Georgia Tech researchers have created a new AI model for decision-focused learning (DFL), called Diffusion-DFL. Recent tests showed it makes more accurate decisions than current approaches.

Along with optimizing industrial output, Diffusion-DFL lowers costs and reduces risk. Experiments also showed it performs across different fields. 

Diffusion-DFL doesn’t just surpass current methods; it also predicts more accurately as problem sizes grow. The model requires less computing power despite these high-performance marks, making it more accessible to smaller enterprises.

Diffusion-DFL runs on diffusion models, the same technology that powers DALL-E and other AI image generators. It is the first DFL framework based on diffusion models.

“Anyone who makes high-stakes decisions under uncertainty, including supply chain managers, energy operators, and financial planners, benefits from Diffusion-DFL,” said Zihao Zhao, a Georgia Tech Ph.D. student who led the project. 

“Instead of optimizing around a single forecast, the model evaluates many possible scenarios, so decisions account for real-world risk and become more robust.”

[Related: GT @ ICLR 2026]

To test Diffusion-DFL, the team ran experiments based on real-world settings, including:

• Factory manufacturing to meet product demand
• Power grid scheduling to meet energy demand
• Stock market portfolio optimization

In each case, Diffusion-DFL made more accurate decisions than current methods. It also performed better as problems became larger and more complex. These results confirm the model’s ability to make important decisions in real-world scenarios with noisy data and uncertainty.

The experiments also show that Diffusion-DFL is practical, not just accurate. Training diffusion models is expensive, so the team developed a way to reduce memory use. This cut training costs by more than 99.7%. As a result, Diffusion-DFL can reach more researchers and practitioners.

“Our score-function estimator cuts GPU memory from over 60 gigabytes to 0.13 with almost no loss in decision quality, reducing the requirement for massive computing resources,” Zhao said. “I hope this expands Diffusion-DFL into other domains, like healthcare, where decisions must be made quickly under complex uncertainty."

Beyond decision-making applications, Diffusion-DFL marks a shift in DFL techniques and in the broader use of generative AI models. 

In supply chain management, planners estimate future demand before deciding how much product to stock. In this DFL problem, engineers align ML models with predetermined decision objectives, like minimizing risk or reducing costs. 

One flaw of DFL methods is that they optimize around a single, deterministic prediction in an uncertain future.

Diffusion-DFL takes a different approach. Instead of making a single guess, it determines a range of possible outcomes. This leads to decisions based on many likely scenarios, rather than on a single assumed future.

To do this, the framework uses diffusion models. These generative AI models create high-quality data from images, text, and audio. 

The forward diffusion process involves adding noise to data until it becomes pure noise. Models trained via forward diffusion can reverse diffusion. This means they can start with noisy data and then produce meaningful insights from training examples. 

Real-world data is often noisy and uncertain. Traditional DFL methods struggle in these conditions, but diffusion models are designed to handle them.

Because of this, Diffusion-DFL can explore many possible outcomes and choose better actions. Like image-generation AI, the model works well with complex data from different sources. This enables its use across different industries.

“Diffusion models have achieved significant success in generative AI and image synthesis, but our work shows their potential extends far beyond that,” said Kai Wang, an assistant professor in the School of Computational Science and Engineering (CSE).

“What makes Diffusion-DFL unique is that the specific downstream application guides how the model learns to handle uncertainty.

“Whether we are scheduling energy for power grids, balancing risk in financial portfolios, or developing early warning systems in healthcare, we can explicitly train these highly expressive models to navigate the unique complexities of each domain.”

Zhao and Wang collaborated with Caltech Ph.D. candidate Christopher Yeh and Harvard University postdoctoral fellow Lingkai Kong on Diffusion-DFL. Kong earned his Ph.D. in CSE from Georgia Tech in 2024.

Wang will present Diffusion-DFL on behalf of the group at the upcoming International Conference on Learning Representations (ICLR 2026). Occurring April 23-27 in Rio de Janeiro, ICLR is one of the world’s most prestigious conferences dedicated to artificial intelligence research.

“ICLR is the perfect stage for Diffusion-DFL because it brings together the exact community that needs to see the bridge between generative modeling and high-stakes decision-making for real-world applications,” Wang said.

“Presenting Diffusion-DFL allows us to challenge the traditional training framework of diffusion models. It’s about sparking a broader conversation on how we can align the training objectives of generative AI directly with actual, downstream decision-making needs.”

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

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

Read the full article in ISyE Magazine 

New cybersecurity research from the Georgia Institute of Technology, however, revealed that crews aboard commercial vessels were often not adequately prepared to manage cyberattacks effectively due to systemic training gaps.

The findings are based on interviews conducted by researchers with more than 20 officer-level mariners to assess the maritime industry’s readiness to handle cybersecurity attacks at sea.

"Historically, cybersecurity research has focused heavily on cyber-physical systems like cars, factories, and industrial plants, but ships have largely been overlooked,” said Anna Raymaker, Ph.D. student and lead researcher.

“That gap is concerning when more than 90% of the world’s goods travel by sea. Recent incidents, from GPS spoofing to ships linked to subsea cable disruptions, show that maritime systems are increasingly part of the global cyber threat landscape.”

The researchers proposed four practical strategies to strengthen maritime cyber defenses and close the training gaps. Their findings were presented recently at the ACM SIGSAC Conference on Computer and Communications Security (CCS).

1. Make Cybersecurity Training Actually Maritime

Many of those interviewed for the study described current cybersecurity training as “boilerplate” — generic modules that don’t reflect real shipboard risks. 

Researchers recommend:

• Role-specific instruction: Navigation officers should learn to detect and identify GPS spoofing. Engineers should focus on vulnerabilities in remotely monitored systems.
• Bridging IT and Operational Technology: Crews need to understand how attacks on IT systems can trigger physical consequences in operational technology — including collisions, groundings, or explosions.
• Hands-on delivery: Replace passive PowerPoints with drills and in-person exercises that build muscle memory.
• Accessible standards: Training must account for the wide range of educational backgrounds across crews and be standardized across ranks.

2. Move Beyond “Call IT”

At sea, crews can’t simply escalate a cyber incident to a shore-based IT department and wait. Operational resilience requires onboard readiness.

Researchers recommend:

• Vessel-specific response plans: Ships need clear, actionable protocols for threats such as AIS jamming or radar manipulation.
• Military-style drills: Adopting MCON (Emission Control) exercises — used by the U.S. Military Sealift Command — can train crews to operate safely without electronic systems.
• Stronger connectivity controls: High-bandwidth satellite systems like Starlink introduce new risks. Clear policies and network segregation are essential to prevent new entry points for attackers.

Related Article: When GPS lies at sea: How electronic warfare is threatening ships and their crews by Anna Raymaker

3. Create Unified, Ship-Specific Regulations

Maritime cybersecurity regulations are often reactive and fragmented. Researchers argue the industry needs a cohesive, domain-specific framework.

Key recommendations include:

• A unified global model: Like the energy sector’s NERC CIP standards, a maritime framework could mandate baseline controls such as encryption, network segmentation, and anonymous incident reporting.
• Rules built for real crews: Regulations designed for large naval operations don’t translate well to smaller merchant or research vessels. Standards must reflect actual shipboard conditions.
• Future-proofing requirements: Autonomous ships and remotely operated vessels expand the cyber-physical attack surface. Regulations must proactively address these emerging technologies.

4. Invest in Maritime-Specific Cyber Research

Finally, the researchers stress that long-term resilience requires deeper technical research focused on maritime systems.

Priority areas include:

• Real-time intrusion detection systems tailored to shipboard protocols.
• Proactive security risk assessments of interconnected onboard systems.
• Cyber-physical modeling to better understand cascading failures in complex maritime environments.

The Bottom Line

Cyber threats at sea are no longer hypothetical. Mariners report real-world incidents ranging from GPS spoofing to ransomware that disrupts global trade.

“Through our interviews with mariners, I saw firsthand how much dedication and pride they take in their work,” said Raymaker. “Our goal is for this research to serve as a call to action for researchers, policymakers, and industry to invest more attention in maritime cybersecurity and support the people who risk their lives every day to keep global trade, food, and energy moving."

A Sea of Cyber Threats: Maritime Cybersecurity from the Perspective of Mariners was presented at CCS 2025. It was written by Raymaker and her colleagues, Ph.D. students Akshaya Kumar, Miuyin Yong Wong, and Ryan Pickren; Research Scientist Animesh Chhotaray, Associate Professor Frank Li, Associate Professor Saman Zonouz, and Georgia Tech Provost and Executive Vice President for Academic Affairs Raheem Beyah.

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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.

More than 6,200 high school students across Georgia tuned in for Engineers Week 2026. Through a series of online talks, Georgia Tech researchers shared a glimpse of the technologies shaping the future.  

The partnership has driven progress in areas ranging from hypersonics to bioscience, while also deepening institutional ties beyond research. Joint faculty appointments — such as Anirban Mazumdar, who holds roles at both Sandia and the George W. Woodruff School of Mechanical Engineering — demonstrate how closely the organizations work together. The collaboration has also expanded student talent pipelines, providing more avenues for Georgia Tech students to pursue careers at the national lab.

“At its core, this partnership is about people,” said Tim Lieuwen, executive vice president for Research at Georgia Tech. “Sandia and Georgia Tech share a commitment to discovery and developing the talent, creativity, and collaboration our nation needs.”

The renewed MOU, he said, “strengthens connections between our researchers, opens new doors for our students, and builds meaningful career pathways into national service. When our communities work together to address national priorities, we not only accelerate technological advances — we expand opportunities for the people who will shape the future of our nation’s security.”

Under the new MOU, Sandia and Georgia Tech will focus on integrated research across key national security‑aligned areas, including secure artificial intelligence and computing, quantum technologies, critical minerals, advanced manufacturing, energy and grid resilience, and hypersonics. The partnership emphasizes connecting manufacturing, computation, and systems approaches directly to national security applications.

“Together, we have been solving new and unprecedented challenges in science and engineering, and now we have a great opportunity to develop this partnership,” said Dan Sinars, Sandia’s deputy chief research officer. “Our research benefits both national security and national prosperity, and keeps the country at the forefront of the world.”

With this strengthened connection, the partners aim to grow their shared research footprint through increased funding, publications, and faculty-led startups. Over the long term, Georgia Tech intends to become one of Sandia’s top hiring pipelines, ensuring that talent developed through joint research continues into national security careers.

History of the Partnership

The Institute’s collaboration with Sandia began in the mid‑2010s, when the labs selected Georgia Tech as one of its partner institutions. The first MOU, signed in 2015, formalized the relationship and outlined initial technical focus areas. 

In 2018, George White, executive director of strategic partnerships, and Olof Westerstahl,  senior director strategic initiatives in the Office of Corporate Engagement, helped expand the partnership. They launched “Sandia Day,” an event designed to introduce Georgia Tech faculty to Sandia researchers and spark new collaborations. By 2020, the organizations signed a second MOU that expanded the partnership’s technical focus areas to include energy and grid security, materials and nanotechnology, advanced electronics, advanced manufacturing, advanced computing, cyber and information security, bioscience, hypersonics, quantum information science, and engineering sciences.

The results have been substantial. Since 2018, Sandia has sponsored $35 million in research collaborations with Georgia Tech. Researchers from both institutions have co-authored 450 publications since 2016. Research activity continues to accelerate, with $1.6 million in new contracts in the past year alone. As of August 2025, Sandia employs 325 Georgia Tech alumni — a testament to the impact of the growing talent pipeline.

“We view our work with Sandia as the model for engagement with other national labs,” said White. “With the new MOU, we will continue to grow the Sandia partnership. I would like to see our footprint double in scope in the next five years.”

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.

Diabetic foot ulcers, for example, are slow to heal and can increase the risk of infection, hospitalization, and even amputation. 

To address this critical challenge, researchers at the Georgia Institute of Technology (Georgia Tech) and the Georgia Tech Research Institute (GTRI) have developed a sensor designed to monitor chronic wounds in real-time. Embedded directly into a bandage, this flexible, low-cost device could transform wound management for diabetic patients and other critical applications — such as providing direct treatment to soldiers on the battlefield or managing chronic wounds in elderly populations and patients with limited healthcare access — by reducing invasive bandage changes and ensuring timely medical intervention.

“For diabetic patients with foot ulcers, long-term monitoring and care are essential,” said GTRI Principal Research Engineer and Project Lead Judy Song. “We were inspired by the success of wearable glucose monitors to develop a compact, affordable sensor tailored to wound care.”  

This project was supported by GTRI’s Independent Research and Development (IRAD) program between 2022-2025 and reflects the strength of interdisciplinary collaboration across Georgia Tech. Researchers from three out of GTRI’s eight laboratories developed the sensor with experts from the George W. Woodruff School of Mechanical Engineering, the H. Milton Stewart School of Industrial and Systems Engineering and the Wallace H. Coulter Department of Biomedical Engineering at Tech and Emory University.

About one in four people with diabetes will develop a foot ulcer at some point in their lives, making it one of the leading causes of foot amputations. For these patients, nerve damage and poor blood flow hinder the body’s natural healing process and allow wounds to linger and worsen. 

During the initial phases of their research, the team noted that nitric oxide (NO) had been previously identified as a key biomarker for wound health due to its central role in the healing process. Nitric oxide improves blood flow, reduces inflammation, promotes tissue growth and fights infection. By tracking nitric oxide levels in wounds, clinicians could determine whether a wound is improving or detect early signs of trouble. 

"Nitric oxide plays a fascinating, almost paradoxical, role in wound healing,” said GTRI Senior Research Engineer Victoria Razin, who is co-leading the project. “It’s essential for processes like blood flow and tissue repair, but can also signal when something is going wrong.”

At the core of the smart bandage is a flexible sensor powered by a three-electrode system capable of detecting changes in nitric oxide. The team used advanced Aerosol Jet® printing techniques to fabricate the sensor, significantly reducing production costs from thousands of dollars to just a few dollars per unit and making the design more affordable and scalable.

“Typically, prototyping these sensors can cost thousands of dollars, but our approach brought costs down dramatically,” said Chuck Zhang, the Eugene C. Gwaltney, Jr. Chair and Professor in ISYE and a program director at the National Science Foundation (NSF), who oversaw sensor fabrication for this project. “Lower costs let us iterate quickly and deliver something that could have real healthcare impact.”

To test the sensor’s accuracy, the team conducted extensive laboratory studies in both biological and simulated wound conditions. 

In one set of experiments, endothelial cell cultures were used to create “wounds” by scraping the cell layers. As the cells migrated to repair the gap, nitric oxide production increased, and the sensor successfully tracked these changes in real-time. Additional fluid tests using blood plasma and red blood cells demonstrated that the sensor could reliably detect nitric oxide in a variety of conditions that closely mimic real-world wound environments.

These experiments confirmed that the sensor can identify the fluctuations in nitric oxide associated with different phases of wound healing. 

Lab testing was led by Dr. Wilbur Lam, a professor in the Department of Biomedical Engineering and at Emory University School of Medicine, with support from Kirby Fibben, a biomedical engineering Ph.D. student at Tech. 

"There’s a significant clinical need for real time, minimally invasive sensor technologies that detect nitric oxide,” said Dr. Lam. “While we’re starting with wound healing, there’s multiple other applications for vascular, hematologic, and pulmonary diseases as well.” 

The next step in the project is integrating the sensor into a functional wearable device. The team is combining the sensor with a miniaturized potentiostat (MicroPS) – a small electronic device that measures chemical signals – along with flexible electronic components and a system to transmit data to a mobile app. 

The MicroPS, designed by the GTRI research team, led by GTRI Research Engineer Curtis Mulady, enables compact electrochemical measurements and the wireless platform transmits nitric oxide readings from the bandage to a mobile app via Bluetooth. The app uploads the data to a cloud platform, giving clinicians the ability to remotely monitor wound progress in real time. This system could reduce the need for frequent in-person checkups, enabling earlier interventions and improving outcomes for patients.

Future iterations of the bandage aim to include “closed-loop” systems capable of both monitoring and treating wounds, said GTRI’s Song. For example, sensors could trigger a response, like releasing therapeutic agents or antimicrobials directly to the wound, when abnormalities are detected.

The researchers are also exploring commercialization pathways, including partnerships with medical device companies or the formation of a startup. 

“This sensor meets a real need for early detection of infection and to evaluate wound healing, and I believe it could have significant commercial success,” said Peter Hesketh, a professor in the School of Mechanical Engineering who led sensor design and performance testing. 

Other contributors to this project from GTRI include Mulady, Cora Weidner, Maxwell Blanchard, Rachel Erbrick and Christopher Heist. Zhaonan “Zeke” Liu, a postdoctoral fellow in ISYE, assisted with sensor fabrication, while Rizky Ilhamsyah, a graduate research assistant in the School of Mechanical Engineering, contributed to sensor design and performance testing. 

Writer: Anna Akins 
Photos: Sean McNeil 
GTRI Communications
Georgia Tech Research Institute
Atlanta, Georgia USA

For more information, please contact gtri.media@gtri.gatech.edu. 

To learn more about GTRI, visit: Georgia Tech Research Institute | GTRI

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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?

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Current cybersecurity approaches have been shown to be insufficient in blocking sophisticated attacks aimed at networked robotic motion-control systems.

To address this gap, Jun Ueda, Professor and ASME Fellow in the George W. Woodruff School of Mechanical Engineering at Georgia Tech, has been awarded approximately $700,000 by the National Science Foundation to establish methods to enhance cybersecurity for networked motion-control system. The research will focus on the unique geometric vulnerabilities in networked robotic systems and stealthy false data injection attacks that exploit geometric coordinate transformations to maintain mathematical consistency in robotic dynamics while altering physical world behavior.

Using an interdisciplinary approach that will combine research methodology from system dynamics, control, communication, differential geometry and cybersecurity engineering, Ueda hopes to establish new mathematical tools for analyzing robotic security and develop safer networked robotic systems that successfully repel system intrusion, manipulation attacks, and attacks that mislead operators. 

This article refers to NSF Program Foundational Research in Robotics (FRR) Award # 2112793 
A Geometric Approach for Generalized Encrypted Control of Networked Dynamical Systems

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University research drives U.S. innovation, and Georgia Institute of Technology is leading the way.  

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

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

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

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

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

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

Manufacturing Aluminum

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

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

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

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

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

Sustainable Impact 

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

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

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

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

Funding: Army Research Office

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

From fighter jets to medical devices, today’s most advanced machines depend on parts as intricate as their missions. These components aren’t just geometrically complex — they’re made from specialized metals engineered to withstand extreme heat, friction, and wear. But that strength comes with a challenge. How do you shape metals tough enough to survive the heat of a jet engine? 

For decades, manufacturing has been synonymous with job creation, a pillar of economic growth and stability. Today, the industry is evolving into something far more dynamic: a hub for innovation, sustainability, and purpose-driven careers. Experts say this transformation is reshaping not only what manufacturing looks like but why it matters. 

The Blavatnik Awards are presented by the Blavatnik Family Foundation and are administered by the New York Academy of Sciences. They honor the most promising early-career researchers in the U.S., across life sciences, chemistry, and physical sciences, and engineering. The awards are among the most prestigious and competitive in science.  

This dual recognition underscores Georgia Tech’s growing national leadership in high-impact, interdisciplinary research. 

Ryan Lively, Thomas C. DeLoach Jr. Endowed Professor in the School of Chemical and Biomolecular Engineering, is recognized in the Chemical Sciences category for pioneering scalable technologies that will reduce industrial carbon emissions and energy use. He develops new materials that can capture carbon and separate chemicals, using much less energy than conventional methods. His innovations could make industry cleaner and play a key role in addressing climate change. 

Matthew McDowell, Carter N. Paden Jr. Distinguished Chair in the George W. Woodruff School of Mechanical Engineering holds a joint appointment in the School of Materials Science and Engineering. Recognized in the Physical Sciences and Engineering category for groundbreaking battery research, he and his team develop new materials to make batteries last longer and store more energy. He has discovered ways to visualize how battery materials change during use — insights that help improve the performance and safety of future energy technologies. 
 
This year’s 18 finalists were selected from 310 nominees. On Oct. 7, 2025, three laureates will be announced at a gala at New York City’s American Museum of Natural History. Each laureate will receive $250,000, the largest unrestricted scientific prize for early-career researchers in the U.S.  

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.

Georgia Institute of Technology has been ranked 7th in the world in the 2026 Times Higher Education Interdisciplinary Science Rankings, in association with Schmidt Science Fellows. This designation underscores Georgia Tech’s leadership in research that solves global challenges. 

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.

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.

“Saving 10 hours a week with GenAI tools and techniques? That attracts leaders in this field,” said Chris Gaffney, Managing Director of the Supply Chain and Logistics Institute (SCL) and presenter of the seminar. “But they also seek deeper expertise that addresses what leaders need to know now about AI, including prompting as a strategic skill, AI policy implications for both students and companies, and real examples of how GenAI can move the needle on decision speed and quality.”

Gaffney is also the Edenfield Executive-in-Residence and a Professor of the Practice in Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering.

The free seminar was part of a regular “Learners and Leaders” series, which usually meets before work over breakfast. The interactive session began by defining confusing terms in AI and the significance of its rapid development, then focused on use cases and strategies. It presented emerging trends and a new Advanced Analytics Learning Ladder, an actionable guide to training teams in AI.

Georgia Tech-Savannah, an educational outreach arm of Georgia Tech to the Coastal Empire of Georgia and beyond provides a range of learning experiences, including education for veterans, K12 STEAM enrichment and outreach, leadership training, OSHA training, and more.

It is also home to the region's Enterprise Innovation Institute's office for the Advanced Technology Development Center (ATDC), the Georgia Tech Manufacturing Extension Partnership (GaMEP), and Georgia Tech’s regional presence for Apex Accelerator.

In a recent applied research partnership, the Georgia Ports Authority along with Georgia Tech experts from the GT Supply Chain and Logistics Institute (led by Gaffney) showed through research that routing Asia cargo through the Port of Savannah delivers lower costs, greater reliability, and comparable transit times versus West Coast ports.

This Learners and Leaders seminar series responds to regional needs and offers practical strategies and solutions to workplace or educational challenges. This includes the supply and logistics sector, predominant in the Savannah region, but also extends to other topics like K-12 education, safety and health, workforce demands, etc. Among more than 110 attendees in September (face-to-face and online) were representatives of the largest regional companies, the Georgia Ports Authority, local universities, and local economic development authorities.

Georgia Tech-Savannah plays a vital role in the College of Lifetime Learning efforts to address the needs of learners in timely and meaningful ways that help the workforce remain agile, capable, and engaged. 

“It’s not just about creating jobs, it’s about filling them,” says Tom Kurfess, Regents’ Professor in mechanical engineering and executive director of the Georgia Tech Manufacturing Institute (GTMI). “To do that, we need to show students how exciting and innovative manufacturing can be. Manufacturing has really changed over the past few years. Today, going from an idea to a physical part is much easier to do. It is fun and exciting to bring ideas to life and to actually hold the results in your hands.”

GTMI is working to reignite student interest in the art and science of making through its new K–12 initiative: the Advanced Manufacturing Pathways (AMP) Program. Modeled after Georgia Tech’s Rural CS Initiative, AMP empowers schools with faculty expertise, cutting-edge equipment, and a hands-on curriculum to give students early exposure to the tools, technologies, and creativity behind modern manufacturing while building a pipeline of future talent ready to thrive in high-tech careers.

Funded by the Southwest Georgia Regional Commission (SWGRC), AMP is kicking off in three school districts this fall — Decatur County, Thomas County, and the city of Thomasville  — with plans to expand to additional schools in the spring of 2026. The program will start by engaging more than 200 students through hands-on learning, virtual instruction, and in-person lab experiences led by Georgia Tech researchers and faculty.

“Here in Southwest Georgia, we believe that opportunities like this are vital for integrated learning in schools and for growing our future workforce,” says Beka Shiver, economic development and transportation planner for SWGRC. “Workforce development and K-12 integration are at the heart of our Southwest Georgia Ecosystem Building Project, and we are so pleased to be able to provide funding for this program.”

The launch of the AMP Program is centered around Design, Build, Race, a course putting a modern spin on the classic pinewood derby. Students will use digital design, 3D printing, and machining to build and race custom cars, while also learning how to collect and analyze performance data to improve their designs and predict outcomes. The course blends engineering with data science, sparking curiosity and showing students how modern manufacturing is powered by both technical skills and smart data. 

“This program delivers real-world industry experience to students while strengthening the talent pipeline that drives innovation, competitiveness, and resilience in advanced manufacturing”, says Steven Ferguson, interim director of operations at GTMI and one of the project’s leaders. “After more than 20 years of driving education and workforce development innovation, I’m more energized than ever to help launch the AMP program to open doors for students and advance U.S. manufacturing leadership.”

Building the Blueprint

Before it evolved into the AMP Program, Design, Build, Race was a course developed by GTMI research engineer Kyle Saleeby in 2023. Originating in GTMI’s Advanced Manufacturing Pilot Facility (AMPF), the course was designed to introduce Morehouse and Georgia Tech students to the possibilities of modern manufacturing through digital design, 3D printing, machining, and competitive creativity.

“Even after the first week, it was powerful to watch students discover how exciting it is to design and manufacture a competition-ready car in a matter of hours,” said Saleeby. “That’s when I knew we were onto something special.”

Saleeby teamed up with Ferguson to transform the course into a broader initiative. The duo engaged colleagues from STEM@GTRI and secured funding from SWGRC to modify the curriculum and scale the course for a high school audience. 

“We are thrilled that we have been able to take the lessons learned during the development of the Rural Computer Science Initiative and expand opportunities for students in Southwest Georgia,” says Sean Mulvanity, a senior research associate in the Georgia Tech Research Institute. Mulvanity is one of the founders of the initiative and has been a key contributor to the AMP Program. “We hope this program can grow and expose students across the state to the field of advanced manufacturing.” 

Though granted by the SWGRC, funds for the program were provided by Georgia Artificial Intelligence in Manufacturing, a statewide initiative founded by GTMI and Georgia Tech’s Enterprise Innovation Institute to advance AI-driven manufacturing.

To bring AMP into classrooms, Southern Regional Technical College helped set up labs and provide technical support, ensuring schools were ready to launch. 

“At all levels, the community has rallied around this program,” says Saleeby. “Providing students with a unique experience learning advanced manufacturing technologies will open countless career opportunities. I cannot wait to see where they go.” 

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.

The Georgia Tech Manufacturing Institute (GTMI) recently welcomed its first Hiring Our Heroes (HOH) Fellow to help address this growing need. Lukas Berg, a retiring U.S. Army officer, will be working with GTMI to support new education and training programs aimed at preparing Georgians for careers in advanced manufacturing.

“Lukas Berg brings a unique blend of operational experience, academic insight, and a deep commitment to service,” said Thomas Kurfess, executive director of GTMI. “His perspective will be invaluable as we work to build stronger connections between Georgia’s communities and the advanced manufacturing sector.”

Hiring Our Heroes is a nationwide initiative led by the U.S. Chamber of Commerce Foundation that helps veterans and military spouses transition into civilian careers through short-term fellowships. Since 2021, Georgia Tech has hosted more than two dozen HOH fellows, beginning with U.S. Army veteran Erik Andersen, who now serves as interim deputy director for the Research, Electronics, Optics, and Systems Directorate at the Georgia Tech Research Institute (GTRI), where he also helps lead the HOH program. 

Berg is the first fellow to be placed outside of GTRI, a sign of the program’s growing reach across campus and its potential to support a broader range of workforce development efforts.

“It’s been exciting to see how the Hiring Our Heroes program has grown at Georgia Tech,” said Andersen. “Berg’s placement at GTMI reflects the Institute’s commitment to connecting military talent with real-world innovation and workforce development. Veterans bring a unique perspective and skill set to these challenges, and I’m proud to see the program expanding to new parts of campus.”

Berg’s military career includes aviation command roles, teaching positions at West Point and the Joint Special Operations University, and deployments across multiple regions. At GTMI, he will be contributing to a new initiative that partners with rural school districts to introduce students to hands-on learning in advanced manufacturing, an effort designed to spark interest in high-potential career paths and support long-term workforce readiness.

With personal ties to Georgia Tech and a strong sense of purpose, Berg sees this fellowship as a meaningful next step. We spoke with him to learn more about what brought him to GTMI and how he views the role of manufacturing and workforce development in shaping the country’s future.

What inspired you to pursue a fellowship at the Georgia Tech Manufacturing Institute after your military service?

Last year, I visited Georgia Tech with many of the junior officers and pilots assigned to my helicopter battalion in Savannah. Our agenda included stops at the Georgia Tech Manufacturing Institute and the Advanced Manufacturing Pilot Facility, both of which struck me as being absolutely vital to maintaining the technological edge required to fight and win on the modern battlefield. Pursuing a fellowship at GTMI felt like a natural extension of my military service, and I suspected that it would put me back at the intersection of thinkers and doers (where I have always felt most at home). 

You mentioned your grandmother taught at Georgia Tech for over 30 years — how has her legacy influenced your academic and professional journey?

My grandmother, Maria Venable, was the first woman to serve as a full-time faculty member in Georgia Tech’s School of Modern Languages. She poured herself into both her family and her students, and I was lucky to count myself in both populations, as she agreed to tutor me for the AP German exam in high school (but only if I behaved as well as her students at Tech). Her example inspired me to pursue a teaching assignment at West Point halfway through my Army career, and I experienced the same joy in teaching that she did. It’s something that I will continue to do for the rest of my life, whether in a formal or informal capacity.

Can you share more about the specific initiatives you'll be working on at GTMI related to advanced manufacturing education?

Most immediately, I am joining a new GTMI initiative that partners with rural school districts to deliver several weeks’ worth of curriculum and hands-on practice in advanced manufacturing. We just kicked off a pilot program with Bainbridge High School in Decatur, and it’s exciting to see their students leveraging sophisticated systems to design and build Pinewood Derby cars that would make Cub Scouts across the country green with envy. Beyond this initiative, I hope to contribute to other efforts that get young people excited about careers in manufacturing and that assist adult learners in re-skilling and up-skilling for this high-potential industry.

What are you most looking forward to as you begin your fellowship at GTMI?

Georgia Tech feels like a physical and intellectual crossroads of modern civilization. I’m excited to not only contribute as a member of GTMI but also to learn about the countless other departments, institutes, and programs that are convening talent to solve the world’s thorniest problems. 

What skills or insights are you hoping to gain during your time at GTMI that will support your next career chapter?

As an Army officer, I’ve been stationed across the country and deployed around the world, but Georgia has always been home. (Gladys Knight’s “Midnight Train to Georgia” has been a fixture on my playlist since I left for West Point at the age of 17.) Now back with my family, I look forward to using my time at GTMI to learn about my home state and identify ways that I can contribute to its near and long-term prosperity, whether through roles in academia, government, or private industry. I also look forward to expanding my network in all these communities, as no single one has a monopoly on problem-solving.

Why do you believe rebuilding America’s industrial base and manufacturing workforce is critical to national security today?

As a career aviator, much of my professional life was spent agonizing over the availability of parts to repair my helicopters. It seemed like there were never enough, and they always took too long to get to me. This experience, coupled with lessons learned from our support of Ukraine’s self-defense, contrasted starkly with my recent study of America’s 20th-century role as the “arsenal of democracy.” I’m convinced that we need to regain that reputation, and I would like to see Georgia at the forefront of associated design, manufacturing, and education initiatives.  

How do you see veterans playing a unique role in strengthening the U.S. manufacturing workforce?

I think veterans are the most natural candidates in the world for roles in the manufacturing workforce. They possess the knowledge, skills, and abilities to be successful in most endeavors, but most are looking for ways to extend their service beyond their time in uniform. What better way than to contribute to a field that is so vital to our national security and prosperity?

What does “Progress and Service” mean to you, and what does it mean to you personally to be contributing to that mission?

I love Tech’s motto. I grew up in a family and community that reinforced at every turn the idea that our highest potential as human beings is realized when we serve others. This motivated my choice to serve in the military for the past 20 years, and it remains my North Star for this next chapter. I also love the idea of technological progress being the vehicle by which Georgia Tech collectively serves others, and I hope to accelerate this progress during my time at GTMI. 

If you could give one piece of advice to other service members considering a fellowship like this, what would it be?

Inventory your passions and define your purpose. Then start reaching out to people in related fields. I have been amazed at how generous people have been with their time and how eager they have been to help me find my second calling and related opportunities.

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

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

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

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

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

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

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

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

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

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

Access the abstract on arXiv.

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.

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.
 

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

Professor Thomas holds a joint appointment in the H. Milton Stewart School of Industrial & Systems Engineering and in the School of Public Policy at Georgia Tech. Dr. Thomas's research interests include energy systems, sustainability, industrial ecology, technology assessment, international security, and science and technology policy. Her current research projects include the environmental impacts of biofuels and electricity system policy and planning. Dr. Thomas is a member of the USDA/DOE Biomass Research and Development Technical Advisory Committee. In 2004-2005, she was the American Physical Society Congressional Science Fellow. Dr. Thomas is a Fellow of the American Association for the Advancement of Science, and of the American Physical Society, and has been a Member of the U.S. EPA Science Advisory Board. She is currently a member of the board of the Southeast Energy Efficiency Alliance and a member of the Federation of American Scientists Board of Experts. She has previously worked at the Department of Engineering and Public Policy at Carnegie Mellon University, and at Princeton University’s Environmental Institute. Dr. Thomas received a B.A. in physics from Swarthmore College and a Ph.D. in theoretical physics from Cornell University.

Election of new NAE members, the culmination of a yearlong process, recognizes individuals who have made outstanding contributions to "engineering research, practice, or education, including, where appropriate, significant contributions to the engineering literature" and to "the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education."  

“It’s the honor of a lifetime to be recognized by the National Academy of Engineering for the impact we’ve have on understanding lung injuries in the critical care unit and traumatic brain injuries in children,” said Margulies, chair of the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University and, with Brown, one of just three women on the Georgia Tech faculty accorded NAE membership – one of the highest professional distinctions an engineer can receive.

“Our work is deeply collaborative, and I am grateful to the engineers, scientists, physicians, and patients who are partners in our journey,” Margulies added.

Margulies, a researcher in the Petit Institute for Bioengineering and Bioscience at Tech and a Georgia Research Alliance Eminent Scholar in Injury Biomechanics at Emory, was elected, “for elaborating the traumatic injury thresholds of brain and lung in terms of structure-function mechanisms,” according to the NAE announcement.

Using an integrated biomechanics approach, Margulies’ research program spans the micro-to-macro scales in two distinct areas, traumatic brain injury and ventilator-induced lung injury. Her work has generated new knowledge about the structural and functional responses of the brain and lungs to their mechanical environment. Margulies came to Georgia Tech in 2017 from the University of Pennsylvania, where she’d been a professor of bioengineering, and had earned her Master of Science in Engineering and Ph.D. in Bioengineering.

Brown, a Regents and Brook Byers Professor of Sustainable Systems in the School of Public Policy, was co-recipient of the Nobel Peace Prize in 2007 (for co-authorship of the Intergovernmental Panel on Climate Change Working Group III Assessment Report on Mitigation of Climate Change, Chapter 6). 

She joined Georgia Tech in 2006 after a career at the U.S. Department of Energy's Oak Ridge National Laboratory, where she led several national climate change mitigation studies and became a leader in the analysis and interpretation of energy futures in the United States. Her research at Tech focuses on the design and impact of policies aimed at accelerating the development and deployment of sustainable energy technologies, emphasizing the electric utility industry. She was elected to NAE “for bridging engineering, social and behavioral sciences, and policy studies to achieve cleaner electric energy.” 
 
Brown, who earned her Ph.D. at the Ohio State University, co-founded and chaired the Southeast Energy Efficiency Alliance, served two terms as a presidential appointee on the board of the Tennessee Valley Authority – the nation’s largest public power provider – and also served two terms on the U.S. Department of Energy’s Electricity Advisory Committee, where she led the Smart Grid Subcommittee. 

“The most rewarding feature of my career has been working toward solutions with colleagues across disciplines,” Brown said.

Shapiro is the Russell Chandler III Chair and professor in the H. Milton Stewart School of Industrial and Systems Engineering, where his research is focused on stochastic programming, risk analysis, simulation-based optimization, and multivariate statistical analysis.

In 2013, he was awarded the INFORMS Khachiyan Prize for lifetime achievements in optimization. He received the 2018 Dantzig Prize from the Mathematical Optimization Society and the Society for Industrial and Applied Mathematics.

Since earning his Ph.D. in applied mathematics-statistics from Israel’s Ben-Gurion University of the Negev in 1981, Shapiro has made substantial contributions to the fields of optimization and large-scale, stochastic programming, and he was elected to NAE “for contributions to the theory, computation, and application of stochastic programming.” 

Kurfess is professor and HUSCO/Ramirez Distinguished Chair in Fluid Power and Motion Control in the George W. Woodruff School of Mechanical Engineering, where he has helped guide the evolution of technology as a pioneer in the digital transformation of manufacturing. 

Improving manufacturing technology is a pursuit that has roots in his childhood. “I grew up in my father’s machine shop,” said Kurfess, who has a special fondness for mom-and-pop operations. He was elected by the NAE “for development and implementation of innovative digital manufacturing technologies and system architectures.”

“I’m proud that the work we do has a positive impact on small and medium-sized enterprises, which are about 99% of the manufacturing operations, as well as large operations,” said Kurfess, who earned all of his degrees at MIT. “Our work targets people who are implementing the digital thread in manufacturing, and what the digital thread will do is make sure those smaller enterprises, those mom and pops, can have access to the latest and greatest technologies.”

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Media Relations Contact: John Toon (404-894-6986) (jtoon@gatech.edu).

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

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

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

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

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

Filling the Workforce Gap

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

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

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

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

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

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

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

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

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

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

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.

The MNCP is a National Science Foundation (NSF) funded collaboration between the Institute for Matter and Systems (IMS), Georgia Piedmont Technical College (GPTC) and Pennsylvania State University’s Center for Nanotechnology Education and Utilization. 

The spring 2025 cohort was comprised of three individuals with non-technical backgrounds. For 12 weeks, they split time between online lectures and hands-on training in the Georgia Tech Fabrication Cleanroom where they immersed themselves in advanced microelectronic fabrication techniques. Their training included thin film deposition, photolithography, etching, metrology, laser micro-machining, and additive manufacturing. They gained hands-on experience with industry-standard equipment, even creating their own custom designs on 4-inch silicon wafers.

“The program really helps people get their head start, especially for those who don’t really have the educational background,” said Lauren Walker, one student from the cohort. Walker applied for the program after hearing about it from a colleague and was able to get a job as a laboratory technician with help from the program resources.

“[The program] gave me everything I needed to know for new skills and things like that for the industry,” said Walker. “It helped me eventually get another job. I say it helped because of the workshops they had.”

Under the direction of Seung-Joon Paik, IMS teaching lab coordinator, the cohort spent two days a week in the IMS cleanroom working on research projects with IMS staff. Michelle Wu, a research scientist in IMS, served as lab instructor throughout the program and oversaw the training on cleanroom tools. 

“As their lab instructor, I’ve been thoroughly impressed with their passion, patience, and unwavering dedication to this program,” said Wu.

The program is supported by the Advanced Technological Education program at the National Science Foundation and is free for all participants. 

Learn more about the Microelectronics and Nanomanufacturing Certificate Program

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.

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.

 

She wanted to connect her experience in manufacturing with her burgeoning interest in data science but wasn’t sure which way to turn. Then, she stumbled upon a job opportunity that brought both into one path forward: A fellowship focused on artificial intelligence in manufacturing through the Partnership for Inclusive Innovation, or PIN.

“I happened upon this fellowship and the vertical I landed on was AI in manufacturing, which was a good marriage of the two disciplines,” said Cook, who began the one-year paid position over the summer. The PIN fellowship, part of Georgia Institute of Technology’s Enterprise Innovation Institute, places early career professionals into public and private opportunities.

The fellowship is made possible through support from Georgia Artificial Intelligence in Manufacturing, or Georgia AIM. Georgia AIM supports several PIN fellows each year through the AI in Manufacturing vertical. Participants spend six months working on a research project through the Georgia Tech Manufacturing Institute (GTMI) and then six months with a partner company where they focus on a project that enhances the use of smart technologies.

Cook recently completed her first six-month rotation as a researcher with the Melkote Advanced Manufacturing Research Group at Georgia Tech, working with GTMI Associate Director Shreyes Melkote. She is now in her next role at Carbice, an Atlanta semiconductor manufacturer.

That’s the interesting part of the PIN fellowship: those accepted into the program gain experience in both the public and private sectors. Upon completing the program, fellows enter the workforce with a unique, innovative skillset that contributes to the emerging roles AI is creating in manufacturing.

The PIN program also helps address a gap in the workforce. There is a growing need for professionals who understand AI and smart technologies, and the program’s public/private partnership provides useful training and experience to early career professionals who are eager to solve these challenges.

In Cook’s case, her first job after college was with a small manufacturer doing engineering design and CAD work. Her role expanded a bit to accommodate her data analytics background while working on her master’s degree practicum project. But due to the size of the company, her work returned to strictly engineering after she graduated. In contrast, through the PIN fellowship, Cook is working on developing machine learning models that can be used to search for parts in a database of CAD designs. This would allow manufacturers looking for CAD drawings or 3D models to find similar parts with designs already created, saving time by giving engineers a starting point. This research allows her to leverage both her analytics and engineering knowledge.

"I feel like I am learning a lot,” said Cook. The research position allows her to apply theoretical knowledge from her master’s degree in a research environment. “That’s been very interesting and eye-opening. I’m still early in my career and my only experience is fairly traditional corporate jobs, so working in the realm of the unknown is a different situation. With research, you’re just exploring and have no assurances that what you’re doing is going to work. ”

Moving to Carbice for the second half of her fellowship adds another layer of learning, she added, because it’s one thing to test out a theory in a lab; it’s different when you are working for a company that needs to see results.

“Working in the private sector allows you to identify and reality-check the needs of actual workplaces,” she added. “Because sometimes you have a compelling idea and interesting research, but in a corporate setting, first, is it useful, and second, if it is useful, is it even something the industry wants or is willing to adopt?”

This is a paradox Cook will face not only during the second half of her fellowship, but also going forward in her career. The foundational experiences attained through the PIN fellowship will give Cook an edge as she moves into her next role. Many manufacturers are interested in adopting AI and smart technologies, but the challenge is in identifying problems to solve.

Cook said she is confident the fellowship will give her new insights that can be beneficial to future employers. The program also offers networking opportunities and connections with respected professionals that will be beneficial in years to come, she added.

“It’s really good to have both the public and private perspectives. And because I’ve worked in a couple different manufacturing environments, I’m interested in how different my manufacturing rotation will be and if I can identify patterns, similar issues, or inefficiencies. And all that is useful knowledge to have,” she said. “For me specifically, the content of this work is going to be very helpful in tying my whole resume together.”

For more details on the AI and Manufacturing-focused PIN fellowship supported by Georgia AIM, visit the PIN website.

“At Georgia Tech, innovation isn’t just about discovery — it’s about solving real-world challenges,” said Executive Vice President for Research Tim Lieuwen. “Georgia Quick Start ensures that cutting-edge research in advanced manufacturing translates into practical training solutions. Together, we are equipping Georgia’s workforce with the skills needed to drive economic growth and industry advancement.”

As manufacturing technologies and artificial intelligence continue to evolve, U.S. manufacturers increasingly require skilled workers experienced in advanced manufacturing. For decades, Georgia Quick Start, administered by the Technical College System of Georgia, has been addressing this need and has been recognized as the country’s top workforce training program for 15 years. 

Now, researchers at Georgia Tech will collaborate with Georgia Quick Start to enhance these efforts by developing Extended Reality (XR) training programs, providing a scalable and experiential solution to meet the growing demand for training. 

“We have been so successful for so many years because we stay focused on relevance, flexibility, and responsiveness,” said Scott McMurray, deputy commissioner for Georgia Quick Start. “This partnership is an example of how Quick Start is able to develop and deliver effective training even for companies working on the leading edge of advanced manufacturing technologies.”

Extended Reality, Scaled Training

XR technologies use a combination of virtual and augmented reality to create immersive, interactive experiences. By simulating real-world manufacturing environments and processes, XR has the potential to allow trainees to practice and refine their skills in a controlled, risk-free setting through standardized training experiences. This not only enhances the learning experience but also ensures consistency in training quality across a large workforce.

“Virtual reality scales training by gamifying complex tasks and removing the need for costly or hazardous physical equipment. Augmented reality scales on-the-job training by providing adaptive, context-aware guidance exactly when and where it’s needed, reducing the need for expert supervision,” said manufacturing XR researcher Mohsen Moghaddam, Gary C. Butler Family associate professor in the H. Milton Stewart School of Industrial and Systems Engineering and the George W. Woodruff School of Mechanical Engineering. “Together, they make training more consistent, up-to-date, accessible, and safe, especially for workers who may hesitate to ask for assistance from peers or supervisors out of fear of judgment.”

The collaboration will leverage Moghaddam’s research and the AR/VR training space within the expanded Advanced Manufacturing Pilot Facility, providing a state-of-the-art environment for developing and deploying XR training technologies. Researchers from the Georgia Tech Manufacturing Institute (GTMI) and Georgia AIM(Artificial Intelligence in Manufacturing) will also play pivotal roles in the development of these training programs.

“Partnerships like these highlight the power of the integrated University of Georgia and Technical College System of Georgia’s workforce development ecosystem,” said Thomas Kurfess, Regents’ Professor and GTMI executive director. “Our country not only needs the creation of new jobs but also the skilled workforce to fill them. At Georgia Tech and GTMI, we are serving as an enabler of innovation in that workforce development.”

Kinaxis, a global leader in supply chain orchestration, and the NSF AI Institute for Advances in Optimization (AI4OPT) at Georgia Tech today announced a  new co-innovation partnership. This partnership will focus on developing scalable artificial intelligence (AI) and optimization solutions to address the growing complexity of global supply chains. AI4OPT operates under Tech AI, Georgia Tech’s AI hub, bringing together interdisciplinary expertise to advance real-world AI applications.

This particular collaboration builds on a multi-year relationship between Kinaxis and Georgia Tech, strengthening their shared commitment to turn academic innovation into real-world supply chain impact. The collaboration will span joint research, real-world applications, thought leadership, guest lectures, and student internships.

“In collaboration with AI4OPT, Kinaxis is exploring how the fusion of machine learning and optimization may bring a step change in capabilities for the next generation of supply chain management systems,” said Pascal Van Hentenryck, the A. Russell Chandler III Chair and professor at Georgia Tech, and director of AI4OPT and Tech AI at Georgia Tech.

Kinaxis’ AI-infused supply chain orchestration platform, Maestro™, combines proprietary technologies and techniques to deliver real-time transparency, agility, and decision-making across the entire supply chain — from multi-year strategic orchestration to last-mile delivery. As global supply chains face increasing disruptions from tariffs, pandemics, extreme weather, and geopolitical events, the Kinaxis–AI4OPT partnership will focus on developing AI-driven strategies to enhance companies’ responsiveness and resilience.

“At Kinaxis, we recognize the vital role that academic research plays in shaping the future of supply chain orchestration,” said Chief Technology Officer Gelu Ticala. “By partnering with world-class institutions like Georgia Tech, we’re closing the gap between AI innovation and implementation, bringing cutting-edge ideas into practice to solve the industry’s most pressing challenges.”

With more than 40 years of supply chain leadership, Kinaxis supports some of the world’s most complex industries, including high-tech, life sciences, industrial, mobility, consumer products, chemical, and oil and gas. Its customers include Unilever, P&G, Ford, Subaru, Lockheed Martin, Raytheon, Ipsen, and Santen.

 

About Kinaxis
Kinaxis is a global leader in modern supply chain orchestration, powering complex global supply chains and supporting the people who manage them, in service of humanity. Our powerful, AI-infused supply chain orchestration platform, Maestro™, combines proprietary technologies and techniques that provide full transparency and agility across the entire supply chain — from multi-year strategic planning to last-mile delivery. We are trusted by renowned global brands to provide the agility and predictability needed to navigate today’s volatility and disruption. For more news and information, please visit kinaxis.com or follow us on LinkedIn.  

About AI4OPT
The NSF AI Institute for Advances in Optimization (AI4OPT) is one of the 27 National Artificial Intelligence Research Institutes set up by the National Science Foundation to conduct use-inspired research and realize the potential of AI. The AI Institute for Advances in Optimization (AI4OPT) is focused on AI for Engineering and is conducting cutting-edge research at the intersection of learning, optimization, and generative AI to transform decision making at massive scales, driven by applications in supply chains, energy systems, chip design and manufacturing, and sustainable food systems. AI4OPT brings together over 80 faculty and students from Georgia Tech, UC Berkeley, University of Southern California, UC San Diego, Clark Atlanta University, and the University of Texas at Arlington, working together with industrial partners that include Intel, Google, UPS, Ryder, Keysight, Southern Company, and Los Alamos National Laboratory. To learn more, visit ai4opt.org.

About Tech AI
Tech AI is Georgia Tech's hub for artificial intelligence research, education, and responsible deployment. With over $120 million in active AI research funding, including more than $60 million in NSF support for five AI Research Institutes, Tech AI drives innovation through cutting-edge research, industry partnerships, and real-world applications. With over 370 papers published at top AI conferences and workshops, Tech AI is a leader in advancing AI-driven engineering, mobility, and enterprise solutions. Through strategic collaborations, Tech AI bridges the gap between AI research and industry, optimizing supply chains, enhancing cybersecurity, advancing autonomous systems, and transforming healthcare and manufacturing. Committed to workforce development, Tech AI provides AI education across all levels, from K-12 outreach to undergraduate and graduate programs, as well as specialized certifications. These initiatives equip students with hands-on experience, industry exposure, and the technical expertise needed to lead in AI-driven industries. Bringing AI to the world through innovation, collaboration, and partnerships. Visit tech.ai.gatech.edu.

"Joining the NSTC is a significant milestone for Georgia Tech," said George White, senior director for strategic partnerships. "This partnership will enable us to collaborate with leading experts in the semiconductor field, drive groundbreaking research, and contribute to the advancement of semiconductor technology in the U.S."

The NSTC is operated by Natcast (National Center for the Advancement of Semiconductor Technology) and supported by the Department of Commerce through the CHIPS and Science Act. NSTC brings together key stakeholders from academia, industry, and government to create a robust semiconductor ecosystem. As a member, Georgia Tech will have access to a wide range of benefits, including research grant opportunities, participation in NSTC-led research projects, and access to state-of-the-art facilities and resources.

Georgia Tech's involvement in the NSTC will focus on several key areas, including workforce development, research and development initiatives, and fostering collaboration between academia and industry. By participating in the NSTC, Georgia Tech aims to enhance its research capabilities, support the growth of the semiconductor industry, and contribute to national economic and security goals.

Learn more about CHIPS initiatives at Georgia Tech:

$100M Investment Will Propel Absolics Inc., Georgia Tech’s Advanced Packaging Research

Georgia Tech Joins $840M DoD Project to Develop and Manufacture Next-gen Semiconductor Microsystems

Semiconductor Research Corp. and Georgia Tech Secure $285M SMART USA Institute

How did you first hear about CREATE-X?

We did the CREATE-X Capstone with an initial team of seven people, later transitioning to Startup Launch in the summer. Capstone required a hardware product, but for several reasons, we pivoted to software. By that point, we already had a grasp on the problem that we were working on but didn't have the resources to start working on a large hardware product.

Why did you decide to pursue your startup?

One of our close buddies was an emergency medical technician (EMT), and we also had family connections to EMTs. When we were doing our customer interviews, we found out that Emergency Medical Services (EMS) had multiple problems that we thought we'd like to work on and that were more accessible than the broader medical technology industry. 

What was Startup Launch like for you?

Startup Launch seemed to transition pretty seamlessly from the Capstone course. We came to understand our customer base and technical development better, and the program also led us through the process of starting and running a company. I found it very interesting and learned a whole lot.

What was the most difficult challenge in Startup Launch?

Definitely customer interviews. We spent a lot of time on that in the Startup Launch classes. It's a difficult thing to have a good takeaway from a customer interview without getting the conversation confused and being misled. We didn't mention the product, or we tried to wait as long as possible before mentioning the product, so as to not bias or elicit general, positive messaging from interviewees. 

We're working in EMS, and the products we are building affect healthcare. EMS is a little informal and a little rough around the edges. Many times, people don't want to admit how bad their practices are, which can easily lead to us collecting bad data. 

What affected you the most from Startup Launch?

The resources at our fingertips. When we were running around, it was nice to be able to consult with our mentor. It's great having someone around with the know-how and who's been through it themselves. I revisit concepts a lot.

How did the partnership with NGHS come about?

During Demo Day, we met a Georgia state representative. He put us in touch with NGHS. They were looking for companies to work with through their venture arm, Northeast Georgia Health Ventures(NGHV), so we pitched our product to them. They liked it, and then we spent a long time banging out the details. We worked with John Lanza, who's a friend of CREATE-X. He helped us find a corporate lawyer to read over the stuff we were signing. It took a little back and forth to get everything in place, but in September of last year, we finally kicked it off.

What’s the partnership like?

We provide them a license to our product, have weekly meetings where experts give feedback on the performance of the system, and then we make incremental changes to align the product with customer needs. 

While we're in this developmental phase, we're kind of keeping it under wraps until we make sure it’s fully ready. Our focus is primarily on emergent capabilities that NGHS and other EMS agencies are really looking for. Right now, the pilot is set to be a year long, so we're aiming to be ready for a full rollout by the end of the year. 

How did you pivot into this other avenue for your product?

EMS does not have many resources. That makes it not a popular space as far as applying emerging technologies. There's only competition in this very one specific vein, which is this central type of software that we plug into, so we're not competing directly with anyone.

EMS agencies, EMTs, and paramedics - the care that they give has to be enabled by a medical doctor. There has to be a doctor linked to the practices that they engage in and the procedures that they do. With the product that we're making now, we want to provide a low-cost, plug-and-play product that'll do everything they need it to do to enable the improvement of patient care. 

How are you supporting yourself during this period? 

I was paying myself last year, but we're out of money for that, so we're not currently paying for any labor. It's all equity now, but our burn rate outside of that is very low. The revenue we have now easily covers the cost of operating our system. I'm also working part-time as an EMT now. This helps cover my own costs while also deepening my understanding of the problems we are working on.

How are you balancing your work?

It's hard to balance. There's always stuff to do. I just do what I can, and the pace of development is good enough for the pilot. Every week, and then every month, Kevin and I sit down and analyze the rate at which we're working and developing. Then we project out. We're confident that we're developing at a rate that'll have us in a good spot by September when the pilot ends.

What’s a short-term goal for your startup?

Kevin and I are trying to reach back out and see if there's anyone interested in joining and playing a major role. The timing would be such that they start working a little bit after the spring semester ends. I think most Georgia Tech students would meet the role requirements, but generally, JavaScript and Node experience as well as a diverse background would be good.

Where do you want your startup to be in the next five years?

I want to have a very well-designed system. Despite all the vectors I’m talking about for our products, everything should be part of the same system in place at EMS agencies anywhere. I just want it to be a resource that EMS can use broadly.

Another issue in EMS is standards. Even the standards that are in place now aren’t broadly accessible. I think that these new AI tools can do a lot to bridge the lack of understanding of documentation, measures, and standards and make all of that more accessible for the layperson.

What advice would you give students interested in entrepreneurship?

Make sure the idea that you're working on, and the business model, is something you enjoy outside of its immediate viability. I think that's really what's helped me persevere. It's my enjoyment of the project that's allowed me to continue and be motivated. So, start there and then work your way forward.

Are there any books, podcasts, or resources you would recommend to budding entrepreneurs?

 I’d recommend Influence to prepare for marketing. I have no background in marketing at all. Influence is a nice science-based primer for marketing.

 I reread How to Win Friends and Influence People. I am not sure how well I'm implementing the concepts day-to-day, but I think most of the main points of that book are solid.

I also read The Mom Test. It's a good reference, a short text on customer interviews.

Want to build your own startup?

Georgia Tech students, faculty, researchers, and alumni interested in developing their own startups are encouraged to apply to CREATE-X's Startup Launch, which provides $5,000 in optional seed funding and $150,000 in in-kind services, mentorship, entrepreneurial workshops, networking events, and resources to help build and scale startups. The program culminates in Demo Day, where teams present their startups to potential investors. The deadline to apply for Startup Launch is Monday, March 17. Spots are limited. Apply now.

As part of the class, Trigger made several trips to the Georgia Tech Manufacturing Institute (GTMI). When the faculty asked if anyone wanted an internship, Trigger raised his hand. 

“Coming to Georgia Tech allowed me to clarify what I wanted to do,” he said. “I’ve always been in service-based jobs, but I was interested in additive manufacturing,” or 3D printing.

For five months every weekday, Trigger drove from his home in Macon to Georgia Tech’s campus for his internship. The paid internship took place at Tech’s Advanced Manufacturing Pilot Facility (AMPF). This 20,000-square-foot, reconfigurable facility serves as the research and development arm of GTMI, functioning as a teaching laboratory, technology test bed, and workforce development space for manufacturing innovations.

During his time there, Trigger focused on computer-aided manufacturing and met with faculty and students to learn about their research. The internship wasn’t convenient, but it was worth it. 

“From our campus visits, I understood the mission of AMPF, so the fact they offered me this opportunity was huge for me,” he said. “The internship had a big impact on my life in terms of the technical and soft skills I gained.”

Building the Workforce

Launching new careers is just one of AMPF’s goals in testing new manufacturing and growing the future U.S. workforce. Since 2022, AMPF has improved the manufacturing process at all parts of the talent pipeline — from giving corporate researchers space to test and adopt AI automation technologies to training and upskilling their employees. Collectively, GTMI and AMPF’s efforts have led to a stronger, bigger network of manufacturers that other companies and the U.S. government can rely on. 

“We are going to need to manufacture more in the U.S. — from computer chips to cars — so we want to create jobs and fill them,” said Tom Kurfess, GTMI’s executive director. “We need more people working in the manufacturing sector, and we've got to make these jobs better and make people more efficient in them.” 

AI is one way to boost efficiency, but artificial intelligence won’t cut humans out of the process entirely. Rather, people will be integral to monitoring the systems and advancing them. As AI becomes more widely adopted, a college degree won’t necessarily be required to work in the AI field.

“Our workforce is going to need the next generation of employees to be amenable to retraining as the technology updates,” said Aaron Stebner, a co-director of the Georgia Artificial Intelligence Manufacturing program (AIM). A statewide program, Georgia AIM helps fund AMPF and sponsored Trigger’s internship. “Education is going to be more of a lifelong learning process, and Georgia Tech can be at the forefront of that.”

While GTMI already integrates AI into many processes, it remains committed to staying ahead of the curve with the latest technologies that could boost manufacturing. The facility is in the process of an expansion that will nearly triple its size and make AMPF the leading facility for demonstrating what a hyperconnected and AI-driven manufacturing enterprise looks like. This will enable GTMI to build and sustain these educational pipelines, which is key to its work.

“We’re developing the workforce for the future, not of the future,” explained Donna Ennis, a co-director of Georgia AIM. “It’s AI today, but it could be something else five years from now. We are focused on creating a highly skilled, resilient workforce.”

Part of Georgia AIM’s role is creating the pipelines that people like Trigger can follow. From bringing a mobile lab to technical colleges to hosting robotics competitions at schools, these efforts span the state of Georgia and touch populations from “K to gray.” 

“Kids don’t say they want to be a manufacturer when they grow up, but that’s because they don’t know it’s a viable career path,” Ennis said. “We’re making manufacturing cool again.”

Creating Corporate Connection

To create these job opportunities, GTMI is also partnering with corporations. Companies can join a consortium to access the AMPF research facilities and collaborate with researchers. Any size or type of company can take advantage of AMPF facilities — from corporations including AT&T and Siemens to small startups like Alegna, which licenses and commercializes Navy research.

“The ability to manufacture domestically is critical, not only for national security purposes, but also to keep the U.S. economically competitive,” said Steven Ferguson, a principal research scientist and executive director for the GT Manufacturing 4.0 Consortium. “Having the AMPF puts Georgia Tech within the innovation epicenter for these areas and will help us reshore manufacturing.”

The benefit of such an arrangement is twofold. Companies can work with the newest manufacturing technologies and make their own advances, and Georgia Tech builds a network of manufacturers across the state and world that students can work with. For example, AT&T uses the AMPF to test sensors for expanding personal 5G networks, and George W. Woodruff School of Mechanical Engineering Professor Carolyn Seepersad has Ph.D. students funded by a Siemens partnership through AMPF.

Trigger was able to connect and collaborate with some of these corporations and researchers during his internship. “I told them about my interest in machine learning because I wanted to see how they were integrating machine learning into their research projects,” he said. “All of them invited me to come by to observe and be part of the research.”

Starting a New Path

Because of his research collaborations during his AMPF internship, Trigger now has a new focus. “The internship clarified for me that AI is where everybody is going,” he explained. He wants to be at the forefront of AI manufacturing and hopes to pursue a certificate in machine learning next.

While he knows he still has much to learn, AMPF gave Trigger a foot in the door and confidence about the future. He — and other veterans like him — will help build the workforce that propels America forward in manufacturing.

Wednesday, May 11 at 12:00p
Tom Kurfess - Callaway building, 813 Ferst Drive, NW, Room 114 or link here for virtual participation

Thursday, May 12 at 2:00p
Chuck Zhang – Callaway building, 813 Ferst Drive, NW, Room 114 or link here for virtual participation  

- - - - - - Short Bios - - - - - 

Tom Kurfess
Wednesday, May 11 at 12:00p 
Callaway building, 813 Ferst Drive, NW, Room 114 
Virtual option - link here

Thomas R. Kurfess is the HUSCO/Ramirez Distinguished Chair in Fluid Power and Motion Control and Professor of Mechanical Engineering at Georgia Tech. He received his S.B., S.M. and Ph.D. degrees in mechanical engineering from M.I.T. in 1986, 1987 and 1989, respectively. He also received an S.M. degree from M.I.T. in electrical engineering and computer science in 1988. During 2019-2021 he was on leave serving as the Chief Manufacturing Officer at Oak Ridge National Laboratory (ORNL), where he was responsible for strategic planning for ORNL in advanced manufacturing. He was also and was the Founding Director for the Manufacturing Science Division at ORNL. During 2012-2013 he served as the Assistant Director for Advanced Manufacturing at the Office of Science and Technology Policy in the Executive Office of the President of the United States of America, where he was responsible for coordinating Federal advanced manufacturing R&D. He was President of SME in 2018, and currently serves on the Board of Governors of the ASME. His research focuses on the design and development of advanced manufacturing systems targeting secure digital manufacturing, additive and subtractive processes, and large-scale production enterprises. He is a member of the National Academy of Engineering and is a Fellow of ASME, AAAS, and SME. 

Chuck Zhang
Thursday, May 12 at 2:00p 
Callaway building, 813 Ferst Drive, NW, Room 114
Virtual option link here

Dr. Chuck Zhang is a Harold E. Smalley Professor in the H. Milton Stewart School of Industrial & Systems Engineering at Georgia Tech. He is also an affiliated faculty member with Georgia Tech Manufacturing Institute (GTMI). He serves as the Director of the newly established Center for Composite and Hybrid Materials Interfacing (CHMI) which is a three-university NSF Industry/University Cooperative Research Center (IUCRC) headquartered in GTMI. Dr. Zhang’s current research interests include advanced composite/nanocomposite structures manufacturing and maintenance, additive manufacturing, bio-manufacturing, and manufacturing cybersecurity. As PI and Co-PI, he has led or conducted over 50 research projects sponsored by numerous federal agencies including Department of Defense, Department of Veterans Affairs, Food and Drug Administration, National Institute of Standards and Technology and National Science Foundation, as well as industrial companies such as ATK, Cummins, Delta Air Lines, and Lockheed Martin. He currently serves as a Co-lead of the “Soft Robotics Technology Working Group” for NextFlex, one of the Manufacturing USA Network Institutes. Dr. Zhang is a fellow of Institute of Industrial and Systems Engineers (IISE). He has published over 220 refereed journal articles and 230 conference papers. He also holds 26 U.S. patents.