Supply Chain Intelligence: How AI Is Redefining the Supply Chain for Manufacturers

“AI is not going to make the decisions for you.” – Mike Sibley

In this Moore on Manufacturing episode, Mike Sibley breaks down supply chain intelligence and how AI can help manufacturers stay ahead. He explains why traditional supply chain models fall short, where AI is making a real impact and why security and governance should come before anything else.

Inside the Discussion

Supply chains are changing faster than ever, and the data behind them is changing just as quickly. Mike shares what supply chain intelligence looks like in practice, how manufacturers can use AI to model risk and why the biggest opportunities come from building AI into everyday workflows.

He also covers the most common mistakes companies make when adopting AI, how to balance automation with human judgment and what the first step should be for manufacturers who haven’t started yet.

Resources

James Moore Manufacturing Services
Moore on Manufacturing Series
Watch: How AI Is Redefining the Supply Chain

Transcript

[00:02] Host: Hi everyone and welcome. Today we are here with Mike Sibley. Hi Mike. How are you?

[00:02] Mike Sibley: Good. How are you?

[00:02] Host: Good. So today we’re talking about supply chain intelligence and how AI helps manufacturers stay ahead. So the first question is, in your opinion, why are traditional supply chain models no longer enough in today’s market?

[00:02] Mike Sibley: Well, I think information is moving fast. Changes are happening really really quickly, whether it’s because of geopolitical reasons or other costing issues. I mean, the easiest example, you look at this conflict going on over in Iran and what’s happening to resin prices. And so things are changing really really fast. That means customer data is changing really really fast, which means you have to be very nimble in your supply chain forecasting analysis. And that’s why it’s just not enough to do the same old same old with supply chain anymore.

[00:58] Host: Absolutely. What does supply chain intelligence actually mean in practice, not just as this buzzword that people are talking about?

[00:58] Mike Sibley: Well, I think intelligence, like anything, is having information and being able to act on that information. And so supply chain intelligence is really understanding the downstream and the upstream of what is happening, kind of like the example I just gave. It’s understanding that and using that information to then plan properly for what you’re going to do. So that intelligence, that’s going after information in a digestible way. Manufacturers and distributors, they’ve got all sorts of data, and the biggest problem with data is it can be overwhelming. You get to the point of analysis paralysis. So what do you do with it? Maybe you don’t do anything with it, which can be very detrimental.

[01:53] Host: Absolutely. It can be very overwhelming for sure. So where is AI making the biggest impact right now? Do you feel like it’s forecasting, logistics, cost management?

[02:13] Mike Sibley: Well, I think manufacturers are still trying to figure that out. It’s one of the things that we’re helping them do. You hear a lot of people talk about AI for emails and doing Excel spreadsheets and things like that. I look at AI having the biggest impact when you can insert it into your workflows. There’s starting to be AI supply chain software. There’s also, when you look at Claude and ChatGPT and all these, other ways to actually build some of your own basic systems. But I look at it as AI needs to be built into the workflows, which means you actually need to understand what your workflows are. Manufacturers are really good about having flowcharts and production charts, but what about what I call the administrative side? So the non-production stuff like supply chain, like finance, like HR, and understanding those workflows and inserting AI into the workflow.

[03:06] Host: Absolutely. How are leading manufacturers using AI to predict disruptions before they happen?

[03:06] Mike Sibley: Well, I think that’s the modeling. It’s really important. I mentioned upstream and downstream. It’s really important to understand what your customers are doing, what your market is doing, where it’s headed and why it’s headed that direction. Then use that information in your modeling, in your understanding of where the risks are and where the market risks are, especially if you’re overseas, if you’re looking at, again, the geopolitics. And then also where your supply chain is coming from, what is going on, what’s disrupting them and how does that impact your business? And again, you can use AI to help take all this information and help put it down into something, but AI is not going to make the decisions for you. And I think that’s one of the things that sometimes we get into. People are like, “Oh, AI did it, so it’s got to be right.” No, maybe someday, but right now it’s not like that. So you’ve got to be able to use all that to help you model and then really understand what’s going on and use it to forecast your business.

[04:24] Host: Absolutely. What’s the biggest mistake companies make when trying to implement AI in their supply chain?

[04:24] Mike Sibley: Well, I think you’ve got to start off, so I’ve kind of built a framework of AI that I think about. I’ve got a nice picture of it that I like to use. You really have to start first and foremost with security and making sure your data is secure. Then I look at it as you need a governance policy. How are you going to govern the use? Where can AI be used, where can it not be used? Because there’s a big risk that you have employees who are just throwing data into an unsecure version or connecting straight to data sources, which you may or may not want. And so I think a big mistake is not learning how to use it and not learning how to use it right, and again not putting it into your workflows. But an even bigger mistake is to not look at the overall infrastructure. Look at your AI framework first and then go from there down in.

[05:30] Host: Absolutely. And do you come across clients that you work with that are resistant to learning AI? Do you feel like they would rather keep it the old traditional way?

[05:30] Mike Sibley: I don’t know. I think at first there was sort of like, “I don’t understand it, and so we’re not using it.” I think what you’re seeing is a slow adoption of it. But I think there’s really a lack of understanding. So that’s why we’re spending a lot of time on training clients and companies on using it and where it can be used. But again, we start with the framework side of things. And that’s the thing I want to caution more than anything else. Don’t just go sign up for an AI account on any of the platforms and start throwing all your data into it. That’s not a good way to go. You can really open yourself up to releasing information that you don’t want released.

[06:16] Host: Oh, absolutely. How do you balance AI-driven automation with human decision-making and expertise? You kind of touched on this before, but this is actually a really important question.

[06:44] Mike Sibley: It is, because I think right now you have to look at AI as a tool. Think about Excel. Excel is a tool, and we build all sorts of automations in models in Excel. But when we do that, we go and we check it. We make sure it makes sense. We make sure that the information coming out of the model makes sense, because if you have a formula that’s wrong, it can lead you to a wrong decision. The same thing with AI. It’s learning how to put the right data and information into it and how to prompt, and then when you get it, read it and make sure it makes sense. Because AI can help take a lot of information and boil it down really, really quickly. But at the end of the day, you have to use your experience and other data sources combined with that to say, “Okay, now I’m going to use this to make a decision.” So it’s a tool. Use it as a tool. Don’t use it as your sole advisor on how you’re going to manage and run your business.

[07:42] Host: Yeah. Like, “Okay, my brain can take a break and it can do it for me.” I’ve seen the silliest mistakes that people make because they don’t read anything or look over anything, and I’m like, guys…

[07:59] Mike Sibley: It’s not perfect.

[07:59] Host: You really need to use your discernment in a lot of ways.

[07:59] Mike Sibley: Absolutely.

[07:59] Host: Yeah, with the stuff it’s spitting out. Can AI actually help reduce costs in a meaningful way, or is that just an overhyped notion?

[07:59] Mike Sibley: No, I think it can, and I think we’re really at the starting point of how it can help with that. I mentioned workflows before, and inserting AI technology and tools into your workflows can be a huge source of, first, let’s just talk about opportunity cost. I’m a big fan of taking employees and upskilling them. There’s people out there saying, “Oh, we’re going to get rid of employees.” I mean, you’re seeing it in the news, right? You see some of these mega tech companies saying that. But for manufacturers, what I look at is if you’re trying to grow, you can possibly grow without adding employees, at least for a little bit longer. What AI can do is help alleviate or reduce the amount of time spent on tasks, particularly repeatable tasks. That means you’re going to open up more time for those people, who can be upskilled, learn new skills and take on other tasks that can be leveraged better. And so you’re opening up those opportunity costs and possibly deferring costs. Maybe, just maybe, through attrition, some of the technology can replace the need to hire new people. But I’m not one that’s saying, “Hey, you just need to go and lay off a bunch of people and replace them with AI.” I think it’s really reducing some of those manual repeatable tasks that will allow you to save costs over time.

[09:18] Host: Absolutely. Okay, so the last question is, if a manufacturer hasn’t started using AI yet, which you kind of touched on earlier, what’s the first step they should take today?

[09:40] Mike Sibley: Call us and we’ll help them. Well, in all reality, I think the first step is understanding. Get an understanding. I go back to the framework that I’ve kind of built out for myself and some of my clients. Make sure you’ve got security, governance, those kinds of things in place, and then training is part of that. You can dive in and start playing around with it, but I think training is a big part of it, the right utilization in the right ways. Going back to what we were talking about earlier, it’s pretty easy to throw something into ChatGPT and assume, “Okay, this is what it gave me.” And you look at it and you’re like, “This isn’t even about our business. What are you trying to do here?” I’ve seen and heard early on people at manufacturers and other businesses start taking data, throwing it into a personal ChatGPT account and starting to work with it. It’s like, you just put private data into an open LLM. So I think that’s why it’s important that companies get control of it, do the proper training and put security and governance around first and then build from there.

[10:53] Host: Absolutely. Well, this was a great conversation today, and I think it’s a really important one. Sometimes people feel a little lost or overwhelmed and don’t know where to start. So I think you’re a great resource for anyone who is looking to be educated, know more or expand their business in these ways. It was great talking to you today, Mike, and we’ll talk to you again soon.

[11:10] Mike Sibley: All right. Thank you. Thanks. Bye.

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