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

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

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

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

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

Start with what is already true.

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

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

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

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

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

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

Here is what the modeling shows.

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

Three findings stand out.

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

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

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

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

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

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

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

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

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

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

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

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

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

The larger point.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Engineering a Better Game Day Experience (Team Linebackers)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In this issue:

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

A Subtle but Growing Concern

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

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

But the same concern keeps surfacing across those conversations:

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

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

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

What the Experience Taught Us

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

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

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

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

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

Little Rock: When Feasibility Isn’t Reality

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

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

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

Mini Cans: When the Thinking Matches the Model

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

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

Most of the Work Is Outside the Model

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

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

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

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

Where the Breakdown Happens

Before the Model: Skipping the Hard Questions

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

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

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

After the Model: Mistaking Mathematical Accuracy for Business Validity

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

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

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

What Critical Thinking Actually Means in Supply Chain

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

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

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

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

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

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

A Five-Question Diagnostic

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

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

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

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

This Gap Is Well Documented

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

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

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

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

The 4 C’s: A Decision-Focused Framework

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

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

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

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

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

Where to Start

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

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

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

Questions or comments? Reach out to SCL.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

When Capability Becomes Cheap

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

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

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

The discussion quickly moved beyond software to a broader question:

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

Brand as a Promise

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

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

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

AI is changing that balance.

Lower Barriers, Higher Expectations

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

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

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

AI Raises the Execution Standard

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

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

That is changing.

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

From Disruption to Preventability

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

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

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

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

Brand Is the Delivered Experience

From a brand perspective, that is a fundamental shift.

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

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

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

The Weakest Node Defines the Brand

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

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

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

Implications for the Supply Chain Ecosystem

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

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

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

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

Winners and losers are being judged daily.

What This Means for Leaders

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

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

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

One Question to Answer Now

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

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

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

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

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

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