How are the booming technology trends like artificial intelligence and large language models impacting the supply chain? In this Executive Perspectives episode, Dave Tuttle, Vice President of Data Intelligence at Breakthrough, discusses how AI is being leveraged toward more informed decision making and more successful operational executions.
This interview was edited and formatted for clarity.
Tyler Fussner, Managing Editor, Supply Chain Connect
Hi, Dave. Thank you for joining us today.
Dave Tuttle, VP of Data Intelligence, Breakthrough
Hey, it’s my pleasure. Happy to be here.
Fussner
And if you could please introduce yourself to our audience.
Tuttle 0:28
My name is Dave Tuttle, Vice President of Data Intelligence with Breakthrough. Breakthrough is a transportation technology organization that helps our clients to manage their costs, and service, and even sustainability as it pertains to transportation and supply chain.
The company’s been around for 15-plus years, and we’re lucky enough to service some really cool and interesting customers, some of the biggest names—or names that anybody would recognize—with products that people consume every single day. We get to take part in helping people save money. That’s always a great thing to be able to do for your clients, and it makes what we do pretty easy when we can come to our customers and offer them opportunities to reduce their total cost or optimize the way they do business.
Fussner 1:22
Well, Dave, I’m excited to sit down and talk to you today about some of the trends that we are seeing in supply chain. My first question for you: What are some of the bigger technology trends that you’re seeing in use?
Tuttle 1:36
I’d be remiss not to talk about generative AI or LLMs—large language models have certainly been a hot topic; was a hot topic through 2023, not going anywhere in 2024. I think that all also leads back into AI and ML. We see these things as some of the most important technologies that will shape the way that supply chain, logistics, transportation works and a number of other industries going forward.
Someone made the comment to me that 2023 was the year of exploration and discovery. We found out what are these things, or the more of the masses were introduced through companies like open AI and Microsoft and other organizations that are really embracing this. Now it’s adoption and application of these tools. I think that those are going to be some of the big tech breakthroughs or technologies that get implemented or utilized in our space.
Fussner 2:39
And that’s my follow up question to you. With the onset of adoption, the adoption rates certainly about to increase this year, what do you see as the best use cases for AI or LLMs in supply chain?
Tuttle 2:51
I think one of the most interesting use cases will be capacity management. An opportunity to find capacity that we may not have understood even existed. The ability to go through and build optimizations or string together information that some of it that may be well organized data combined with context, that these models can now bring together, pair appropriately, and show us things that we may not have been able to see otherwise.
And so I do think, if we think about what we saw through COVID, and I know in the space that I was working in (I spent a lot of that time in distribution), we saw this crunch—this capacity crunch. How do we create more throughput? How do we do more with less? Or more with the same? I think that’ll be a big part of it.
If we think beyond just that supply chain logistics space, I think that’ll be a key unlock for a lot of industries, or even industries that service logistics and supply chain. If we can go and pull together or look at large amounts of information as it pertains to say legislative change or any kind of documentation that might impact the way we do business, these models can help us go through that and pair up information that might be helpful in the way that we make decisions.
The other side is, and we’ve been doing this for a long time, continuing to improve the way that we forecast. We’ve seen the introduction of machine learning into forecasting models, which has really created some interesting, albeit sometimes more complex, but interesting ways of forecasting and looking at what the future might bring. I think we’re going to continue to see improvements there. And all that leads to optimization, right? And things like inventory optimization, throughput optimization. I think these models, these tools, will have an impact on all of those pieces.
Listen to the rest of the episode on the podcast player above or below, or wherever you listen to podcasts, picking up at the 4:50 mark!
