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.

Shalin Bhatia - Uniquely positioned to apply knowledge and skills to make a difference in people’s lives 

Within the Millennium Fellowship is there any U.N. Sustainable Goals you are targeting? Are there any specific outcomes you hope to achieve in this experience? Within the Millennium Fellowship, I am targeting Goal 9 (Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation) and Goal 16 (Promote just, peaceful and inclusive societies). Through this experience, I aim to alleviate socioeconomic disparities in my community and promote fairer systems in society.

As a Stamps President’s Scholar and Industrial Engineering student, how do the mentorship and analytical training you’ve received so far at Georgia Tech shape the way you diagnose problems and design solutions? The mentorship and analytical training I’ve received so far at Georgia Tech have made me much more technical and curious in the way I approach problems. Rather than analyzing the nature of the problem, I aim to understand the root of the problem and design quantitative solutions to truly alleviate the problem.

For students considering the Millennium Fellowship next year, what advice could you share? As Georgia Tech students, we are uniquely positioned to apply our knowledge and skills to make a difference in people’s lives. To create a better future for ourselves and others, I’d suggest we do just that and turn opportunity into action.

What do you think defines a strong, SDG-aligned project? I think that a strong, SDG-aligned project encompasses a genuine interest and motive in being involved in the surrounding community. It doesn’t have to be the most advanced, technical, or complex; it just needs a good purpose.

Priyanka Joseph - Advancing Gender Equity in Industrial Engineering 

Are there any challenges that your Millennium Fellowship looks to aid and/or is there a particular UN Sustainable Development Goal (SDG) you are targeting? My Millennium Fellowship project is focused on addressing gender inequity in Industrial Engineering. Girls in IE is a student-led organization I am building, inspired by my experience as the only female engineer and first woman of color co-op on my team at L’Oréal. The organization will provide industry-specific mentorship opportunities, structured recruiting pipelines with leading companies, and resume support through workshops and feedback. It will also give students hands-on project experience to strengthen their skills and host panels with female industrial engineers to encourage networking and visibility. By combining mentorship, career preparation, and community, Girls in IE directly advances SDG 5: Gender Equality by ensuring that women in Industrial Engineering are equipped to succeed, supported by a network, and empowered to lead. 

You study Industrial & Systems Engineering with a strong interest in management and sustainability. Do you think you will be able to combine any or all those threads in your work for this Fellowship? My interest in management and operations shapes how I approach Girls in IE, since building an organization requires designing structures that are both effective and scalable. I envision future opportunities to partner with industry leaders who are prioritizing sustainability in their supply chains and operations. 

You are working as an Industrial Engineering co-op in L’Oreal’s Professional Products Division under the Continuous Improvement Department. What has that experience taught you that you can bring to your Fellowship work? Working as an Industrial Engineering co-op in L’Oréal’s Continuous Improvement Department taught me the importance of building structures that create lasting impact. Projects such as improving warehouse process flows and leading time studies require me to listen to people on the ground, understand their challenges, and design solutions that make their work easier and more effective. These same lessons connect directly to my Fellowship project, where creating mentorship and support networks for women in Industrial Engineering also depends on listening first, identifying barriers, and then building sustainable systems of support.

What leadership skills are you most focused on developing through this fellowship—and why? Through the Millennium Fellowship, I want to strengthen my ability to scale an organization, mentor others, and lead with influence. As the founder of Girls in IE, that means learning how to recruit and empower members, build a strong leadership pipeline, and sustain partnerships with industry and faculty. These skills are essential if I want Girls in IE to grow into a lasting community that creates real opportunities for women in Industrial Engineering.

Shivani Murugapiran - Do you remember when we ran out of eggs?

Are there any challenges you look like to take on during this experience, particularly those that align with the UN SDGs? Do you remember when we ran out of eggs? And everyone all over the country was running around trying to find eggs. And the skincare brand, The Ordinary, ironically started selling eggs in a fridge in their New York stores. And then we had eggs, overflowing eggs. I threw out some eggs once and felt really bad about it. That’s what keeps me up at night. Before you think I’m out of my mind, let me tell you that it’s because I’m incredibly passionate about behavioral economics. I’m obsessed with socioeconomic equality and building financial resilience. At both local and global levels, I believe that education and financial literacy can bridge societal barriers and reduce vulnerability to shocks (like the Great Egg Crisis of ’25), building a more robust society. During this Fellowship, I’d like to take on the challenge of designing solutions that don’t just respond to crises but actually prepare communities to withstand them. For me, this ties directly into the UN SDG 8 (Decent Work and Economic Growth) and SDG 10 (Reduced Inequalities). We live in an era of economic uncertainty, and I hope to equip people with the tools to not only survive but THRIVE in a new world order. 

Which Industrial & Systems Engineering tools such as data analytics, simulation, network flows, etc., do you think you can apply to the Fellowship and the project? During the Fellowship, I plan to draw heavily on data analytics to parse large-scale social and economic datasets and uncover meaningful trends. I will also use simulation modeling to test interventions under uncertainty. My coursework has prepared me well for this. CS 2316 (Data Input & Manipulation) and ISyE 4031 (Regression & Forecasting) gave me a strong foundation in Python, R, and SQL, equipping me to not only organize and analyze data effectively but also translate those insights into actionable strategies.

You’ve engaged with policy and international affairs programming at Georgia Tech. How has that experience influenced the way you approach partnerships and governance, and do you think those experiences will have an impact on this Fellowship? My time in Brussels shaped how I think about partnerships and governance. I remember being in a policy briefing when the speaker paused to announce that the EU’s AI Act had just passed in the very building we were sitting in. It made governance feel immediate, knowing that decisions ripple outward and change the conversation in real time. My involvement with Model NATO reinforced this, showing me that effective partnerships start with listening. More specifically, they require cross-cultural literacy and the awareness that different societies and institutions may be further along on certain challenges. Approaching those differences with openness rather than condescension is key. This perspective will guide my Fellowship work by helping me build trust, align diverse stakeholders, and design solutions that are both technically rigorous and contextually relevant.

For students who want to apply for this Fellowship next year, what advice would you give them?  Think deeply about what keeps you up at night. The impossible, the mundane, everything in between. The United Nations SDGs are broad, all-encompassing, and often daunting to discern. Start with something you’ve experienced and felt, translate it into a focused problem statement, and see who else it affects. Stop thinking, for a moment, about your resume or your list of projects in the backlog that you’ve always wanted to do. Think about who you are, what you care about, and where you want to leave a mark in your community. Find a problem, resonate with it, and home in on its implications. Stress test this by finding a professor or mentor who knows you and tell them about your thought process and ideas. 

You can view the complete list of 2025 MillenniumFellows here. 

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.

Introduction

This year has felt like a lifetime in the Generative AI (GenAI) world. Tools, capabilities, and best practices are shifting monthly, sometimes weekly. For supply chain professionals, the message is clear: ongoing development is not optional. Like lean, analytics, or S&OP in prior decades, GenAI proficiency is quickly becoming a differentiator. The question is not if you’ll integrate GenAI into your workflow, but how quickly and effectively. 

The Evolution of GenAI in 2025

When we look back to January, it’s striking how much progress has been made in less than a year. Early in 2025, the conversation centered on agentic AI and larger models. GPT-5 and Claude 4 improved reasoning and context windows, while OpenAI introduced ChatGPT Agent in preview, able to carry out bounded multi-step tasks like retrieving files, browsing the web, and drafting structured outputs. In supply chain, this translated into early experiments with automating shipment steps or running contract reviews in a single query — tasks that were pilot-level at best in January.

By mid-year, multimodal capabilities and enterprise copilots began shifting from concept to daily use. Users could combine text, image, and voice inputs to detect defects or summarize complex documents, and copilots became embedded inside SAP, Oracle, Microsoft, and Google platforms. For the first time, GenAI wasn’t just a tool "off to the side" but something integrated directly into the systems supply chain professionals rely on.

In the second half of the year, new capabilities started layering on: memory, specialized small models, and synthetic data with digital twins. Memory allowed copilots to recall context from prior chats or S&OP cycles, reducing rework. Domain-tuned models made GenAI lighter, cheaper, and faster for logistics, procurement, and planning tasks. And digital twin integration allowed organizations to stress-test networks under disruption scenarios, from weather to labor shortages.

Enterprises also moved closer to operations with AI at the edge, using IoT data for predictive maintenance or real-time routing. At the same time, guardrails and compliance became a central topic, with more organizations creating clear "green/yellow/red" tiers for safe use. And in Q4, collaboration AI and hybrid architectures came to the forefront — copilots that can negotiate contracts in multiple languages, and architectures that blend closed and open-source models to balance sovereignty, cost, and security.

For mainstream individual users, the picture is simpler but still powerful. Anyone with ChatGPT Plus or Copilot today can take advantage of:

• Memory and custom instructions to save preferences and formats across sessions.
• Project-only memory (rolling out) to organize work by context.
• Agent previews like Operator to see how automation might work on bounded tasks.
• Connectors and file uploads to bring internal data into conversations. 

For leaders, the focus is on policy, safe pilots, and scaling. They are:

• Sponsoring agent experiments in low-risk domains (like supplier alerts).
• Embedding copilots in enterprise systems for daily planning and reporting.
• Formalizing AI use policies so employees know what’s encouraged, conditional, and off-limits.

The net result: what started in January as experimentation has, by October, become a layered landscape. Individual users now have practical tools to reclaim time, while leaders are piloting more ambitious integrations and building the governance to make adoption sustainable.

1. Action Planning is Critical

The pace of change makes a one-and-done training activity insufficient. Think of GenAI skills like fitness: it requires steady reps over time. Professionals who set quarterly development goals — experimenting with new tools, building prompt libraries, testing workflows — will not only stay current but pull ahead.

💡 Try This Quarter:

• Build a custom prompt library for routine tasks (e.g., supplier follow-ups, KPI summaries).
• Test one open-source tool such as LangChain or Haystack.
• Use AI to summarize two recent meetings and validate output with your notes. 

2. Prompt Maturity is the New Literacy

I’ve personally learned the most about prompting by asking ChatGPT to critique my style against a 12-step framework. The feedback gave me a process improvement plan I still use today. Prompt maturity isn’t abstract — it’s a measurable, improvable skill.

💡 Applied step: Rewrite one work prompt per week by climbing the ladder. 

3. Unlocking Personal Productivity

One of the fastest returns from GenAI comes from personal productivity. In our short courses this year, I’ve seen learners gain comfort and lower stress as they practice more with the tools. Many reclaimed time by using GenAI for emails, presentations, meeting notes, and data prep.

While the list of GenAI time-saving strategies is broad, some uses are already mainstream and validated by thousands of professionals. The table below organizes these strategies into categories, provides guidance on how to accomplish them, and highlights common watch-outs to ensure they deliver value without risk.

💡 Try this week: Track one workflow where AI saved time and estimate the hours reclaimed.

4. Critical Thinking: Ironically More Important than Ever

We wrote about critical thinking and added it to our curriculum after studies raised concerns about overreliance on AI. The smarter the tools become, the more important it is to validate their outputs.

💡 Applied step: Take one AI output this week and run it through the checklist — you’ll see both strengths and blind spots.

5. Advocating for Strategy and Guardrails

We’ve seen firsthand how AI policies can evolve. One major retailer shifted in less than a year from a rigid “only data scientists experiment” model to encouraging all employees to try safe versions of multiple LLMs. This shift shows why professionals should advocate for strategy and guardrails that evolve with the technology.

💡 Ask your manager: Which of our daily tasks fall into green, yellow, and red today? 

6. Agents: Early but Essential

Many industry partners are actively testing agents. Our software partners are hitting singles and doubles now, with bigger “home run” opportunities still developing. Agents aren’t fully reliable yet, but they are advancing quickly and will increasingly appear in ERP, TMS, and WMS platforms. 

In practice, most organizations today sit between Level 1 (Exploratory) and Level 2 (Task-Specific Agents), with early pilots pushing into Level 3 (Augmented Workflows). Tech-forward enterprises — particularly in retail, e-commerce, and global manufacturing — are building domain-specific agents for forecasting, procurement support, and transportation planning, often embedded inside ERP or planning platforms. These companies are experimenting with multi-agent coordination but keep humans firmly in the loop. By contrast, mainstream companies are still largely in the exploratory stage: individuals using general copilots for drafting documents or ad hoc analysis, without enterprise integration, security controls, or governance. The gap is widening — forward-leaning firms are developing playbooks for orchestrated workflows, while many organizations are just beginning to set policies and figure out where AI fits safely into their operations.

Looking ahead, Level 4 (Collaborative Automation) is where the near-term breakthroughs will happen. In the next 3–5 years, we can expect multi-agent orchestration to become a practical tool for managing recurring disruptions — think transportation rerouting during weather events or automated supplier alerts when delivery milestones are missed. Early adoption will occur in large, tech-forward enterprises with strong governance and secure infrastructure. Level 5 (Autonomous Resilience) remains aspirational: while the vision of end-to-end supply chain automation is compelling, regulatory hurdles, trust, and explainability challenges mean human oversight will remain essential. The more realistic trajectory is that enterprises will selectively automate narrow disruption scenarios while maintaining tight human control, with broader autonomy coming only as governance, standards, and trust mechanisms mature.

💡 Applied step: Identify one repetitive process in your work that could be a candidate for an agent. 

7. Human in the Loop: Non-Negotiable

Competition has improved model quality this year — but hallucinations and memory issues remain. That’s why “human in the loop” is not just a principle; it’s operational reality. AI is still an assistant, not a replacement.

💡 Applied step: Write down one checkpoint you always apply before sharing AI outputs.

Conclusion

These observations — from teaching courses, updating curriculum, and watching partners experiment — motivated this article. GenAI is evolving at extraordinary speed, and our profession must evolve with it. Build your plan, refine your prompts, reclaim time, apply critical thinking, advocate for strategy, explore agents, and always keep the human in the loop. Those who do will thrive in 2026 and beyond.

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?

A Growing Manufacturer Facing Volatile Demand 
Synthetex is a Georgia-based manufacturer of geosynthetic fabric systems used for erosion control and infrastructure protection, with headquarters in Peachtree Corners and a production facility in Toccoa. With dozens of products and extensive customization, the organization operates in a complex manufacturing environment shaped by fluctuating construction demand. Historically, Synthetex relied on a make-to-order system, leading to sharp swings between overtime-heavy production and prolonged idle periods, delayed material procurement, and workforce challenges. These factors pushed average lead times beyond 12 weeks, sometimes as long as six months, putting customer deadlines at risk. To address these challenges, the winning senior design team developed a comprehensive plan to transition Synthetex to a proactive, hybrid production model. 

Introduction

The supply chain labor market has been through one of the most dramatic swings in modern history. During the COVID-19 era disruption, talent shortages were acute, and the pendulum swung decisively toward employees. Companies paid top dollar, offered unprecedented flexibility, and competed fiercely for planners, warehouse leaders, S&OP talent, logistics managers, strategic sourcing leaders, and procurement specialists.

But the pendulum swung back in the opposite direction, from whence it came: in favor of the employers.

The past 18–24 months have seen hiring across supply chain cooling. Many large companies are now signaling they intend to grow revenue without necessarily increasing headcount. At the same time, AI and automation have gotten to the point where employers can get more productivity from existing teams. The result is not necessarily indicative of a recessionary job market but a “Great Hiring Pause”: low hiring, low firing, and a clear tilt of bargaining power back toward employers.

The key question now is whether this moment represents a temporary pause or the new normal. Additionally, what does this mean for both hiring managers and early to mid-career supply chain professionals who want to stay competitive in the workplace?

We’ll explore what this means for all stakeholders as we wrap up the year, looking at how the supply chain job market evolved in 2025 and what we expect to see in 2026.

The Pendulum has Swung from Employee Power to Employer Advantage

If you had as little as 5 years of supply chain experience in late 2020–2022, you may have found yourself with competing job offers. Compensation packages offered were lucrative and filled with relocation fees or even 100% remote job offers.

Without a doubt, this shaped the next 2–3 years of the supply chain labor force. Office space sat empty. Employees moved out of the city into the suburbs. Work-life balance improved for everyone. Employers fretted over rents and mortgages on office space and whether their highly compensated employees were actually working. Threats of a pending recession loomed but never materialized. (fingers crossed, knock on wood). Employers ran a bit lean but then found themselves needing more people to keep up with demand.

In early 2025, we wrote about this swing and the influence AI and automation had on supply chain hiring. Companies seemed to be focusing more on how they could accelerate the performance of existing teams while navigating new cost influences and demand swings. Anxiety about the economy amid never-before-seen tariff whims made it increasingly difficult for employers to plan reliable growth strategies for 2026.

And now here we are. The prevailing mindset as we close out a volatile 2025, where AI and tariffs took center stage, is for growth without as much hiring. So what does that mean for 2026 for employers and employees, or aspiring employees?

Growth Without Hiring: Why Companies are Staying Lean Across Supply Chain and Logistics

Executives are treating hiring as a last resort and not a first resort. JP Morgan Chase’s CFO reportedly said the firm has a “strong bias” against reflexively hiring new people. Walmart, Inc. has signaled plans to grow revenue without increasing employee numbers, instead relying more on automation/AI and efficiency improvements.

As mentioned above, market indicators have become increasingly unreliable. Recent Black Friday consumer spending data indicate that people are financing their purchases on credit and using buy-now, pay-later plans. This means less cash injected into the economy in the short term, along with increased interest payments for 95% of the purchases made on Black Friday. Retailers rely heavily on consumer spending and demand, which dictate their growth or lack thereof.

Businesses have also decided to engage in what some are calling “The Great Freeze”, which is not to hire but also to not fire—holding steady on headcount until they can get a better feel for what 2026 will offer from a demand and affordability sense. High inflation affects everyone, which is why many employers are riding it out for a while.

The Risks of Going Too Lean: Burnout, Fragility, and a Shrinking Talent Pipeline

For supply chain organizations, running lean means pressure to improve throughput, reduce waste, and automate more tasks. While the rapid emergence of AI and automation has greatly improved efficiencies, you still need people to understand the best use cases for all of these tools. They can certainly be enhancements, but will backfire if they are seen to be wholesale replacements for full-time employees. This backlash is being felt and mentioned a lot more consistently. AI shouldn’t replace humans, but rather, make them superhuman.

Firms may invest in upskilling existing staff rather than hiring large numbers of junior or mid-level staff. This could help manage costs in a turbulent economy. This is a tricky game, though. Keeping headcount flat while demands increase can lead to burnout, skill gaps, or degraded service if not managed carefully. Productivity gains might be possible, but at what cost? Change management, culture shift, lack of future talent pipeline, and succession planning can place your supply chain at great risk. Think about it: What will you do about career progression, worker loyalty, and organizational capability in 5–10 years? Yes, AI and automation are force multipliers, but not force replacers.

The people who succeed are those who take a measured approach to talent decisions. It is a refrain that has been emphasized for years. Overly lean operations become fragile, just as banking talent balloons your costs. The goal is to strike a balance between the two.

Will the Pendulum Swing Again?

The short answer: not anytime soon. Today’s flat hiring environment is not just a reaction to inflation or a temporary post-COVID correction or regression to the mean. It is influenced by other structural forces like AI maturity, demographic shifts (including the aging of the workforce), productivity pressure, and a corporate mindset increasingly comfortable with “growth without headcount.”

So what now? Employees should pay attention to these moves and make themselves more valuable by staying proactive. Do not wait for a chance to improve your position. Seek it out.

Find collaborative opportunities with your peers outside of your specific silo. Cross-functional literacy takes center stage to increase one’s value. There has been career acceleration among mid-level supply chain professionals who can work across the organization and become proficient in a multitude of functions. Increase your functional knowledge base and increase your organizational value at the same time.

This is not the time to be complacent or average. Employers still need people with elite soft skills such as leadership, personnel management, communication, and initiative. Visible contributions are essential and will separate those who thrive from those who are content to endure.

There is also hope on the horizon. An elite supply chain institution recently reported that more than 85% of their spring graduates received high-level roles. Another hopeful metric is the rise in offers coming to every supply chain graduate. These numbers are all trending up, which means that the supply chain is strong and in need of a robust talent pipeline.

Employees must demonstrate they can become experienced—if not fluent—with AI tools that make individuals more productive. Use them to lift your value. Differentiation is the name of the game in a field where the top 10–15 percent of talent still commands a premium.

This was explored further in an article written for Georgia Tech this summer. AI is not the end, it is the beginning:

I firmly believe professionals—especially early in their careers—should spend 3 to 5 years in front-line roles. No AI tool can replicate the kind of intuition you build by seeing how things work, where they break, and how people respond in real time. That foundation lasts an entire career.

There will always be a place where the human edge is necessary. The goal is to find where you fit and how you can use AI to your advantage while honing and refining your soft skills. Do not be afraid to make mistakes, either. It is one of the best ways to learn.

Conclusion: Planning for Stability in an Unstable Market

The supply chain talent pendulum has clearly swung back toward employers, and the forces keeping it there are unlikely to fade any time soon. AI maturity, demographic stagnation, post-COVID overcorrections, and a corporate appetite for “growth without hiring” all point to a labor market that may remain employer-favored through 2027 or 2028. But the story does not end there. The pendulum can shift again, and it will if several conditions align: steady consumer demand, renewed business investment, lower interest rates, stable inflation, and a labor market that stays tight enough to force companies to compete for talent rather than squeeze more productivity out of smaller teams.

For employees, waiting for that moment is a recipe for disaster and is not a strategy for success. This is the time to skill up, stand out, and become visibly indispensable. Become more proficient with AI tools, expand your cross-functional range, and build the soft skills that technology cannot replace. Your competition now becomes yourself. There is no better time to be a “self-starter” than now.

For employers, running lean perpetually will not provide a bulletproof bottom line. There is risk to succession planning and employee morale through burnout and stagnation. Continue strategically building internal pipelines. The job market has plenty of talent at a premium right now, so find people who can help you maintain operations and grow into more senior roles as the economy rebounds. Workforce resilience cannot be built overnight, and organizations that fail to adequately invest now will struggle later.

“Steady-Eddie” remains the preferred path. Do not overhire or overfire. Aim for a sweet spot that maintains growth, protects margins, and creates a small cushion of resilience for the labor pool. The companies that invest smartly and the employees who stay adaptable, proactive, and highly visible have the chance to define the next era of supply chain leadership, no matter where the pendulum lands.

Call to Action: What This Means for You—and What to Do Next

If these dynamics feel familiar—or unsettling—you are not alone. Moments like this are precisely when intentional investment in skills, talent pipelines, and professional networks matters most.

For students and early-career professionals

This is the time to differentiate, not wait. Employers are hiring selectively, and they are looking for candidates who combine foundational supply chain experience with strong communication, cross-functional literacy, and practical fluency with analytics and AI-enabled tools. Georgia Tech’s Supply Chain and Logistics Institute (SCL) offers professional education courses designed to build exactly these capabilities—grounded in real-world application, not theory alone.

For working professionals

If you are navigating growth-without-hiring realities, reskilling and upskilling are no longer optional. SCL programs help professionals sharpen decision-making, leadership, and applied technical skills that increase both individual and organizational resilience—especially in environments where headcount is constrained but expectations are rising.

For hiring managers and employers

Even in a cautious hiring market, the competition for top-tier supply chain talent has not disappeared—it has become more targeted. Engaging early with Georgia Tech SCL allows you to connect with high-caliber students, support a durable talent pipeline, and partner on developing skills that align with where supply chains are headed, not where they have been.

Readers are also encouraged to explore SCM-focused podcasts and practitioner conversations—including leadership, career-path, and “day-in-the-life” perspectives—that help translate these labor market shifts into practical guidance. These voices complement formal education by offering lived experience and real-world context during periods of uncertainty.

For those wondering how to navigate what comes next, staying connected with Georgia Tech SCL can be valuable. In a January 2026 webinar, the team will preview an emerging trend expected to materially shape supply chain roles, workforce expectations, and talent strategies over the next 3–5 years—particularly at the intersection of AI enablement, front-line experience, and leadership readiness.

This moment favors those who engage early, build capability deliberately, and stay connected to credible institutions shaping the future of supply chain practice.

This content was developed in collaboration with SCM Talent Group, a supply chain recruiting and executive search firm.

Resources

• Associated Press — “US hiring stalls with employers reluctant to expand...” (reports just ~22,000 jobs in a month). AP News
• CBS News — Supporting story on same 22,000-job report / labor-market cooldown. CBS News
• PBS NewsHour — Analysis of U.S. hiring stall and its implications. PBS
• Business Insider — Coverage of weak August 2025 jobs report and growing caution in labor markets. Business Insider
• The Wall Street Journal — “Jobs Report Shows Hiring Slowed in August 2025” (subscription-gated). The Wall Street Journal
• Bloomberg — Reporting that job openings and hiring have decoupled despite rising corporate capital expenditures; signals firms are investing without matching headcount growth. Bloomberg
• Walmart / Newsweek — Recent article on Walmart celebrating automation and signaling flat headcount even as business grows. Newsweek

His grandfather, Peter Petrecca, had studied aerospace engineering at Tech and had a long career in aviation, engineering, and product development. No one would celebrate the news more, so Rogers called him with the news before he even told his parents. 

Petrecca had raised three daughters and exposed them to engineering and making things. But none had been interested enough to make it a career — or study at Tech.

“Then Cole came along, and I had another opportunity,” Petrecca said. “We made model cars and motorcycles together and did other things. I wasn't sure he was going to go the engineering route, but I was thrilled when he got accepted.”

Now Rogers is graduating with his industrial engineering bachelor’s degree, and in the sometimes funny way history echoes itself, he’ll walk across the stage exactly 60 years after his grandfather finished his own degree.

It’s a path that probably has been quietly paved throughout Rogers’ life, during all his visits to his grandfather’s house.

Read the full story here.

Andrea Laliberte (IE 1982, M.S. IE 1984, HON Ph.D. 2025) has also invested in the program to ensure future engineers have the support they need. Laliberte shared that she champions the cohort model, which fosters leadership and connection.

• Industrial Engineering undergraduate student Lukas Anisansel used his enrichment grant to study abroad at Georgia Tech-Europe, visiting 14 countries and shaping his global career aspirations. 
   
• Karina Osma Perez (IE 2025) found community and leadership growth through off-campus enrichment experiences like Climate Week in New York. 

Previous recipients include:

2020 Cohort: 
Bethanie Penna (IE 2021) – Tech Strategy Consultant, PwC

2021 Cohort:
Sofia Varmeziar (IE 2025, Graduate MS Operations Research Student)

2022 Cohort:
Lukas Anisansel
Carson Veal

2024 Cohort:
Tiffany Laij
Karina Osma Perez (IE 2025)

2025 Cohort: 
Jose Gallen

Read the full story here.

So, when he had the opportunity to learn more about it through the Medical Bioscience Academy at his high school, Ganesh Kumar enrolled immediately. The program exposed him to healthcare, patient systems, and the science behind clinical care. Becoming a physician seemed like the natural next step.

But by the end of his junior year, everything changed.

He had taken an AP Statistics class, and that opened his eyes to the ways data could spot patterns, strengthen decisions, and, ultimately, improve the ways that systems support people. It was a revelation — one that pushed him to think about industrial engineering, a field that merged his mathematical mindset with his determination to do something meaningful in healthcare and something that felt deeply human.

“What I love about IE is how it lets you take a systems approach to problems that affect people directly,” said Ganesh Kumar, now a fourth-year student in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE). “In healthcare, even small process improvements can make a real difference in someone’s care.” 

As he works toward that future of making a difference for people, he’s trying to make a positive impact on campus first. He’s found ways to connect his interests and his personal values: mentoring younger students, conducting research on using data to improve medical outcomes, and organizing events as president of the Georgia Tech chapter of the Institute of Industrial and Systems Engineers (IISE) professional organization.

This semester’s Capstone Design Expo showcased the ingenuity and problem-solving skills of more than 118 student teams across seven disciplines. Among them, 17 teams represented H. Milton Stewart School of Industrial and Systems Engineering (ISyE), presenting a wide range of solutions, from optimizing scheduling for medical clinics, to refining inventory management for a major auto manufacturer, to enhancing sepsis detection through data-driven patient monitoring. 

Prior to joining One MHS, Scott was Director of Worldwide Design and Engineering for Retail Core Fulfillment/Transportation at Amazon, where he was responsible for the design and development of global supply chain capability to support business growth across Amazon's vast network. He led critical design reviews with senior executives, effectively communicating vision, technology development roadmaps, and solutions to make compelling business cases at the VP, SVP, and CEO levels. 

His expertise covers the full spectrum of Amazon's supply chain operations, including first mile facilities (transload facilities, import processing centers, and inbound cross docks), production on demand (books, disks, custom merchandise), fulfillment centers (both Amazon Robotics and traditional facilities supporting conveyable and non-conveyable products across diverse merchandise categories), air and ground transportation (ground hubs, sort centers, air hubs and air gateways), and seasonal/specialty operations (quick-deploy, returns processing, and reverse logistics). 

Since joining Amazon in 2015, Scott has been influential in technological breakthroughs in robotics and AI, enabling new opportunities to broaden the types of deployable systems by using computer vision and machine learning to unlock new capabilities. He leads the development of integrated systems-of-systems that balance process optimization and intentional automation to ensure humans and technology work together safely and efficiently. During his tenure Amazon has achieved the largest deployment of industrial robotics and mechatronics on earth.

Prior to Amazon, Scott served as Project Manager and Lead Engineer for Direct Fulfillment Supply Chain at The Home Depot from 2011 to 2014, where he developed comprehensive omni-channel supply chain architecture and was recognized as Supply Chain "Leader of the Month" for his work on e-commerce facility network design and  startup. Earlier in his career, he spent six years at Office Depot as Senior Manager for Engineering, Continuous Improvement, and Supply Chain, where he received the Global Innovation Award for implementing lean principles to achieve 57% cycle time reductions across the fulfillment network.

Scott holds both Bachelor's and Master's degrees from the Georgia Institute of Technology — a Bachelor of Science in Industrial Engineering (2004) and a Master of Science in Systems Engineering (2011). His graduate work included analyzing future cargo aircraft and automotive designs, supply chain network simulations, advanced supply chain robotics, and autonomous robotics integration with human systems.

As an Industrial and Systems Engineer with over 20 years of industry experience, Scott brings expertise in strategic business planning, logistics network analysis and design, automation and robotics, statistical modeling, continuous process improvement, and team leadership.

SCL appreciates Scott's participation and will leverage his extensive expertise in global supply chain design, automation, robotics, and systems engineering to help shape our strategic initiatives and provide valuable insights to our research and educational programs.
 

In today's supply chain environment, the pace and scale of change are no longer episodic — they are constant. Network redesigns, automation investments, digital transformation, new product and business models, shifting customer expectations, cost pressure, and talent dynamics all converge at once. 

Here is the most direct insight I can offer — and one I have come to believe deeply through experience:

“If you want your organization, automation, or Digital/AI investments to pay off, change management is not optional. It is the highest-leverage point of failure or success.”

Despite decades of innovation, the uncomfortable truth is that most large-scale supply chain transformations still fall short. According to a recent Bain survey, 70% of major transformations fail to meet their objectives — a number that has remained stubbornly consistent over time. The reasons vary, but the most common root cause is not the technology — it’s the people side of the change.

This is why change management must be treated as a leadership discipline at the center of supply chain excellence. And it is why this topic continues to rise in conversations I have with industry partners, consulting clients, and the students entering the field. 

Where I First Learned the Power of Change Leadership 

This isn’t an abstract subject for me — it is something I experienced in my career. When I worked at The Coca-Cola Company, the business went through multiple waves of transformation over a 10–15 year period: acquisitions and integrations, major information-system deployments, shifts in the beverage portfolio, and cultural changes as carbonated soft drink growth slowed.

As the company diversified into new beverage categories, the economics shifted and productivity expectations rose. The technical challenges were significant, but what stood out to me was this:

“The difference between transformations that succeeded and those that stalled was how effectively people were brought into the change — how well they understood it, aligned with it, and adapted to it.”

Strong technical designs struggled if people weren’t aligned. But “good enough” solutions thrived when the organization invested in communication, role clarity, and capability-building.

Later in my career, during my time as President of Coca-Cola Supply, we made one of the most durable leadership investments I’ve ever seen: certifying the entire organization in the Coca-Cola change model. Many of those leaders still apply the same principles today — 15 to 20 years later — because the skills became part of how they led, not something they had to remember.

That experience shaped how I see change leadership today.

What Today’s Supply Chain Landscape Is Telling Us

Across industries — and especially across complex supply chains — the same patterns repeat.

WMS and automation vendors now budget change management into implementation plans. They’ve learned that even well-designed systems fail if associates fear job loss or can’t visualize the “after” state of their work.

Consulting firms see adoption challenges as the biggest barrier to client success. A firm we taught recently added change management to their executive education curriculum because their teams saw change gaps in almost every engagement. Months later, that module remains the highest-value part of the course.

Network design firms observe cultural resistance across geographies. Even optimized solutions don’t transfer cleanly from one region to another. Culture, norms, and expectations matter — often more than the math.

Robotics and automation projects fail for people reasons, not engineering reasons. At the recent RoboGeorgia Forum, the keynote emphasized that a surprising percentage of large automation investments fail because of unclear roles, resistance, weak communication, and fear — not limitations in the technology.

AI adoption mirrors these challenges. According to a recent McKinsey Global AI survey, only one-third say they are scaling AI enterprise-wide, and just 39% report measurable EBIT impact. The survey reinforces that even when technology works, the real barrier is organizational readiness — leadership alignment, redesigned processes, clear governance, and a reskilled workforce — not model performance.

There is also strong evidence showing that when change leadership is done well, project outcomes dramatically improve. In a benchmarking study of more than 2,600 initiatives, Prosci found that 88% of projects with excellent change management met or exceeded their objectives, compared with only 13% of those with poor change management. Projects with excellent change management were also 5 times more likely to stay on or ahead of schedule and 1.5 times more likely to stay on or under budget. These findings reinforce a simple truth: effective change leadership is directly correlated with higher performance, better adoption, and faster time to value.

Put simply:

“Technical innovation moves faster than organizational adoption — and the gap costs time, money, and credibility.”

Why We Still Struggle With Change, Even Though We “Know Better”

Here's where a critical-thinking lens helps:

• We have 50 years of research on how change works.
• We have widely used models.
• We have entire consulting practices devoted to change.
• And most leaders have lived through multiple transformations.

So why does the gap persist?

Leaders confuse technical readiness with organizational readiness. A strong design doesn’t guarantee strong adoption.

Self-interest is underestimated. Logic rarely moves people. Personal impact does.

Urgency pressures force shortcuts. Go-live dates push leaders to cut corners on communication, training, and role clarity — the exact things that prevent failure.

Leaders assume operations teams “will adjust.” This is the most common miscalculation. Operational excellence does not automatically translate to change readiness.

These points explain the paradox: even experienced leaders underestimate the work of leading people through change. 

The Two Leading Change Management Models: Kotter and ADKAR

Dozens of frameworks exist, but two stand clearly above the rest in terms of use, validation, and practical effectiveness in modern supply chain and technology environments: Kotter’s 8-Step Process and the Prosci ADKAR model.

Frameworks like Kotter and ADKAR are powerful, but they don't replace judgment. Real change leadership requires applying these tools with situational awareness, not following them mechanically.

Kotter’s 8 Steps focus on organization-wide transformation:

1. Create a sense of urgency: Show why change is necessary and the potential consequences of not changing.
2. Build a guiding coalition: Assemble a team with enough power and influence to lead the change effort and encourage teamwork.
3. Form a strategic vision: Develop a clear vision for the future and strategies to achieve it, making it clear how things will be different.
4. Communicate the change vision: Widely and often communicate the vision to get buy-in and inspire action from others.
5. Empower broad-based action: Remove obstacles and barriers, such as outdated processes or resistant individuals, to enable employees to act on the vision.
6. Generate short-term wins: Plan for and celebrate early successes to build momentum and prove that progress is being made.
7. Consolidate gains and build on the change: Use the credibility from initial wins to tackle larger, more complex changes, and don't declare victory too early.
8. Anchor new approaches in the culture: Reinforce the new behaviors, processes, and practices until they become a permanent part of the organization's culture. 

ADKAR focuses on individual adoption:

• Awareness  – Of the need for change
• Desire – To Participate and support the change
• Knowledge  – On how to change
• Ability  – To implement required skills and behaviors
• Reinforcement – To sustain the change

The synthesis: 
Kotter shows leaders how to orchestrate change. 
ADKAR shows leaders how to scale it through people. 
Supply chain leaders benefit from understanding both.

What Supply Chain Leaders Can Do on Monday

A practical call to action for building your own change leadership muscle:

1. Run a 15-minute clarity check with your team.

Ask:

• What change is coming?
• Why is it happening?
• Who will feel it most?
• What might they fear losing?

2. Identify the two individuals most affected by the change.

Ask:

• What will their new day actually look like?
• What one action can support them?

3. Choose one communication habit and make it consistent.

Options include:

• A Friday “What’s coming next” email
• A weekly dashboard
• A Monday 10-minute huddle

4. Map one current project against Kotter or ADKAR.

• Pick a project already underway.
• Identify the missing step.
• Strengthen it.

5. Model the behaviors you want to see.

• Be the first adopter.
• Be transparent.
• Be steady.

A Personal Reflection (Full Circle)

Looking back at my time at Coca-Cola Supply, the decision to certify the entire organization in change leadership stands out as one of the smartest investments we made. It gave us a shared language and a shared discipline for supporting people through transformation.

Fifteen to twenty years later, I still see those leaders applying those principles instinctively. That’s what happens when change management becomes part of a leadership culture — a natural reflex, not a task.

My hope is that every supply chain professional, whether student or senior leader, will build this capability. Because:

“Technology will keep evolving. People will remain the center of every transformation.”

Final Thought: “Says Easy, Does Hard” — But Always Worth It

Supply chains do not succeed because of perfect plans or flawless systems. They succeed because the people who operate them understand the change, believe in it, and are supported through it.

This is a muscle worth building. And it’s one that lasts.

If You Need Support — We’re Here to Help

If your organization is navigating a transformation and wants support building these capabilities, please reach out to us at the Georgia Tech Supply Chain and Logistics Institute (SCL). We are actively working with companies across Georgia and beyond, sharing what we’ve learned and offering short, practical workshops on change leadership for supply chain teams. We’re always happy to help organizations strengthen this essential muscle.

The Moment That Changed How I Listen 

When I chaired the National Product Supply Group at Coca-Cola, one of our most respected board members was Jeff Edwards. Jeff had decades of experience and commanded respect without ever seeking attention. In a four-hour meeting, Jeff might speak two or three times—never more. But when he did, everyone stopped to listen.

What made Jeff so impactful wasn’t the number of words he used—it was the care behind them. He listened intently, gathered information, built context, and added value only when his perspective would move the conversation forward. His real skill was not speaking—it was listening with purpose.

That experience stayed with me, especially because earlier in my own career, I had a very different experience. While working at AJC International, I attended a leadership program at the Center for Creative Leadership. Early in the program, a cohort of about twenty of us sat in a facilitated discussion. What we didn’t know was that we were being filmed.

Later that day, each of us reviewed our videos one-on-one with an instructor. Watching myself was humbling. I saw a young professional trying too hard to prove himself—talking far too much, jumping in before others, and dominating the conversation. It was uncomfortable to watch, but invaluable. It forced me to face how insecurity can manifest as over-talking and how much more powerful restraint and self-awareness can be. I’ve been on a "less is more" journey ever since.

Why Communication Is a Supply Chain Differentiator 

We often talk about supply chain as end-to-end, but that phrase means something deeper than process visibility—it implies constant collaboration. Supply chain professionals must connect with suppliers, customers, and internal stakeholders across every function. 

That means communication is the connective tissue of our profession.

• Upstream and downstream, we are translators—interpreting complex data, system logic, and network realities for people who make decisions.
• Inside organizations, we act as bridges between technical teams and commercial leaders.
• Across tiers, we negotiate, influence, and build trust with partners who don’t see what we see every day.

Even as automation expands, supply chains remain messy, human, and physical. Systems can handle the routine, but edge cases, disruptions, and exceptions still rely on judgment—and judgment relies on communication. The ability to see, listen, and convey context in real time is what keeps operations resilient when variability strikes.

In our earlier SCL articles, we wrote that skills that survive AI are the ones that emphasize human discernment—and that critical thinking is about interpreting and questioning rather than accepting data at face value. Communication is where these two intersect. It is how human understanding flows across the supply chain network.

When Communication Breaks Down

I once worked with a technically gifted colleague—let’s call him Forrest—who had deep analytical capability but struggled to speak up in group settings. His insights were sharp, but his inability to communicate them left him isolated. Eventually, he left the organization. It was a tough reminder that technical strength without communication is unrealized potential.

In a global supply chain, it’s not enough to know the answer. You have to make others understand why it’s the answer—and what to do with it. Communication is how insight becomes action. 

The Many Dimensions of Communication

We tend to equate communication with speaking, but it’s much broader. Great communicators master four dimensions:

1. Speaking – Conveying information clearly, concisely, and confidently.
2. Writing – Capturing ideas and decisions in a way that travels across teams and time zones.
3. Listening – Absorbing context before contributing, and letting others be heard.
4. Observing – Seeing what others miss and using that insight to guide action.

The fourth one—observing—is often overlooked.

Recently, while reading with my granddaughter, she picked out a children’s book titled Bud Finds Her Gift. It’s about discovering one's special ability, and Bud's gift turned out to be observation—simply noticing things others missed. Watching her read that story reminded me how powerful observation really is.

I thought of my former colleague, Tim Harville, with whom I worked at Coregistics. Tim often walked the warehouse with new supervisors, teaching them to "see the operation"—to notice what looks good, what's out of place, and where waste or opportunity hides in plain sight. His goal wasn't to test them—it was to train their eyes. Observation, in that sense, is a key communication skill. You can't describe, explain, or improve what you haven't first seen clearly. 

Can Communication Be Taught? Absolutely.

I’ve seen it done.

At Frito-Lay, we invested in communication training for new managers—everything from eliminating filler words to using purposeful body language and structuring messages with intent. At Coca-Cola, Toastmasters chapters gave leaders a safe space to practice public speaking, storytelling, and feedback.

And beyond formal training, there's practice in the everyday moments—taking notes in meetings, volunteering to summarize a discussion, representing a project team, or offering to speak at a class or event. Every repetition builds comfort and clarity.

My own Center for Creative Leadership experience was the beginning of that practice for me. Decades later, I still catch myself needing to slow down, listen, and wait for the right moment. The lesson never stops.

Painting the Picture: When It Works and When It’s Missing

When communication works, credibility follows. Jeff Edwards didn’t have to compete for airtime; his credibility made his words count. When it's missing, even talented people like Forrest can struggle to influence or grow.

Both extremes teach the same lesson: communication isn't about more or less—it's about meaning. It's knowing when to speak, what to say, and how to connect it to the needs of others. 

Practical Ways to Build Communication Strength

• Listen to learn. Take notes, paraphrase what you've heard, and confirm understanding
• Translate technical into practical. Explain what data means for the business, not just what it shows.
• Observe before you act. Practice "seeing" your operation or process with fresh eyes.
• Simplify your writing. Clarity beats cleverness every time.
• Seek feedback. Ask trusted peers to tell you how your communication lands.
• Prepare with intent. Know your audience, outcome, and key message before you speak. 

Reflection Questions

• Where in my current role does communication make or break outcomes?
• When was the last time I adjusted how I communicate to fit my audience?
• Do I listen more than I speak—and what might I learn if I did?
• How can I model communication that builds understanding rather than winning airtime? 

Closing Thought

Technical skills and analytics may earn you a seat at the table, but communication determines whether your ideas move the organization forward.

In a world of AI, automation, and constant change, the ability to listen, observe, and translate context into action remains our most human—and most valuable—differentiator.

Monteiro has been a leading figure in continuous optimization for decades, recognized for combining deep theoretical advances with practical algorithm design that has shaped modern optimization. His pioneering work includes foundational contributions to interior-point methods, the influential Monteiro–Zhang framework for semidefinite programming, and the Burer–Monteiro method, which made it possible to tackle massive optimization problems across areas such as machine learning, data science, and engineering.

The John von Neumann Theory Prize, awarded annually by INFORMS, honors a scholar (or scholars in the case of joint work) whose body of research represents fundamental, sustained contributions to theory. Prize criteria include significance, innovation, depth, and scientific excellence, with emphasis on work that has stood the test of time. Named for the legendary mathematician John von Neumann, the prize commemorates his extraordinary contributions to mathematics, computing, and applied science. Von Neumann’s work on the stored program concept and the IAS computer laid the foundation for modern computing architecture. He also played a pivotal role in advancing computational methods for solving some of the most complex scientific and engineering challenges of his time.

“Dr. Monteiro’s work exemplifies the spirit of the John von Neumann Theory Prize,” INFORMS noted in its announcement. “His contributions combine mathematical depth with wide-reaching impact, influencing generations of researchers and practitioners.”

Monteiro will receive the award, which includes a $5,000 honorarium, a medallion, and a citation, during the INFORMS Annual Meeting award ceremony in Atlanta on Sunday, October 26, 2025.

INFORMS, the world’s largest association for professionals and students in operations research, AI, analytics, and data science, recognized Ding for his significant contributions to data and quality science, and for the broad impact of his work on wind energy and manufacturing, as well as his dedicated service to INFORMS communities.

Ding joins a distinguished group of 12 honorees whose pioneering research and leadership have advanced the fields of artificial intelligence, supply chain resilience, clean energy, healthcare delivery, and transportation systems.

“I’m deeply honored to be named an INFORMS Fellow and to join such a distinguished group of researchers whose work has shaped our field,” said Ding. “This recognition reflects not only my own journey but also the exceptional community of researchers at Georgia Tech ISyE.”

Ding’s recognition highlights ISyE’s continued leadership in advancing data-driven innovation and excellence in research.

Read the full story here.

Liu serves as the principal investigator (PI), joined by co-PIs from five universities: Hiba Baroud (Vanderbilt University), Linyin Cheng and Song Yang (University of Arkansas), Jennifer Pazour (Rensselaer Polytechnic Institute), Yisha Xiang (University of Houston), and Xiang Zhou (Harvard University). Together, they are launching Resilient-NET, a collaborative research team and network designed to connect U.S. and international research communities focused on the resilience of urban socio-technical systems.

The project takes a holistic approach to urban resilience, focusing on three interwoven dimensions:

• Technology (the “New Dimension”) – examining the integration of AI-enabled systems into infrastructure and mobility networks.
• People (the “Missing Puzzle”) – exploring the complex and evolving interactions between humans and technology.
• Environment (the “Uncertainty Accelerator”) – addressing the rising frequency of extreme natural events that disrupt urban systems, with the goal of improving predictive modeling, disaster preparedness, and the development of urban digital twins.

By combining these perspectives, the team aims to generate new insights into how technology, society, and climate intersect, and to chart pathways toward resilient, sustainable, and livable cities.

“Urban resilience is an interdisciplinary grand challenge,” Liu said. “It requires coordination of talents and resources to create transdisciplinary solutions. Through Resilient-NET, we hope to consolidate a roadmap for resilient urban ecosystems and nurture the next generation of leaders who will tackle these problems on a global scale.”

The project benefits from the support and collaboration from multiple research fields within ISyE such as System Informatics and Control, Energy and Sustainable Systems, Data Science and Statistics, and Supply Chain and Logistics Institute (SCL) and AI4OPT of Georgia Tech, domestic partners including the Weatherhead Center for International Affairs at Harvard, the Natural Hazards Center at the University of Colorado, IBM Research, the Taylor Geospatial Institute, and Argonne National Laboratory, as well as international partners, including the Singapore-ETH Center on Future Resilient Systems, Karlsruhe Institute of Technology in Germany, Global South partners such as Tecnológico de Monterrey and Universidad Panamericana Guadalajara, and the 4TU Centre for Resilience Engineering in the Netherlands.

Looking ahead, the researchers envision Resilient-NET as a long-term hub that not only advances scientific understanding but also equips students and early-career researchers with global leadership skills. At a time when artificial intelligence is rapidly reshaping infrastructure and mobility, Liu and his collaborators see this work as critical to ensuring that urban systems continue to function in the face of uncertainty.

Liu shares, “Our vision is that Resilient-NET will serve as a bridge, linking disciplines, institutions, and countries, to build cities that are not only technologically advanced, but also safe, welcoming, and adaptable for generations to come.”

Read the full story here. 

The H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech hosted its annual MentIE Program Kickoff Event on September 23, bringing together students and alumni to celebrate a new year of mentorship, networking, and professional growth. 

It means that our faculty community has specific rights and responsibilities that allow them to govern themselves and uphold high academic standards. The success of this model hinges on faculty contributions and voices.  

On a busy campus filled with enough buzz to keep schedules fully booked, Dima Nazzal, chair of the Faculty Executive Board, and Adam Steinberg, secretary of the faculty, explain why faculty governance is a commitment that matters.  

Q: Why do you think faculty governance matters, especially given everything else faculty juggle?

Nazzal: The faculty handbook says it’s both a right and a responsibility. Faculty are responsible for maintaining the highest standards for teaching, research, and resources. Many of us don’t think about how policies are made until we’re part of the process. Faculty governance gives us that voice.

Steinberg: For me, it’s about impact. I could publish a paper that gets cited hundreds of times over the years. But through governance, I can help make a decision that affects 50,000 students immediately. That’s powerful.

Q: What makes Georgia Tech’s governance structure unique compared with other universities?

Nazzal: One major difference is that half of our faculty are research faculty — many in GTRI. That’s unusual, and it shapes governance. We have both an Academic Faculty Senate and a Research Faculty Senate, with the Faculty Executive Board and Standing Committees of the Faculty bringing those together. Another unique element is that the president presides over the Senate, whereas at many universities it’s an elected faculty member.

Steinberg: Beyond structure, the culture matters. At Tech, governance is collaborative, not combative. Even when there’s tension, the spirit of the faculty community is aligned with our mission to support students.

Q: Why should students and staff care about faculty governance?

Steinberg: Students come here for the best education, and faculty governance is how we oversee, adapt, and advance the academic mission. Everything from degree programs to academic integrity policies to campus services passes through this system.

Nazzal: And it’s not just faculty voices. Students and staff serve as full members on many standing committees. Their input shapes outcomes, whether it’s curriculum, services, or even student fees.

Q: Could you share how you first became involved in faculty governance at Georgia Tech?

Nazzal: My first service involvement beyond my School came during the pandemic. As an industrial and systems engineer, I volunteered for the Covid-19 recovery task force. That experience showed me how Georgia Tech runs — how every person and unit is essential. After that experience, I decided to run for the Faculty Services Committee, then the Faculty Senate, then the Faculty Executive Board. Eventually, I was persuaded to run for vice chair and chair of the Faculty Executive Board.

Steinberg: I came to Tech as an associate professor and joined the Institute Undergraduate Curriculum Committee (IUCC). At first, I just observed. Later, I chaired a subcommittee during a major curriculum review, and that’s when I really engaged. From there, I became vice chair of IUCC, and then I was encouraged to run for secretary of the faculty.

Q: For faculty who are skeptical, what would you say about the impact they can have through governance?

Nazzal: Some impacts are easy to measure, like new degrees or policies approved through committees. But much of the work is behind the scenes — building trust, shaping resolutions, finding alignment, and preventing conflicts. It’s hard to see unless you’re inside, but it matters.

Steinberg: If governance stopped, you’d notice immediately. New programs wouldn’t move forward, and policies wouldn’t be adapted. The deeper you engage, the more you see how essential it is.

Q: If I’m a faculty member who has never been involved, what’s the best way to start?  

Nazzal: Many people start with service in their School or College committees, then move into Institute-level work. At the Institute level, you can run for one of the 15 standing committees, each with a clear focus — like benefits, accessibility, IT services, or curriculum.

Steinberg: Standing committees do the detailed work, while the Faculty Senate deliberates and acts on those recommendations. Both are good entry points, and temporary committees also provide opportunities to engage when new issues arise.

Q: What’s your elevator pitch for why faculty should participate?

Nazzal: Three things: You grow your network, you gain perspective on how Georgia Tech works, and most importantly, you get to influence how things get done here.

Steinberg: I’d add that it’s also leadership development. Many of Tech’s current academic leaders cut their teeth in faculty governance. It’s a way to make Tech better and grow professionally at the same time.

There are opportunities throughout the year to become involved in Faculty Governance at Georgia Tech. You can find out more about the various committees and branches where you can plug in via the Faculty Governance website.

Award Recognition

This year, the committee received 39 exceptional submissions, resulting in a highly competitive selection process. After two rigorous rounds of voting, only three papers were selected to receive awards, highlighting the exceptional quality of Wang's research.

Groundbreaking Research Impact

The focus of the research tackles a critical real-world problem: how emergency response teams can efficiently assess earthquake damage when resources are limited. In the chaotic aftermath of a major earthquake, inspection teams must quickly determine which buildings are safe and which pose risks to public safety.

The authors transformed this challenge into an innovative vehicle routing optimization problem. Unlike traditional routing that simply moves vehicles from point to point, their approach strategically deploys inspection teams to collect the highest-quality damage assessment data possible.

Technical Innovation

The team developed advanced mathematical methods that measure data quality using sophisticated criteria, ensuring every inspection contributes maximum value to emergency response planning. They validated their methodology through realistic case studies using cutting-edge earthquake simulation technology, proving their system can significantly improve disaster response efficiency.

About the Organization

The INFORMS Transportation Science & Logistics Freight Transportation and Logistics Group focuses on research spanning trucking, rail, shipping, air cargo, and intermodal transportation. Their work encompasses planning, real-time control, pricing, demand management, and risk analysis across global supply chains.

Developed in partnership with the College of Computing, the minor offers students a powerful blend of financial theory and technological expertise, equipping them for careers in algorithmic trading, blockchain, digital banking, and financial data analysis. Since its launch, the program has quickly gained traction, now boasting 228 enrolled students – a testament to the rising demand for tech-savvy finance professionals.

Among the first to graduate with the minor are Alexandre Abchee, BSIE ’25, and Logan Roach, BSBA ’24, whose experiences reflect the program’s interdisciplinary strength and real-world impact.

Major: Industrial Engineering
Minor: Fintech

What excites you most about fintech?

Fintech is driving a new era, and blockchain’s integration into traditional finance is the most exciting part. It’s reshaping everything from settlements to cross-border payments, making systems more transparent and accessible. It’s thrilling to be part of the generation leading this change.

What was your favorite Scheller course?

My favorite course was MGT 4074, "Fintech and Crypto Tokens," with Dr. Joseph Hall. Dr. Hall brings incredible energy to the class. My final project involved building a decentralized exchange, which pushed me to understand bonding curves and liquidity pools.

By the end, I had developed a strong grasp of how decentralized exchanges function, as well as how token-specific rules and smart contract inheritances shape the trading experience. It was the perfect blend of technical problem-solving and applied financial theory.

Have you had any internships or practical experiences related to fintech?

I interned at a wealth management firm in Geneva. During my internship, I applied my fintech knowledge to support investment meetings and analyze digital assets. I shared insights on regulations like the GENIUS Act and created materials on blockchain-based products. It was rewarding to see how technical expertise, paired with clear communication, can shape real-world financial decisions.

What resources (e.g., clubs, events, networking opportunities) at Georgia Tech have been most beneficial for your fintech journey?

Georgia Tech gave me the tools to succeed in fintech. GT Venture Capital (GTVC) offered hands-on experience with startups, while events with firms like Millennium and BlackRock helped me connect with industry leaders. Scheller’s professors showed me how to apply my engineering background to solve financial challenges, building both my technical foundation and professional network.

What advice do you have for students considering a career in fintech?

My advice is simple: don’t be afraid to innovate. Always ask questions, share your ideas, and find ways to bring them to life. In fintech, you can embrace opportunities to test, learn, and iterate. The industry moves quickly, and curiosity is one of your greatest assets. Embrace experimentation, learn through experience, and stay open to new technologies and perspectives. The willingness to take calculated risks and adapt will set you apart.

What’s next for you?

I will be starting my master’s in Operations Research at Georgia Tech in Fall 2025. After graduation, my goal is to work in a fintech company as an operations analyst – a role I believe bridges the gap between industrial engineering and financial technology by combining process optimization with innovative financial solutions.

Read the full story here.

For the 25th year in a row, Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering (ISyE) has been named the nation’s top undergraduate program in its field by U.S. News & World Report. The 2026 Best Colleges rankings, released September 23, 2025, reaffirm ISyE’s long-standing reputation for excellence in education and research. 

"While rankings are just one measure of our impact, they reflect the respect ISyE has earned among peer institutions and the broader academic community," said Pinar Keskinocak, professor and H. Milton and Carolyn J. Stewart Chair. "Most importantly, they also underscore our shared commitment to preparing the next generation of leaders in our field." 

Steimle’s work applies operations research and machine learning to improve medical decision-making and advance population health, with a focus on maternal and child health. Her recent projects explore maternal healthcare access, prevention of severe maternal morbidity from cardiovascular conditions, and strategies to prevent and control poliovirus outbreaks.

Read the full story here.

Finding Flexibility and Purpose
For Amanda Ehrenhalt, a fourth-year student from the east coast, the path to IE and Georgia Tech wasn’t straightforward.

"I'm originally from Philadelphia and I didn't know a ton about Georgia Tech, I didn't know a ton about IE, or anything. I have always been pretty sustainability, change and impact-driven. And when I came to Georgia Tech, I came in hoping to make my difference through sustainability as an environmental engineer. I quickly realized that the lab side of things was not my calling! So I changed majors to IE and what I tell everyone is that it is the best thing that I could have ever done for myself. It allows me to lean into the technical side of large-scale issues and topics. So things like optimization, efficiency, systems, all of these learnings can be applied to any interest and have intersection to everything in the world. I want to use the education I'm receiving and apply that to what I'm really interested in — making a change in this world through energy. I like that I get to use all the technical pieces of my degree towards a future career where I can make an impact.”

A Mindset for Problem Solving
Like Ehrenhalt, Andrew Pietkiewicz, a fourth-year IE, started his college journey in another discipline before finding his fit.

"Originally, coming into Georgia Tech, I wasn't sure what I wanted to study. I knew that I wanted to major in engineering. I knew that I liked math, I was interested in coding. But above all else I was interested in problem solving and trying to find a degree where I felt I really could make an impact on the world around me. I came into my first semester Georgia Tech as a chemical engineering major and then I enrolled in our introductory engineering seminar course and was introduced to industrial engineering, and I was really excited by it. I think industrial engineering is special because it's so broad and rather than just being a discipline it’s also really, a skill set, and honestly, a mindset for tackling a variety of different problems, for thinking about the world around you, and identifying ways to improve it, make it more efficient, and to make it better. That’s kind of what drew me in. In my four years here as an industrial engineer, I'm happy about the decision I've made. I've loved the breadth of my curriculum and the flexibility."

Early Interest, Big Goals
For some, like Bryce Cannon, IE was the clear choice from the beginning.

“I knew I wanted to major in engineering in college, and I looked at each of the types of engineering [schools] at Georgia Tech. I saw that ISyE was business related, and I want study data analytics. And I also saw that ISyE ranks as the number one undergraduate program, so that's why I came here.”

Amelia Lemmon, a fourth-year student, connected the dots between event planning in high school and the analytical problem-solving skills she found in ISyE.

"As a high school student, I had the opportunity to plan a large-scale event for around 500 people, and it truly ignited a passion for me. Finding the best way to make the event run smoothly, and ensure that no one was ever bored, hungry, or had to wait in a line gave me such rewarding challenges to solve. From then, I worked to find a field that I could solve those problems but still heavily use analytical thinking. And IE has given me exactly that ever since! Studying IE has equipped me with a toolkit I can use to solve a variety of problems in both my personal and professional life. I would love to work in a supply chain or manufacturing role after I graduate, and ISyE has equipped me with the education and network to get there. More than anything, I want to land in a career that truly embodies Progress and Service.”

Paths of Discovery
For others, IE was a discovery they didn’t expect at first.

Tanijoluwa “Tani” Alofe, a second-year student, explained, “I did not know about IE when I first arrived to Tech, as I originally applied as a biology major. I am interested in entrepreneurship, and I took the S1301 course, and IE piqued my interest. I love computer science and so now I'm on a concentration for data analytics. I'm hoping to one day have my own business.”

Transfer student Brian Castro shared how his family’s background shaped his decision.
“I found out IE is like ‘business’ engineering. When I was growing up, my dad owned a landscaping business. He wasn’t the most fluent in English, so I was a sixth grader helping him communicate with clients. And I felt always smart enough to be an engineer, so I figured why not major in something I've grown up around.”

And for David Baker, a first-year IE, it’s the versatility of the major that stands out.
“IE is sort of the broad scope of engineering; in terms of the things you can do. Whether working with different companies to solve problems, like supply chain, or tackling real-world challenges that are facing our community, as I’ve seen capstone projects that worked with food banks. I am not quite sure what I want to do yet, but it’s the interdisciplinary approach with the major that interests me.”

Jingye Xu, Fourth-year Ph.D. 
Focus Area: Algorithms, Combinatorics, and Optimization 

How has the Beerman Presidential Fellowship impacted your academic journey or research opportunities?
The Beerman Fellowship provides me with both financial flexibility and academic confidence. It helps me to conduct ambitious research where pursuing some research questions that are high‐risk but potentially high reward. It also signals recognition of my potential and help me to build my confidence.

Could you share more about your research focus area? Is there a problem you hope your research will solve or a system you would like to improve?
My research focuses on integer programming, a core area of operations research that deals with optimization problems involving discrete decisions. Many real-world systems—such as supply chain design, scheduling, and network planning—depend on solving large and complex integer programs efficiently. I hope my work contributes to developing methods that not only improve computational performance but also make optimization more accessible for practical, large-scale decision-making.

Georgia Tech’s ISyE program is consistently ranked No. 1 nationally. In your experience, what makes ISyE such a strong environment for success?
ISyE is unique in both technical depth and breadth. The faculty like Arkadi Nemirovski and Alexander Shapiro are leaders in their optimization. The program’s breadth—spanning optimization, statistics, machine learning, and applied domains—creates an environment where interdisciplinary ideas thrive. Students are constantly exposed to real-world problems, ensuring that research is both theoretically strong and practically relevant.

What factors influenced your decision to continue your education at Georgia Tech?
During my undergraduate studies, I first encountered Arkadi Nemirovski’s textbook on optimization, which deeply shaped how I viewed the field and inspired me to pursue research in integer programming. When I later discovered that Georgia Tech’s ISyE program is home to Professor Nemirovski and other leading scholars in optimization, it became clear that this was the ideal place to continue my education. The chance to learn directly from faculty whose work had already influenced me, combined with ISyE’s unmatched reputation in operations research, made Georgia Tech the natural choice.

How have ISyE faculty or resources shaped your learning or research so far?
My research is shaped by many ISye faculties. For example, taking Professor Arkadi Nemirovski’s convex optimization class was a formative experience for me. His lectures not only deepened my understanding of fundamental optimization theory but also showed me how mathematical rigor can be combined with practical insight. That experience has strongly influenced how I now approach research in integer programming, by focusing on both the theoretical foundations and their real-world relevance. Beyond coursework, ISyE’s research seminars and collaborative environment have further broadened my perspective and sharpened my research skills.

Rui Gong, Third-year Ph.D.
Focus Area: Operations Research 

How has the support of the Beerman Presidential Fellowship enabled you to pursue your research goals and growth at ISyE? The Beerman Fellowship has given me meaningful flexibility in my research. In my first year, it allowed me to serve as a research assistant rather than a teaching assistant, which freed sustained time to develop ideas and build momentum. The support also let me explore collaborations with multiple professors without worrying about their immediate funding, so I could try different topics and ultimately commit to a direction I’m excited about. Just as important, the fellowship reduces financial pressure, so I can focus fully on my studies and research.

How does your research aim to make a difference, whether by solving a problem or improving a system? My work sits at the intersection of combinatorial optimization, dynamic programming, and convex relaxations. I design semidefinite programming (SDP) relaxations for complex combinatorial and dynamic decision problems. For example, in stochastic scheduling—where customers requested schedules or job characteristics are random—exact solutions are often intractable. Carefully crafted relaxations preserve essential structure while yielding tractable approximations and performance guarantees. My goal is to provide tools that not only solve these problems faster and more reliably in practice but also offer insights other researchers can adapt to related problems.

What do you think makes ISyE such a unique and supportive environment for students? ISyE combines depth in optimization, statistics, machine learning, healthcare and stochastic processes with a collaborative, intellectually open culture. Students benefit from rigorous coursework, active seminars, and mentoring from faculty who span theory and high-impact applications—so you can do first-rate theory while staying close to real systems. I’ve also found the faculty and staff exceptionally professional and responsive; whether it’s research, coursework, or logistics, there’s always someone prepared to help, which keeps projects moving.

What drew you to Georgia Tech when you were considering your next step in education? Fit and focus. My interests align closely with ISyE faculty, offering both strong mathematical training and opportunities to test ideas on meaningful applications. I was also drawn to the program’s support for industry engagement and internships. Finally, the BeermanFellowship was a major factor—it reduces teaching obligations and gives me the time needed to pursue ambitious research early in my PhD.

What role have ISyE faculty played in shaping your experience as a student and researcher? Faculty mentorship has influenced how I pose questions as much as how I answer them. Regular meetings and reading groups refined my problem formulations and helped connect ideas across optimization, probability, and computation. Seminars and visiting speakers have introduced techniques that directly informed my current projects, accelerating both the depth and the scope of my work.

Jyotishka Ray Choudhury, First-year Ph.D.
Focus Area: Machine Learning 

How has the Beerman Presidential Fellowship shaped your journey as a graduate student at ISyE? The Fellowship has been a tremendous support for me. Of course, it eases financial pressures during my Ph.D., but more importantly, it is a great honor that motivates me to pursue ambitious research directions with confidence. This recognition encourages me to push my work in theoretical statistics and machine learning to the highest standards.

How does your research aim to make a difference, whether by solving a problem or improving a system? My research lies in theoretical statistics and machine learning, with a focus on problems in high-dimensional settings. I study two types of problems: statistical inference when data is (a) incomplete and (b) evolving over time. For example, when parts of a dataset are missing completely at random, or when there is an abrupt change in the underlying data distribution (a “change-point”). These situations arise naturally in large-scale applications such as medical monitoring, finance, and modern data science pipelines. A central goal of my work is to design principled methods that are both statistically reliable and computationally efficient, so that practitioners can detect changes or handle missing data, reinforced with strong theoretical guarantees.

ISyE is consistently ranked the top program in the country. What do you feel contributes to that success? What makes ISyE unique is its remarkable scope combined with rigor. If we combine the research interests of all the faculty, the department spans an extraordinary range -- from optimization, statistics, and probability theory to operations management and machine learning. This creates an environment where one can explore both deep theoretical questions and impactful applications. As a first-year student, I’ve already found ISyE to be highly collaborative. I was always encouraged to think broadly, ask difficult questions, and connect ideas across disciplines. The culture is both challenging and supportive, which I think is key to its sustained excellence.

What drew you to Georgia Tech when you were considering your next step in education? I chose Georgia Tech, ISyE in particular, because it offers the right balance between theory and applications. My research is strongly theoretical, but I wanted to be in a place where theory is closely connected to real-world systems and impactful research. The faculty members at ISyE have outstanding research profiles, spanning both rigorous statistical foundations and meaningful applications in data science, engineering, and operations. Combined with a collaborative research culture and Atlanta’s vibrant academic community, this made Georgia Tech the ideal choice for my Ph.D. journey. Discussions with my thesis advisors (both of whom are in ISyE) have been central in shaping my academic direction, encouraging me to approach problems with both rigor and creativity. Weekly seminars, courses taught by faculty members, and talks by invited researchers from leading universities and labs have further broadened my perspective. Together, these opportunities motivate me to frame my work in ways that are both deep and impactful.

How did your experience as a GT football player influence the way you approach challenges in your career? I like to tell people that I use what I learned in the classroom at Tech every day, but the lessons from the football field, I use minute by minute. Teamwork, resilience, relying on the person next to you, the daily discipline and rigor, the importance of preparation and constant practice—these are the fundamentals of being an athlete. They’re also the qualities that set leaders apart and drive lasting success, especially in business.

Can you share an example of a work situation where you applied a lesson learned from football? I was a walk-on at Tech—not the biggest, not the fastest, and I was a long way from having the most athletic talent. But I had a role. As a scout team tight end, my job each week was to help prepare the defense for our opponent. If I didn’t show up ready and work hard, along with the rest of the scout team, we weren’t putting the team in the best position to succeed. That lesson translates directly to business: everyone has a role, and every contribution is critical to the success of the organization. In my career, I’ve tried to build a culture around that idea. For our sales team, the goal isn’t just closing a deal, it’s setting up our Customer Success team for a strong implementation. When implementation exceeds expectations and delivers real value, it empowers sales to go back and sell even more value to the client. Success comes when everyone understands their role and how it helps their teammates.

What advice would you give current student-athletes who are pursuing rigorous academic programs like industrial engineering? Enjoy the journey! Whether you realize it or not, your time at Tech is laying the foundation for a life of accomplishment, success, and leadership. When I think back on my experience as a student, an athlete, and a active member of campus life, I remember incredible moments made even more meaningful by the challenges that pushed me and those around me to grow. Growth requires hard challenges, and with those challenges, failure is inevitable. But failing doesn’t make you a failure. Athletics teaches you that no one wins 100% of the time, even the best stumble sometimes. You don’t always learn that in the classroom, but I truly believe we’re built for the struggle of life. So embrace it, and enjoy it.

Derek Goshay (IE 1993) – VP, Genuine Parts Company (Retired) 
Tight End #88 (1988 - 1993) 

What strategies or habits helped you successfully balance the demands of athletics, academics, and personal life while at Georgia Tech? Georgia Tech wasn’t an easy academic environment, but I always kept my ultimate goal in mind. I had friends and family back home counting on me to succeed. As a first in my direct line and the first of my generation to attend college, there were eyes on me I couldn't let down. I was determined to do whatever was necessary to succeed. 

Were there moments when your academic and athletic commitments felt overwhelming, and how did you overcome them? Yes, it felt overwhelming every day, but you had to keep grinding. When you represent something bigger than yourself, you keep pushing. When you realize you’re living an opportunity your parents and grandparents could never have imagined, you keep pushing. And when you carry the weight of those you left back home, as well as the generations who came before you, there’s no choice but to keep grinding—no matter the cost.

How has the discipline or resilience you developed in athletics helped you solve problems in your career? Discipline and resilience are what keep you in the game. You will have setbacks and failures, but the willingness to keep going in spite of them comes from discipline and the resilience to push forward. In engineering, few problems are solved on the first iteration. You have to keep working through it until you find the solution.

Kofi Smith (IE 99, MBA 09) - CEO, Keystone Management 
Cornerback #43 (1995-98)

Can you share a specific example of a work situation where you applied a lesson from your time as an athlete? My first job out of college was with Milliken & Company in LaGrange, GA. The plant manager asked me, ‘Do you want to be a traditional Industrial Engineer running cost analyses and time studies, or do you want a team?’ I asked for the team—and he gave me C Shift. At the time, they were struggling with process reliability, production, and quality. It was sink or swim. Within six months, that team became the best in all areas, breaking and setting records. I drew on a lesson I learned on The Flats: everyone has a specific role that maximizes their unique strengths. My associates were in the wrong positions, so I shifted them, and we started winning the game.

Were there times when juggling academics and athletics felt overwhelming, and what helped you get through them? As a Division I athlete pursuing an ISyE degree, there were plenty of times at Tech when I felt overwhelmed. What I learned then is what guides me today: I pray, trust in God, put in the work, and keep moving forward.

What skills from the football field have benefitted your career as an IE? The top skill I carried from the gridiron into corporate America is the ability to see the whole field. I look at both the individual and collective movements of each player, then zero in on the most important aspects to make the play. I use that same approach today in high-stakes customer meetings and negotiations.

As part of the recognition, Ziani has been invited to present his work in the MIF Early Career Award session at the 2025 INFORMS Annual Meeting in Atlanta. His talk, titled “Towards Inclusive and Human-Centered AI: Research and Service at the Intersection of Algorithms and Society,” will take place on Monday, October 27, 2025.

In his presentation, Ziani will highlight how his research redefines fairness in algorithmic decision-making, treating it not simply as a technical requirement but as a property shaped by broader socio-economic contexts. His work leverages methods from computer science, operations research, and economics to study both immediate and long-term disparities and to evaluate the societal impacts of algorithm-driven systems.

“This award is a recognition not only of my research but also of the importance of building inclusive structures that support the next generation of researchers,” Ziani said.

Beyond research, Ziani has dedicated his career to supporting emerging scholars in the field. He has spearheaded initiatives such as ISyE-MS&E-IOE Rising Stars Workshop, in conjunction with Stanford University Management Science and Engineering and University of Michigan Industrial and Operations Engineering, and has served as Doctoral Consortium Chair for the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) for the past four years.

For more information on 2025 INFORMS Annual Meeting, please visit the INFORMS website.

A newly released study confirms what many shippers have suspected: Atlanta-bound cargo through Savannah offers shippers lower costs, greater reliability, and similar transit times compared to West Coast ports. 

To read the full announcement, view the story here. 

We congratulate Professor Li on this well-deserved recognition. To read the full announcement, view the story here.

Tsiotras and Kajani spent the summer working on the NSF-funded project titled Fostering Smart and Sustainable Travel through Engaged Communities under the guidance of Srinivas Peeta, Frederick R. Dickerson Chair and Professor in the H. Milton Stewart School of Industrial Engineering and School of Civil and Environmental Engineering. The project leverages Peachtree Corners, GA, as a living lab to co-design data-driven interventions that promote sustainable travel. 

The students received mentorship from Gulam Kibria, a Ph.D. student in Operations Research at ISyE, and Viswa Sri Rupa Anne, a Ph.D. student in CEE, both working under Professor Peeta’s supervision in his Autonomous and Connected Transportation Lab. Kibria’s research focuses on promoting sustainable travel through optimization and the integration of emerging mobility modes and technologies, while Rupa’s research centers on encouraging sustainable travel behavior through the design and evaluation of monetary and non-monetary behavioral interventions.

During the internship, Tsiotras and Kajani contributed to key components of the project, including the development and testing of AI/ML-based models to classify travel modes using GPS data, the creation of data pipelines to process large-scale location data, and the evaluation of app-based behavioral interventions designed to nudge users toward more sustainable travel options. Their work focused on translating raw mobility data into meaningful behavioral insights to support more informed and effective community-level transportation planning. Their poster presentation highlighted key contributions in data engineering, app refinement, and behavioral intervention analysis, reflecting the depth and real-world impact of their summer research.

This recognition underscores ISyE students’ growing impact in applying analytical and computational tools to solve real-world sustainability challenges. It also highlights the importance of experiential learning opportunities like PIN in bridging academic research and community innovation.

Introduction

Artificial intelligence has entrenched itself in almost every aspect of the professional world. From copywriting tools to search engine optimization and image generation, professionals and laypeople alike use this new technology to streamline daily activities. But, before AI, there was high-level analytics and machine learning in supply chain. Analysts across the supply chain used machine learning to interpret high volumes of data and turn it into predictive algorithms for inventory planning, demand planning, and more. Now, AI is generating these analytics at a much faster, real-time pace.

This shift raises important questions. What does this mean for technology professionals in the supply chain world who once made a living doing these jobs? And what can we expect for aspiring supply chain pros or mid-career professionals who want to increase their value to the team in an age of accelerated technological advances? 

The fact of the matter is that AI is now everybody’s job. Standing still will ensure that you get left behind by your peers or the talent pipeline from colleges and universities. The question then becomes, how can I upskill and use what I already know to add value to my role and ensure that my AI competencies allow me to compete in today’s supply chain workforce?

We’ll look at the ladder as a series of increasing levels of complexity and AI activity—what we’ll call ‘maturity levels’: descriptive, diagnostic, predictive, prescriptive, cognitive/autonomous, and integrated enterprise.

Some things to bear in mind as we progress through this topic:

1. Everybody is somewhere on the ladder, so everyone has the opportunity to climb the ladder.
2. Analytics are no longer just for specialists. AI allows analytics to be an access point to the ladder. You no longer have to rely on someone else higher up on the ladder, and it’s in your best interest to climb higher, regardless of your job description.
3. There are lots of resources freely available to allow you to climb the ladder. But in most companies, you can find a mentor who is further along on a ladder, and perhaps they can help you up-skill your operational knowledge and help you advance your capabilities to ascend the ladder. 

We’re here to discuss to what degree you should so you can optimize your career opportunities and not be left behind. 

How Did We Get Here?

In the field of supply chain we’ve always been ahead of the curve when it comes to these types of innovations. Before AI, we were using machine learning and predictive analytics to enhance our understanding of real-time supply issues. We worked a lot on optimizations at Coke and started utilizing machine learning tactics almost 10 years ago. While I wasn’t the hands-on user of the technology, I took it upon myself to try and understand exactly what was happening and how it was working.

That was a large corporate machine–one of the biggest brands in the world–utilizing the latest in predictive analytics technology. And now we have a democratization of this technology being spread across industries. You no longer need to be part of such a high-powered team to make use of these tools. 

We have now entered into an era where artificial intelligence has become omnipresent across almost every supply chain practice and industry, or any other career discipline. The key is understanding best practices is making use of AI in your field, and how you can add value and incorporate it into your everyday work-life. 

Descriptive Level: From Rearview Mirror to Forward Thinking Decisions

“If you have some proficiency in Excel, then you’re on the ladder.” - Chris Gaffney

The lowest rung on the AI ladder is the descriptive level. Excel knowledge and experience resides here and can be the access point for most people. This level helps us describe what is happening with numbers and data. Reporting dashboards can be crafted here, and we can run trend analysis using basic inference to see what is happening and where to make adjustments, if necessary.

Excel tells us what did happen - not what could happen. These are important functions, to be sure. However, they only look behind us. They tell us what and why. Today’s supply chain landscape requires tools that allow us to make decisions based on what could happen in the future. We don’t have the power to make proactive decisions or to navigate uncertainty and factor in variables of change.

Our competitive edge is sharpened by having the capability to shape the future, not just explain the past. In order to do so, we need to move up into predictive and prescriptive AI territory.

Up until very recently, this descriptive capability was enough. Analysts, planners, and buyers were all able to produce data that helped others to understand what was happening. The data then required synthesis and analysis. The whys and so whats were human functions performed by different team members and used to measure the efficacy of various inputs and outputs throughout the supply chain. As one moves up the chain of command, so to speak, the ability to interpret the data and findings becomes even more important. However, the numbers crunching and analytics were more siloed.

And now, everyone has access to AI’s ability to synthesize and analyze raw data. But very few “off-the-shelf tools” can answer the why, let alone the ‘what should we do about it’ questions. Planners and managers need to upskill and ensure that they are up to speed on the capabilities and deficiencies of these platforms and insert themselves and their skillsets to close those gaps.

Roles at this level:

• Transportation analysts
• Warehouse supervisors reviewing daily throughput metrics
• Demand planners tracking forecast accuracy from the last quarter

Working in hindsight by monitoring and measuring data is important, albeit limiting. This looking backward in the world of supply chain decision making at a time when forward thinking is essential for future proofing your supply chain organization. Staying here too long limits your ability to prevent problems before they escalate.

What to do next?

• Learn Power BI or Tableau for interactive dashboards
• Get comfortable using large data sets from your ERP or WMS
• Start asking, “why” and “so what”

Diagnostic Level - Information into Insight

“This is where you start to become more valuable because now you can help the team avoid repeat issues.”

So you’ve now measured what happened. The next logical question is why?  Here’s where many companies fall short by relying on only internal historical data. The real learning happens when you bring in external variables like weather, economy, labor, or competitive actions. Diagnostics help uncover root causes and patterns across time and systems. What does this mean for you and the AI ladder?

This could mean combining two different datasets using SQL to pull deeper reports or identifying correlations between variables. You need to be able to get inside of your supply chain to see what’s really going on, much like a physician will draw blood or perform various scans to get a more vivid and comprehensive picture of what’s happening.

Examples from the field:

• A demand planner diagnosing why forecasts were consistently off by adding external factors outside your control.
• A transportation analyst finding route disruptions correlated with labor strikes and weather trends - kinda like WAZE.

What you can do

• Add layers of internal and external factors
• Use Power BI or Excel to show the impacts of external events
• Start to track leading indicators, not just lagging ones.

Predictive - Seeing What’s Coming

“Most of the tools we have heavily leverage your own history. But your ability to sell a product next year is different because you don’t control everything.”

Predictive analytics enables supply chain professionals to see trends, forecast disruptions and plan proactively.

As we mentioned earlier, most forecasting tools rely too much on internal history. Predictive power comes from adding things like economic trends, labor availability, weather, etc., to your forecasting models.

My first exposure to the broader umbrella of machine learning, falling under AI, was while working at Coke. Every night, our machines processed enormous volumes of data to track how much of each type—across countless product combinations—was being used. This data was being used to predict when the fountain machines would fail so that we could prepare a replacement without losing time or operational capacity. Basically, this meant we could allocate maintenance resources proactively instead of reactively.

This machine learning doesn’t have to be intimidating. In fact, machine learning was the #1 skill in supply chain job postings in 2024. Python and machine learning are much more accessible tools than they once were, and many professionals are teaching themselves the basics using online resources that are much more prevalent than they once were. Again, the democratization of AI tools means everyone can level up a lot faster.

Roles Seeing This Shift

• Demand planners and sourcing managers are combining historical sales information with things like inflation, trade wars, and taste evolutions.
• Transportation teams are integrating weather trends and traffic data to reroute loads

What Can You Do:

• Learn the basics of Python’s forecasting libraries
• Pull in a single external variable, like weather or labor availability, into your demand forecast.
• Track model accuracy over time to see where it succeeds and, most importantly, fails.

Prescriptive: Deciding What to Do About It

"We don’t want analytics experts. We want people who are applied analytics or applied AI experts.”

It’s not just identifying the risk. The key is choosing a more effective path forward. And this requires modeling scenarios in a way that lets you take action rather than just be an observer. 
A lot of companies stop at prediction. The ones that get ahead of the pack are those that are able to simulate outcomes and use this logic in daily decisions. Just remember that context is everything. Those with very impressive technical skills can sometimes miss the mark because they didn’t understand the business. There are also supply chain planners with moderate technical skills who can make major contributions because they knew what mattered and where to apply it.

The supply chain AI ladder is crucial, but only as effective as the depth of the supply chain knowledge base.

Cognitive and Integrated is When AI Starts to Work With You

This is the very top of the ladder or the tip of the AI ladder iceberg, if you will. This is the realm of AI agents that are learning and acting in an intelligent and sometimes autonomous manner. The cognitive tier blends into the integrated enterprise, where systems and data are connected. Warehouses talk to the forecast, which communicates with sourcing, which can adjust production. This is kind of futuristic, but based on how AI has evolved, it will likely be ubiquitous within a couple of years.

How to Apply Cognitive and Integrated AI:

• Learn how to build a basic GenAI or logic-based agent using online tutorials or sandbox tools
• Make sure the AI Agent’s work is sound before turning it loose on our business. The human element is still crucial in these cases.

Role of Leadership in Deploying the Supply Chain AI Ladder

“This can’t be a black box to you.”

Leaders need to know just enough about AI to advocate for it. If you’ve hired the right people, then you trust them to do the job that you hired them to do. If they’re telling you that AI tools will help them do their jobs better, then listen to them. Find out what your team needs and get them to explain to you how AI can unlock more benefits for your business.

Encourage them to pursue professional development courses and to experiment in a safe environment until they feel confident integrating the tools into regular operation.

Conclusion: Don’t Stand Still and Be Left Behind

The supply chain AI ladder is real, and it’s climbable. You are not too late to get on board and begin using AI to increase your personal value at your company. It doesn’t matter how old you are - whether you’re an entry-level professional with an MBA, a mid-career professional, or a seasoned C-suite executive. There is a place on the ladder for you.

The most valuable assets that employees can bring to bear right now in this tech immersion context. Those who have been in the workforce for a few years are able to mix their experiential knowledge with the tools and assets available through AI to translate technology into real-world wins for your supply chain teams. Your value increases significantly if you pair your knowledge with proactive learning tools.

Take the time to self-assess and figure out where you are on the ladder.

Don’t try to jump too high up on the level. Take it one rung at a time. Then reassess.

Commit to the 70/20/10 rule. 70% on-the-job learning, 20% learning from peers and mentors, and 10% formal training.

Apply what you’ve learned and stay curious. Just don’t get complacent. This is not the time to rest on your laurels because someone who is hungry for knowledge will be on your heels.

This content was developed in collaboration with SCM Talent Group, a supply chain recruiting and executive search firm.

Every few weeks these days, a new AI breakthrough makes headlines. Models get sharper and more capable. Language tools get more fluent. Claims of agent breakthroughs and embedded autonomy in tools are everywhere.

And each time, the question resurfaces: What’s left for people to do as this wave progresses?

It’s a fair question. But from what I’ve seen throughout my career—from managing logistics in a Frito-Lay regional DC to transportation and distribution operations at AJC International and Coca-Cola, and now through executive education, consulting, and applied research at Georgia Tech—I believe we’re asking the wrong question.

Instead of asking what AI can do, we should be asking: Where is the human edge—and how do we keep it sharp?

1. Collaboration Across Boundaries Still Wins the Day

Whether in manufacturing, logistics, commercial and customer teams, or strategy, success still hinges on people working together—often across silos, systems, or supply chains. At Coca-Cola, some of the most impactful progress we made didn’t come from technology upgrades. It came from aligning teams that didn’t naturally collaborate—finance with planning, supply chain with sales, bottlers with company.

From what I see in my advisory work and interviews with supply chain leaders, that hasn’t changed. AI can improve visibility. It can suggest decisions. But it doesn’t build consensus, resolve conflicts, or create shared understanding. That’s human work—and it often makes the difference between potential and progress.

2. When the Plan Breaks, People Step Up

During my time in global logistics at AJC International, unexpected events were the norm: shipping delays, capacity shortages, regulatory changes. AI may help flag risks, but when the plan breaks, it’s still people who step in, prioritize under pressure, and find creative solutions.

This same theme came up in a recent SCM Talent podcast conversation. When I asked a senior supply chain leader what traits define her most effective team members, she didn’t hesitate:

“A drive for results. Problem solving. The ability to work in teams. And the ability to influence others.”

Those aren’t going out of style. They’re still what carries teams forward when the data model breaks or the shipment gets stuck.

The professionals I see excelling—especially in moments of disruption—aren’t just technical experts. They’re problem solvers who own the outcome and stay focused when others get stuck.

Drive, persistence, and adaptability aren’t things you automate. They’re human qualities that remain essential.

3. Hands-On Context Isn’t a Field Trip—It’s a Foundation

At Frito-Lay, I worked in a regional distribution center and breakbulk operation managing warehouse activities and dispatching drivers. Later, I spent a full year as an operations manager at one of our plants, where I led drivers and worked with plant warehouse teams and schedulers to ensure load readiness and on-time dispatch to local DCs.

Those weren’t just jobs—they were formative experiences. They taught me how decisions affect execution in the real world, and how the rhythm of operations shapes everything else in the supply chain.

That’s why I firmly believe professionals—especially early in their careers—should spend 3 to 5 years in front-line roles. No AI tool can replicate the kind of intuition you build by seeing how things work, where they break, and how people respond in real time. That foundation lasts an entire career.

4. Communication and Leadership Will Always Matter

In every role I’ve had—from the plant floor to corporate teams to Georgia Tech—I’ve seen that clear communication and authentic leadership are force multipliers. They carry more weight now, not less.

AI might help with drafting, summarizing, or visualizing, but it doesn’t earn trust. It doesn’t mentor a new team member or guide a group through a difficult change. That takes listening, emotional intelligence, and personal credibility.

Those leading change in today’s organizations—whether rolling out a new system or rebuilding after disruption—are the ones who can communicate with clarity and lead with steadiness. That’s not something AI can learn.

5. The Edge Is Where Humans Live

There’s a space at the boundary of every operation—the “edge”—where plans meet real-world variability. And that’s where humans remain essential.

Whether it’s spotting an issue before it escalates, reading between the lines of a conversation, or connecting seemingly unrelated problems across functions, that kind of judgment is rooted in experience. It can’t be downloaded or inferred from data alone.

In my work at Georgia Tech, across executive education, consulting, and applied research, I regularly see the difference it makes when decision-makers bring not just technical knowledge, but lived context from the field. That human edge is where resilience is built—and where strategy becomes reality.

6. Humans and AI: Better Together

To be clear: this isn’t about rejecting AI. The smartest teams I work with aren’t afraid of it—they’re learning how to use it. AI tools can improve productivity, identify trends, and help people make better decisions. But they need to be paired with human insight.

AI suggests. People choose. AI speeds up planning. People keep it grounded. The professionals who combine digital fluency with interpersonal skill, operational awareness, and strategic judgment? Those are the ones who will lead in the next era.

So What Should You Do?

If you want to build a career that endures—and evolves—with AI, here are seven things I recommend:

1. Invest in the front line. Not just a tour. Spend 3–5 years in a real operations or customer-facing role. It will shape how you lead for decades.
2. Build bridges. Learn how sales thinks. Understand finance’s constraints. Connect systems, teams, and people.
3. Volunteer when the extra project comes up. These stretch roles are often tied to strategic initiatives and senior leadership. Saying yes can accelerate learning and visibility—especially when others hesitate.
4. Take roles at the intersections—not the cul-de-sacs. Look for positions that connect functions, partners, or ecosystems. Exposure to diverse perspectives sharpens insight and multiplies your value.
5. Sharpen your communication. Speak with intent. Write with clarity. Listen deeply. These skills amplify everything else.
6. Evolve with AI—or fall behind. You don’t need to code, but you do need to understand how AI is changing your domain. Through continuing education, hands-on learning, or professional development, stay curious and current.
7. Never stop learning. At Georgia Tech, I see firsthand how ongoing learning—through executive education, research engagement, or new assignments—helps professionals lead through change. Keep asking: what haven’t I seen yet? Who could I learn from?

Final Thoughts

The future of work isn’t about humans vs. machines. It’s about people who can lead, decide, and connect—with AI as their force multiplier.

We may automate tasks. But judgment, trust, and empathy? Those are human domains. And in times of uncertainty, it’s the people who can navigate complexity, rally teams, and adapt with integrity who make the difference.

So yes, learn the tools. Embrace the change. But never underestimate the power of experience, context, and connection.

That’s your edge. And that’s not going anywhere.

A Personal Wake-Up Call

I’ve always considered myself a reasonably strong critical thinker—someone who asks good questions, challenges assumptions, and doesn’t adopt a viewpoint just because it’s popular. But a recent experience humbled me. I took an open-source critical thinking test and didn’t do nearly as well as I expected.

This led me down a deeper path of inquiry. I was already concerned about how two decades of social media have shaped the way we consume and respond to information—short, sensational content delivered by algorithm. And now, with the rapid rise of generative AI, I worry we may be trading our thinking for speed and scale.

I use AI tools daily, and I advocate for their use—especially in supply chain applications. But I’ve also come to believe this: if we’re not careful, we risk outsourcing the very thinking that makes us human and effective decision-makers.

Why Critical Thinking Matters More Than Ever—Especially in Supply Chain

Critical thinking isn’t just a defense mechanism—it’s a differentiator. In a world where AI can generate answers instantly, the professionals who ask the right questions will stand out.

Supply chain professionals operate in environments where second and third-order consequences matter. We are called on to make decisions under uncertainty, weigh risks, balance competing priorities, and understand interdependencies.

Judgment—tempered by experience, structured analysis, and humility—is the edge. Tools can help you scale, but they cannot replace the human responsibility to challenge, reflect, and adjust.

What Is Critical Thinking?

Critical thinking is the ability to think clearly and rationally about what to do or believe. It involves:

• Questioning assumptions
• Evaluating evidence
• Recognizing biases (ours and others’)
• Drawing reasoned conclusions
• Reflecting on one’s own thought process

Said simply, it’s self-awareness of your thinking style—how you form your views, test them, and revise them when new evidence emerges.

It requires effort. It requires slowing down. It requires, at times, being wrong.

Facione, in his Delphi Report, defines it as "purposeful, self-regulatory judgment."

Kahneman reminds us that our brains are wired for shortcuts—“System 1” thinking is fast and efficient but often error-prone. True critical thinking requires “System 2” effort: slow, reflective, and disciplined.

Are We Losing It?

There’s growing evidence we are.

Social media echo chambers reduce exposure to opposing views. Short-form content conditions us to expect fast answers. And according to the MIT Media Lab (Kosmyna et al., 2024), students using ChatGPT retained less, showed reduced cognitive effort, and had lower originality.

“When ChatGPT was used, cognitive effort declined.”

And yet—this is not a moment for despair. It’s a call to discipline. Because critical thinking, practiced intentionally, can become a personal and professional superpower.

Applying Critical Thinking in Supply Chain Decisions

Supply chain professionals face complexity daily—inventory tradeoffs, supplier uncertainty, resource constraints, policy risk. Many of these decisions can’t be answered by tools alone—they require judgment. Critical thinking lives in that judgment.

Whether you're building a forecast, evaluating a supplier, responding to a disruption, or modeling risk exposure, structured thinking provides a path. The steps are familiar:

• Define the problem clearly
• Clarify what information is available—and what’s missing
• Analyze root causes or future implications
• Generate multiple options
• Establish decision criteria
• Choose a path—and test it before launch
• Monitor and adjust as feedback arrives

This process resembles A3 thinking or supply chain analytics. But what makes it powerful is doing it intentionally—even under pressure.

The best professionals I’ve worked with practice it on small decisions as well as large ones. They don’t confuse speed with clarity.

Practicing Critical Thinking When Using Generative AI

AI tools are powerful—but without deliberate use, they can dull our thinking. Here's how to make AI work with your brain—not instead of it:

• Document your assumptions before prompting
• Journal your intent: What are you trying to decide or explore?
• Ask AI to provide counterarguments or alternative views as well as sources for you to research and draw your own conclusions
• Look for what’s missing or oversimplified
• Summarize AI output in your own words
• Track and reflect on how AI influenced your decisions

Treat AI like a research assistant—not a strategist. Use it to extend your reach, not replace your reasoning.

Final Thought and Your Next Steps

Critical thinking is no longer optional. Not in business. Not in education. Not in leadership.

It is a skill. A discipline. And a mindset that pays dividends over a lifetime.

If you’ve read this far, take this challenge seriously:

1. Write out how you form your opinions—on paper.
2. Practice structured thinking on small problems weekly.
3. Use AI with intention—never outsource your judgment.
4. Teach someone else how you reached a conclusion.
5. Be humble. Ask yourself: what if I’m wrong?
6. Keep a thinking journal for 30 days.

The goal isn’t to be right all the time. It’s to be reflective, rigorous, open to challenge, and consistent over time. That’s what the world needs more of. That’s the edge AI can’t replicate.

So think before you automate.

And never stop questioning.

Ms. Ananthachary brings a wealth of experience in enterprise software and cloud technologies to the SCL Advisory Board. She has a proven track record of building and mentoring high-performing global engineering teams and driving large-scale strategic initiatives. Her leadership has played a key role in delivering transformative, cloud-based enterprise applications and advancing digital solutions in the supply chain space.

“I’m honored to join Georgia Tech’s SCL Industry Advisory Board,” said Ms. Ananthachary. “As someone deeply passionate about the future of supply chains, I’m inspired by the SCL team’s commitment to innovation, education, and impact. I look forward to learning, contributing, and collaborating with this exceptional community.”

Ms. Ananthachary holds an MBA from Georgia State University and a Bachelor of Science from the National Institute of Technology Jamshedpur. She brings both technical expertise and a strategic business perspective to her advisory role.
 

Titled “Data Modality, Data Science, and Multistage Manufacturing,” Shi’s address emphasized how the integration of cutting-edge technologies (sensors, artificial intelligence, and data science) is reshaping the way engineers understand, monitor, and optimize complex production systems.

“Multistage manufacturing refers to multiple machines, stations or operations interconnected to produce a final product.”

He further explains how at each stage, quality is affected not only by local variation but also by upstream influences. With the rise of advanced sensors, researchers can now gather vast amounts of heterogeneous data. But the challenge lies in merging this data with engineering expertise using data science tools.

Shi’s keynote provided a comprehensive look into how the co-evolution of sensing data modality and data science methodologies is driving advances in variation modeling and analysis of multi-stage manufacturing. He discussed how past research and current applications are unlocking new capabilities in manufacturing productivity and sustainability. 

His talk included real-world examples of industrial projects where machine learning and AI models are being applied to solve long-standing manufacturing challenges.

Beyond technology, Shi also reflected on the broader impact of hosting this year’s conference in Atlanta, particularly for Georgia Tech’s ISyE program, ranked the #1 undergraduate program in the nation by U.S. News and World Report.

“The ISyE School will greatly benefit from the annual conference being held in Atlanta,” he noted in a recent Q&A with IISE.org. “Many [of our] faculty and students will present their work and assist with the event conference. Attendees often visit the Georgia Tech campus and our school during their stay, enhancing their understanding of our top-ranked industrial and systems engineering program.”

Shi also expressed his own enthusiasm for the event, highlighting the joy of connecting with colleagues and discovering emerging trends in the field. 

“My favorite aspect is reconnecting with old friends and meeting new ones within the ISE community. Additionally, learning about new research topics and trends through sessions and discussions is always exciting. Hosting the conference in Atlanta is especially meaningful, offering opportunities to showcase and our wonderful city.”

As industries continue to evolve with digital transformation at the forefront, Professor Shi’s keynote underscored a central message: the future of manufacturing lies at the intersection of engineering, data science, and intelligent sensing – an arena where Georgia Tech’s ISyE is leading the way. 

The agenda featured a strong lineup of speakers covering use-inspired R&D, workforce development, translation and commercialization, and ecosystem sustainability. Highlights included insights from leaders at the Strategic Energy Institute, Georgia Cleantech Innovation Hub, Savannah River National Lab, Southern Company, and others. I contributed a perspective on the critical role of supply chain design in optimizing the development of any new critical mineral supply chain—ensuring we design networks from the start that are scalable, resilient, and efficient.

Perhaps the most valuable elements of the day were the breakout sessions and informal networking, where participants explored how we can collectively advance resource development with greater speed, innovation, and shared benefit. The level of engagement and openness to collaboration was impressive.

We’re now turning our attention to shaping a full proposal to support this initiative, and I’m encouraged by the alignment and energy coming out of this session. Many thanks to Dr. Yuanzhi Tang and the organizing team for bringing this community together in such a purposeful way.

Chris Gaffney
Managing Director, Georgia Tech Supply Chain and Logistics Institute

In his address, Shi reflected on Deming’s enduring impact on the global quality movement, noting his pioneering quality management principles and his role in popularizing Walter Shewhart’s statistical process control (SPC) techniques in Japan. Researchers often utilize SPC to ensure that production processes operate efficiently, creating more specification-conforming products with less waste scrap.

Shi also discussed the evolving landscape of modern manufacturing, highlighting how increasingly complex processes and the abundance of in-situ measurement data have created a need for a new approach. He introduced a quality engineering framework designed to enhance in-process quality improvement (IPQI) by integrating statistics and data science with engineering expertise and system theory. This approach leverages real-time sensing data to enable process fault detection, root cause diagnosis, automatic compensation, and defect prevention in smart and data rich manufacturing systems. 

“Quality control is very important in all manufacturing systems,” said Shi. “Many pioneer works, like Taguchi’s robust design, Shewhart’s SPC control charts, and Deming’s Total Quality Management, have been widely used for quality control applications. However, one critical information, in-site sensing signals that are readily available in manufacturing systems, are typically not used directly for quality improvement. Thus, IPQI takes advantage of in-situ sensing data, combining data science with engineering domain knowledge, for quality improvement, which leads to a paradigm shift in quality control methodology development and implementation.”

The IPQI framework has already demonstrated significant social and economic impact across industries including automotive, aerospace, semiconductor, and steel manufacturing. With advances in sensing technologies, the Internet of Things (IoT), and computational power, as well as the rapid growth of machine learning and data science, Shi believes the IPQI approach is poised to become a cornerstone of smart and autonomous manufacturing systems.

Looking ahead, the continued development of IPQI will require strong interdisciplinary collaboration in both education and research. By bringing together experts from diverse fields, researchers can push the boundaries of IPQI applications and expand its role in shaping the future of advanced manufacturing. The framework not only enhances quality control processes but also represents a key step toward building more efficient, data-driven, and intelligent production systems.

“I am deeply honored and grateful to receive this award, and its recognition of the IPQI concepts, methodologies, and implementations,” Shi shares. “I wish the Deming Lecturer will motivate more researchers and engineers contributing to the IPQI development and implementation in modern production systems."

Artificial intelligence (AI) increasingly shapes critical decisions in everyday life, from who sees a job posting or qualifies for a loan, to who is granted bail in the criminal justice system. These systems rely on historical data to learn patterns and make predictions. For example, an applicant might be approved for a loan because an AI system recognizes that previous borrowers with similar credit histories successfully repaid. But when the underlying training data is incomplete or low-quality, the consequences can be serious, disproportionately affecting those from groups historically excluded from such opportunities.

Ziani's research will explore how the economic value of data, combined with the effects of data markets and network dynamics, can lead to incentives that naturally improve dataset robustness. By identifying the conditions under which the supposed efficiency trade-off disappears, Ziani and his collaborators hope to open the door to more reliable and equitable AI systems.

Traditionally, researchers have assumed that making AI-assisted decision-making more robust and representative comes at the expense of efficiency. This assumption treats training data as fixed and unchangeable, which can place limits on the potential of AI systems. But as large-scale data platforms grow and the exchange of data becomes more accessible, the conventional trade-off between robustness and efficiency may no longer apply.

“Our project demonstrates how carefully designing incentives—both for data producers and data buyers—can enhance the quality and robustness of datasets without compromising performance,” said Ziani. “This has the potential to fundamentally reshape the way AI systems are trained and how data is collected, shared, and valued.”

With this work, Ziani aims to advance both the theory and practice of AI and data economics, ensuring that as AI continues to transform society, and does so in a way that is fair, accurate, and trustworthy.

This spring, 30 graduate students from the Master of Science in Analytics (MSA) program chose to collaborate with the Fulton County Schools IT Division for their capstone practicum experience. This semester-long project allowed students to apply their data science training to real-world challenges faced by the school district.

MSA students worked with Fulton County Schools IT staff; the 4th largest school district in Georgia, the 35th largest school district in the United States, and the largest non-contiguous school district in the nation. The district’s size and geography offer additional data interoperability challenges. These challenges include fragmented data systems, difficulty obtaining real-time insights, and equitable access to reliable data. 

For several months during the spring semester, these talented teams worked closely with the school district’s IT division to develop tools designed to gain insights intended to improve how the district serves its over 86,000 students. Some of their impressive works include:

• Building Microsoft-based dashboards: These dashboards help educators take actionable steps aligned with Georgia Department of Education metrics, providing a clearer picture of student performance and areas needing attention.
• Implementing an AI-powered Virtual Counselor chatbot: This innovative tool supports students in meeting graduation requirements by offering personalized guidance and resources.
• Creating a recommendation engine: This engine matches students with personalized learning resources, ensuring that each student receives the support they need to succeed.
• Designing models to forecast budget needs: By using historical trends and enrollment projections, these models help the district plan and allocate resources more effectively.

Georgia Tech students submitted a total of 13 projects focused on continuous improvement in the school district.We extend our heartfelt thanks for their creativity, commitment, and belief that data and technology can drive meaningful changes in education. Their work not only showcases the power of collaboration but also sets a precedent for future partnerships aimed at improving student outcomes.

To shed light on student reflections, Alejandra Sevilla, an MSA student, recently commented, “It’s been incredibly rewarding to use data to support K-12 education and create something that could actually help real students and educators.”  Another student, Hariprasad Ramamoorthy, explained that “working with FCS has been a valuable and insightful experience, and I truly appreciate the collaboration and support throughout.”

Students were encouraged to submit a project that could be implemented in Fulton County’s current established Microsoft ecosystem. MSA students and Fulton staff met regularly to discuss the various MSFT tools available to Georgia Tech students in weekly meetings and how to implement their solutions. These collaborative meetings also offered students a chance to learn about FERPA compliance, student data requests, legal issues surrounding cameras in classrooms, handling outliers and duplicate records, technical challenges in data scraping, data modelling, and data governance. 

Georgia Tech alum Heather Trotter Clark (MSA 21) says the collaboration goes beyond analyzing data and test scores. It’s providing students with meaningful, actionable support on their path to success.

“The tool is there for them to use in a safe, secure environment, completely customized to their academic environment, their academic experience, in real time,” said Trotter Clark, who serves as Director of Data Management for Fulton County Schools and is a former schoolteacher. “Students and their parents have something tailored to them specifically to say, ‘OK I can do this because everything I need, I see it, I have it right here in front of me.’” 

This initiative exemplifies a successful collaboration between higher education institutions and public-school systems to work together to address real-world challenges that ultimately benefit students and educators alike.

Author: Heather Trotter Clark - Data Management Director, Fulton County Schools

Born on July 26, 1938, to Glenn Irvin and Edna Volberg Johnson, Dr. Johnson was raised on a farm near Athens, Georgia. He embarked on his academic journey at Georgia Tech, earning a B.S. in Applied Mathematics in 1960, and later, a Ph.D. in Operations Research from the University of California at Berkeley in 1965, under the tutelage of George Dantzig, a luminary in the field. 

He began his professional teaching career in ISyE at Georgia Tech in 1995. Working alongside Professor George Nemhauser, he co-established and co-directed the university’s Logistics Engineering Center, which would form what is now the Supply Chain and Logistics Institute.  

He was also instrumental in the creation and early development of the elite Algorithms, Combinatorics and Optimization PhD program.   

Dr. Johnson, a distinguished figure in research and optimization, has been honored with several prestigious accolades, highlighting his exceptional contributions to the field.  

Notably, he received: 

• 1981, The Alexander Von Humboldt Senior Scientist Award, at the University of Bonn, Germany
• 1983, The Frederick W. Lanchester Prize, with publication in the journal, Operations Research
• 1985, The George B. Dantzig Prize, a triennial award for original research in mathematical optimization
• 1988, Elected as a member of the National Academy of Engineering (NAE)
• 2000, The John von Neumann Theory Prize, acknowledging his foundational work in integer programming and combinatorial optimization
• 2002, The Daniel H. Wagner Prize for Excellence in Operations Research Practice
• 2002, Name as a Fellow of the Institute for Operations Research and the Management Sciences (INFORMS) 

His achievements have more than solidified his status as a visionary and trailblazer in the realm of optimization research.  

George Nemhauser, Institute Professor Emeritus in ISyE, reflected on Johnson’s profound influence on the industry, remarking, “Ellis Johnson was a world-renowned figure in the field of operations research and optimization... he was largely noted for his work with Ralph Gomory, on integer programming; and while at Tech, Ellis was a devoted advisor to many PhD students and a great colleague.” 

His legacy endures through his family, including his wife Crystal, his three children, and grandchildren, and through his profound impact on operations research and industrial engineering.

A commemorative service to honor his memory is scheduled for Saturday, March 23, 2024, at 10:00 a.m., taking place at the Sugar Creek Chapel within Hundred Acre Farm. This will be followed by a reception at the Red Barn. 

____

Author: Atharva Anand Dave

The H. Milton Stewart School of Industrial and Systems Engineering (ISyE) is proud to recognize and celebrate exceptional students who were honored at the annual Student Honors Celebration, held on Thursday, April 24.

This event served as a meaningful recognition of students who demonstrated outstanding academic performance, leadership, and service throughout the year.

Alpha Pi Mu Academic Excellence Award

Christopher Liding
Christopher Liding was awarded the Alpha Pi Mu Academic Excellence Award in recognition of his exemplary academic record, research excellence, and active engagement in scholarly activities. His name will be added to the Alpha Pi Mu perpetual plaque displayed in the ISyE main office.

Evelyn Pennington Outstanding Service Award

Aron Cheng
Aron Cheng received the Evelyn Pennington Outstanding Service Award for his exceptional contributions to the ISyE community. His commitment to service and support of student initiatives, while maintaining academic excellence, exemplifies the values of this honor.

IISE Excellence in Leadership Award

Lucia Touma
Lucia Touma was recognized with the Institute of Industrial and Systems Engineers (IISE) Excellence in Leadership Award for her impactful leadership within ISyE student organizations. Her efforts have significantly enhanced student engagement and strengthened the sense of community within the program.

📸 View photos on Flickr.

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.

Donehew was one of seven members inducted on Saturday, joining Alana Beard, Sue Bird, Mark Campbell, Sylvia Fowles, Lucille Kyvallos and Cappie Pondexter.

A four-year letterwinner at Georgia Tech (1996-00), Donehew left her mark as a Yellow Jacket on and off the court. She helped Tech to a WNIT postseason appearance, while setting the program record with eight made three-pointers in a game twice and still owns the record for three-pointers made in a single season with 86 in 1998-99. She was a two-time academic all-ACC and CoSIDA academic all-district, and was awarded the ACC Postgraduate Scholarship in 2001. In 2013, Donehew was selected as Georgia Tech’s ACC Legend.

Read the full story here.

Anish Bhatt (IE 10)

Anish Bhatt is a product innovator transforming global finance and commerce. As a product leader at Jeeves, he helped secure the company’s Series C funding and scaled its cross-border platform to over $100 million in gross monthly volume, empowering thousands of businesses with access to capital and modern expense management tools across North America, Central America, South America, the U.K., and Europe. He is the named inventor of Apple Pay Cash, now used by millions.

Tanay Rajore (IE 14)

Tanay Rajore is a director in PwC Consulting’s Tech Strategy practice, where he leads PwC’s digital product management and launch specialty, advising global clients on bringing new platforms and products to market

Learn more about our alumni here!

The MentIE program, hosted by The Center for Academics, Success, and Equity (CASE), connects world-class alumni with students, offering more than guidance — it builds life changing relationships that open doors to new career opportunities.  

The American Society for Quality (ASQ) has awarded Dr. Jianjun Shi the Shainin Medal "for his invention and implementation of in-process quality improvement methodologies that integrate data science and systems theory to analyze in-process sensing data, enabling root cause diagnosis, automatic compensation, and defect prevention across the automotive, aerospace, steel mill, and semiconductor industries.” 

Each year, only one nominee receives this prestigious award, based on the criteria that a unique and/or creative method for improving quality or products, processes, or services has been developed.  

Dr. Jianjun Shi is the Carolyn J. Stewart Chair and Professor in the H. Milton School of Industrial and Systems Engineering, with joint appointment in the Woodruff School of Mechanical Engineering. Dr. Shi is a pioneer in the application of data fusion for quality improvements, and his work in the development and implementation of in-process quality improvement (IPQI) methodologies puts an innovative spin on the traditional quality control concepts. Dr. Shi’s methodology focuses on integrating data science and system theory to achieve process monitoring, diagnosis, and control.  

Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering (ISyE) welcomes seven new members to its Advisory Board, whose terms will begin on July 1, 2025. The newest board members include Sheereen Brown, Joe Depa, Jason Dorris, Carson Earnest, Matlock Rogers, Kofi Smith, and student member Sujan Ganesh Kumar.  

The H. Milton Stewart School of Industrial and Systems Engineering (ISyE) proudly celebrates Assistant Professor Debankur Mukherjee, who has been named the 2025 recipient of the ACM SIGMETRICS Rising Star Research Award.  

This semester 29 ISyE senior design teams were showcased in the Capstone Design Expo, nearly double the amount of the Fall 2024 cohort!

Below are the awardees: 

Graduate Awards

The program bestows multiple awards and fellowships to graduate students who have been recommended for quality and positive performance within the last year. 

1. Atlanta Air Cargo Association PhD Student Fellowship for Research Excellence
   1. Joseph Boone
2. Shabbir Ahmed PhD Student Fellowship for Research Excellence
   1. Neelkamal Bhuyan & Jingye Xu
3. Thos and Claire Muller PhD Student Fellowship for Research Excellence
   1. Yuming Sun
4. Anderson-Interface PhD Student Fellowship for Research Excellence
   1. Kevin Wu
5. Jerry and Harriett Thuesen PhD Student Fellowship for Research Excellence
   1. Diptangshu Sen
6. Robert Goodell Brown PhD Student Fellowship for Research Excellence
   1. Mengqi Lou
7. Ed Iacobucci PhD Student Fellowship for Research Excellence
   1. Yuhao Wang
8. Margaret and Stephen Kendrick PhD Student Fellowship for Research Excellence
   1. Alexander Bukharin
9. Angela P. and Reed J. Baker PhD Student Fellowship for Research Excellence
   1. Young In Kim
10. ISyE Outstanding Graduate Student Instructor of the Year Award
    1. Meen-Sung Kim
11. The Philip J. and Delores A. Scott Graduate Student Health and Wellness Award
    1. Sixtine Guerin
    2. Himadri Pandey
    3. Zihan Zhang
    4. Tanuj Deshmukh
    5. Thomas Cabe
    6. Sharay Gao
12. Alice & John Jarvis Best Paper Award - Winner
    1. Tianjiao Li
13. Alice & John Jarvis Best Paper Award - Honorable Mention
    1. Shaan Ul Haque
14. Alice & John Jarvis Best Paper Award - Honorable Mention
    1. Neelkamal Bhuyan

Undergraduate Awards

The undergraduate awards offer a number of scholarships and recognition to students each year, based on recommendations, past performance and the student's current resume. To learn more about each award, read here.

1. Institute of Industrial and Systems Engineers Excellence in Leadership Award
   1. Lucia Touma
2. The Evelyn Pennington Outstanding Service Award
   1. Aron Cheng
3. The Evelyn Pennington Student Health and Wellness Award
   1. Shirley Sharp
   2. Piya Khakharia
   3. Caroline Steiger
   4. Brennan Haynes
   5. Mohamad El Abou Khoudoud
   6. Shalini Mehra
4. ISyE Alpha Pi Mu Academic Excellence Award
   1. ZhiXian (Chris) Liding
5. Kurt Salmon & Associates Scholarship in Industrial and Systems Engineering
   1. Anna Park, Noreen Ali, Sristi Karamchandani
6. KS2 Technologies, Inc. Entrepreneurship Award
   1. Guy Mastrion
7. KS2 Technologies, Inc. Innovative Technology Award
   1. Sahana Yerneni
8. Nicholas & Aurora Suarez Condezo International Award
   1. Mariana Anargyrou
9. Institute of Industrial and Systems Engineers Outstanding Senior Award
   1. Sam Deckbar
10. Institute of Industrial and Systems Engineers Outstanding Freshman Award
    1. Mariana Anargyrou
11. Institute of Industrial and Systems Engineers Rising Star Award
    1. Aron Cheng
12. COE Honors Day Award
    1. Rohin Shah

At the heart of this family connection is a beautiful academic journey shared between three generations of Yellow Jackets: ISyE Associate Professor, Gunter Sharp (IE ’65, IE PhD ’73), his son Alexander Sharp (B.S. in Applied Biology ‘89, and M.S. in Environmental Engineering ‘99), and his granddaughter, Melina Sharp (IE ‘25).