Georgia Tech Supply Chain and Logistics Institute

10/16/2026

Supply chain people know a thing or two about things getting lost at sea.

So we're all in on this year's Georgia Tech Homecoming theme: "Lost at Sea, Found at Homecoming."

Join GT SCL for our Homecoming 2026 Reception, an informal networking gathering with our Industry Advisory Board members, alumni, faculty, staff, and friends of Georgia Tech.

📅 Friday, October 23 | 2–4 p.m. ET
📍 Herren Family Rooftop Terrace, 15th floor of the GEORGE tower

It's the new home of the Stewart School of Industrial and Systems Engineering and SCL, and the view from up there is worth the trip on its own.

Heading to Buzz Bash afterward (5–8 p.m.)? Catch a GT bus from Technology Square straight to the GT Exhibition Hall.

Come get found. Register at the link in the comments. ⬇️

10/14/2026

The scariest horror story in operations is not a shutdown.
It is a slightly misaligned label.

Because tiny defects scale fast:
rework time
mispicks
carrier rejects
customer returns
inventory accuracy drift

A simple label quality checklist that prevents a lot of pain:
First-print verification at the start of every shift
Scan test every new roll, every new template
Standard label placement, same spot every time
Printer calibration checks, scheduled, not reactive
A clear rework lane, so defects do not re-enter flow

One small improvement here often pays back immediately, because it removes hidden labor.

What “small” defect causes the biggest downstream chaos in your operation?

Sign up for the GT SCL newsletter for practical supply chain insights: https://bit.ly/47zMtbS

10/13/2026

Progress is a habit, Georgia Tech has been practicing it since 1885.

On October 13, 1885, the Georgia School of Technology was founded with a clear purpose, help turn a region into an industrial economy. It opened a few years later after Tech Tower and the first shop building were constructed.

That origin story still shows up in how Tech operates today:
curious minds
hands on building
solutions that leave the lab and show up in the real world

From all of us at GT SCL, happy birthday, Georgia Tech!

10/06/2026

Last call for the Octl Savannah cohort.

If your team is exploring AI but still lacks a clear framework for applying it, this course gives you the structure, tools, and guided experimentation to build real capability. Each Savannah group so far has pushed the work forward in surprising ways.

Registration closes in 72 hours:https://buff.ly/bEtT2Ef

10/05/2026

Dashboards are great, until you still have to decide.

If your team is stuck arguing about production plans, network tradeoffs, or routing rules, you do not have an analytics problem. You have a decision problem.

Supply Chain Optimization and Prescriptive Analytics is built for that moment. You learn how to use prescriptive analytics and mathematical optimization techniques, including linear programming, mixed integer programming, and heuristics, to make real supply chain choices with clarity.

What you will work on:

Production planning, distribution network optimization, and routing decisions
Building and evaluating optimization models, with hands on practice in Python and Power BI (with prep resources if you are getting started)
A capstone style hackathon using the Cardboard Company case to connect the methods to business outcomes

Next session:
Nov 2 to Nov 5, 2026 (Online)
Register by Oct 29, 2026

If you have been waiting for the moment when analytics turns into action, this is it.

09/17/2026

09/08/2026

Forecast accuracy is not the only question.
Forecast confidence is the one planners wish everyone asked.

A forecast number without context creates two bad outcomes:
overconfidence, you under-buffer and miss service
underconfidence, you over-buffer and tie up cash

Three quick ways to make forecasts more usable:
Segment demand, stable items get a different approach than volatile ones
Track bias, not only error, consistent over-forecasting is a decision problem
Set confidence bands by horizon, week 1 is not week 12

A practical habit: every forecast review should include one sentence, “Here is what would have to be true for this to be wrong.”

What drives the most forecast uncertainty for you, promos, new products, supply constraints, or customer behavior?

Sign up for the GT SCL newsletter for practical supply chain insights: https://bit.ly/47zMtbS

09/08/2026

Forecast accuracy is not the only question.
Forecast confidence is the one planners wish everyone asked.

A forecast number without context creates two bad outcomes:
overconfidence, you under-buffer and miss service
underconfidence, you over-buffer and tie up cash

Three quick ways to make forecasts more usable:
Segment demand, stable items get a different approach than volatile ones
Track bias, not only error, consistent over-forecasting is a decision problem
Set confidence bands by horizon, week 1 is not week 12

A practical habit: every forecast review should include one sentence, “Here is what would have to be true for this to be wrong.”

What drives the most forecast uncertainty for you, promos, new products, supply constraints, or customer behavior?

Sign up for the GT SCL newsletter for practical supply chain insights:

09/03/2026

If AI can sense, decide, and execute faster than any team you could hire, what's left for a supply chain leader to manage?

That's the question Chris Gaffney and Zaid Duwayri open with in Part 1 of our new series, The AI-Managed Supply Chain: What Changes, What Doesn't, and What Leaders Must Do.

They start with a thought experiment: what would the ideal supply chain actually look like?

One that senses. Anticipates. Synchronizes. Responds. Learns continuously while balancing service, cost, cash, growth, resilience, and responsibility.

That vision isn't new. Our ability to build it is.

The conversation gets into:
→ How AI is collapsing the time between sensing, deciding, and executing
→ Why physical constraints still bite even when decision latency approaches zero
→ What shifts when intelligence goes from mostly human to human-and-machine

Here's the part worth sitting with. As AI absorbs more of the decisions, management moves up a level: To managing the decision system itself. What it optimizes for. What authority gets delegated. Where the boundaries sit. How outcomes get governed.

Which raises a talent question we don't have a good answer to yet:
If people make fewer of the routine calls that used to build their judgment, how do we develop the next generation of supply chain leaders?

Watch Part 1 on the SCL YouTube channel. Link in the comments.

If you're leading a supply chain team right now: where is your judgment actually being built? Tell us below.

08/28/2026

Supply chain utopia isn't a new idea.

Ask a room of leaders to describe the ideal system and you'll get some version of what's in this image. You'd have gotten roughly the same answer in 1995.

The goal hasn't moved. What's moved is everything standing between us and it.

Compute got cheap. Data got fast. The distance between sensing, deciding and executing is collapsing and physical AI is starting to close the last stretch, the part where a decision has to turn into a pallet that actually moves.

Which means the constraint was never the vision. It was the friction.

And that surfaces a question the field has mostly been able to avoid:

If the system can sense, decide, and execute on its own, what is management actually for?

Chris Gaffney and Zaid Duwayri take that on September 3. Part 1 of three.

What would you add to the list? Or take off it?