Improving First-Year Registration: Georgia Tech Office of the Registrar - Winner 

Partnered with Georgia Tech’s Strategic Consulting and the Office of the Registrar, this team focused on improving registration outcomes for incoming first-year students. Among their key recommendations was an “AP reserve” system designed to better support students awaiting Advanced Placement credit determinations while still allowing them to register for the right courses, reducing downstream schedule conflicts and improving placement accuracy. 

Team members: Alexis Almeida, Irene Chang, Zarah Khan, Mahathi Manikandan, Shaan Patel, Madeline Sanders, Zach Thomas, Claire Wu 

Client Sponsor: Georgia Tech Senior Director of Academic Administration Andrew Eichel 

Faculty Advisor: Associate Chair Dima Nazzal

Aluminum Production Scheduling Optimization - Novelis 

Working with Novelis, a global leader in aluminum rolling and recycling, this team developed solutions to reduce production scrap and improve manufacturing efficiency through process optimization. Their scheduling approach addressed complex interdependencies across production lines, providing Novelis with a more systematic framework for sequencing orders and reducing costly waste. 

Team members: Nivi Dileep, Charles Hill, Davis McLanahan, Myiesha Rahman, Mahathi Siripurapu, Martin Svobodkov, Jason Warner, Peter Zagrobelny 

Client Sponsor: Novelis Supply Chain Senior Manager James Praino 

Faculty Advisor: Associate Professor Mathieu Dahan 

VayKLife Route and Capacity Optimization: VayKLife - Winner 

Partnered with VayKLife, this team optimized delivery routing and vehicle space utilization for beach rental equipment, helping the company reduce operational costs while maintaining high service levels during peak seasons. Their solution tackled a challenging last-mile logistics problem with significant variability in demand, product size, and delivery timing, producing routing recommendations that VayKLife can deploy in real operations. 

Team members: Lilly Aabye, Jessa Brown, Will Craycraft, Tyler Estridge, Lucy Fantz, Chance O’Donnell, Rohan Prabhuram 

Client Sponsors: Senior Logistics Analyst Matt Durigon and COO/CTO, Managing Parter Erik Weston 

Faculty Advisor: Professor Alejandro Toriello 

After deliberation, "FASET Your SEATbelts" and "Buzz on the Beach" were declared winners, each receiving a $1,500 award from ISyE. "Shift Happens" and "Complex Marriages" tackled large-scale problems with major corporations, American Airlines and Novelis respectively, and their solutions have the potential to generate tremendous operational impact. The margin between all four teams was exceptionally narrow. 

“What stood out this semester was how consistently the finalist teams connected their methodology to measurable outcomes,” said ISyE Senior Lecturer and Senior Design Team Examiner Laura Li. “These students didn’t just build models, they demonstrated, with data, that their solutions work.”

Category 2: KS2 Technologies Design with Impact Award 

Sponsored by KS2 Technologies, the Design with Impact Award recognizes outstanding Senior Design projects with a strong focus on implementation; projects that do not simply recommend solutions but demonstrate how those solutions can be put into practice to create measurable value for clients and communities. Each winning team received a $1,250 award from KS2 Technologies. 

Claims Concierge Process Improvement - Claims Concierge Inc. 

Working with Claims Concierge Inc., this team analyzed and redesigned key workflows within the company’s claims processing operations to reduce cycle time, improve throughput, and increase consistency. Their process improvement recommendations were grounded in detailed data analysis and structured for immediate implementation. 

Team members: Sylvia Shyrel Bentata, Lauryn Carter, Quincy Howard, Nevin Jiang, Octavio Plate, Catalina Segura Vargas, Matias Torres, Rasin Vasaya 

Client Sponsor: Co-Founder and COO Ariel Oken 

Faculty Advisor: Professor Nicoleta Serban 

Now You See Us - Elevate Solutions Group 

Partnered with Elevated Solutions Group, this team redesigned the client's multi-person assembly line into an innovative single-assembler model, eliminating double handling, decreasing interdependency between employees, and speeding up the material flows.  The implementation of the new system was done at a very low cost and is expected to save over $120K annually.  

Team members: Saba Ansari Mohseni, Justin Collins, Pardha Collins, Skyler Malmberg, Hannah Mathew, Visakhi Miriyapalli, Nicholas Nist, Esha Pentakota 

Client Sponsor: General Manager Paul Larate 

Faculty Advisor: Associate Professor Anton Kleywegt 

The presentations also featured some memorable moments, including one especially energetic presentation from KS2 Technologies Design with Impact Award-winner “Claim to Fame” opening with a surprising “BAM! Car accident!”, a moment that immediately startled the room and drew laughs from the audience.  

Category 3: Senior Design Honorable Mentions 

Honorable Mention teams were strong contenders for finalist recognition; projects distinguished by their analytical depth, quality of execution, and value to their clients. 

Pitching the Perfect Promotions - Atlanta Braves Marketing 

Working with the Atlanta Braves marketing team, this group developed a data-driven framework for evaluating promotional strategies and their impact on game attendance. Their analysis helped the Braves better understand which promotions drive incremental attendance versus those that attract fans who would attend regardless, enabling smarter, more targeted marketing investments. 

Team members: Nadia Bozeman, Joshua Canlas, John Cellitti, Vivian Chung, Sarah Copeland, Katie Hamfeldt, Avinash Haritsa, Curran Mitra 

Client Sponsor: Atlanta Braves Director of Platforms and Applications Paul Lupcke 

Faculty Advisor: Professor Craig Tovey 

Improving Operations at Wellstar’s Consolidated Service Center - Wellstar Health System 

Partnered with Wellstar Health System, this team redesigned Wellstar’s Consolidated Service Center into a system capable of absorbing future growth while controlling costs and maintaining service levels through re-slotting across the different pick zones and staffing improvements. 

Team members: Eric Baw, Aron Cheng, Mackenzie Hulsey, Sujan Ganesh Kumar, Junwon Min, Alka Rao, Nano Suresettakul, Patrick Tangente 

Client Sponsor: Executive Director Adam Flood 

Faculty Advisor: Associate Professor (Retired) Gunter Sharp 

The eight recognized projects were just a slice of a remarkably diverse cohort. 

“The range of industries represented by this cohort, from airline operations to health systems to aluminum manufacturing, reflects the versatility of what ISyE students are trained to do,” said ISyE Senior Lecturer and Senior Design Examiner Gamze Tokol-Goldsman. “The quality across the board made this a genuinely difficult set of decisions.” 

For the students, the luncheon marked the end of their capstone journey and the beginning of their transition into industry and future careers. As they shared their results one last time, the event celebrated not only exceptional projects but also the growth and accomplishments of the minds behind them as they prepare to take the next step in life. 

ISyE extends sincere thanks to the client sponsors, faculty advisors, Daniela Estrada, program manager, for her behind-the-scenes work keeping the program running smoothly, and Brandy Blake for her dedicated work supporting teams' technical communication throughout the semester. Special thanks also to Andrew Leipold and Meg Bailey at the Office of General Counsel for managing the legal agreements and NDAs that make client partnerships possible. 

The award recognizes contributions that benefit Georgia Tech, the profession and the public. Her contributions reflect all three, through leadership in shared governance, education, and community engagement. 

Nazzal has had a central role in faculty governance as chair of the Faculty Executive Board (FEB), where she is currently serving her third term. In this elected role, she has helped guide faculty governance and collaborated with the GT Administration though a period of significant complexity in higher education, bringing a steady, thoughtful approach that builds trust across faculty and administration while advancing difficult but necessary conversations. 

“I'm deeply honored. The work that led to this award has been some of the most meaningful of my career, not because of any single project, but because of the people it connected me to: students, faculty and staff colleagues, and the broader community Georgia Tech serves," said Nazzal. "Receiving recognition from the Class of 1934 makes it feel especially significant." 

Her leadership has extended to Institute-wide priorities, such as serving as Chair of the Georgia Tech Faculty Executive Board, Director of Education and Projects for the Center for Health and Humanitarian Systems, and service on the Georgia Tech Athletic Association Board of Trustees. 

Within ISyE, Nazzal has made lasting contributions to education. She founded the School’s Cornerstone Design course and led the Senior Design program for seven years, overseeing large-scale experiential learning for hundreds of students annually. These efforts have strengthened how students connect theory to practice while maintaining a consistently high-quality learning experience. 

Her research and applied work extend this impact beyond campus, with contributions in public health and healthcare operations. 

As Associate Chair, Nazzal provides strategic oversight of undergraduate and graduate education in one of the largest industrial engineering programs in the country, integrating curriculum, accreditation, and long-term planning.  

By Chris Gaffney, Managing Director of the Georgia Tech Supply Chain and Logistics Institute and a former Vice President of Global Strategic Supply Chain at The Coca-Cola Company.

Three weeks ago, I started hearing from contacts in my network. Senior supply chain executives, people who have managed through COVID and the Suez Canal blockage, were expressing concern. The kind of concern that doesn’t make it into earnings calls or press releases. The kind that shows up in private conversations between people who actually move goods around the world for a living.

Their worry wasn't about crude oil prices. Crude oil prices are now widely discussed. Their worry was about what happens after crude oil prices. About the plastic in your water bottle, the fertilizer going into this year's corn crop, the engine oil in your car, the polyester in your running shoes.

Those conversations sent me back to the data. The geopolitical crisis and the energy shock are now well-documented in mainstream reporting. What is less discussed and what my conversations with experienced practitioners suggested was being systematically underestimated is the operational cascade downstream of that energy shock. I wanted to answer a specific question: given that the Strait has been effectively closed since February 28, what aspects of the downstream impact are already locked in regardless of a diplomatic solution, and what is still unfolding? Could I use publicly available data, straightforward analytical tools, and accessible modeling to produce a defensible, quantified view of that question?

The answer, after several weeks of work, is yes. And what the analysis shows is more operationally significant than most of the public commentary has yet captured.

Start with what is already true.

The International Energy Agency (IEA) has characterized this as what it describes as one of the largest supply disruption in the history of the global oil market. Flows through the Strait fell from roughly 20 million barrels per day before the conflict to low single-digit levels in March and early April. Asian crude stocks dropped 31 million barrels in March alone, with further declines expected through April. Global refinery runs in Asia were cut by around 6 million barrels per day. Middle distillate prices in Singapore hit all-time highs.

But energy prices, as alarming as they are, are the visible part of this problem. The less visible part is what those commodities become.

Naphtha, a petroleum derivative most people have never heard of, is the feedstock for the polyester in your clothing, the polyethylene terephthalate (PET) in your water bottle, the polypropylene in your food packaging, the polyvinyl chloride (PVC) in your plumbing. Roughly 80 percent of the naphtha imported into Asia comes from the Middle East. South Korean petrochemical plants were running at 60 to 70 percent of capacity by late April. Japanese crackers at 65 to 75 percent. The IEA confirmed it in plain language: Asian petrochemical plants curtailed operating rates as feedstock supply dried up.

Liquefied petroleum gas (LPG) is the cooking gas that 60 percent of Indian households depend on for daily meals and was the first fuel to be rationed. Queues formed as deliveries were delayed. This reflected physical supply constraints alongside severe price pressure.

Fertilizer prices hit 49 percent above last year's levels by April, according to DTN data. Corn planting intentions dropped 3.5 percent. The math on that is straightforward: the food prices that result from this spring’s planting decisions will show up at the grocery store in 2027. The disruption has a long tail, and most of that tail is still ahead of us.

The question isn’t whether this will affect what you pay for everyday goods. It already is. The question is how far the cascade goes and how long it lasts.

Here is what the modeling shows.

Working from publicly available IEA, U.S. Energy Information Administration (EIA), and commodity price data, I built a scenario model that tracks 12 commodity-region pairs through a 300-day simulation horizon. I then ran that model over 1,500 times with slightly varying assumptions to produce a range of outcomes rather than a single point estimate. That range is more honest than a single number, because the genuine uncertainty in this situation deserves to be represented.

Three findings stand out.

First: a diplomatic deal today would be unlikely to quickly reverse what has already happened. This is the finding that surprised me most, and it held across almost every simulation. The high-import-dependency commodities have already depleted enough inventory that functional shortage is already embedded in the near-term outlook regardless of when the Strait reopens. The diplomatic question determines how long the pain lasts and how severe the recovery will be. For consumers, this means the effects may show up long after the headlines fade through higher prices, product shortages, and delays in everything from clothing and packaging to fertilizer-dependent food production.

Second: Europe's most visible supply chain story, airlines canceling flights, is a price story, not a physical shortage story. The IEA documents approximately six weeks of European jet fuel supply. Airlines are grounding aircraft because fuel has doubled in price, not because airports are running dry. Meanwhile, Asian petrochemical plants are curtailing because feedstock physically stopped arriving. These two situations look similar in the headlines. They require completely different responses. For consumers, the difference matters because one problem mainly makes travel and goods more expensive, while the other can interrupt the actual production of the products modern life depends on.

Third: the recovery will be harder and longer than most public commentary assumes. S&P Global estimates five weeks to seven months for full supply normalization after a reopening, depending on infrastructure damage. Mine clearance alone requires 60 to 90 days of sustained operations before commercial vessels can transit safely. Insurance premiums will not normalize until underwriters see months of safe transit. And when supply does restart, suppressed demand returns simultaneously with a supply base that is still rebuilding. The EIA's 2027 demand forecast of 1.6 million barrels per day growth (nearly three times the depressed 2026 rate) makes this concrete. We have seen this pattern before. COVID demonstrated it at scale. The bullwhip effect, applied to a supply-side energy shock, produces a second dislocation on the back side of the crisis.

What this means for your grocery bill, your gas tank, and your business.

The analysis maps 36 supply chain pathways from raw commodity to consumer shelf across 15 product categories. Here are three examples that are or will be visible to you.

Take construction materials. PVC pipe, insulation, and window profiles all begin with petrochemical feedstocks moving through the Gulf region. PVC resin prices in India rose nearly 80 percent in March. Since PVC pipe is largely PVC resin, the pass-through to construction costs is immediate and difficult to absorb. The result is likely to show up in higher prices for building materials, repairs, and infrastructure projects long before most consumers connect the cause.

The same pattern is unfolding in synthetic motor oil. Shell's Pearl Gas-to-Liquid facility in Qatar — one of the world's most important sources of premium Group III base oil — was taken offline by missile strikes. Producers in Bahrain and the UAE have declared force majeure. Roughly 40 percent of global Group III supply is now offline or unable to ship. For consumers, that eventually means higher oil-change costs, more expensive industrial lubricants, and added operating costs moving quietly through trucking, aviation, manufacturing, and delivery networks.

Food arrives later, but it arrives. Fertilizer prices are already sharply elevated, and planting decisions are being made right now under those conditions. The agricultural calendar creates a lag most consumers do not see. Disruptions this spring can become higher grocery prices many months from now. That is not speculation. It is simply how agricultural supply chains work.

We tend to underestimate the breadth and duration of these events while they are happening, and overestimate how quickly things return to normal after they appear to resolve.

What we did, and why it matters how we did it.

Every number in this analysis traces to a cited source. Where data was insufficient and judgment was required, those judgment calls are labeled as such. The model is not a black box. It is a documented, reproducible simulation that any researcher can run independently.

I also used AI — specifically Claude by Anthropic — as a partner to help analyze and build this work. While I provided the analytical framework, the practitioner judgments, and the validation of assumptions, the AI assisted with drafting, building models, computation, and data synthesis. This collaboration is fully detailed in the paper.

This represents a new way of performing analytical work. The results are significant: a quantified, sourced, and reproducible analysis of a complex disruption in the actual world. What usually takes a traditional research team months was completed in weeks. That speed is vital when a situation is still unfolding.

The larger point.

Sixty-seven days in, the global supply chain community is navigating a disruption that has no precise historical parallel. The 1973 OAPEC embargo lasted months and produced lasting structural change in how the world consumes energy. The 1990 Gulf War shock was brief enough that it produced relatively mild downstream consequences. The 2022 European energy crisis showed us what happens when industrial feedstock costs become uneconomic for months at a time: capacity comes offline, and some of it does not come back for a long time.

The 2026 Hormuz closure is now 72 days old. It has already lasted longer than the 1990 Gulf War shock. It is approaching the territory where the worse historical outcomes become the more relevant comparators. Every additional week of closure moves the probability distribution toward the scenarios that produced lasting structural damage.

Both public and private entities may be underestimating the magnitude of what recovery will require. Restoring normal supply chain function after an event of this scale and duration is not a matter of reopening a waterway. It is a matter of rebuilding inventory buffers, restarting industrial capacity, normalizing insurance markets, reestablishing commercial relationships, and managing the demand surge that hits simultaneously with the supply restart. The organizations that are planning for that recovery now will be materially better positioned than those that wait.

The people I talked to three weeks ago were right to be concerned. Their concern was based on experience and instinct and what they were seeing in their own business. Our work over the past weeks validates their perspective.

An enduring diplomatic solution is the essential precondition for any of this to improve. Without it, the cascade continues. With it, the hard work of recovery begins. Either way, the time to understand the full scope of what is in motion is now  and not after the headlines move on.

Editor’s note:
View the related report: technical analysis, scenario modeling, Monte Carlo simulation methodology, consumer impact assessment.

Valerie Thomas, Anderson-Interface Chair of Natural Systems and Professor at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE), launched the program in 2020 to educate future leaders through research collaboration and global, context-rich experiences.

Through partnerships with the University of Rwanda and local institutions, Georgia Tech students learn directly from Rwandan faculty, students, and professionals working in sustainability.

“Direct engagement brings Georgia Tech students face-to-face with high levels of expertise and professionalism in Rwanda, and by extension in other countries,” says Thomas. “This program provides our students with greater understanding of how Rwanda is working on sustainability challenges. It also requires our students to up their game, participate at a high level, and adjust in real time.”

Over the years, Rwanda Study Abroad has evolved into a cross-campus initiative involving the Schools of Industrial & Systems Engineering, City and Regional Planning, and Modern Languages, as well as the Scheller College of Business.

In past years, the study abroad program primarily served undergraduate students. However, this year, the program has expanded with the addition of a concurrent graduate opportunity: the MBA International Practicum at Scheller College, which is a section of the Spring 2026 Sustainable Business Consulting Practicum, co-taught by Professor of the Practice Michael Oxman and Lecturer Bob Lax.

Oxman’s collaboration with Thomas on a National Science Foundation planning grant led to him co-leading a trip to Rwanda in 2025. On that trip, he met with clients and identified projects for the Spring 2026 practicum. He said, “Rwanda was a compelling new destination for MBA students in the International Practicum, and I was pleasantly surprised by the interest of the 26 MBA students who participated.”

The interdisciplinary structure of the program allows students to examine sustainability, energy, development, and planning challenges from multiple perspectives.

“I enjoyed the presentations and meetings with the students at the University of Rwanda,” said Franklin Heath, a fourth-year ISyE major. “It was interesting to talk with the students, see what projects they were working on, and learn about their classes. I realized that our lives are more similar than I had imagined. Also, the Rwanda Mines, Petroleum, and Gas Board (RMB) provided lots of insight into the mining industry and its logistics that I otherwise would not have thought about.”

Divya Cherukupalli, an Evening MBA student, wrote, “I chose to participate in the practicum because I wanted the opportunity to experience a different culture and understand how other countries approach sustainability compared to the United States. Being able to see these perspectives firsthand made the experience especially meaningful. The International Practicum was a truly transformative experience for me.”

Georgia Tech students move beyond observation and into deeper engagement through structured opportunities with Rwandan students, government officials, entrepreneurs, and members of local communities working toward similar goals. These interactions provide unique context and can shape how students understand challenges when they return to campus.

“I have seen students grow significantly – especially in their confidence. Their excitement about learning seems supercharged as well. I think all the students on this recent trip felt a deep bond amongst themselves and with the instructors,” reflected Tony Giarusso, professor of the practice in the School of City & Regional Planning and co-lead for the program. “Many of the student projects are set before we arrive in Rwanda, but our engagement with government officials and industry help fine tune student ideas while also providing them with field observation data and connections for data resources.”

Oxman agreed with Giarusso regarding the fruitful relationships in Rwanda. In the practicum, Oxman and Lax led students through a structured, hypothesis-driven approach to problem solving, and the students successfully leveraged that approach to add value to their clients’ objectives. Smayah Uwajeneza, a professional from Elevate Through Coffee, a client, wrote: “We’re proud to collaborate with the students in shaping a more sustainable and inclusive future, where knowledge exchange and partnership create real impact beyond borders.”

Other clients offered unsolicited testimonials (see Rwanda Energy Group, RMB, and Rwanda Polytechnic). These comments demonstrate the importance of sustainability not only for the good of society also for business value. Specifically, clients noted that the students’ work strengthens entrepreneurship, accelerates innovation, and delivers solutions that respond to the needs of society. They said the work also created actionable pathways for improving sector governance, increasing resource efficiency, enhancing economic returns, and turning research ideas into industry-ready solutions.

It was important to program leaders that students receive cultural insight and understand cultural continuity. While working with groups such as ESRI Rwanda, students and Georgia Tech as a global community build long-lasting relationships with local partners, helping participants develop a true understanding of life and the work being done in Rwanda.

Language and culture play a critical role in this process. Organizers emphasize the importance of linguistic knowledge and cultural awareness in accessing research, communicating effectively, and making informed decisions in international contexts.

“Language expertise greatly facilitates work on the ground and gives students access to significant research written in other languages than English,” said Christophe Ippolito, professor of French in the School of Modern Languages, who embedded the trip to Rwanda in the Africa: Serve, Learn, Sustain VIP. The course provides an opportunity for undergraduates from across Georgia Tech to engage in sustainable development research in the African context. He remarked, “I have been on the ground, having worked in Africa and visited 30+ countries there. So, for instance, I could tell students which kind of machine would work better for irrigation in some regions.”

Organizers agree that the program has led to students gaining a significant growth in confidence and teamwork skills, often producing strong bonds within the cohort, reinforcing collaboration and engagement, beyond the study abroad experience.

Oxman reflected, “From my career, having been a consultant on sustainable business in emerging markets, I know that the only way to truly drive business and sustainability is through local immersion and understanding. I’m so grateful to be able to facilitate this kind of experiential learning opportunity for our Georgia Tech students.” Oxman said he’s excited to be planning another international practicum in Rwanda in Spring 2027. 

Looking ahead, Thomas said she expects the program to continue expanding with additional pathways for undergraduate and graduate students, deeper collaboration with Rwandan institutions and other partners, and project formats that further integrate research, teaching, and global engagement.

“A hallmark of the program will be immersion and reciprocity,” said Thomas, who envisions the program expanding to Rwandan colleagues visiting Georgia Tech and an urban planning studio in Rwanda. “We have faculty across the Institute with the talent, knowledge, and capability to support students in study abroad in Rwanda. Drawing on the existing talent and expertise across the Institute is making the Rwanda program robust and multi-faceted.”

ISyE brought the most teams of any school to the expo, where the senior design teams had the opportunity, for the first time, to present their problems and solutions to the public. In the packed McCamish Pavilion, teams erected their poster boards and demos, ready to explain their work to other interested students, donors, parents, and leaders from other schools at the Institute.

The projects spanned industries from aluminum smelters and artisan popsicle companies, NCAA football stadium queues and hospital emergency rooms, Atlanta nonprofits and Marine Corps operations. The breadth reflected both the versatility of industrial engineering and the program's reach into Atlanta's business community and beyond. One team, “Shift Happens,” showed how they improved dispatcher scheduling for American Airlines. Their solution takes employees’ preferences, flight schedules, the dispatcher’s flexible day use, and training days into account to produce an improved schedule that minimizes the number of understaffed and overstaffed shifts. The scale of their solution was enormous. 

“When we combine that all into our one model, we have three shifts to account for: morning, evening, and midnight. We have eight regions to account for with American Airlines. It’s a massive model: twenty-one million variables, thirty-five million constraints,” explained “Shift Happens” team member Colin Fravel.

With their improved schedule, they expected a 73% reduction in unmet demand where there is no dispatch to work a flight. By better distributing dispatchers, they also estimate that their solution will save American Airlines $1.3 million dollars in reduced staffing costs over the course of a year.

Another team, “Buzz on the Beach,” worked with company VayKLife — a guest engagement and beach gear rental platform — to optimize transportation resource usage by reducing the number of trucks required, total miles traveled, labor hours, fuel consumption, and reliance on rental equipment during peak seasons. The team developed a routing model that accounts for inventory availability and vehicle capacity constraints, enabling the company to maintain its current level of service while operating more efficiently with fewer resources.

“In peak season, we were able to see the savings of nine, almost ten thousand dollars in Charleston alone. And then in the off-season, we’re able to save almost 13.7 thousand dollars, just in Charleston,” said Rohan Prabhuram, one of the “Buzz on the Beach” team members.

At the end of the expo, industry judges gave “Contact Point” the Industrial Engineering monodisciplinary award. Team members Skyler Malmberg, Visakhi Miriyapalli, Nick Nist, Hannah Mathew, Justin Collins, Pardha Kanchiraju, Esha Pentakota, and Saba Ansari developed a work-process solution for Elevate Solutions Group.

Their new process improves the way Elevate Solutions Group loads trays with contact lenses, as specified by their clients. The old process involved four to six workers loading individual trays, manual sorting, and long travel times between steps. “Contact Point’s” solution included constructing a new workstation for loading trays, a handheld scanner, custom software, and a tray attachment to make loading easier. They also counterintuitively decreased the number of workers at a time to just one.

“We were able to note an almost up to 50% decrease in time usage per tray,” Collins explained. “We estimated this will save them over $126,000 over the first year on just this one product line that we're working with.”

Most of that cost savings came from labor, which they reduced by an average of 9 hours per client order. The team’s impact, their client’s satisfaction, and the rapid adoption of their solution were decisive factors in awarding this team the top prize.

For most of the 237 students in the ISyE senior design groups, the expo was the most public moment of their time throughout this experience. They showed not just a semester’s work, but what four years of study, dedication, and determination look like. This final event was the culmination of all the steps it took for each one of them to become a Georgia Tech industrial engineer.

In this issue:

• The real blind spot in analytics teams
• Three failures where the model was “right” and the decision was wrong
• A five-question checklist to run before anything goes to leadership.

A Subtle but Growing Concern

Over the past several months, I have had conversations with senior leaders at several large, well-established supply chain organizations with strong teams responsible for Integrated Business Planning (IBP) and supply chain network design and optimization.

These teams are technically strong. They know how to build models. They are comfortable with large data sets. Many are now incorporating AI tools into their workflows.

But the same concern keeps surfacing across those conversations:

The analytical capability is improving—but the decision-making discipline around it is not keeping pace.

Analysts move quickly to building models without fully defining the business problem. Assumptions are not always surfaced or challenged. Outputs are evaluated mathematically, not operationally. And recommendations are not always translated into real-world implications.

Leaders are concerned about this and are looking for ways to address. I share their concern because I have been in their shoes.

What the Experience Taught Us

Earlier in my career, across different roles at Coca-Cola, we did not formally teach critical thinking. We learned it through experience and often through mistakes. Three situations shaped how I think about this today.

Powerade: When the Model Works but the Thinking Doesn’t

While working with optimization groups at Coca-Cola North America, we overbuilt capacity for Powerade. The model did exactly what it was supposed to do. The problem was upstream of the model.

We took the demand forecast at face value. At the time, we deferred to the brand teams without interrogating their assumptions. We never asked what was driving the projected volume—whether the competitive dynamics supported it, whether the channel assumptions were realistic, whether pricing and distribution plans were grounded, whether overall market growth would materialize as projected.

The consequence was idle capacity, production lines that were purchased and never installed, write-offs, and a fundamental change to our process. Going forward, brand and supply chain teams were both required to sign off on future business cases. The model was technically correct. The thinking around the model had not been.

Little Rock: When Feasibility Isn’t Reality

Later, within Coca-Cola Supply, we made a network decision to close a plant in Little Rock. On paper, the remaining system had the capacity to absorb the volume. The model said so.

What the model assessed was production capacity based on rated line speeds. What it did not account for was dock and storage capacity at peak, or the practical limitations of standing up a new shift at the receiving plants. Those constraints were real. They were also invisible in the model.

In the short term, we had to source sub optimally from other plants—which directly undermined the business case we had built to justify the closure. The math was right. The operational validation was incomplete.

Mini Cans: When the Thinking Matches the Model

By the time I led the National Product Support Group, we had evolved. Decisions like the launch of mini cans required cross-functional alignment, scenario-based thinking, and a clear understanding of how demand would actually be generated across channels and routes to market.

We got that one right, not because the model was more sophisticated, but because the discipline around the model was stronger. We had learned, the hard way, to ask the questions the model could not ask for itself.

Most of the Work Is Outside the Model

There is a line I first heard from Chris Janke: "Most of the work is outside the model." He may have learned it from someone else; I don’t know the original source, but it is the framing that has stayed with me. With the advances in data and machine learning we have seen over the past decade, that proportion may be closer to 75 percent today.

We are better than ever at collecting and cleansing large data sets, processing high volumes of information, and identifying mathematical errors. But the most important work still happens outside the model: defining the right business question, building meaningful scenarios, interpreting outputs in real-world terms, and stress-testing the assumptions that drive the recommendation.

Janke captured this precisely in documenting his own experience with a modeling error that illustrated the point. An analyst had validated the math on a labor cost model—everything checked out numerically. But when the output was translated into real-world terms, it implied production workers earning roughly $300,000 per year while working approximately 60 hours total annually. The math was internally consistent. The result was operationally impossible. The question that should have been asked early: does this make sense in the context of how the business actually operates? It was not asked until after the analysis was complete.

The discipline to ask that question is not modeling skill. It is a critical thinking skill.

Where the Breakdown Happens

Before the Model: Skipping the Hard Questions

A common pattern today is that analysts move quickly to building the model. The harder and more important step of defining the business decision before the model is built gets compressed or skipped entirely. The questions that require that step are not complicated, but they take time and engagement to answer well:

• What business decision are we actually trying to make?
• What scenarios matter, and why?
• What does success look like—not mathematically, but operationally?
• What constraints are real versus assumed?

These questions are not as clean as coding a model. They require conversations with people who understand the constraints, not just the data. That is part of why they get skipped.

After the Model: Mistaking Mathematical Accuracy for Business Validity

This is where more serious errors occur. Model issues can usually be fixed with more time. Misinterpretation of output leads to bad decisions that are much harder to unwind.

The Powerade and Little Rock situations both illustrate this. In each case, the model was not wrong in any technical sense. What was missing was the translation layer— where someone asks, “what changes on a Tuesday night shift, at Plant B, when demand spikes 12 percent?”

That translation layer does not happen automatically. It has to be built into how teams work. And it is exactly the discipline that gets squeezed when organizations reward speed and analytical sophistication above everything else.

What Critical Thinking Actually Means in Supply Chain

Critical thinking in supply chain is not skepticism for its own sake, and it is not a soft skill that sits alongside the analytical work. It is a discipline applied to decisions and not just to models. The word itself points to what we mean: kritikos, the Greek root, means skilled in judging, able to discern*. That is the right definition for our purposes.

It means asking whether the right question is being answered before investing in answering it well. It means making the assumptions that drive a recommendation visible and testable. It means translating analytical output into operational consequence: what actually changes, for whom, at what cost, and under what conditions the answer flips.

That discipline shows up or breaks down at four specific moments:

1. Before the model is built:  Is the business question defined precisely enough to model?
2. While the model is running:  Are the assumptions embedded in the data realistic and challenged?
3. When the output is ready:  Does this result make sense in how the business actually operates?
4. Before the recommendation goes forward:  Have we planned for how this will be received, and by whom?

When these moments are skipped because of time pressure, overconfidence in tools, or a culture that rewards analytical speed over decision rigor the gap between analysis and action grows. The Powerade and Little Rock situations were both failures at these moments, not failures of the models themselves.

*DeCesare, M. (2009). Casting a critical glance at teaching “critical thinking.” Pedagogy and the Human Sciences, 1(1), 73–77.

A Five-Question Diagnostic

Before an analysis or recommendation moves forward, teams should be able to answer five questions clearly. If any of them cannot be answered, the analysis is not ready—regardless of how strong the model is.

Figure 1: A Five-Question Diagnostic (accessible version)

These are  questions that should have specific, grounded answers before a recommendation reaches leadership. If the team cannot answer question two (what assumption would flip the result) then the recommendation rests on unexamined ground. If question four cannot be answered, the change management work has not started yet.

In the Powerade situation, questions one and two were the misses. In Little Rock, it was question three. The models were not the problem. The diagnostic would have surfaced both gaps before the decisions were made.

This Gap Is Well Documented

What I am describing from my own experience is consistent with what the research shows.

A long-running finding in operations research is that many models are built and comparatively few actually drive decisions, and the breakdown is organizational, not technical. A widely cited review in the European Journal of Operational Research frames this as an implementation problem rooted in how models are connected (or not connected) to the people and processes that own the decision. 

Professional credentialing bodies have recognized the same gap. The INFORMS Certified Analytics Professional blueprint explicitly lists business problem framing, stakeholder analysis, and business case development as core analytics competencies—not optional additions. The signal is clear: being analytically strong is necessary but not sufficient.

On the training side, a field study published in the European Journal of Operational Research tested the effects of structured decision training across roughly 1,000 decision makers and analysts. The results showed measurable improvement in proactive decision-making skills and decision satisfaction. The gap is real, and it is addressable. It is a training and design issue, not a talent issue.

The 4 C’s: A Decision-Focused Framework

At Georgia Tech SCL, we organize this thinking around what we call the 4 C’s. These soft skills play a key role in the decision process. Each one asks a specific question about whether the decision, not just the analysis, was made well.

Figure 2: The 4 C’s: A Decision-Focused Framework (accessible version)

Notice what this framework does not include: model accuracy, data quality, or visualization quality. Those matter, and they are inputs to the decision. But a team can have a perfect model, a clean dataset, and a compelling dashboard and still fail all four of these tests.

The Powerade situation failed the Collaboration test The supply chain team did not sufficiently interrogate the brand team’s assumptions. Little Rock failed the Critical Thinking test: the right question was not asked about what the model was not capturing. In both cases, the Communication and Change Management failures followed directly from those upstream gaps.

When all four are present, analysis becomes a decision. When one or more is missing, the analysis and translation to a solid recommendation are at risk.

Where to Start

This topic keeps coming up in conversations with companies, in work with practitioners, and in what we hear from students as they move into industry roles.

The tools are not the problem. AI-assisted analytics, optimization models, and advanced forecasting are real assets. But tools amplify the thinking behind them. Weak decision discipline and better tools is a faster path to the wrong answer.

If this shows up in your org, try the five-question diagnostic on your next recommendation before it hits leadership. If it surfaces gaps you cannot close quickly, SCL can help. We are building workshops and courseware on decision-focused critical thinking, and we will cover this in our June Lunch and Learn.

Questions or comments? Reach out to SCL.

Alpha Pi Mu Academic Excellence Award
Yubin Kim

Yubin Kim received the Alpha Pi Mu Academic Excellence Award in recognition of outstanding academic achievement, research excellence, and active involvement in scholarly activities. As part of this honor, Kim’s name will be added to the Alpha Pi Mu perpetual plaque displayed in the ISyE office.

Evelyn Pennington Outstanding Service Award
Phong Nguyen

Phong Nguyen was honored with the Evelyn Pennington Outstanding Service Award in recognition of his exceptional contributions to the ISyE community. His dedication to service, support of student initiatives, and commitment to academic excellence embody the spirit of this award.

IISE Excellence in Leadership Award
Sujan Ganesh Kumar

Sujan Ganesh Kumar received the Institute of Industrial and Systems Engineers (IISE) Excellence in Leadership Award in recognition of impactful leadership within ISyE student organizations. Through dedicated efforts to foster student engagement and collaboration, Kumar has helped strengthen community and enrich the student experience within the program.

The School congratulates all honorees for their dedication, leadership, and contributions to the ISyE community. To view the full list of honorees, click this link. 

Li worked alongside ISyE Assistant Professor Xiaochen Xian, Georgia Tech School of Electrical and Computer Engineering (ECE) Assistant Professor Baoyun Ge, ECE graduate students Piyush Chauhan and Yuanhao Mo, and Le Chang, an engineer from General Motors. The team was honored for its innovative approach to modeling magnetic systems, an area critical to the performance and efficiency of modern power electronics.

The MagNet Challenge tasks student teams with developing advanced software algorithms that learn from existing training data and accurately predict magnetic behavior in previously unseen materials and operating conditions. Competitors are evaluated on both modeling accuracy and robustness, with applications spanning electric vehicles, power converters, motors, and transformers.

Drawing on engineering principles in modeling, optimization, and data-informed decision-making, the team developed a physics‑informed prediction model based on mechanical analogies for magnetic systems. The approach allowed the model to explicitly capture complex nonlinear effects, including saturation, hysteresis, eddy currents, and displacement currents, that have long challenged engineers and researchers.

Their model significantly reduced prediction errors across five different testing materials, operating over a wide range of switching frequencies (from 50 kHz to 800 kHz) and temperatures between 25°C and 70°C, outperforming all other competitors.

The project spanned 10 months, beginning in March 2025, and reflects ISyE’s growing role in tackling complex, interdisciplinary engineering challenges through advanced modeling and data-driven methods. The team has since submitted two provisional patents related to the technology developed during the competition.

The award was formally presented at the IEEE Applied Power Electronics Conference on March 24, 2026, in San Antonio, Texas. Hosted by Princeton University and Dartmouth College, the MagNet Challenge was sponsored by the IEEE Power Electronics Society along with industry leaders including Nvidia, Texas Instruments, Würth Elektronik, ITG Electronics, and pSemi.

For ISyE, the win highlights the impact of its students and faculty in shaping next-generation solutions at the intersection of systems engineering, computation, and emerging energy technologies.

The study emphasizes that gateway decisions should not be based solely on ocean rates or sailing time. While trans-Pacific routes to the West Coast are shorter at sea, researchers found that congestion, cargo rehandling, and inland transportation complexity often introduce delays and variability. In contrast, Savannah's efficient port operations, on-terminal rail service, and direct interstate access help offset longer ocean voyages with faster inland movement and greater predictability.

Researchers analyzed vessel and inland transportation data from ten Asian ports to the three Southeastern markets. Their findings showed that Savannah's reliable port processing and inland logistics significantly reduce congestion exposure and transit variability, making it a more dependable gateway for shippers seeking consistent delivery performance.

The study was conducted by Georgia Tech faculty and PhD students at the Institute's Physical Internet Center and reinforces previous Atlanta-focused research demonstrating similar benefits of East Coast routing. The findings support the growing role of the Port of Savannah as a strategic gateway for U.S. supply chains serving inland Southeast markets.

Read the original press release from the Georgia Ports Authority here:
Georgia Tech research shows East Coast gateway best choice for Atlanta, Memphis and Nashville
 

For the 36th year in a row, Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering (ISyE) has earned the No. 1 spot in the 2026 Best Engineering Schools ranking released by U.S. News & World Report.   

ISyE researchers are applying AI to complex manufacturing environments, including multistage production systems, asset management, quality improvement, and human‑centered manufacturing. Faculty leaders emphasize the importance of contextualizing large volumes of manufacturing data so AI can support reliable decision‑making, efficient operations, and sustainable outcomes. At the same time, the initiative acknowledges challenges such as data integration, system complexity, and the need to balance automation with human involvement. Together, these efforts position ISyE at the forefront of shaping AI‑powered manufacturing systems that are innovative, resilient, and socially responsible.

Read the full article in ISyE Magazine 

Nguyen began his academic journey as an undergraduate at Minerva University, where he studied in a different country nearly every semester. This experience abroad shaped his approach to problem-solving. After graduating, Nguyen remained passionate about mathematics but became interested in applying theory to real-world challenges.

“I still wanted to do math, but I wanted to apply my mathematical research to some tangible applications,” Nguyen said. “I wanted to see the meaning behind my research.”

That desire, along with ISyE’s long-standing top national ranking in industrial engineering, led Nguyen to pursue his doctoral studies at Georgia Tech. His primary research focuses on applied probability, with an emphasis on bridging theoretical models and practical systems.

Nguyen received the IBM Fellowship in recognition of his ongoing research. One of his current research projects examines how far a process is from the steady state and seeks to better understand the finite-time behavior of the system and to make accurate real-time decisions. This work has meaningful applications in many real-world service systems models, such as the load balancing algorithms found in data centers and ride-hailing systems.

In additional his work in applied probability, Nguyen is exploring ways to improve artificial intelligence reasoning. His research investigates how large language models can verify their own outputs using mathematical heuristics and training data. By identifying and correcting discrepancies before displaying results to the user, the system could become more accurate and reliable. 

Nguyen contributes much to the mentorship of his advisor, Professor Siva Theja Magulur.

“I would like to thank my advisor, Professor Siva Theja, for supporting me through this journey,” he said. “He's an extremely caring, insightful, and attentive professor. He's also very supportive of me pursuing the AI reasoning research at Google DeepMind, although this is not his main research. Over the years, I have learned a lot from him as his student.”

The IBM Fellowship is the latest in a series of achievements for Nguyen. In 2024 and 2025, respectively, he was part of a Google DeepMind team that earned silver and gold medals in the International Mathematical Olympiad. He also won second place at the ACM SIGMETRICS 2025 Student Research Contest for his work on the finite-time behavior of queuing systems.

As he continues his doctoral studies, Nguyen remains focused on advancing his research and contributing to both theoretical and applied fields.

“My dissertation focused on maternal health in the U.S., particularly in Georgia,” Meredith said. “Women are experiencing maternal morbidity and mortality at much higher rates than in other high-income countries. There are also significant racial, ethnic, and rural-urban disparities. All of this points to systemic issues in how maternal healthcare is provided.”

Meredith’s research combined data analysis, modeling, and policy-focused tools to understand and improve access to obstetric care. A major focus was how access is measured and how to use existing resources more effectively.

“One widely used metric, ‘maternity care deserts,’ looks at access by county,” she explained. “But in Georgia, someone might be labeled as lacking care even if a hospital is just a mile across the county line. We realized that this metric was measuring the wrong thing in Georgia. Our work focuses on providing actionable insights to policymakers so they can ensure women have high-quality care nearby.”

Her research was motivated by the urgent need to address poor maternal outcomes in Georgia, which ranks among the worst states in the U.S. for both maternal mortality and racial disparities. “We see that half of women who die due to pregnancy-related causes die after delivery,” Meredith said. “Pregnancy is a critical opportunity to engage women in their healthcare, and it’s essential to focus on both maternal and fetal health.”

Meredith’s contributions aim to bridge the gap between complex operations research models and real-world healthcare decision making. “We want to take the guesswork out of policy decisions,” she said. “If a hospital closes or receives funding, our models can estimate the impact on care access and outcomes.”

Receiving the IISE award underscores the impact of Meredith’s research. “We were so focused on improving maternal health in Georgia,” she said. “This recognition shows that meaningful healthcare research can also advance the field of operations research and industrial engineering.”

Meredith credits much of her success to her advisor, Professor Lauren Steimle, who nominated her for the award and provided guidance throughout her PhD program, which she began during the height of the COVID-19 pandemic. “Her support was critical, especially during the first year and a half of online learning and isolation,” Meredith said. She also highlighted the importance of her women-led team of collaborators, including physicians and epidemiologists, working on this women-centered research.

Currently a postdoctoral researcher at NYU Langone Health, Meredith is applying her expertise to organ transplant policy, a field where operations research directly informs life-saving decisions. “Access, efficiency, and equity are still central themes,” she said. “The goal is to use powerful tools to improve care outcomes wherever they’re needed most.”

Tens of millions of brackets have been filled out ahead of the NCAA men’s and women’s basketball tournaments. Some fans will choose winners based on the higher seed, others will try to predict shocking upsets, and some may choose who advances based on which mascot would win a fight, but a Georgia Tech professor has his bracket down to a (data) science.   

We recently wrapped our semi‑annual industry advisory board meeting, where a core element of the agenda is a set of "hot topics" sourced in advance from our member companies, curated, and facilitated to reflect what is most top of mind in the field. This cycle, one of those topics focused on the impact of AI on supply chain technology investment.

What began as a discussion on technology quickly surfaced a broader issue:

AI is not just changing supply chains—it is raising the standard for execution, and in doing so, redefining what it takes to sustain a brand.

When Capability Becomes Cheap

Within that discussion, a simple example sparked debate. Most of us would trust a platform like DocuSign without hesitation. It has earned that trust through reliability, security, and consistent performance.

But what if a new entrant—call it “FredSign”—offered similar functionality, powered by AI, at lower cost and with comparable features? Would you use it?

The room split. Some argued that established brands are durable because of the trust they have built over time. Others pushed back, suggesting that AI‑enabled challengers could close that gap faster than expected, making brand less relevant.

The discussion quickly moved beyond software to a broader question:

In a world where AI lowers the cost of building capability, does trust shift from brand to performance—or does brand become even more important?

Brand as a Promise

From a supply chain perspective, this is no longer theoretical. It is already happening.

At its core, a brand is a promise. For product companies, that promise is built on quality, consistency, and the experience of using the product over time. For supply chain technology and service providers, it is grounded in reliability, security, and confidence in execution.

Historically, brand has been reinforced by performance—but also protected by time, scale, and familiarity.

AI is changing that balance.

Lower Barriers, Higher Expectations

On one hand, AI lowers barriers to entry. New entrants can replicate functionality faster, improve user experiences, and target specific gaps in incumbent offerings.

In supply chain technology, this is particularly relevant. Many organizations have made significant, long‑term investments in systems that have not always delivered as expected. That creates an opening for AI‑enabled providers to enter through narrow use cases, solve specific problems better, and establish a foothold. Over time, they build credibility.

But there is a second dimension that is more immediate—and more consequential.

AI Raises the Execution Standard

One way to frame this is simple: data is a terrible thing to waste.

For years, supply chains have generated vast amounts of data across planning systems, transportation networks, warehouses, and customer interactions. Much of that data has been underutilized—captured, stored, but not fully leveraged to anticipate risk or improve outcomes.

That is changing.

The capability now exists—and is rapidly maturing—to sense, interpret, and act on that data in ways that were not previously practical. Risks can be identified earlier. Disruptions can be predicted. Corrective actions can be taken before the customer ever feels the impact.

From Disruption to Preventability

Over the past week, in the span of just six days and four unrelated conversations with members of my network, I heard a series of examples that all pointed to this shift.

• A global food company managing risk tied to a critical supplier whose quality issues could impact multiple major brands—raising the question of whether AI could have surfaced a near sole‑source dependency earlier.
• An e‑commerce retailer using machine learning to reduce theft and damage in its fulfillment network, improving the customer experience.
• An organization proactively shifting its fulfillment partner mix based on AI‑driven insights into which nodes can and cannot handle surge capacity.
• A high‑end clothing shipment arriving wet due to a fulfillment breakdown—where the loss was not just the product, but a time‑sensitive moment that could not be recovered.
• A consumer receiving an empty box after successfully purchasing a limited‑release product that could not be replaced.

These are not isolated anecdotes. The common thread is not disruption—it is preventability.

As AI enables earlier detection of risk, better prediction of disruptions, and faster response to exceptions, the tolerance for failure is declining. Companies are no longer judged simply on whether something went wrong. They are judged on whether it should have been avoided.

Brand Is the Delivered Experience

From a brand perspective, that is a fundamental shift.

A product brand may invest heavily in innovation and customer engagement. But if the product arrives damaged, late, or not at all, the customer does not distinguish between the brand owner and the supply chain behind it.

There is only one experience—and therefore only one brand.

In an AI‑enabled supply chain, failure is no longer just a risk—it is increasingly a choice.

The Weakest Node Defines the Brand

A brand is now only as strong as its weakest node.

That node may be a supplier, a logistics provider, a fulfillment partner, or a technology platform. Many sit outside the direct control of the brand owner, yet their performance is inseparable from the customer’s perception of the brand.

AI makes it possible to identify and address these weak points—but it also makes it more apparent when companies fail to do so.

Implications for the Supply Chain Ecosystem

This dynamic extends directly to platform and software providers. In an AI‑enabled environment, it is no longer sufficient for supply chain technology to be stable or functionally adequate. It must evolve—continuously—to sense risk earlier, enable better decisions, and improve execution outcomes. If it does not, its limitations will be exposed quickly, and alternatives will emerge.

Technology providers are not insulated by their brand; they are judged by the outcomes they enable. Their brand will strengthen if their platforms improve execution—and erode if they do not.

Product companies must use AI to protect the customer experience end‑to‑end. Logistics providers must adopt AI to remain credible partners. Technology providers must evolve their platforms to meet a higher execution standard.

If one part of the system advances while another does not, the gap will be visible—and acted upon quickly.

Winners and losers are being judged daily.

What This Means for Leaders

None of this suggests that brand is no longer important. In high‑trust, high‑risk environments—contracts, financial transactions, healthcare, and other sensitive use cases—brand remains critical.

Even in this environment, trust must be continuously reinforced through performance. Leaders must clearly understand what underpins their brand. Brand is not an asset to be protected; it is the result of consistently delivering on a promise. Any performance gaps must be addressed before others move in. AI‑enabled challengers will not challenge strengths—they will target weaknesses.

Finally, leaders must elevate their ecosystem. Brand performance is now inseparable from partner performance. That requires greater visibility, tighter integration, and higher expectations—not only internally, but across suppliers, logistics providers, and technology partners.

One Question to Answer Now

This execution dimension is only one part of how AI is reshaping brand—but it is already decisive.

A great product can still win. A strong brand can still endure. But in an AI‑driven world, where disruptions can be anticipated and failures mitigated, the margin for error is disappearing.

And in many cases—especially where the purchase is infrequent or the moment is critical—you only get one shot. At the conclusion of our discussion, one participant framed it simply:

What is our secret sauce—and what are we doing to build on it?

That is the question every supply chain leader should be answering now.

Because in an AI‑enabled world, your brand will be defined by what your system consistently delivers.

The sisters shared that their love of math and science started at a young age, naturally excelling in both subjects throughout middle and high school. That passion was further nurtured at home, where their parents, Maria and Troy Larson, played a central role. After school, they would turn to their mother for help with math homework, a routine that became a foundation for their academic confidence.

“Our mom pushed us, and I think that we’re the better for it. She always pushed us to excel. It was a matter of making sure that we got As. Bs weren't really quite acceptable,” Shelley explained.

While math and science were always part of their foundation, the field is vast, with countless specializations and career paths that branch off in different directions. Yet all three sisters ended up choosing the same major at the same school. For Allison, the middle sister, the path was partly paved by watching her older sister go first.

“Seeing that Shelley kind of made a path, and I knew that it was one that I could take and I could follow, made it very easy. I’m also good at things that she's good at. Might as well just kind of do what she's doing. And I think industrial engineering itself. From everything we knew and learned about it, it offered a lot of different opportunities,” Allison said.

Isabella echoed those sentiments, adding that having a sibling go through the program first made the experience far less daunting. Many of the unknowns had already been navigated, and once all three were at Tech together, they became an informal support system for one another, helping with coursework, class selections, and the everyday grind of an intense program.

“If I was really struggling with the concept in the particular class, I would always call Allison to help me with my computer science homework, because I just wasn’t able to understand the concept. So it was almost [like] having additional teachers and resources that we could always lean on. Also, just to talk through challenges that we were facing and have an extra support system in that way because they've gone through it a couple of years prior,” Isabella explained.

The Larsons also found time to build a rich campus life. All three pledged Alpha Delta Pi sorority, finding a close-knit community outside of the classroom. They also each served as resident assistants, an experience they valued for the opportunity to connect with students from all different backgrounds. Allison and Isabella added club tennis to the mix, rounding out lives that were full both academically and socially.

After graduating, the sisters remained in the industrial engineering field but carved out distinct paths. Shelley moved into customer success, Allison into product management, and Isabella into consulting. The drive to keep growing didn't stop at graduation, either. Shelley later returned to Georgia Tech for her MBA, and Allison came back for a Master of Science in Analytics. 

That ambition didn't emerge in a vacuum. The role models in their lives, chief among them their mother, shaped the women they became. She didn't simply set high standards for her daughters, she lived them. They grew up knowing her story: how she left Peru at 20 and moved to Michigan without speaking a word of English, building a life through sheer determination. As young girls, the sisters remember watching her rise early every morning to get to work and provide for the family.

“Seeing hard work firsthand, the early mornings and the hard days, she always made it work. And of course, she always prioritized us, but it was just always at the forefront that you have a really hardworking mother,” Isabella said.

Their mother's example also carried a deeper message, one about what women are capable of. In a field where female role models have historically been few and far between, the sisters never saw those limitations as barriers. That confidence, in large part, was something their mother imparted in them.

“My mom's a woman in work, a woman in STEM. She's always instilled in us that you always work: that it's your independence,” Shelley said.

Xiaoming Huo, A. Russell Chandler III Professor in the H. Milton Stewart School of Industrial and Systems Engineering and Associate Director for Research in the Institute for Data Engineering and Science (IDEaS), served on the event’s organizing committee. He underscored the importance of collaboration in advancing responsible and effective AI tools for medicine.

“The forum provides an opportunity for collaboration and team forming. This is critical in developing AI tools for medical and health care research,” Huo said.

The keynote address was delivered by Hongtu Zhu of UNC, who presented his work on Causal Generalist Medical AI (GMAI). The model integrates multiple data sources to recommend treatments to physicians and incorporates causal reasoning to strengthen reliability. Zhu demonstrated that embedding causal elements into medical AI systems can improve generalizability by supporting evidence-based decision-making rather than relying solely on predictive outputs. He also outlined both growth opportunities and ongoing challenges that must be addressed before GMAI can serve as a robust clinical decision-support tool.

Additional presentations highlighted the range of AI applications in health care. Professor Omer Inan of Georgia Tech’s School of Electrical and Computer Engineering (ECE) shared research from his lab on AI-enabled wearable technology designed to detect heart conditions. The device he demonstrated uses a vibrometer to measure heart timing and subtle vibrations. AI algorithms then filter the signal to identify abnormalities that may indicate deeper cardiovascular concerns, signals that might otherwise go unnoticed.

Students also played a central role in the forum, presenting their research in a poster competition. One project detailed the development of a retrieval-augmented generation (RAG) model designed to answer questions about drug interactions and flag potential side effects when incompatible medications are combined. Researchers found that incorporating a RAG framework can reduce AI hallucinations, an especially critical concern in medical contexts.

Another student team explored how AI can identify candidate proteins that may aid in treating blood cancers. Using data from the Worldwide Protein Data Bank, the researchers trained AI models to predict which proteins could disrupt processes that limit the immune system’s ability to target cancer cells. Given the complexity of protein interactions, AI offers a powerful tool for identifying promising therapeutic pathways that would be difficult to isolate manually.

Across keynote talks and student presentations, BERD illustrated both the breadth and precision of AI applications in medicine. From at-home wearable diagnostics to advanced computational modeling for cancer research, presenters emphasized that AI is already delivering tangible value. At the same time, speakers noted that the field remains in its early stages, with continued collaboration and innovation essential to improving care delivery and advancing healthier outcomes.

Entering 2026, one thing is clear: staying on the sidelines is no longer a viable option. We both agree that 2025 was the last year when being “behind” on AI adoption could be rationalized. In 2026, leaders cannot stay in the foxhole. They need to move forward, doing so in a way that reduces the risk of failure.

The past two years have been full of promise for AI in supply chain: we have seen impressive pilots, compelling research findings, and no shortage of claims about what agents and large language models can do. At the same time, many supply chain leaders are frustrated; there has been significant activity and investment in centralized capabilities without meaningful results in the supply chain. Too many efforts stall. Too many pilots never scale. Many organizations feel they have kissed a lot of frogs and are still waiting for something that works reliably.

The question for 2026 is no longer whether to engage with AI, but how to do so in a way that consistently delivers results. This is the year to put points on the board through disciplined, repeatable progress rather than moonshots.

Two Principles Separate Progress from Experimentation

Across our work and conversations with supply chain leaders, organizations that are driving tangible results tend to follow two principles, sometimes explicitly, sometimes intuitively:

1. Leverage GenAI Where It Adds Differential Value

Large language models are exceptionally strong at working with language. They summarize, explain, code, and translate intent into logic. This makes them powerful tools for accelerating development, analysis, and communication.

Much of supply chain execution, however, depends on precision. Planning rates, forecasts, production schedules, routing logic, and inventory policies rely on structured data, mathematical relationships, and deterministic logic. In these environments, hallucinations or probabilistic answers are not just inconvenient. They can be operationally disruptive.

Many early failures stem from applying LLMs where deterministic logic is required, rather than using them to support the creation, maintenance, and monitoring of that logic. In practice, GenAI is most effective upstream, helping teams build analytics faster, surface issues earlier, and lower the friction of development and maintenance.

2. Design with People in the Loop

This is not only a philosophical stance. It reflects technical reality. While recent research shows that collections of agents can outperform humans in controlled settings, production supply chains are not laboratories. They are complex, interconnected processes and organizations that operate in a dynamic, ever-changing environment. In contrast to AI that augments workers, fully autonomous systems introduce risks—technical, organizational, and reputational—that erode the incremental value relative to the increased costs to develop and maintain them.

Human-in-the-loop is not a concession. It is a design principle.

From Ideation to Error-Proofed Execution

Most supply chain organizations are not short on AI use cases. What they lack are clear, high‑probability paths to value creation.

A familiar pattern plays out: organizations rush into pilots without a clear view of where AI adds value. Results are mixed and hard to interpret. When early efforts disappoint, leaders become more cautious, not because they doubt AI’s potential, but because they are wary of repeating visible failures.

One executive described this dynamic as being "tired of kissing frogs." After aggressively leaning into new technologies early, the organization became skeptical, insisting on external proof and peer validation before investing further.

The more productive question is no longer "What is the most advanced thing we can try?" but instead: "What can we do today that has a high probability of working, scaling, and building our capabilities?"

How to Put Points on the Board in 2026

Across our experimentation and advisory work, two areas consistently emerge where GenAI is already delivering value.

Enterprise Productivity: The Safest On-Ramp

The most reliable progress comes from improving everyday productivity.

Most organizations take a restrictive approach, limiting AI access to a small group or tightly controlled pilots led by centralized technical teams, only to realize they were slowing learning and adoption across the enterprise. In one large retailer, leadership initially centralized AI use due to security and governance concerns. Over time, they shifted to enterprise licensing that centralized risk management while allowing broader employee access within guardrails.

The result was not chaos or "shadow IT." It was productivity: meeting summaries, analysis support, presentation development, and faster access to internal knowledge.

These gains may sound modest, but they matter. Giving people five to ten hours per week back changes how employees experience AI. It becomes a tool that helps them do their jobs better, not a signal that their jobs are being automated away.

For leaders, this means actively enabling access to approved tools, supporting skill development, and encouraging experimentation within clear boundaries. This is one of the most straightforward ways to quickly and visibly put points on the board.

Decision Intelligence: Rewiring the Operating Model

Advanced analytics, optimization, and planning systems predate GenAI. What is new is not the math, but rather the speed, accessibility, and maintainability of building and sustaining advanced analytics solutions.

GenAI acts as an accelerator. It reduces the friction of writing code, standing up, monitoring logic, and explaining results. It brings advanced capabilities closer to the business, rather than confining them to a small central team.

A concrete example comes from production planning. Planned production rates are often set during commissioning or early ramp up and then reused for long periods. Over time, changes in labor mix, maintenance practices, or product complexity cause actual throughput to drift. Plans continue to run, but they quietly degrade.

In effective implementations, GenAI does not update the planning system autonomously. Instead, it operates adjacent to it. It helps teams build monitoring logic that compares planned versus actual performance, surfaces statistically meaningful drift, and generates candidate adjustments with supporting context. Planners review and approve changes before they are re-ingested into the APS.

The system of record remains intact. Human accountability is preserved. What improves is the speed, frequency, and quality of assumption hygiene, enabling earlier detection of problems before they cascade into service, cost, or inventory issues.

Avoid Kissing Frogs: Technology and Organizational Choices

Many organizations “kiss frogs” not because the new technology is flawed, but because they are not ready to adopt it.

To avoid this fate, successful efforts often include the following elements:

1. Leverage existing, approved AI platforms rather than onboarding new technologies
   • Accelerates time to value
   • Helps define the true limitations of your current technology stack to guide future platform selection
2. Maximize the value of current systems (e.g., APS, production scheduling software) instead of chasing new applications
   • Existing, complex supply chain software often under-delivers on its promised value
   • AI agents and workflows are highly effective at improving master data quality and ensuring planning parameters are accurate
3. Foster ideation and solution development with internal teams, while using third parties to accelerate capability building—not to replace it
4. Make progress visible by sharing early wins, curating employee-driven experiments, and scaling what works

Change management is not an option; it must be designed into every aspect of an AI program from the start. When organizations invest heavily in advanced capabilities at the top while doing little to equip everyday employees, the message received is often, "This is happening to you, not for you." That perception creates resistance, fear, and organizational drag.

Effective leaders communicate a clear vision for how new capabilities will augment, not replace, their teams, so that scarce human intellect is applied where it adds the most value.

Key Actions to Win in 2026

The principles are clear. The opportunity is real. The question now is execution.

If 2026 is the year to put points on the board, supply chain leaders must move from experimentation to engineered progress. That begins with clarity.

1. Define a Multi-Year AI Value Vision

Develop a concrete view of how AI will create value in your organization over the next several years. Not a collection of pilots. Not a list of tools. A clear articulation of where and how AI will improve productivity, strengthen decision quality, and increase operational reliability.

That vision should:

• Clarify where AI will augment human decision-making versus automate tasks
• Identify the business outcomes you expect to improve (service, cost, inventory, resilience, productivity)
• Guide decisions on organizational design, platform selection, governance, and partnerships
• Establish sequencing - what you will enable now versus what must wait

Without a defined direction, AI efforts default to software deployment. With it, technology becomes a lever for measurable operational improvement.

2. Enable Broad, Responsible Access

Capability development accelerates when access is not unnecessarily constrained. Ensure that team members at every level - from executives to frontline planners - have access to approved enterprise AI tools and agent-building capabilities, along with practical training tied to real workflows.

Effective enablement includes:

• Enterprise licensing and governance that remove friction while protecting data
• Hands-on guidance tied directly to day-to-day supply chain work - reporting, master data cleanup, production monitoring, inventory analysis, schedule validation
• Clear operating guardrails that define appropriate data use and boundaries
• Leadership support for responsible experimentation

Restricting access may feel prudent. In practice, it slows learning and reinforces dependency on centralized teams. Broad enablement builds capability across the organization.

3. Create Local Ideation and Scaling Mechanisms

Durable progress does not originate only from centralized programs. It often begins at the front line.
Leaders should create simple, visible mechanisms for individuals and teams to experiment within defined guardrails and to share what they are building.

This includes:

• Recurring forums or showcases where teams present working solutions
• Curated libraries of effective prompts, workflows, and agents
• Clear channels for submitting ideas and documenting results

Most importantly, organizations must be able to move from local experimentation to scaled adoption. That requires:

• Identifying the strongest minimum viable solutions emerging from the field
• Refining and hardening them into repeatable workflows
• Productizing and scaling what demonstrably improves performance

The objective is not activity. It is building capability that compounds over time.

These steps are straightforward. They require intention and follow-through. That is what separates durable capability from scattered experimentation.

It is not too late to lead. The last several years have provided lessons - technical, organizational, and cultural. Leaders who absorb those lessons and design deliberately for scale will build AI capabilities that strengthen over time.

That kind of progress is not flashy. It does not depend on moonshots or fully autonomous systems operating in isolation. It depends on clarity, access, discipline, and accountability.

In 2026, novelty will attract attention. Durability will create an advantage.

The organizations that win will not be the ones with the most pilots. They will be the ones who consistently translate AI into measurable operational improvement.

This is the year to move from experimentation to engineered results.

Put points on the board.

Xie is one of 20 mid-career scientists, engineers, and medical professionals in the 2026–2028 cohort. Her research develops statistical and computational foundations for sequential inference, high-dimensional change-point detection, robust decision-making under uncertainty, and generative modeling for inference and decision-making, with applications in public safety, power grid resilience, and biomedical and health systems.

During her two-year term, Xie will have the opportunity to contribute to National Academies’ consensus studies and convening activities, collaborate on interdisciplinary projects, and engage with peers to address critical national and global challenges. Cohort members meet monthly in virtual sessions and gather twice annually for in-person meetings.

“I am honored to join the New Voices program and to contribute perspectives from statistics, data science, machine learning, and operations research to the National Academies’ work on AI and public decision-making,” said Xie. 

New Voices members are supported by National Academies staff and guided by an advisory committee of senior leaders. They include elected members of the National Academy of Sciences, National Academy of Engineering, and National Academy of Medicine, as well as program alumni.

With the addition of the 2026 cohort, the New Voices network now includes 80 members across four cohorts. Alumni of the program have gone on to serve on dozens of National Academies committees and to represent U.S. mid-career STEM perspectives at major national and international events.

Xie will be the third Georgia Tech faculty member to participate in New Voices. Lauren Stewart (School of Civil and Environmental Engineering) and Omar Asensio (School of Public Policy) served in previous cohorts.

“Now more than ever, we need to nurture the next generation of talented American researchers, who are the future leaders of the U.S. science and innovation enterprise,” said Marcia McNutt, president of the National Academy of Sciences.

Businesses depend on strong supply chain expertise to keep Valentine’s Day running smoothly. While overall demand mirrors other holiday periods, certain products like flowers, candy, and cards see sharp spikes, and these peaks can ripple across other goods and services as well.

Candy
Candy makers are well-versed in navigating peak sales periods throughout the year, but holidays like Valentine’s Day bring a distinct set of challenges. Beyond meeting higher demand, manufacturers must redesign packaging and, in some cases, reshape products (think heart-shaped candies and festive wrapping) to capture the spirit of the season and stand out on store shelves.

To make this transition seamless, many large companies treat the holiday as an innovation cycle, assigning dedicated teams to plan months in advance. These teams develop new packaging designs and product variations while ensuring changes integrate smoothly into existing production schedules and supply chain operations.

“For Reese’s to make a tree versus a bunny versus a heart. They figured that out. That's kind of in the final element of actually bringing, in their case, the peanut butter and the chocolate together. So it's going to run in an existing production facility. It may even run on an existing production line with some very unique change parts that would be unique for that physical product,” explained Chris Gaffney, managing director of Supply Chain and Logistics Institute and academic program director in Georgia Tech Professional Education.

Flowers
Demand for flowers operates differently than demand for products like candy. While there are predictable peaks around holidays such as Valentine’s Day, demand also rises and falls throughout the year. Growers must determine weeks in advance how many flowers to plant, carefully balancing the risk of overproduction with the need to meet customer demand at precisely the right moment.

To make these decisions, growers invest significant resources in building accurate and reliable forecasts. Historical sales data offers a logical starting point, providing insight into seasonal patterns and prior performance. But past demand can only serve as a guide. Economic conditions, consumer confidence, and shifting purchasing behaviors all influence how many flowers customers ultimately buy.

Forecasting is becoming increasingly complex. Research from the Federal Reserve Bank has shown that consumers’ perceptions of the economy are growing more disconnected from their actual household financial situations. That misalignment makes traditional indicators less dependable. As a result, forecasters are turning to new metrics and advanced technologies, including artificial intelligence and machine learning, to analyze massive volumes of data and uncover hidden patterns.

“This is where we get into machine learning. You have people who will get 10,000 different data streams, cash register spending, other things that might be correlating events and try to sit here and say, can I create a machine learning model that predicts [demand] better?” Gaffney said.

Restaurants
The restaurant industry thrives on filling every available seat, and there’s rarely much extra capacity to spare. This year, restaurants may get a small reprieve, as Valentine’s Day falls on a weekend. Diners are more likely to spread out their visits throughout the day, rather than all arriving at once after work, easing the typical rush and helping restaurants manage demand more smoothly.

Caregivers
Perhaps even more lucrative, Gaffney explained, is the babysitting market. With a limited number of sitters available, rates can soar on Valentine’s Day, and anyone willing and able to work can expect to earn significantly more than usual.

“The really interesting hedging might be the babysitter; responsible kids older than 18 who could stay out late or a high school kid who can stay out until 1:00 in the morning. A very reliable babysitter might make a lot of money on a day and might have to be booked in advance for Valentine's Day,” Gaffney said. 

The Makerspace offers a wide range of equipment to support XR projects and experimentation, including Apple and Meta headsets, augmented reality glasses, haptic gloves, motion-tracking cameras, and 3D printers, enabling student and faculty research and exploration in extended reality.

XR is an umbrella term encompassing everything that merges physical and virtual worlds. It includes augmented reality (where digital objects are added to the physical world), mixed reality (where digital elements can interact with the physical world), and virtual reality (which uses a completely virtual environment). Steven Yoo, graduate student and XR Captain who leads a team of five other student crew members who will operate the Makerspace, said that exploring XR in an industrial engineering context enables a more human-centered approach, allowing engineers to better understand how real people interact with their design solutions. 

Tech Square 3, officially named George Tower | Scheller Tower, will reach a major milestone on Monday, Jan. 12, offering the campus community access to the first three floors of the new facility.  

People often ask me a simple question: “You always recommend a good book to read; what have you read lately?”

I usually give them my version of a money-back guarantee. I haven’t had to pay up yet!

The Thinking Machine, Stephen Witt’s book on Jensen Huang and NVIDIA, is one of those recommendations.

It’s a fast, engaging read that packs a lot of insight into a book you can finish in just a couple of days. It’s also one of the most interesting books I’ve read this past year out of a stack of twenty or thirty. Most importantly for my world, it’s a book from which supply chain students, young professionals, and senior leaders can all take something different.

What many supply chain readers may not realize is that NVIDIA’s story is, at its core, a case study in supply chain design, constraint management, and long-horizon system building played out on a global stage.

This book matters to me because it pulls back the curtain on the largest technology shift impacting supply chains this century. It shows it not just as a technology story, but as a supply chain, leadership, and ethics story hiding in plain sight.

More Than a Tech Book

On the surface, this is a story about GPUs, artificial intelligence, and one of the most important technology companies in the world. But underneath, it’s really a story about context: how ideas evolve, how industries form, and how long-term decisions compound over decades.

You don’t need to be an engineer to enjoy it. By the time you’re done, you’ll have a much better grasp of:

• why chips matter,
• why AI depends on physical infrastructure,
• and why supply chains quietly shape what’s possible.

That combination makes the book especially relevant for anyone building a career in supply chain, operations, or industrial leadership.

The Immigrant Story — Still Worth Protecting

One of the most powerful threads running through the book is Jensen Huang’s immigrant story.

His family worked hard to come to the United States. He grew up in modest circumstances, and through persistence, opportunity, and relentless effort, he helped build a company with global impact.

For many of our ancestors, this story feels familiar. For many who come to the U.S. today, it still represents hope. The book serves as a quiet reminder that this pathway from modest beginnings to meaningful contribution is not accidental; it is something that needs to be protected.

The United States is far from perfect, but it remains a remarkable place to innovate and to start businesses. Supply chains are both a driver of that innovation and a beneficiary of the new ideas that emerge.

A Startup Story With Real Twists and Turns

The founding of NVIDIA is not a clean, linear success story.

The original big idea wasn’t necessarily the one that ultimately “won,” and the initial target market wasn’t always the right one. The company faced near-death moments, pivots, resets, and more than a few reasons to walk away.

For students and young professionals considering startups, whether founding one or joining one, this book offers a realistic picture of what that path looks like. It reinforces a few hard truths:

• the probability of failure is high,
• the work ethic required is enormous,
• and the rewards, if they come, often come much later.

I often describe this as a “one scoop now, two scoops later” dynamic. Early effort is rarely rewarded proportionally; patience matters more than hype.

Innovation Is a Team Sport

While Jensen Huang is clearly the centerpiece of the book, one of its strengths is that it avoids treating innovation as a solo act.

Many other players, sometimes knowingly and sometimes unwittingly, contributed research, ideas, and decisions that ultimately shaped where we sit today. The book does a good job showing how progress builds through layers of contribution, often across institutions and generations.

This matters, especially for students and early-career professionals. Breakthroughs rarely come from a single moment or a single person; they come from systems that allow ideas to accumulate and translate into real-world application.

From Basic Engineering to Neural Networks

Several chapters walk through the literal evolution of the technology, and this is where the book is both accessible and impressive.

Even if you can only “just barely hang on” technically, the narrative is clear: today’s AI capabilities are the result of layered progress. Hardware advances built on earlier hardware, software abstractions built on earlier software, and research findings translated into application over time.

Many of the contributors moved fluidly between academia and industry, reinforcing a core lesson: foundational science and engineering still matter. For those of us who remember an analog world, it’s fascinating to see how decades of incremental progress led to the current state and potential of AI.

A Supply Chain Story Hiding in Plain Sight

From a supply chain perspective, The Thinking Machine reads like a case study hiding in plain sight.

NVIDIA is an American innovation success story that is, at the same time, deeply dependent on global supply chains. Its relationship with TSMC in Taiwan, the scarcity of advanced manufacturing capacity, the national security implications of certain chips, and the need to serve global markets all create a complex and fragile operating reality.

One of the quieter but most powerful lessons in the book is how much supply chain design matters. Product success here isn’t just about better ideas; it’s about how effectively those ideas are translated into scalable, resilient, global systems.

AI may feel digital, but its limits are profoundly physical.

Leadership Results — and a Real Paradox

The book also forces an uncomfortable but important leadership conversation.

Jensen Huang is demanding, intense, and uncompromising. While the results are undeniable, I don’t advocate for many aspects of his leadership style. I believe similar outcomes could be achieved without subjecting employees to public humiliation.

Results matter, but how we get them matters too.

Reading this book reminded me that some of the most valuable leadership lessons I’ve learned came from watching both how to lead and how not to lead. I’ve had bosses who modeled the kind of leader I wanted to become, and a few who taught me just as much by showing me what I wanted to avoid. Both experiences have been valuable.

That tension is worth sitting with, especially for those mentoring the next generation of leaders.

Computer Vision, GPUs, and Adaptability

Computer vision plays a supporting role in the story: not the headline act, but an important early driver. Graphics and vision workloads helped shape GPU architectures long before today’s generative AI boom.

Over time, those architectures generalized to support a wide range of parallel computation, including neural networks. It’s a reminder that technologies often succeed not because of a single application, but because they are flexible enough to evolve.

Ethics, Uncertainty, and Responsibility

Finally, the book leaves us with unresolved questions, and that may be its most honest contribution.

AI is resource-intensive, it will reshape work and livelihoods, and it raises real ethical concerns. Opinions vary widely on whether this moment resembles past industrial revolutions or represents something fundamentally different.

I teach and advocate for the application of AI, but I personally struggle with these ethical dilemmas. Rather than avoid them, I try to address them head-on by highlighting the risks and encouraging students to stay informed so they can be voices for responsible, positive use.

In today’s global and regulatory environment, it’s unrealistic to expect a pause in research or application. Education, not avoidance, may be the most practical form of governance we have.

We can’t guarantee how this plays out over the next decade, but we can prepare.

Why I Keep Recommending This Book

If you’re a supply chain student looking for context, a young professional navigating career choices, or a senior leader trying to understand how AI, supply chains, leadership, and ethics intersect, this is a book worth your time.

It’s engaging, timely, and surprisingly human.

And when someone asks me, “What are you reading?”

This is the book I’ll keep recommending.

The Thinking Machine succeeds because it reminds us that behind AI are people, supply chains, and long-term decisions, all operating under real constraints. That’s a lesson worth revisiting as we set the pace for the months ahead.

A Closing Question

This book highlights traditional supply chain constraints that NVIDIA faced in its growth journey, such as single source supply, perceived lead times, capacity at key suppliers, demand volatility, and talent gaps. Where have you seen or faced these, and how have you and your company navigated them?