Srinivas Peeta, the Frederick R. Dickerson Chair in Transportation Systems at Georgia Tech’s School of Civil and Environmental Engineering, has been appointed Co-Editor-in-Chief of Transportation Research Part B: Methodological. This prestigious journal focuses on the mathematical and analytical foundations of transportation systems, addressing critical challenges in areas such as traffic flow, network design, control and scheduling, optimization, queuing theory, logistics, and behavioral modeling. 

Transportation Research Part B complements other journals in the series—Part A (Policy and Practice), Part C (Emerging Technologies), and Part D (Transport and Environment)—forming a comprehensive suite of publications that collectively represent the forefront of transportation science. The journal serves a diverse and specialized audience, including operations researchers, logisticians, economists, econometricians, mathematical modelers, transportation engineers, geographers, and planners.

Professor Peeta brings decades of experience to this role. His research spans dynamic traffic assignment, congestion mitigation, and the development of resilient transportation networks. His association with Transportation Research Part B began in the early 1990s as a reviewer, and he has since published approximately 25 papers in the journal. Since 2019, he has served as an Associate Editor, playing a key role in managing the editorial process and upholding the journal’s high standards.

Please join us in congratulating Professor Peeta for this well-earned recognition. We are confident he will continue to guide Transportation Research Part B with excellence and vision, shaping the future of transportation research.

By Chris Gaffney, Managing Director, Georgia Tech Supply Chain and Logistics Institute | Supply Chain Advisor | Former Executive at Frito-Lay, AJC International, and Coca-Cola

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.

By Chris Gaffney, Managing Director, Georgia Tech Supply Chain and Logistics Institute | Supply Chain Advisor | Former Executive at Frito-Lay, AJC International, and Coca-Cola

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.

By Chris Gaffney, Managing Director, Georgia Tech Supply Chain and Logistics Institute | Supply Chain Advisor | Former Executive at Frito-Lay, AJC International, and Coca-Cola

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.

Researchers at Georgia Tech have developed a new artificial intelligence tool that dramatically improves how companies plan their supply chains, cutting down the time and cost it takes to generate complex production and inventory schedules. 

The tool, known as PROPEL, combines machine learning with optimization techniques to help manufacturers make better decisions in less time. It was created by researchers at the NSF AI Institute for Advances in Optimization, or AI4OPT, based at Georgia Tech under Tech AI (the AI Hub at Georgia Tech).

The technology is already being tested on real-world supply chain data provided by Kinaxis, a Canada-based company that supplies planning software to global manufacturers in industries ranging from automotive to consumer goods.

Vahid Eghbal Akhlaghi, senior research scientist at Kinaxis and former postdoctoral fellow at AI4OPT and the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech, said, “Our industry partner has been instrumental in shaping PROPEL’s capabilities. By validating the approach with real operational data, we ensured it addresses true bottlenecks in supply chain planning.”

"PROPEL represents a leap forward in how we tackle massive, complex planning problems," said Pascal Van Hentenryck, lead researcher, the director of Tech AI and the NSF AI4OPT Institute, and the A. Russell Chandler III Chair and Professor at Georgia Tech with appointments in the colleges of engineering and computing. "By combining supervised and reinforcement learning, we can make near-optimal industrial-scale decisions, an order of magnitude faster."

Traditional supply chain planning problems are typically solved using mathematical models that require immense computing power—often too much to meet real-time business needs. PROPEL, short for Predict-Relax-Optimize using LEarning, reduces this burden by teaching the AI model to first eliminate irrelevant decisions and then fine-tune the solution to meet quality standards.

Reza Zandehshahvar, one of the paper’s co-authors and postdoctoral fellow with the NSF AI4OPT and the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech, said the breakthrough lies not just in the AI algorithms but in how they're trained and deployed at scale.

“Many AI models struggle when applied to problems with millions of variables. PROPEL was built from the ground up to handle industrial complexity, not just academic examples,” Zandehshahvar said. “We’re seeing real improvements in both solution speed and quality.”

 In trials using Kinaxis’ historical industrial data, PROPEL achieved an 88% reduction in the time needed to find a high-quality plan and improved solution accuracy by more than 60% compared to conventional methods.

While many AI methods in supply chain rely on simulated data or simplified models, PROPEL’s performance has been validated using real-world scenarios, ensuring its reliability in high-stakes operational settings.

The Georgia Tech team says PROPEL could benefit industries that manage large, multi-tiered production networks, including pharmaceuticals, electronics, and heavy manufacturing. The researchers are now exploring partnerships with additional companies to deploy PROPEL in live environments.

Access the abstract on arXiv.

I want to recommend a book that I think is especially timely for anyone working in, studying, or simply trying to understand today’s supply chain landscape. Peter Goodman’s How the World Ran Out of Everything takes you inside the global disruption we all lived through — but it also challenges some of the assumptions we've made about how supply chains are supposed to work.

This isn’t a technical manual. It’s a well-researched, human story — with frontline accounts from truckers, factory workers, port operators, and business leaders — and it puts real names and faces behind the headlines. For those of us who’ve been in this field for a while, many of the companies and consultants referenced will be familiar. I’ve worked in and with those same types of organizations, and I’ll say this plainly: so much of what happens in supply chains comes down to incentives. And that’s a thread this book pulls on again and again.

Why I Think It’s Worth Reading Now

 

It lays bare the tension between short-term profitability and long-term resilience.

That balance is hard — even for well-run companies. This book doesn’t offer easy answers, but it helps you see the tradeoffs more clearly.

It’s realistic about reshoring and nearshoring.

Yes, they’re happening. But unless you’re Walmart or a top-tier buyer, they’re not easy plays. The book does a good job showing why that’s true.

It tackles the complexity of working with China.

Like many of you, I’ve been in conversations where we talk about moving away from China — and then realize how difficult (and costly) that would be. This book captures that paradox well: we can’t live with them, but we can’t live without them either.

It reminds us that behind every system are people.

This part resonated with me. From seafarers stuck at sea to small businesses trying to stay afloat, it brings the human side of supply chain to the forefront.
 

Who Might Enjoy This

• Practitioners thinking about how to build more resilient systems
• Early-career professionals who want to see how theory meets practice
• Anyone who wants a thoughtful, readable entry point into the “why” behind the supply chain headlines

As we explore new solutions — whether AI, circular supply chains, or new sourcing strategies — it's worth pausing to ask: what were we solving for before? And are the incentives any different now?

This is a good summer read to help frame that discussion.

Sowmya Ananthachary is Vice President of Software for the Americas region at Dematic. In this role, Sowmya leads Dematic’s software strategy, overseeing the development, implementation, and optimization of software solutions. Working closely with cross-functional teams and key partners, Sowmya ensures Dematic’s software strategy aligns with business objectives, market demands, and customer needs.

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.
 

Aluminum scrap is one of the most common materials found on military bases and aircraft carriers worldwide. Now, the U.S. Army has tapped Georgia Tech to help turn that waste into power that can be generated off the grid and on demand. 

The Army Research Office awarded Georgia Tech and its partners $20 million to develop scalable, efficient methods for transforming aluminum into hydrogen energy. The project could lead to a new, low-cost, clean, and efficient energy source powered by discarded materials. 

Aaron Stebner, professor and Eugene C. Gwaltney Jr. Chair in Manufacturing in the George W. Woodruff School of Mechanical Engineering and professor in the School of Materials Science and Engineering, will oversee the multi-year effort at Georgia Tech together with Scott McWhorter, lead for Federal Initiatives at the Strategic Energy Institute.

In addition to several team members from Georgia Tech and the Georgia Tech Research Institute, the project includes researchers from Fort Valley State University, the 21st Century Partnership, MatSys, and Drexel University. 

“Aluminum already reacts with water — even wastewater and floodwater — to create hydrogen gas, power, and thermal energy,” McWhorter said. “If aluminum can be efficiently upcycled into stored energy, it could be a game-changer.” 

The team’s goal is to experiment with aluminum’s material properties so it can be inexpensively manufactured to create a highly effective reaction that produces low-cost, clean hydrogen.

“Having this ability would allow military bases to be less dependent on the use of a foreign country’s electrical grids,” said Stebner, who is also co-director of Georgia Artificial Intelligence in Manufacturing and faculty at the Georgia Tech Manufacturing Institute. 

Manufacturing Aluminum

Several years ago, the Army Research Lab discovered and patented the basic technology for recycling aluminum to produce hydrogen gas. However, current manufacturing methods require too much energy for the amount of hydrogen energy produced.  

To make the technology viable and effective, Stebner and his colleagues will research alternate manufacturing processes and then develop automated methods for safely producing and storing stable aluminum. They also plan to optimize these processes using digital twin technologies.

Currently, manufacturers use large machines to grind up and tumble the aluminum in very controlled environments, because stray aluminum powder can be explosive. These methods are very costly. 

Stebner and the team are looking into small, modular technologies that could allow for convenient, onsite energy generation. According to Stebner, they are interested in determining how these smaller machines could be so efficient that they could be powered using solar panels. 

Stebner envisions that a field of solar panels could power the aluminum-processing modules — the aluminum recycling could be done while the sun shines and produce power 24/7. 

Sustainable Impact 

Once they have developed the manufacturing techniques and processes, the team plans to test their efficacy by generating power for rural Georgia communities. Success here would prove the technology could be viable for military deployments and other off-grid scenarios. 

“The Deep South — especially middle and southern Georgia, Alabama, Mississippi, and Louisiana — often has enormous energy disruptions during hurricanes or power outages due to flooding and severe rains,” Stebner said. “Manufacturers can be hesitant to build big plants there, because the grids aren’t as stable. This same technology that the Army plans to use for remote military bases could be a game-changer in rural Georgia.”

If power is unexpectedly cut in those areas, floodwater could then be used to make hydrogen gas. While hydrogen has not yet had its day in the sun, it has great potential as an alternative to fossil fuels, Stebner says. 

“From a sustainability perspective, any time you can take something that’s already waste — like scrap aluminum and wastewater — and turn it into a high-value product that can be used to power communities, that is a huge win.” 

 

Funding: Army Research Office

Thomasville, Georgia, is a hub of training and talent for local manufacturers. But Mason Miller could tell there was something missing.

“We didn't have any training for advanced manufacturing in our area,” said Miller, vice president of Academic Affairs at Southern Regional Technical College (SRTC), which offers education and training programs in technical and manufacturing fields. “Companies had to go out and recruit people from Michigan to run their machines. That's when we said, ‘We don’t want that to happen — we need to be doing that right here.’”

That’s where the Georgia Tech Manufacturing Institute (GTMI) stepped in. Working with partner program Georgia Artificial Intelligence in Manufacturing (Georgia AIM), GTMI helped connect SRTC with the resources and expertise needed to develop a robust training program tailored to the needs of local manufacturers.

Miller said at first, he was skeptical. “When GTMI said they wanted to be partners, I thought, ‘OK, this is another situation where we're going to talk for a minute, everybody says things and then goes away — and that’s it,’” said Miller. “That's not how it's been at all.”

Rather, it’s been a true partnership driven by SRTC, with curriculum focused on automation and robotics developed by the Technical College System of Georgia and GTMI. The curriculum is also shaped by local industry input to directly address workforce gaps in the region’s manufacturing sector. 

“As a state institution, we're here to serve you,” said Steven Sheffield, senior assistant director of Research Operations at GTMI and a point person of the partnership. “Tell us the problem, and we will work hard to try to solve it with you.”

Filling the Workforce Gap

Miller was committed to giving SRTC students the advanced manufacturing skills needed to stand out in the workforce. Yet the evolving manufacturing landscape and the needs of local manufacturers revealed gaps in SRTC’s curriculum, particularly in AI, automation, and robotics.

With GTMI and Georgia AIM researchers contributing key expertise to the expanded smart manufacturing curriculum, Miller noted the partnership is “opening our eyes to what we can do with AI. We're going to start integrating that into our programs.”

Beyond AI and robotics, SRTC leadership identified a crucial gap in their program: training in precision machining, a skill that local manufacturers like Check-Mate Industries sorely needed. 

“If we want to attract new business and industry to Georgia, we need to be able to show them we can provide a skilled workforce,” said Miller. 

To address this missing piece, GTMI and Georgia AIM helped procure funding to acquire and refurbish precision-machining equipment from longtime partner Makino. Georgia AIM also supported the renovation and outfitting of two SRTC lab spaces with additional updated equipment. 

Last fall, SRTC launched its new Precision Manufacturing & Engineering and Manufacturing Engineering Technology programs, with instructors trained by GTMI faculty in precision manufacturing. The new program at SRTC is one example of the ways GTMI experts are working with communities across the state to expand access to training and new technology.

“Not a lot of technical colleges have this type of machinery,” said Marvin Bannister, SRTC precision machining and manufacturing program chair. Instructors like Bannister received specialized training at GTMI’s Advanced Manufacturing Pilot Facility to ensure they felt confident teaching students how to operate the machinery. “Not only is it something else to add to my skill set, but the most important thing is that I'll be able to train other students who desire to learn on a machine like this.”

Because of SRTC’s expanded offerings, the technical college has strengthened partnerships and developed new internship programs with local manufacturers. “We all want the same thing,” said Miller, “which is to grow industry partnerships and to create a talent pipeline for our state.”

GTMI and Georgia AIM also support STEM programs with Thomasville area schools and internship programs for K-12 teachers with local manufacturers such as Check-Mate. These efforts deepen the connections between students and manufacturers, opening doors to future careers in the sector.

“We’re here to connect the dots and enable these types of partnerships,” says Steven Ferguson, a principal research scientist with GTMI and co-director of Georgia AIM. “When teams and their networks come together to solve a challenge for just one manufacturer, the impact can reach across an entire region.”

Last month, I had the opportunity to represent Georgia Tech SCL at the joint GEMs-GRACE workshop in Macon, hosted by partners from Georgia Tech, the Georgia Mining Association, and the Middle Georgia Regional Commission. The event brought together 70 participants from 36 organizations across economic development, academia, national labs, non-profits, and industry—underscoring the importance and growing momentum around critical mineral development in our region.

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