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

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

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

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

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

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

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

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

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

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

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

In this issue:

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

A Subtle but Growing Concern

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

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

But the same concern keeps surfacing across those conversations:

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

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

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

What the Experience Taught Us

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

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

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

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

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

Little Rock: When Feasibility Isn’t Reality

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

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

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

Mini Cans: When the Thinking Matches the Model

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

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

Most of the Work Is Outside the Model

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

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

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

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

Where the Breakdown Happens

Before the Model: Skipping the Hard Questions

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

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

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

After the Model: Mistaking Mathematical Accuracy for Business Validity

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

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

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

What Critical Thinking Actually Means in Supply Chain

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

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

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

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

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

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

A Five-Question Diagnostic

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

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

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

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

This Gap Is Well Documented

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

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

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

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

The 4 C’s: A Decision-Focused Framework

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

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

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

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

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

Where to Start

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

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

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

Questions or comments? Reach out to SCL.

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
 

Drawing on his expertise in supply chain economics and transportation systems, Gaffney discussed how disruptions in global energy markets can ripple through logistics networks, ultimately affecting consumers and businesses across Georgia and the Southeast.

Read the full Atlanta News First article and watch the related video: Experts Warn War With Iran Could Raise Costs, Georgia Fuel Prices Leading the Way

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.   

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.

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.

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. 

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

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.

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. 

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.

Geotab and Georgia Tech have formalized their collaboration through a Master Agreement, facilitating joint research initiatives between Geotab teams and Georgia Tech faculty and their students. This strategic partnership emphasizes knowledge transfer and practical outcomes.

Read the article in its entirety within the Geotab website.

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." 

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. 

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.

To read the full announcement, view the story here. 

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.

That’s why I’m recommending a summer read that, while not a traditional supply chain book, speaks directly to this spirit of continuous improvement: Better by Atul Gawande.

Gawande, a practicing surgeon and bestselling author, focuses much of his writing on the challenges of delivering better outcomes in medicine. But as you read Better, it becomes clear that the insights he shares transcend healthcare. In fact, many of them connect deeply to the work we do in supply chain. Health care, after all, is a complex supply chain in itself — one that must manage the flow of goods (medicines, equipment) and services (diagnosis, surgery, therapy) under conditions of great uncertainty and high stakes.

Gawande’s work reminds us that the principles that drive improvement in medicine are the same ones that drive improvement in supply chains, manufacturing, transportation, and just about every field where people are trying to do things better every day.

Three Takeaways for Supply Chain Professionals

1. The Relentless Pursuit of Better is Everyone’s Job

One of Gawande’s central points is that better performance is not reserved for “geniuses” or “experts” alone. In medicine, small, consistent improvements — asking an extra question, double-checking a dosage, washing hands properly — save lives. The same is true in supply chain.

In our world, whether it’s taking a second look at an inventory replenishment setting or spending a few extra minutes mapping supplier risks, the incremental pursuit of better outcomes can mean the difference between success and failure. There’s no standing still. Better is a moving target, and everyone on the team has a role in aiming for it.

2. Systems Matter as Much as Skill

Gawande makes a strong case that even the most skilled individuals can fail if the systems around them are poorly designed. A world-class surgeon operating in a broken hospital supply chain still faces high risks of failure.

Supply chains work the same way. Even great people can’t overcome a bad process or poor system design for long. When we evaluate our operations, it’s important to look beyond individual performance and address the structural barriers that prevent consistent execution. Strong systems allow talent to flourish; weak systems exhaust it.

3. Always Ask One More Question

One of my favorite insights from Better is the idea that sometimes, the most powerful thing you can do is simply ask one more question.

Gawande shares examples where small moments of curiosity or concern — asking a patient one more question about their symptoms, or a nurse asking why a process was skipped — led to major improvements or saved lives.

In supply chain, asking one more question can reveal unseen risks, highlight hidden opportunities, and help avoid costly mistakes. When considering a new supplier, a logistics routing change, or a forecasting adjustment, taking the time to dig a little deeper often makes the difference between a smooth operation and a big problem.

As supply chain learners and leaders, developing the habit of curiosity — and the courage to ask that extra question — is one of the simplest and most powerful habits we can cultivate.

Why Better is a Worthwhile Summer Read

What makes Better a great summer read is not just the quality of Gawande’s storytelling, but how accessible and applicable his lessons are. You don’t have to be a doctor to appreciate the challenges he describes, and you don’t have to be in a hospital to face similar decisions about quality, safety, and improvement.

Supply chains, like healthcare systems, are messy, imperfect, and always evolving. Gawande’s stories are a reminder that we improve not by finding perfect solutions, but by persistently chasing better ones — day after day, decision after decision.

If you’re looking for a book that will inspire you to think a little differently about your work, challenge you to ask better questions, and recharge your commitment to doing things better — Better is a worthy addition to your summer reading list.

I hope you’ll find it as insightful and motivating as I did. And as you turn its pages, I encourage you to keep a simple question in mind: What’s one thing I could do a little better today?

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.

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."

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 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.  

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.  

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

The economic impact of Georgia’s transportation and logistics industry was $107 billion in 2023, according to an economic impact study by the University of Georgia’s Selig Center for Economic Growth. These industries supported more than 578,000 Georgia jobs, or one in nine jobs in the state. From 2010 to 2023, transportation and logistics jobs in Georgia grew by 68%, outpacing the national growth rate of 52%. Additionally, in 2023 and 2024 alone, new logistics and distribution sector investments, including cold storage and ecommerce fulfillment centers, totaled $3.8 billion and created over 9,000 new jobs.

“Georgia’s unmatched global connectivity is one of the driving forces behind our economic success. Decades of strategic investment in our logistics and supply chain infrastructure – from our ports and rail lines to our highways and air cargo capabilities – have led to record-breaking economic investments and trade,” said Georgia Department of Economic Development Commissioner Pat Wilson. “The Logistics Summit brings together private sector, government, and education leaders to learn from experts, exchange best practices, and explore opportunities in the rapidly evolving logistics landscape to maximize opportunities. Events like this strengthen collaboration and spark new ideas that keep Georgia businesses competitive on a global scale.”

Georgia Department of Transportation Commissioner Russell R. McMurry highlighted Georgia’s strategic investments and how the Georgia Department of Transportation (GDOT) is leveraging technology to improve freight flow. He cited the leadership of Governor Brian P. Kemp and support from the General Assembly to allocate $1 billion to the newly created Georgia Freight Program over the past two years. Additional investments in transportation infrastructure are advancing the timing for key planned transportation projects that will maintain and improve Georgia’s interstate highway system, roads, and bridges. Georgia’s multimodal transportation network carried nearly half a billion tons of freight in 2019, valued at $673 trillion. Projections show that freight volume is expected to nearly double to 900 million in tonnage and freight value to more than double today’s value by 2050. Working with partners that include the Center of Innovation and the Georgia Institute of Technology, GDOT is focusing on enhancing safety and efficiency, including projects to add 50% more truck parking and installing fiber internet on Georgia interstates.

Georgia Ports Authority Vice President of Operations Susan Gardner provided updates on strategic investments to expand capacity at the ports, and how Georgia Ports Authority (GPA) is leveraging live data to improve safety, track vessel productivity and containers, and eliminate congestion. Gardner emphasized building a technological culture and prioritizing hiring creative employees, as well as harnessing data insights to boost efficiency. GPA is investing in $4.5 billion in improvements over the next decade as part of its port master plan to expand cargo handling capabilities and support future supply chain requirements.

This year’s feature panel, “AI and Beyond: Embracing Digital Transformation in Logistics,” included leadership from The Home Depot, Havertys Furniture, and TOTO USA, as well as research perspectives from the Georgia Institute of Technology. Panelists highlighted the ways digital technologies are reshaping supply chains, including a three- to five-year outlook for the industry, and provided insights attendees can use to shape their strategies to move more efficiently as AI and automation transform the industry.

“Digital technologies are reshaping supply chains in various ways, and Georgia is working to stay ahead of the curve,” said Center of Innovation Executive Director David Nuckolls. “The Center of Innovation and our specialized logistics team work alongside this dynamic industry, helping to position businesses for growth. The annual Georgia Logistics Summit is a powerful opportunity to build connections and equip businesses with crucial knowledge and resources.”

Educating the needed talent was the focus of the event’s final panel, with University System of Georgia Chancellor Sonny Perdue and Technical College System of Georgia Commissioner Greg Dozier providing updates on how the state’s post-secondary institutions are developing a globally competitive workforce. Discussion focused on how these institutions are ensuring the skills they are teaching match the jobs logistics companies are looking for, including creative problem-solving and effective use of new AI and automation tools. The breadth of Georgia’s technical college programs was also discussed, including the High Demand Career Initiatives program and a pilot program called “Dual Achievement” that enrolls students who withdrew from high school in a technical college program, enabling them to earn a high school diploma alongside a technical college certificate, diploma, or degree. Panelists focused on the importance of helping students get where they want to go faster, upskilling the existing workforce, and how connections between industry leaders and educators can help foster greater outcomes.

The Center of Innovation’s Logistics Industry Advisory Board also recognized the winners of the inaugural Future Innovators in Supply Chain competition. The competition invited college students to create videos about supply chain careers, reflecting the Center’s commitment to developing future logistics leaders. Led by professor Parisa Pooyan, student team “The Masters of Logistinomics” from Kennesaw State University won first prize and a $3,000 grant for the university. Eli Hampton, Angeline Harris, Joe Johnson, and Dana Pazhouhesh created the winning video, which can be viewed here.

For additional information on the 2025 Georgia Logistics Summit and to stay up-to-date on next year’s plans, visit galogisticssummit.com.

Participants in the 2025 Georgia Logistics Summit also included leaders from S&P Global Market Intelligence, Boost Phase Ventures, and M.D. Livingstone Consulting.

About the Georgia Center of Innovation
Exclusive to Georgia, the Center of Innovation helps Georgia businesses of all types and sizes find inspired solutions to challenges and opportunities. The Center connects new and expanding businesses with a team of experts, external partners, and independent mentors to tap into the technical expertise and guidance they need. By encouraging collaboration across six key industries: Aerospace, AgTech, Energy Technology, Information Technology, Logistics, and Manufacturing, the Center helps Georgia prepare for growth in strategic industry ecosystems.

About GDEcD
The Georgia Department of Economic Development (GDEcD) is the state’s sales and marketing arm. It is Georgia’s lead agency for attracting new business investment, encouraging the expansion of existing industry and small businesses, and locating new markets for Georgia products. As the state’s official destination marketing organization, it drives traveler visitation and promotes the state as a location for film and digital entertainment projects. GDEcD is responsible for planning and mobilizing state resources for economic development, fostering innovation and the arts to drive opportunity from the mountains to the coast.

As part of their preparation for undergraduate education in the U.S., GTLI students enrolled in the Supply Chain Fundamentals course, a foundational element of our program, recently took their final exam in a truly innovative way.

The students, hailing from GTLI, are part of a program designed to equip international students with the skills they need to succeed in American universities.  Language Institute director, Katherine Samford, emphasizes, “Comprehensive preparation for undergraduate studies for our students means developing their English language skills, helping them build strong academic habits, and providing foundational exposure to content. 

Working with Georgia Tech’s Supply Chain and Logistics Institute is the perfect match for our program mission.”  Since many of these students have expressed a keen interest in pursuing a degree in Supply Chain Management, the Supply Chain Fundamentals course is a critical steppingstone toward their academic and professional goals.

What made this exam particularly unique was its delivery through our new Global Virtual Learning Environment (HITeC). HITeC, which we are also utilizing to modernize the content and pedagogy for our Master's in Supply Chain Engineering program, provided the perfect platform for these students to demonstrate their knowledge. 

The system was developed in collaboration with Dr. Frederick Benaben and our partners at IMT, offering a group-based, collaborative simulation that mirrored real-world supply chain scenarios.

The exam required students to work together to make strategic supply chain decisions using a virtual simulation that focused on the global supply chain for e-skateboards. This allowed the students to interact with suppliers and customers from around the world in a controlled, immersive environment. Students had the opportunity to learn about the complexities of managing a supply chain, the impact of their decisions, and how to improve decision quality in real-time.

Their journey began with the analog Beer Game, a classic exercise in supply chain dynamics. From there, they transitioned into the virtual world, taking on various roles such as ordering, assembly, and shipping, each critical to the successful management of a supply chain. Throughout the course, they played multiple rounds, gaining hands-on experience that culminated in this final immersive simulation.

This initiative marks the first time our students have taken a final exam using HITeC, and the results were impressive. The students successfully navigated the challenges of the virtual environment, supported by a dedicated development team, including virtual teaching assistants live from France.

Ghala Alhajri, Georgia Tech Language Institute student and SABIC Foundation Year Program participant reflects, "We got practical, hands-on experience that will help us in our future job roles at our company, SABIC (Saudi Arabian Basic Industries Corporation). The VR experience showed me how technology can impact our lives in a positive way, and I understood the importance of communication in the field of supply chain management."

Our collaboration with IMT and the work in the Physical Internet (PI) lab at Georgia Tech demonstrates the global reach and cutting-edge nature of the supply chain education we offer. The success of this pilot program highlights the value of blending technology with education to create meaningful, real-world learning experiences.

We are excited to continue modernizing our courses and leveraging platforms like HITeC to ensure that our students are not only prepared for the challenges of today’s supply chain but are also equipped to lead in the supply chain of the future.

Stay tuned for more updates on our educational innovations and how we continue to shape the next generation of supply chain leaders.

Why Organizations Choose Georgia Tech

Organizations turn to us for talent development solutions for several reasons:

A Balance of New and Experienced Professionals: Managing talent means addressing the needs of both emerging professionals who bring fresh energy and experienced workers who provide deep institutional knowledge. Our programs cater to this balance, offering foundational courses for newcomers and advanced training for seasoned leaders. 

A Competitive Edge in Attraction and Retention: Offering world-class development opportunities signals to current and prospective employees that your organization values growth. This commitment is especially critical in today’s job market, where career development ranks high on employees’ priorities. 

Practical, Immediately Applicable Training: Adult learners need training that works in tandem with their day-to-day responsibilities. Our modular programs are designed for immediate real-world application, ensuring learning outcomes translate directly into workplace improvements. 

Scalable Solutions for All Organizations: For growing companies, introducing structure and formal training is often the next step in their evolution. For larger enterprises, we complement internal training programs with flexible, impactful learning options that align with organizational goals.

What Makes Our Offerings Unique

We deliver training solutions tailored to the realities of today’s supply chain and logistics landscape, setting us apart in the industry. Here’s how: 

Flexible Delivery Options: From self-paced courses to live and virtual classes, public programs, and custom corporate training, we offer a variety of formats to meet your needs. For example, our hybrid Engineering the Warehouse course combines online lectures with in-person lab sessions to maximize convenience and impact. 

Bite-Sized, Modular Learning: Our modular approach allows professionals to access content in manageable segments, enabling learning alongside work demands. For instance, our series on supply chain analytics is broken into short, focused modules that let learners immediately apply concepts like inventory optimization and demand forecasting. 

Innovative and Relevant Content: We stay ahead of the curve by addressing emerging industry trends. Our new course on Generative AI in Supply Chain equips professionals to leverage advanced technologies for process optimization and strategic planning. 

Industry-Informed Solutions: Collaboration with industry leaders ensures our content addresses real-world challenges. For example, our program on Supply Chain Risk and Resilience integrates insights from Fortune 500 companies to help businesses mitigate disruptions effectively. 

Grounded in Research: Every program is informed by leading academic research and designed to reflect the best practices of adult learning, ensuring your team gains knowledge that is both current and actionable.

Preparing for 2025: A Competitive Advantage

As we approach 2025, talent development remains central to addressing the challenges of the supply chain industry. Large enterprises continue to seek solutions to skill gaps and organizational transformation, while small and mid-sized organizations increasingly recognize the need for formalized training. Georgia Tech’s offerings serve as a vital resource, ensuring professionals stay competitive, skilled, and current in an ever-evolving landscape.

Investing in your workforce today not only ensures your organization’s readiness for tomorrow’s challenges but also signals a commitment to your team’s growth and success. At Georgia Tech, we are proud to partner with companies of all sizes, providing a unique blend of flexibility, innovation, and industry relevance that drives results. 

Whether you need a program to supplement your internal training, build a comprehensive development strategy, or prepare your team for the future, we’re here to help. Contact us at info@scl.gatech.edu to learn more about how we can support your talent development needs. 

Let’s work together to ensure your supply chain workforce is ready to lead in 2025 and beyond.

As Managing Director of the Supply Chain and Logistics at Georgia Tech, I’ve seen firsthand how development planning can elevate individual careers, strengthen teams, and improve organizational resilience. However, the key to making it work lies in balancing the demands of day-to-day operations with actionable, targeted development efforts.

The Reality of Development Windows

The structure of the working calendar doesn’t make development planning easy. Between the end-of-year holiday season and summer vacation months, supply chain professionals face two primary windows for development:

• Spring (February through May): Coming off the end-of-year slowdown, spring provides the first extended opportunity to focus on growth.
• Fall (Mid-August through Mid-November): After summer, fall offers a second chance to refocus before the holiday rush begins.

For supply chain professionals, these windows represent critical periods to upskill and prepare for the evolving demands of the industry. The spring window is upon us, making now the perfect time to act with urgency and purpose.

The “Why” of Development Planning

In supply chain, the "why" behind development planning is straightforward: the industry is changing faster than ever. New technologies, shifting global trade patterns, and the increasing complexity of operations demand professionals who are both technically skilled and strategically agile.

The supply chain leaders of tomorrow must excel in areas like:

• Data and Analytics: Understanding data and leveraging it for decision-making is no longer optional.
• Automation and Technology: From warehouse robotics to AI-powered forecasting, supply chain professionals need to be tech-savvy to stay relevant.
• Resilience and Risk Management: Building robust supply chains capable of withstanding disruption is now a core competency.

The “What” of Development Planning: Key Focus Areas

Development can be broken into two primary areas:

1. Core Competencies (In-House):

• Communication: Clear, concise, and persuasive communication is critical for collaboration.
• Problem-Solving: Supply chains are inherently complex, and professionals must excel at diagnosing and resolving issues.
• Leadership and Teamwork: Even non-managers need strong leadership and collaboration skills to succeed in today’s cross-functional environments.

Here is a link to one of the best competency guides out there: FYI Resource Center

2. Technical and Operational Skills:

• Advanced Analytics and Visualization: Skills in tools like Power BI, Tableau, or Python are becoming industry standards.
• Supply Chain Systems and Automation: Understanding the functionality and implementation of WMS, TMS, and other critical systems is vital.
• Sustainability and Compliance: Professionals need to navigate increasingly complex sustainability requirements and global regulations.

Beyond these newer technical areas, there remain significant gaps in the application of many core supply chain processes—gaps that cannot be overlooked:

• Production and Inventory Planning: Effective planning processes are critical, yet many organizations struggle to optimize them to balance demand and capacity.
• Warehouse Operations and Optimization: Opportunities remain to improve material flow, labor efficiency, and space utilization in warehouse environments.
• Manufacturing Asset Reliability and Output: Ensuring consistent and reliable equipment performance is essential to maintaining throughput and meeting customer expectations.
• Tradeoff Analysis for Purchasing Decisions: Striking the right balance between low cost, resilience, and sustainability is an increasingly complex challenge, especially in the face of rising customer and consumer pressure for speed and service.

A Quick Action Plan Using the 70/20/10 Model

For supply chain professionals with limited time and access to leading practices, the 70/20/10 model offers a practical framework for development:

• 70% Experiential Learning: Apply learning directly in your work.
• 20% Social Learning: Learn from others in your network or organization.
• 10% Formal Learning: Invest in structured learning opportunities.

Please see attached figure 1 for Development Plan Example to improve Warehouse Operations Capability

Here is a link to a free Individual development GPT in Chat GPT: Individual Development Plan Builder

Call to Action: Take 30 Minutes to Plan Today

For busy professionals, the biggest hurdle to development planning is often finding the time. But a well-crafted development plan doesn’t have to take hours. Here's how you can get started in just 30 minutes:

1. Reflect on Your Growth Needs (10 Minutes): Where do I need to grow most?
2. Set Three Development Goals (10 Minutes): Identify experiential, social, and formal learning goals.
3. Identify Next Steps (10 Minutes): Write down one immediate action for each goal.
4. Schedule time with Manager to review proposed plan and schedule monthly check-ins

Development as a Competitive Advantage

The pace of change in supply chain operations is relentless, but professionals who make development a priority can turn that challenge into a competitive advantage. By leveraging the 70/20/10 model and focusing on intentional, actionable planning, you can position yourself—and your team—for success.

Don’t let this spring window pass without taking steps toward growth. Whether it’s mastering a new technology, improving core processes, or navigating tradeoff decisions, the time to act is now. If your growth plan includes professional education, consider how the Supply Chain and Logistics Institute and Georgia Tech Professional Education might benefit you!

AI Agents and Decision Intelligence

AI is moving beyond forecasting and analytics into autonomous decision-making. AI agents can rapidly process complex scenarios—such as supply disruptions—and generate optimal responses in real time. This shift reduces reliance on manual problem-solving and enables organizations to respond faster and with greater accuracy. These AI-driven systems also make insights more accessible, allowing non-technical professionals to interact with advanced analytics in natural language.

Georgia Tech’s Supply Chain and Logistics Institute is offering education in Generative AI for supply chain, helping professionals understand and apply these tools effectively. The key takeaway? AI isn’t just for data scientists—it’s becoming essential for all supply chain professionals. Investing in AI literacy and decision intelligence training will be critical to staying relevant in the field.

Physical Automation: AMRs Reshaping Warehouses

Automation in warehouses is no longer experimental—it’s here and delivering results. Autonomous mobile robots (AMRs) are replacing traditional automation solutions, offering greater flexibility and adaptability. Unlike AGVs, which rely on fixed paths, AMRs navigate dynamically using AI and real-time mapping, making them well-suited for evolving warehouse environments.

Companies deploying AMRs report increased throughput, reduced labor costs, and improved safety. These robots optimize workflows, assist human workers, and enable 24/7 operations. Georgia Tech researchers are developing human-collaborative robotics, reinforcing that the future is about augmenting—not replacing—workers. Supply chain professionals should focus on developing skills in automation management and AI-driven operations. Understanding how to integrate these technologies into workflows will be a key differentiator.

Data Management: The Foundation for AI and Automation

AI and automation depend on high-quality, well-integrated data, yet many organizations struggle with fragmented systems and poor data governance. Industry surveys consistently highlight that supply chain leaders cite data silos and quality issues as top barriers to digital transformation. Without a strong data foundation, even the best AI models and automation solutions will fail to deliver their full potential.

Modern supply chain visibility platforms and AI-powered analytics tools are helping companies consolidate data for better decision-making. Georgia Tech researchers are advancing digital twin models that simulate supply chain networks, but these rely on robust data integration. For professionals, this underscores the need to develop data literacy and analytical skills. Those who can navigate, interpret, and leverage data effectively will be indispensable in AI-powered supply chains.

Call to Action: Personal Development and Strategic Planning

Emerging technologies in supply chain—AI, automation, data analytics, and logistics AI—are no longer futuristic concepts. They are delivering tangible benefits now, and the gap between early adopters and laggards is widening. If these innovations are not on your radar, you need to take action.

Where to Start:

• Invest in Personal Development: AI, automation, and data skills are becoming core competencies. Take relevant courses, attend industry events, and seek practical experience.
• Assess Business Applications: Identify where these technologies can solve current challenges and improve efficiency in your supply chain.
• Build Data Competency: Understanding how to structure and leverage data is foundational for AI and automation success.
• Experiment with Emerging Tech: Pilot AI-driven decision tools, AMRs, or logistics optimization models to gain insights into their potential.

The future of supply chain management is being reshaped by these technologies, and those who prepare now will define the next era of supply chain excellence. The question is no longer if these tools will impact the industry—it’s how quickly you can learn to use them to your advantage.

The Core Challenge: Complexity Crowds Out Curiosity

In my time in industry, I’ve seen high-stakes decisions unfold under tight timelines. The rigor is there: financial models, market analysis, legal due diligence. But the same pressure that brings focus often narrows the field of vision. Once the strategic goal is clear, the push becomes “get the recommendation ready” or “get the deal done.” Often, the team disbands before the ripple effects have even begun to appear.

In fact, studies of managerial behavior find that decision-makers often prioritize short-term outcomes over long-term implications, making it easy to overlook those downstream impacts.

We rarely paused to ask:

• What happens to our partners, our systems, or our people two or three steps down the line?
• Are we shifting bottlenecks or creating future misalignments?
• Could this solution lock us into a path that becomes hard to reverse?
• Will we be happy with this decision in 5 years?

Not asking these questions isn’t negligence. It’s often a result of how we structure decision processes: focused, time-bound, and oriented toward closure.

When Good Decisions Still Cause Trouble

Let's make this real. I've seen:

• Procurement strategies that focused on driving down cost but over time forced suppliers to reduce investment in quality and continuous improvement resources—eventually leading to a significant quality issue for a key customer.
• Multiple outsourcing efforts that reduced future capital requirements but also reduced flexibility in scheduling and responsiveness to rapid demand shifts or new product innovation.
• Plant closures that optimized total network cost on paper but not in reality, because the remaining plants were not actually equipped to take on more volume and increased complexity.
• A new warehouse management system implementation that promised efficiency gains but created chaos in distribution—not because the software was flawed, but due to unforeseen complexities during implementation.

In each of these, the first-order decision was sound. But the downstream effects caught teams off guard, requiring backtracking, remediation, and even reputational repair.

Even recently, retailers trying to fix 2021 product shortages by ordering more stock found themselves “overwhelmed with inventory” in 2022 when demand eased—a textbook second-order surprise. Likewise, logistics executives admitted they “didn’t anticipate” that 2020’s e-commerce boom would spark a warehouse labor crunch—a side effect that underscores how easily ripple effects can catch us off guard.

Why This Matters—and Why It's Often Skipped

Let’s be honest. Most leaders are moving fast. The idea of adding more process—or imagining abstract future problems—can feel like a luxury. Typical objections sound like:

• "We don't have time for hypotheticals."
• "That's someone else's job—let's just move."
• "We'll deal with it if it becomes a problem."

But here’s the catch: in a complex system like a global supply chain or a tightly coupled stakeholder network, second-order effects are not edge cases—they're part of the landscape.

In fact, recent research in supply chain management finds that such second-order effects are likely ubiquitous and must be anticipated rather than ignored. Ignoring them doesn’t make them go away. It just delays the pain—and multiplies the cost.

Where This Applies in Supply Chain

These second-order thinking practices are especially useful in supply chain decisions where complexity and interdependencies are high. Think about:

• Network redesigns or footprint consolidation
• Sourcing shifts or dual sourcing strategies
• Technology implementations like a new TMS or WMS
• Inventory policy changes that affect fulfillment, customer service, or working capital
• Sustainability initiatives that touch suppliers, packaging, and compliance

Each of these decisions may seem straightforward at first glance, but often carry ripple effects that only surface months later—making this kind of foresight not just useful, but essential.

A Pragmatic Playbook: Small Steps, Big Impact

To embed this thinking into your organization’s DNA, you don’t need to launch a task force. You need lightweight, repeatable tools that shift how teams think. Here are a few that punch above their weight:

✅ Pre-Mortem Workshop

• Time: 60–90 minutes
• What It Is: Imagine the decision failed spectacularly. Ask: what went wrong?
• Value: Surfaces hidden risks early and creates a safe space for dissent.

"This is an insurance policy, not red tape.”


✅ Ripple Mapping

• Time: 1–2 hours
• What It Is: Visually chart the impact of a decision across systems, partners, and people.
• Value: Turns abstract consequences into visible risks and opportunities.

"Helps teams see around corners—and ask better questions.”


✅ Mini FMEA (Failure Modes and Effects Analysis)

• Time: 60 minutes
• What It Is: Identify how key decision elements could fail and what to do about it.
• Value: Helps prioritize monitoring and mitigation during rollout.

"Adapt it from engineering—it works just as well for strategic moves.”


✅ Early Warning Indicators

• Time: Minimal setup, integrated into standard dashboards
• What It Is: Define and track metrics tied to second-order risks (e.g., employee attrition, service delays).
• Value: Helps you course-correct before small issues become systemic.

"It's not just about making the right decision—but making the decision work.” 

Culture Shift: From Transaction to Trajectory

The real unlock comes when we shift the definition of a successful decision. It’s not just about getting a green light. It’s about ensuring the decision holds up over time—operationally, culturally, and reputationally.

To institutionalize this mindset:

• Add a "second-order checkpoint" to strategic review decks or governance templates
• Ask for a "consequence map" alongside the business case
• Celebrate teams who surface risks early, not just those who execute quickly
• Conduct post-mortems (not just pre-mortems) to harvest lessons

"Strategic foresight is not about predicting everything. It's about avoiding the predictable surprises.”
 

Backed by Big Thinkers

This isn't just operational wisdom—it's grounded in thoughtful literature:

• Peter Senge, in The Fifth Discipline, emphasizes how organizations struggle when they fail to see the system-wide consequences of localized actions.
• Nassim Nicholas Taleb, in Antifragile, argues that systems become more vulnerable when decisions are made without consideration for stress-testing and adaptive feedback loops.
• Cass Sunstein, writing on regulatory and policy decision-making, promotes the idea of "decision hygiene”—a systematic process to reduce bias and surface risk.
• Atul Gawande, in his book Better and in his commencement address at Stanford, shared how the habit of asking "just one more question" often uncovered crucial, overlooked insights—just like the disheveled detective Columbo. That final question, the one nobody else asks, frequently makes the difference between surface-level understanding and meaningful action.

Sometimes the last question is the best one. The more complex our systems become, the more important it is to keep asking until we find what we didn’t know we were missing.

Closing Thought: Be the Person Who Asks One More Question

As supply chains become more interconnected and policy environments more volatile, decision quality will increasingly depend on ripple-awareness. You don’t need perfect foresight. But you do need a culture that pauses—briefly—to ask: what might happen next?

Those few extra minutes may be the difference between a great decision—and a regrettable one.

Both Python and R have strong footholds in the analytics space, each with unique advantages. However, industry trends suggest a growing shift toward Python as the dominant tool for data science, machine learning, and enterprise applications. While R remains valuable in specific statistical and academic contexts, businesses must carefully assess which language aligns best with their analytics goals and workforce development strategies.

This article explores the strengths of each language and provides guidance for industry professionals looking to make informed decisions about which to prioritize for their teams.

Why Python Is Gaining Industry-Wide Adoption

1. Versatility and Scalability for Business Applications

Python has evolved into a comprehensive tool that extends beyond traditional analytics into automation, optimization, artificial intelligence, and supply chain modeling. Its key advantages include:

• Scalability: Python handles large-scale data processing and integrates seamlessly with cloud computing environments.
• Machine Learning and AI: Python’s ecosystem includes powerful machine learning libraries like scikit-learn, TensorFlow, and PyTorch.
• Integration Capabilities: Python works well with databases, APIs, and ERP systems, embedding analytics into operational workflows.

2. Workforce Readiness and Talent Development

From a talent perspective, Python is becoming the preferred programming language for data science and analytics roles. Surveys indicate that Python is used in 67% to 90% of analytics-related jobs, making it a crucial skill for professionals. Employers benefit from:

• A larger talent pool of Python-proficient professionals.
• A lower barrier to entry for new employees learning data analytics.
• The ability to streamline analytics processes across different functions.

3. Industry Adoption in Supply Chain Analytics

Python is widely adopted in logistics, manufacturing, and supply chain optimization due to its ability to handle:

• Demand forecasting and inventory optimization.
• Network modeling and simulation.
• Automation of data pipelines and reporting.
• Predictive maintenance and anomaly detection.

Why R Still Has a Place in Analytics

Despite Python’s widespread adoption, R remains a valuable tool in certain business contexts, particularly in statistical modeling and research applications. R’s strengths include:

• Advanced Statistical Analysis: R was designed for statisticians and remains a leader in econometrics and experimental design.
• Robust Visualization Capabilities: Packages like ggplot2 and Shiny make R a preferred choice for creating high-quality visualizations.
• Adoption in Public Sector and Academic Research: Many government agencies and research institutions continue to rely on R.

Strategic Considerations: Choosing Between Python and R

1. Business Needs and Analytics Maturity

• For companies focused on predictive analytics, automation, and AI, Python is the best choice.
• For organizations conducting deep statistical research or working with legacy R code, maintaining some R capabilities may be necessary.

2. Workforce Training and Skill Development

• Companies investing in analytics training should prioritize Python to align with industry trends.
• If statistical expertise is a core requirement, R may still play a supporting role in niche applications.

3. Tool and System Integration

• Python integrates more seamlessly with enterprise software, making it easier to operationalize analytics.
• R is often more specialized and may require additional effort to connect with business intelligence platforms.

4. Future Trends and Technology Evolution

• Python’s rapid growth suggests it will continue to dominate in analytics and AI.
• While R remains relevant, its role is becoming more specialized.

Final Thoughts: A Pragmatic Approach to Analytics Development

For most organizations, Python represents the future of analytics, offering the broadest capabilities, strongest industry adoption, and easiest integration into enterprise systems. However, R remains useful in specialized statistical applications and legacy environments.

A balanced approach might involve training teams in Python as the primary analytics language while maintaining an awareness of R for niche use cases. The key takeaway for business leaders is not just about choosing a programming language but ensuring their teams develop strong analytical problem-solving skills that transcend specific tools.

By strategically aligning analytics capabilities with business goals, organizations can build a more data-driven, adaptable, and future-ready workforce.

First, here is an update on the scale of international trade and its role within the US economy. I’ll use official trade statistics provided by the US Census Bureau. If we look at the trade of physical goods which is the first thing that most people think about when it comes to trade, the US imported US$3.112 trillion worth of goods in FY2023. That is simply a lot of stuff. Note that imported goods can be finished products that are distributed (eventually) through various retail channels to end consumers. But they can also be various inputs to production: supplies, components, or work-in-progress inventory that feeds US manufacturing enterprises. A very good example along these lines is Canadian heavy crude oil, shipped to US petroleum refineries as the key input to the production of refined petrochemicals like gasoline, jet fuel, and other products. You can read elsewhere why the US currently imports heavy crude from Canada when it (already) produces more crude oil than it consumes each year and is thus (already) a net exporter.

Most US consumers understand that large parts of our economy rely on imported goods. Fewer might think about the sheer scale of the US goods export economy. Looking again at FY2023, the US exported US$2.051 trillion worth of goods (includng some of that aforementioned US-drilled crude oil). Wow, again, that is a lot of stuff. But it is true that the balance of trade here currently favors imports over exports. Since we import more goods value than we export, we ran a goods trade deficit with the rest of the world of US$1.061 trillion in FY2023.

A large part of the US economy today is the provision of services and not goods. There are all sorts of services: food service, financial services, educational services, transportation services, consulting services, and so on. And the US does trade in services as well, both importing services from foreign providers while exporting services to foreign customers. In fact, the US ran a trade surplus in services of US$288 billion which reduced the overall net trade deficit to US$773 billion in FY2023.

Now let’s discuss tariffs for a bit, and let’s consider duties on imported goods. If the US places a 10% tariff on a bundle of goods (perhaps a specific category of goods from a specific set of countries), then importers of those goods must pay a customs duty on the declared goods before they can be moved into the US (so-called customs-clearing). As many have noted already, these importers-of-record are firms doing business in the US (or individuals) that have arranged for the importation. Examples of such importers include retailers like Walmart and producers like Ford and ExxonMobil. Customs duties collected go into the US Treasury, similar to personal income taxes, social security and Medicare taxes, and corporate income taxes. However, the fraction of US government revenue raised by tariffs has been very small for a long period of time. In FY2023, the total collected customs duties by the US Treasury was about US$80 billion. In fact, FY2023 trade was down a bit from FY2022 when total goods imports were US$3.35 trillion and total collected duties were US$112 billion, or an average duty of about 3.3%.

So, how much revenue could be raised by new tariffs? Let’s imagine a strange world where new US import duties did not distort the economy in any way: the same value of goods is assumed to be imported even though both demand for those goods would likely adjust and the purchasing power of each US$ might increase. If the average duty were increased to 10%, the total revenue produced to the US Treasury in FY2023 would have been US$311 billion. How about a 25% average tariff? Well, of course, US$778 billion. For comparison, the US Treasury received US$2.43 trillion in personal and US$530 billion in corporate income taxes in FY2023, an amount nearly equivalent to a universal 100% tariff on the imported goods value basis for all imported goods. The tiny yellow sliver in the figure below shows how little total customs duty revenue has been collected over time and how little changed it has been compared to other revenue sources.

Like any other tax, a tariff can be useful to governments as they seek to design mechanisms to fund (important) government activities while distorting economic activity to favor or disfavor various groups of people, businesses, investors, industries, nations, regions etc. It’s also safe to say that, like any other tax, it can be difficult to determine how economic activity will be specifically distorted by any specific tariffs. In fact, it may be more difficult with tariffs for a few reasons. The first is that unlike a sales tax, a tariff on imported goods occurs upstream of the point-of-sale. Instead, tariffs create increases in supply chain costs for importers, and the impact of tariffs on consumers depends on what happens as a result of these cost increases.

First, it should be noted that some supply chain cost increases cannot be borne at all and can lead to the elimination of some products in the marketplace. Why? A cost increase can lead a producer to decide that a product cannot be profitably produced and marketed, and this is true even if a replacement supply source with a lower (tariff-inclusive) cost of supply can be identified. A retailer may make a similar decision for an imported product. If producers or retailers continue to keep a product in the market, they could decide to lower its quality in some way or to pass on portions of the cost increase directly to its customers. But the supply chain cost persists; perhaps a different supplier could be identified not subject to the tariff, but if that supplier were already providing the same input at the same quality for a lower price they would be used already. Since profitability is likely to be impacted, owners and investors as well as employees of the importer will also likely to be impacted. These interactions are all naturally somewhat complex and the outcome is difficult to predict.

I’ll finish with a thought. If a government wishes to use new tariffs to yield a political outcome beyond simply raising revenue, they will likely need to be designed to produce a significant (and noticeable) distortion to some portion of the economy. If the distortion is mild, no change of behavior seems likely to occur. It seems as if the US is about to attempt some new experimentation with tariffs to both influence the behavior of trade partner nations and to create a significant government revenue source. We will likely get to see firsthand what kind of economic distortion they induce.

Here are the post senior design plans from the Spring 2024 cohort (number of respondents = 171):

• Offer rate: 75.6% of job seekers had received an offer by the end of the Spring semester when they completed the survey. This figure has shown a declining trend: Fall ’23: 80.9%, Spring ’23: 87.6%, Fall ’22: 90.4%, Spring ’22: 91.5%, and Fall ’21: 88%
• Starting annual salary: Median = $85K (Fall ’23: $84.5K, Spring ’23: $88K Fall ’22: $85K, Fall ’21: $80K, Spring ’22: $82K). Trend chart below.
• Industry: Reported industry type they are joining: ~37% Consulting (including supply chain consulting), ~18% Financial, 7% Logistics.
• Position: The most common titles are Analyst (40%), Consultant (24%), Engineer (14%).
• Company: Similar to the previous fall semester, Manhattan Associates remains the largest recruiter, with 12% of offer recipients committing to them.. All of MANH recruits have the title: Software or Software Implementation Consultant.
• Location: Of those who disclosed their job location, 52% will be based in Metro Atlanta, followed by NYC. Only 2 individuals reported positions on the West Coast.
• Internship/Co-op: 47.7% have previously interned or participated in a co-op at the company from which they received then accepted an offer.
• Graduate School: 16.7% (29 respondents) intend to pursue graduate studies. This statistic is consistent with most recent semester. Fall ’23: 16.4%, SP’23: 11%, see trend chart below.
• After this spring, one student plans to pursue a PhD at GT, while 27 are pursuing an MS, primarily at GT, with exceptions including 3 at Cornell, 2 at Stanford, and 1 at Minnesota. 

It is clear that we have seen a dip in the past two years for those with offers moving from 91% to 75%.  The data does not capture students with multiple offers but conversation with advisors suggests that more students only had a single offer. 

The field of consulting is very cyclical so it is not a surprise that our offers are down and we know technology in general have seen a reduction in resources.  

We have taken a number of actions to support our Fall Recruiting efforts. We have talked to the Metro Atlanta Chamber and have extended our Supply Chain Day Recruiting information to all the Supply Chain companies in the Metro as well as their new working group on Automation and Robotics. The Atlanta area has strong growth with new companies and startups and not all these companies recruit at Tech. 

I would also observe that the traditional operational roles in Supply Chain in Retail and CPG are still hiring many front line supervisors. We have small participation in our recruiting from companies like Walmart and Kroger and their CPG suppliers but there are many out there who do not recruit at Tech. I do think this connects to our discussion with students about first line operations experience as a valuable first step as an ISYE graduate. Many of our Industry Advisory Board members and faculty have noted a heavy shift into analytics by our students at the expense of context.   

We plan to schedule sessions with undergraduate and Masters students in the Fall to educate on the reality of the market and the likelihood that these conditions will continue through 2025.  We will emphasize the need for traditional supply chain experiences in class projects, internships, coops, student research, and on campus. 

We are also discussing brand image action planning with key companies who view Tech as a long term resource for talent.  We encourage them to take a long term view to their actions as students and faculty have long memories for companies who are not consistent in their hiring presence. 

If you have thoughts on this topic, please reach out to me and we can discuss additional actions for SCL, ISYE, our affiliated partners, students, faculty, and companies. 

Chris Gaffney, SCL Managing Director

Our SCL Industry Advisory Board has asked our team to invest in providing a future perspective on the evolution of Supply Chains and where we think the turns, peaks, and valleys will lie. We will begin an effort to offer a credible future perspective, but it will take some time. In the meantime, we will share some perspective and thoughts on pieces we see that are worth a good read. We will also offer some advice for those who need guidance or an additional data point on their action plans to lead their Supply Chain. The referenced article is a good one. Please scan our summary and if it piques your interest, the article is a good 15-minute read. Here are our thoughts: 

In today's rapidly evolving global landscape, supply chain leaders must adopt a forward-thinking approach to ensure resilience, innovation, and sustainability. As disruption becomes a constant, assessing supply chains from a risk and resiliency perspective is no longer optional—it is essential. This article explores key themes for future-proofing supply chains, offering a self-assessment framework and insights into what leaders are doing to stay ahead. 

Why the Economy Matters

While supply chain, transportation, and technology are obvious areas to focus on, the inclusion of economic insights might seem less intuitive. However, understanding the economy is critical. Supply chains are fundamentally about balancing supply and demand, justifying investments, and fostering business growth. Many inputs are global, the employment market drives wages and employment quality, and macroeconomic factors like growth and interest rates influence numerous aspects of supply chains. Hence, keeping an eye on economic trends is indispensable for any supply chain professional.

Essential Supply Chain Newsletters

Bloomberg’s Supply Lines

One of my top choices for supply chain news is Bloomberg’s Supply Lines. This multi-weekly newsletter offers concise, high quality updates on global supply chain developments. It’s a free resource from a reputable source, making it an accessible way to stay informed about major trends and events affecting our industry.

Dynamo Ventures Dispatch

Another excellent resource is Dynamo Ventures’ Monday newsletter. This newsletter aggregates a wealth of information on current supply chain issues, with a particular focus on supply chain technology and venture capital (VC) and investor interest in the field. It’s an invaluable tool for understanding the intersection of innovation and investment in supply chains.

Must Listen Podcasts

Freightvine with Chris Caplice

For those who prefer podcasts, "Freightvine" hosted by Chris Caplice is a must listen. Caplice is a legend in the world of transportation, and his podcast covers all the key topics in the industry. From transportation management to logistics challenges, Freightvine provides deep insights and expert perspectives.

What Is Going On in Shipping?

If global freight movement is your area of interest, "What Is Going On in Shipping?" on YouTube offers an inside scoop on macro issues in global port and freight flow. The channel provides timely and in-depth analyses of the challenges and trends affecting global shipping, making it an excellent resource for those focused on international logistics.

Macro Musings

I like this one because it goes deep into how the Fed makes decisions on economic policy and actions and includes linkage to business conditions and the macro economy.

Key LinkedIn Influencers

Brittain Ladd

For fulfillment and ecommerce insights, I recommend following Brittain Ladd on LinkedIn. Although I don’t always agree with all of his views, Brittain provides detailed and thought provoking opinions on all aspects of fulfillment. His posts are well researched and cover a wide range of topics, offering valuable perspectives for anyone involved in ecommerce and order fulfillment.

Bridging Supply Chain and Economics

Bloomberg’s Odd Lots Podcast

Lastly, for the intersection of supply chain and economics, Bloomberg’s "Odd Lots" podcast is unparalleled. During the pandemic, the podcast delved deep into supply chain issues, and it continues to feature great guests who provide actionable insights on everything from procurement strategies to retail pricing. The discussions on macroeconomic issues are particularly relevant, offering a broader context that helps in understanding the economic forces shaping supply chains.

Conclusion

In conclusion, staying current in the supply chain field requires a multifaceted approach. By leveraging newsletters like Bloomberg’s Supply Lines and Dynamo Ventures, listening to podcasts like Freightvine and Odd Lots, and following industry influencers on LinkedIn, you can gain a comprehensive understanding of the trends and issues shaping our industry. Each of these resources brings a unique perspective, helping to ensure that you are well informed and prepared to navigate the complexities of modern supply chains.

I also consume many other resources and apologize if I didn’t include your site. I won't hype my own work, but it is out there to consume. How do you stay current in the world of supply chain? We'd love to hear from you about your go to sources and will do a follow up based on crowdsourcing the audience's best resources to stay current in all things supply chain. Please email me at info@scl.gatech.edu with your Go Tos and we’ll create a better list together.

By combining these resources, you can stay ahead of the curve, continuously enhancing your knowledge and skills in this ever evolving field.

Chris Gaffney, SCL Managing Director

Introduction

Predicting the future of supply chains has always been a challenging endeavor. As someone who has spent more than 25 years in the beverage industry, I’ve witnessed firsthand how even the most well thought out predictions can miss the mark. Yet, understanding where we went wrong in the past can equip us with the tools to better anticipate and adapt to future challenges.

In this article, I want to explore the complexities of forecasting in the supply chain realm, reflect on some past predictions that didn’t quite hit the target, and suggest actionable strategies that can help us navigate the uncertainties ahead.

The Challenge of Predicting Supply Chain Trends

The supply chain, particularly in the beverage industry, is a complex web of interdependencies. As we push for innovation—from new ingredients to advanced packaging—our supply chains often struggle to keep pace. Historically, the challenges of maintaining quality, managing costs, and ensuring timely delivery have been compounded by global disruptions, technological advancements, and evolving consumer expectations.

In the 1990s, for example, the advent of RFID technology was hailed as a gamechanger, promising unparalleled visibility and efficiency. While RFID has undoubtedly transformed many aspects of supply chain management, its adoption has been slower and less impactful than originally anticipated. Similarly, the introduction of Enterprise Resource Planning (ERP) systems was expected to revolutionize the way businesses managed their operations. Yet, the promised seamless integration and real time data accuracy have often fallen short, leading to frustrations and costly implementations.

These examples highlight a critical lesson: while technological advancements hold great promise, their real-world application can be fraught with challenges that delay or dilute their impact.

Lessons from Past Predictions

One of the most striking examples of a prediction that didn’t pan out as expected is the Just in Time (JIT) manufacturing model. Initially, JIT was celebrated for its potential to minimize waste and reduce inventory costs. However, the COVID-19 pandemic exposed the vulnerabilities of this approach. As supply chains were disrupted worldwide, many companies found themselves unable to meet demand due to the lack of buffer stock. This has led to a reevaluation of the JIT model, with many businesses now looking to build more resilience into their supply chains by maintaining higher levels of inventory.

Another lesson comes from the early 2000s, when global sourcing was predicted to be the ultimate cost saving strategy. While it did lead to significant cost reductions, it also introduced new risks—ranging from quality control issues to geopolitical tensions—that have since prompted companies to reconsider the balance between cost savings and supply chain security.

The Inherent Risks of Relying on Predictions

One of the inherent risks in predicting supply chain trends is that it often leads to an overreliance on certain strategies or technologies. For instance, the push towards automation and robotics, while offering substantial benefits in terms of efficiency and cost savings, has also led to significant challenges. The initial costs, integration difficulties, and the need for upskilling workers have often been underestimated, leading to delays and unfulfilled promises.

Moreover, as we’ve seen with technologies like blockchain and AI, the hype often outpaces the reality. While these technologies have immense potential to transform supply chain management, their implementation has been slower and more complex than initially expected. This lag can create a false sense of security, leading companies to delay the adoption of alternative strategies or to underinvest in more immediately impactful areas.

Strategies for Navigating the Uncertainty

Given the inherent challenges of predicting the future, how can companies better prepare for what lies ahead? Here are a few strategies that can help:

1. Embrace Flexibility and Resilience: Instead of betting on a single prediction or technology, companies should build flexibility into their supply chains. This might involve diversifying suppliers, maintaining higher inventory levels, or investing in modular production systems that can be quickly adapted to changing circumstances.
2. Invest in Predictive Analytics: While past predictions have often fallen short, advances in AI and machine learning are making it possible to better anticipate supply chain disruptions and demand fluctuations. By investing in predictive analytics, companies can gain more accurate insights into future trends and make more informed decisions.
3. Foster Stronger Relationships with Partners: As supply chains become more complex and globalized, the importance of strong relationships with suppliers and partners cannot be overstated. By working closely with partners, companies can ensure better alignment of goals, improved quality control, and more effective collaboration in the face of disruptions.
4. Prioritize Sustainability: As consumer expectations shift towards more sustainable products, companies that prioritize sustainability in their supply chains will be better positioned to meet future demand. This might involve investing in sustainable sourcing practices, reducing waste, or adopting circular economy principles.
5. Continual Learning and Adaptation: Finally, companies should foster a culture of continual learning and adaptation. By staying informed about the latest trends, technologies, and best practices, businesses can more effectively navigate the uncertainties of the future and seize new opportunities as they arise.

Conclusion

Predicting the future of supply chains is a daunting task, but it’s one that we must continually strive to master. By learning from past mistakes and adopting a proactive, flexible approach, we can better navigate the challenges ahead and turn potential disruptions into opportunities for growth and innovation. As we look to the future, let’s remember that while predictions can guide us, it’s our ability to adapt and respond to the unexpected that will ultimately determine our success.

FAQ

What are the biggest challenges in predicting supply chain trends?

The biggest challenges include the complexity of global supply chains, the rapid pace of technological change, and the unpredictable nature of global disruptions. These factors make it difficult to accurately forecast future trends and adapt to new developments.

How can companies build more resilient supply chains?

Companies can build more resilient supply chains by diversifying their suppliers, maintaining higher inventory levels, investing in flexible production systems, and fostering strong relationships with partners. Additionally, leveraging predictive analytics can help companies anticipate disruptions and respond more effectively.

What role does technology play in modern supply chains?

Technology plays a critical role in modern supply chains, offering tools for real-time tracking, predictive analytics, and automation. However, the implementation of new technologies often comes with challenges, such as high costs and integration difficulties, which must be carefully managed.

Why is sustainability important in supply chain management?

Sustainability is increasingly important as consumers demand more environmentally friendly products. Companies that prioritize sustainability in their supply chains can reduce waste, improve efficiency, and better meet the expectations of consumers and regulators.

How can companies stay ahead of future supply chain challenges?

To stay ahead, companies should embrace flexibility, invest in new technologies, foster strong partnerships, prioritize sustainability, and continually adapt to new developments. Staying informed about industry trends and best practices is also crucial.

What lessons can be learned from past supply chain disruptions?

Past disruptions, such as the COVID-19 pandemic, have highlighted the importance of resilience, flexibility, and strong partnerships. Companies that learn from these events and adapt their strategies accordingly will be better positioned to navigate future challenges.

Chris Gaffney, SCL Managing Director
 

Assessing Risk: The First Step

A critical starting point for risk management is assessing the risks specific to you or your business. For individuals and small businesses, this process doesn’t have to be exhaustive. Rapid risk assessments can identify the most significant vulnerabilities with minimal effort, like assessing how a hurricane or supply chain disruption might impact access to essentials such as power or food. Larger businesses or municipalities might benefit from more systematic assessments, which can be conducted as part of an annual review.

Appetite for Risk vs. Potential Impact

Once risks are identified, it's important to balance your appetite for risk with the potential impact. An individual might be able to weather a short-term power outage, but a logistics provider or hospital must maintain continuous operations. For businesses, supply chain vulnerabilities—especially single-source suppliers—present a significant risk. By understanding where dependencies lie and preparing contingency plans, businesses can significantly reduce potential disruptions.

Low or No-Regrets Actions

The next logical step is to focus on low or no-regrets actions. These are relatively simple actions that provide immediate benefits with little downside. For individuals and families, this can be as straightforward as ensuring access to working flashlights, batteries, and emergency staples like water, rice, and beans to last at least a week.  Developing communication plans with family or neighbors is another critical preparedness step that costs very little but can be lifesaving.

For businesses, conducting an annual risk review is a no-regrets action that can identify critical risks and supply chain weaknesses. For single source suppliers, it may not be easy or practical to find a second supplier.  If not, go deep on that supplier and understand their upstream risks and ensure they are focused on risk mitigation. Small businesses can often a risk review in just a few hours, while larger organizations may need a more comprehensive review. Understanding infrastructure risks—such as power, water, and data reliability—is essential for businesses of all sizes. With each major disruption, such as wildfires or hurricanes, we see large companies caught off guard, often due to inadequate preparation for these well-known risks.

Tools and Resources for Deeper Risk Management

1. Failure Modes and Effects Analysis (FMEA)

FMEA is a widely used tool that helps businesses identify where and how processes might fail. It evaluates the severity, likelihood, and detectability of different failure points, allowing businesses to prioritize actions. In supply chains, FMEA is invaluable for identifying single-source suppliers or fragile logistical routes that could break down under pressure.

2. Business Impact Analysis (BIA)

BIA assesses the potential effects of a disruption on business processes. By identifying critical functions that must continue during an emergency, BIA helps businesses prioritize resources and plan for worst-case scenarios. This is especially useful for small businesses with limited resources, as it pinpoints where to focus recovery efforts during a crisis.

3. Risk Heat Maps

A risk heat map offers a visual way to assess risk by plotting the likelihood and potential impact of various disruptions. By color-coding risks, businesses can quickly see which areas require immediate attention. These maps are especially useful when making quick decisions in the face of a crisis, such as prioritizing responses to supply chain issues or extreme weather events.

4. Scenario Planning

For businesses with complex supply chains or operations, scenario planning helps explore different risk scenarios and develop flexible response strategies. This approach allows businesses to stay agile, with contingency plans ready for natural disasters, industrial actions, or global supply chain disruptions.

Structured Actions: Resources and Support

For those looking to dive deeper into risk management, there are several resources and programs available:

Executive Education Programs: For supply chain professionals, attending executive education programs such as those offered by Georgia Tech’s Supply Chain & Logistics Institute can provide in-depth knowledge and strategies for managing risk. These programs often cover real-world case studies and actionable risk management strategies.

Federal and State Resources: Agencies like the CDC provide accessible resources for disaster preparedness, such as the Zombie Survival Guide, a lighthearted yet effective framework for emergency planning. FEMA also offers guides for Business Continuity Planning (BCP), helping organizations design robust continuity strategies.

State Economic Development Agencies: Many state agencies, chambers of commerce, or small business associations provide business continuity support tailored to specific regions. For example, states prone to hurricanes or wildfires often provide detailed guides on how to prepare for natural disasters and mitigate infrastructure risks.

Building a Resilient Future

The key takeaway for both individuals and businesses is that resilience starts with proactive action. For individuals, preparedness may be as simple as having an emergency kit and communication plan in place. For businesses, risk management should be part of a structured, ongoing process. Annual reviews, risk assessments, and low-regrets actions can protect against significant disruptions, ensuring continuity even in the face of unpredictable events. By incorporating readily available tools and resources, we can all build a more resilient future, whether in supply chain operations, daily life, or community preparedness.

 

MiTEK, a global leader in the construction industry, is known for its commitment to transforming building methods. With this funded granted, ISyE professors will have the potential to propel innovative approaches in construction, enhancing efficiency and connectivity across projects. 

Jihye Jung, Ph.D.

Jihye Jung is a Research Engineer II in ISyE and has worked on mathematical optimization in supply chain, scheduling, transportation, and quantum computing. She is currently a member of the Physical Internet Center, focusing on hyperconnected distributed construction and advanced optimization methods.

Leon McGinnis, Ph.D.

Leon McGinnis is a Professor Emeritus in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE), known for his research in discrete event logistics systems, integrated computational tools and performance assessment models. 

McGinnis has also led initiatives that secured significant funding, including a $1 million TRP grant and over $2 million from the W. M. Keck Foundation – resulting in the creation of the Rapid Prototyping and Manufacturing Institute and the Keck Virtual Factory Lab.

Benoit Montreuil, Ph.D.

Benoit Montreuil is the Coca-Cola Material Handling & Distribution Chair and Professor in ISyE, as well as the Director of the Physical Internet Center and Executive Director of the Supply Chain and Logistics Institute. 

Montreuil’s research interests generically lie in developing concepts, methodologies and technologies for creating, optimizing, transforming and enabling businesses, supply chains and value creation networks to thrive in a fast evolving hyperconnected world. 

Eunhye Song, Ph.D.

Eunhye Song is a Coca-Cola Foundation Early Career Professor and Assistant Professor in ISyE, as well as an NSF CAREER Awardee. With a background in simulation experiments, model risk quantification and simulation optimization, her research interest lie in digital twin simulator validations and experiment design.

Mathematically, these problems get translated into a search for the minimum values of functions. But in all these scenarios, the functions are too complicated to assess directly. Researchers have to approximate the minimal values instead.

It turns out that one of the best ways to do this is by using an algorithm that Isaac Newton developed over 300 years ago. This algorithm is fairly simple. It’s a little like searching, blindfolded, for the lowest point in an unfamiliar landscape. As you put one foot in front of the other, the only information you need is whether you’re going uphill or downhill, and whether the grade is increasing or decreasing. Using that information, you can get a good approximation of the minimum relatively quickly.

Although enormously powerful — centuries later, Newton’s method is still crucial for solving present-day problems in logistics, finance, computer vision and even pure math — it also has a significant shortcoming. It doesn’t work well on all functions. So mathematicians have continued to study the technique, figuring out different ways to broaden its scope without sacrificing efficiency.

Last summer, three researchers announced the latest improvement to Newton’s method. Amir Ali Ahmadi of Princeton University, along with his former students Abraar Chaudhry (now at the Georgia Institute of Technology) and Jeffrey Zhang (now at Yale University), extended Newton’s method to work efficiently on the broadest class of functions yet.

Read the full story here: https://www.quantamagazine.org/three-hundred-years-later-a-tool-from-isaac-newton-gets-an-update-20250324/ 

As a BBISS Faculty Fellow, Dr. Milz will serve as an advisor to the BBISS board, promote sustainability research and education across Georgia Tech, and work to advance the institute's mission of creating a sustainable future. 

Milz's research focuses on optimization under uncertainty and the development of sustainable energy systems, with a particular emphasis on renewable tidal energy farms. 

His contributions to open science and reproducible numerical simulations has made valuable solutions to the field and has been instrumental in fostering collaboration among researchers and practitioners.

We congratulate Dr. Milz on this well-deserved recognition and look forward to his continued impact on the field of sustainability. To read the full announcement, view the story here.

As the Carolyn J. Stewart Chair, with a joint appointment in the School of Mechanical Engineering, Shi’s research and methodologies focus on integrating system informatics, advanced statistics, and control theory for design and operational improvements in manufacturing and servicing systems. 

His keynote, titled “Data Modality, Data Science, and Multistage Manufacturing”, will discuss how new technologies like sensors and data science change the way we study and improve manufacturing processes. He’ll discuss how these advances help us model and analyze the variations in different stages of manufacturing, making overall production better.

The keynote will delve into the following key areas:

• Sensor Technology: Collecting vast amounts of data from different stages of the manufacturing
• Data Science: Analyzing and interpreting to identify patterns, predict outcomes, and optimize processes
• Variation Modeling: Techniques for modeling variations in manufacturing processes
• AI and Machine Learning: Application of AI and machine learning algorithms to solve practical problems in manufacturing
• Real-World Examples: Case studies from past and ongoing research projects demonstrating the practical implementation of these technologies in real-life manufacturing systems.

Shi will also highlight the challenges and opportunities in adopting these advanced technologies in the industry, addressing issues such as data integration, real-time monitoring, and predictive maintenance.

Alongside his work, Dr. Shi has received various accolades including: the NSF CAREER Award, and is the founding chair of the Quality, Statistics and Reliability (QSR) Subdivision at the Institute for Operations Research and Management Science (INFORMS). 

He is also a Fellow of the Institute of Industrial and Systems Engineering (IISE), American Society of Mechanical Engineering (ASME), Institute of Operations Research and the Management Science (INFORMS), an Academician of the International Academy for Quality, and a member of National Academy of Engineering (NAE).

The H. Milton Stewart School of Industrial and Systems Engineering (ISyE), in collaboration with the Center of Academic Success and Engagement (CASE), will be hosting a series of events recognizing alumni and their contributions to industrial engineering.  

This semester 17 ISyE senior design teams were showcased in the Capstone Design Expo, competing against over 100 teams.  

Join us in celebrating the incredible legacy of Professor Alexander Shapiro at ShapiroFest! 

From managing vast quantities of materials to ensuring timely deliverables, the logistics behind each transmission project is a complex puzzle.  

Recently published in the INFORMS Journal Manufacturing & Service Operations Management, their research calls for transparency in the Medicare Advantage (MA) program. 

Their findings share that Medicare Advantage Plans adjust and reallocate funds inappropriately by implementing strategic cross-subsidization, which directly shifts the quality of care between high-risk and low-risk patients. 

With MA Plans supporting over $2 million enrollees, they’ve provided the first empirical evidence of strategic cross-subsidization happening within a large commercial insurance database. 

Ayer collaborated with faculty researchers, Zhaowei She (Singapore Management University), Bilal Gokpinar (University College London), and Danny Hughes (Arizona State University), to study and address the need for Medicare reform and regulating ongoing health equities.

"This study underscores the urgent need for enhanced transparency in Medicare Advantage. The practice of strategic cross-subsidization we've uncovered could exacerbate socio-economic inequalities in healthcare access and outcomes, highlighting the critical importance of rigorous oversight in capitation payment models."

Examples of this can be seen when individuals become ill or considered high-risk, however the healthcare plan may suggest spending less resources on the patient. These specific findings have the potential to alert policymakers and encourage enforcers to revisit the structure behind MA Plans. 

By revealing this unknown issue of resource misallocation, these types of solutions may have the ability to impact millions of beneficiaries across the entire Medicare Advantage system.

To learn more about their research and the full story, read here.

To read their published paper: https://pubsonline.informs.org/doi/10.1287/msom.2023.0637

About INFORMS and Manufacturing & Service Operations Management 

INFORMS is the leading international association for data and decision science professionals. Manufacturing & Service Operations Management, one of 17 journals published by INFORMS, is a premier academic journal that covers the production and operations management of goods and services including technology management, productivity and quality management, product development, cross-functional coordination, and practice-based research. More information is available at www.informs.org or @informs.

Turgay Ayer co-authored the study with his former PhD student, Zhaowei She (now on the faculty at Singapore Management University), as well as Bilal Gokpinar (University College London) and Danny Hughes (Arizona State University). 

_____

Camille Carpenter, Communications Manager

During his 20-year career at The Coca-Cola Company, Martin assumed several roles with increased responsibilities in Houston and Atlanta. In his current role as Vice President, Supply Chain Operations, he leads a team that focuses on delivering our innovation initiatives, driving strategic supply chain solutions, and delivering multi-year Design-to-Value/Sustainability savings.

Martin began his career at the Eaton Corporation in Atlanta, GA where he held several positions including, Plant Financial Controller, Plant Manager, and finally Global Supply Chain Lead. In those roles, he was able to improve manufacturing output, employee engagement and financial performance for the facility he managed.

Martin also provides supply chain and finance consulting as a board member for local non-profit organizations.

Martin is a native of Atlanta, GA. He obtained his bachelor’s degree in Accounting | Finance from Georgia State University and his Master’s Degree in International Business from Mercer University (GA). He is also a practicing CPA in the State of Georgia.

SCL appreciates Mr. Hubbard's participation in our Industry Advisory Board and his willingness to lend his expertise to help shape our strategic initiatives into 2025.