Sustainable Supply Chain

The ChatGPT Phenomenon: How AI is Transforming Supply Chains Worldwide

April 28, 2023 Tom Raftery / Mike Sigler Season 1 Episode 313
Sustainable Supply Chain
The ChatGPT Phenomenon: How AI is Transforming Supply Chains Worldwide
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Show Notes Transcript

In this exciting episode of the Digital Supply Chain podcast, I had the pleasure of talking with Mike Sigler, Senior Director at Nexer. We dove deep into the fascinating world of AI, particularly ChatGPT, and explored its immense potential in the realms of supply chain and manufacturing.

Mike shared his insights on how ChatGPT can help businesses save time, make better decisions, and optimize processes. We discussed numerous use cases for generative AI in the supply chain, from demand forecasting and inventory management to production scheduling and workforce planning.

But it's not all smooth sailing. We also touched on the potential risks and challenges of incorporating AI into the supply chain, such as overreliance on technology, data quality issues, and the so-called "black box problem." Importantly, we emphasized the need for human touch and gatekeeping, especially in the early stages of AI adoption.

We also tackled the critical aspect of change management and user adoption. As Mike pointed out, AI isn't here to replace jobs, but rather to change and enhance them, empowering people to be more efficient and informed in their work.

And as a little bonus, I shared my personal experience using ChatGPT for cooking inspiration and recipe suggestions. Who knew AI could be so tasty?

For more information on Mike Sigler and Nexer, you can find him on LinkedIn or visit the Nexer group website.

I hope you enjoy this insightful conversation as much as I did. Don't forget to subscribe to the Digital Supply Chain podcast for more fascinating discussions on the future of technology in supply chain and manufacturing!

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Mike Sigler:

It is new technology and folks I think have every right to be nervous or cautious about the use of that technology. But at the same time, there is a tremendous amount of potential with it. And I think as we continue to use it, it will just do nothing but improve, and those instances of hallucinations or maybe, false positives or just different, misrepresentations of the results, will gradually, fade into being extremely rare circumstances

Tom Raftery:

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Digital Supply Chain podcast, the number one podcast focusing on the digitization of supply chain, and I'm your host, Tom Raftery. Hi everyone. And welcome to episode 313 of the digital supply chain podcast. My name is Tom Raftery, and I'm excited to be here with you today sharing the latest insights and trends in supply chain. Before we kick off today's show. I just want to take a quick second to express my sincere gratitude to all of this podcasts, amazing supporters. Your support has been instrumental in keeping the podcast going, and I'm truly grateful for each and every one of you. If you're not already a supporter, I'd like to encourage you to consider joining our community of like-minded individuals who are passionate about supply chain. Supporting the podcast is easy and affordable. With options starting as low as just three euros or dollars a month. That's less than the cost of a cup of coffee and your support will make a huge difference in keeping the show going strong. To become a supporter, simply click on the support link in the show notes of this or any episode. Or visit tiny url.com/d S C pod. Now without further ado, I'd like to introduce my special guest today, Mike. Mike welcome to the podcast. Would you like to introduce yourself?

Mike Sigler:

Hey, Tom. Absolutely. Yeah, first off, thanks for having me. My name's Mike Siegler. I'm a senior director at Nexer. A global company that specializes in supply chain logistics.

Tom Raftery:

Okay. Super. And you specialize in Nexer in supply chain logistics, you say, but what aspect of it, because you could be doing anything from driving trucks to, you know, it, it's a big area. What do you guys specialize in, in supply chain logistics?

Mike Sigler:

A absolutely, yeah. It's a broad category, but for my own specialty I'm also a former co-founder of a company called Rangeline Solutions, where our direct focus was on ERP solution providing. And so, my specialty is around just that. So ERP products we're a Microsoft partner so we specialize primarily in that Microsoft flavor of E R P solutions.

Tom Raftery:

Okay, cool. And this is an interesting time both for supply chain with what's been happening the last few years, but also for Microsoft because we've seen in the last few months since November 30th, let's say, when ChatGPT launched a huge upsurge in interest in ai, and particularly in ChatGPT. And Microsoft. I shouldn't say they've jumped on that bandwagon because they launched that bandwagon in a sense, in that they were one of the early backers of OpenAI, the developers of ChatGPT, but now they've put more money into it and they've also started to roll it out in their own products. So we've not seen, or I haven't seen anything yet appear in the supply chain space from Microsoft around generative AI. Is it coming and what kind of things do you think if it does come, it could do.

Mike Sigler:

Yeah. So the, the short answer to your question is yes, it is coming I think faster than a lot of us realize. There are numerous areas in the, the Microsoft Product Suite where AI is very quickly gonna have a large impact in just productivity and, and being able to rapidly make more informed decisions than we could before. Microsoft generally in the ERP space has their kind of flagship product for E R P, Microsoft Dynamics 365. And with that comes a supply chain insight utility, perhaps that is beginning to leverage the co-pilot platform to really quickly drive I guess information and collaboration with the just as a specific example, maybe if you're working in the procurement department, you know, being able to leverage the different information AI can review from perhaps like local news articles for maybe flooding in an area that one of your vendors is located, and being able to quickly recognize that with this vendor you have orders one, two, and three being placed and instantly react to that news, be able to craft through the AI tools, craft an email to this vendor to confirm if the orders that you have with them are being impacted by this data. So, it's very I guess I'll call it collaborative, you know, with your day-to-day role, just helping you have more access to information and react to it more quickly.

Tom Raftery:

Okay. For, people Mike, who might not be familiar with the idea of Co-pilot, maybe you could give people a quick 1 0 1 on that. I know it's what they've added to the likes of Visual Studio and to the office products, but, you know, for, for people who are unaware, maybe walk us through that.

Mike Sigler:

Yeah, absolutely. Co-pilot is very aptly named in my opinion because it does act as your co-pilot, you know, when you're using these different Microsoft products, whether it's the Supply Chain Insights tool, I was somewhat just describing or other Microsoft solutions in their office suite such as, you know, Word or Outlook for crafting emails and that co-pilot tool can leverage AI and some of the different generative language capabilities for analyzing patterns generating responses maybe to questions that you have of, the data trends that you're seeing and perhaps in an Excel spreadsheet. So it, it take a lot of the AI tools that, you know, folks like myself included, can have fun with ChatGPT asking it questions and having it perform a ton of, you know, analysis on just data that's out on the internet and be able to provide very detailed and very natural sounding responses. So it takes those very advanced capabilities and basically integrates it to the Microsoft office platform.

Tom Raftery:

Yeah, yeah, no, true. And so I've been using the search functionality of Bing and the chat part of Bing since shortly after it launched. And I have to say, I've stopped using Google. Because it's so much better. Google is fast, but it it, it's fast at giving you long lists of answers that, you know, you then have to scroll through and scroll through to get past the ads and to find one that I might actually be useful. Whereas on the Bing search with the the chat component, You ask the answer, it gives it to you, as you say, in kind of natural language. And it also tells you why it's giving you this answer. It gives you the, the references, the sources where it found the information. So it 's significantly better in as a search tool than Google has been. I know Google are working with their solution, which they call Bard, and we'll see what that's like when it comes out eventually. The Co-pilot function I've not had a chance to try that yet, but the, the demos I've seen look interesting, particularly in things like I, I assume many people listening to this podcast, for example use Excel regularly, and the co-pilot functionality in Excel sounds really compelling. I know we're not talking Dynamics yet, but even just simple things like Excel. Outlook will have it built in as well. And Teams was one of the first ones they announced. If people are users of Teams, there's gonna be a version of teams with a built-in G P T functionality, which will do things like transcribe the meeting, but it'll also give a summary of the meeting at the end along with a to-do list for people just based on listening to the conversation. Now that's mind blowing enough for me. But talk to me a little bit more about the, the potential for Dynamics and, and for supply chain professionals where you see it really having an opportunity to shine.

Mike Sigler:

Yeah, I, I think you touched on some great points there. I, I think the element of impacting productivity from, they're not, you know, you know, simple applications such as Excel. AI can really shine in its pattern recognition. And as you, you know, touched on as well, the summary capabilities of boiling down some data trends in these spreadsheets that maybe have thousands and thousands of rows. So that's where the pattern recognition and that ability to use some of the natural language components to boil that information down to very digestible components. Mm-hmm. As it relates to the ERP side. And then, you know, somewhat specifically with Dynamics, the components that play into supply chain are where we take those analytical pieces from Excel and like the us were looking at trends and needing to perform day-to-day functions through procurement and making sure that our manufacturing components of the business have the right components and tools they need to deliver products on time. And, and, you know, as and I guess in having the materials that are required, that component, you know, can have a ripple effect through so many different areas of the business. And if you have the right tools with, you know, being enhanced by artificial intelligence or AI, you can react to those things much more quickly. So my example may come back to, you know, if you have you know, years and years of historical data on your purchasing trends for what products arrive on time, and maybe from what particular vendors. AI would be able to quickly help you analyze, you know, what vendors are delivering product on time. So you can maybe quickly adjust that if, you know, maybe a particular vendor is running into a product shortage, you can quickly react to that information and reroute your purchasing from a different vendor so that your processes downstream stream more on the manufacturing side of things may not feel an impact at all because they'll just now have a product when they need it to fulfill basis. So, I think the, the biggest impact is somewhat that ripple effect, that if you solve challenges upstream, your entire business can work, operate more smoothly through the rest of the processes.

Tom Raftery:

Okay. Okay. Regular listeners to this podcast will be aware that, you know, I'm ex-SAP. I worked for SAP for six years up until last year, and one of the things I, I had a public facing role, so I would often be approached by people to say, listen Tom, we want to talk to someone in SAP about X, Y, Z. Who's the best person to talk to? And it's a 100,000 person company with three and a half thousand products in the product catalog. You know, so often I wouldn't know off the top of my head. So I would go away and try and find out internally and come back three or four or five days later and say, okay, I found out now this is who you need to talk to. Chat G P T and G P T large language models have been fed almost all the information that exists on the web. It occurs to me, and I know this is what's going to happen. OpenAI have said they're going to allow companies to feed their data into a large language model so that you can then run the likes of a ChatGPT on your own internal data. And I think something like this would be completely mind blowing because like I said, three and a half thousand products in the SAP product list or whatever it is now. A hundred thousand employees, you know, if I could have just opened up a chat screen in front of me while people are asking me these questions and just find all that information to go, yep, here you go here's who you need to talk to. And you know, That is going to, I feel, be massively transformative for most companies, particularly if they can house the data internally rather than having to upload it to the open AI servers. And I think that's coming as well because there are now open versions of large language models alongside the Open AI one. Right?

Mike Sigler:

Absolutely. I think that's gonna be, you know, an area of concern I, I imagine people will have is, you know, is still revolving around this idea of the cloud. You know, it feels like we've had it for a long time at this point, but I think data security is something that folks will always be cognizant of and, and making sure that their data's well protected. So I definitely agree with your point that if it can be a local instance of, you know, that ChatGPT kind of, AI technology to review local data, I, I think folks will, will absolutely gravitate towards that just to avoid any potential security concerns. But the, yeah, I mean, to your other point as well on, um, you know, leveraging the skillsets of internal employees and being able to quickly identify resources to to work with maybe for particular questions. I think the overall point with some of these technologies is that they're only as good as the information they're given. And so as you bring in these additional data sources to your comment around maybe skillsets, that's already a huge advantage these organizations can use is saying, yeah, who can I talk to that knows X, Y, and Z, you know, about this topic? The other advantage is that as you start to, you know, take that, you know, to the next level of incorporating kind of back to the, the Microsoft side of things maybe Microsoft Outlook, you know, with calendars and being able to, then suddenly you have skill sets on top of that availability. And so you can know, hey, who has availability for a meeting two weeks from now on this topic? And start to layer this information together and have these tools like ChatGPT be able to quickly take that data and boil down the patterns that can identify and give you the information you're looking for.

Tom Raftery:

Yeah, yeah, yeah. I use ChatGPT quite a lot just on these podcasts. For example, I'll take the transcription of the podcast, feed it into ChatGPT, and then, you know, ask it a couple of questions based on the transcription. Sometimes it gets it wrong. Like, I'll ask it to pick out three highlight quotes from the episode and it'll make a couple of them up. So this is a bit of a worry, right? I mean, you can't have that happening with your supply chain. Well, you, you could, but you shouldn't have that happening with your supply chain. Right. How do you, how do you fix that or make sure that doesn't happen?

Mike Sigler:

That's a great question. In a lot of ways, It harks back to the idea of having a healthy dose of skepticism, you know, in just your day-to-day life to a certain extent. You know, it comes back to the, the very old quote at this point of, don't believe everything you read on the internet. Just the idea that it takes a, a level of responsibility around reviewing some information that's, you know, presented to you and, and understanding that this is new technology. It's something that. It is possible, you know, that mistake can happen, but that's the beauty of a tool as advanced as artificial intelligence, is that the more you teach it, the smarter it gets. And so as it is learning, you know, what is correct and what's not, and where there's tolerance for kind of filling in the blanks versus making broad assumptions of in incorrect quotes, or, you know, just presenting information, answer a question despite it maybe not having all the information. So, in summary, I'll say, you know, it is new technology and folks I think have every right to be nervous or cautious about the use of that technology. But at the same time, there is a tremendous amount of potential with it. And I think as we continue to use it, it will just do nothing but improve. And those instances of hallucinations or maybe, um, false positives or just different, misrepresentations of the results will gradually, you know, fade into being extremely rare circumstances.

Tom Raftery:

Okay. And what about implications for things like privacy and well privacy. Let, let's go with privacy right off the top because that's one that some people can be worried about. So, you know, would you trust a G P T with your private information?

Mike Sigler:

That is also a good question. it's. It's something with like ChatGPT and, and my own personal information that in, in some ways I feel that the data collection being done already, you know, before the introduction of chat G P T and before AI has, um, I guess proliferated, you know, throughout so much of our daily lives that, you know, we're asked to sign up, you know, and provide our email information or phone number. Force bold day-to-day services that we use. And so I think there's a bigger topic or a bigger question to address there, just around data privacy as it relates to the individual throughout society. You know, it's, it's kind of a, a, a bigger topic, I think could have its own dedicated podcast related to, but a, as it relates to Chad g p t and ai. At this point, I mean, the information is out there. I think, you know, whether we want it to be or not. And I think having a tool like Chad, g p t, or, you know, in, in different forms, you know, AI be able to review that data and analyze it perhaps in a much more natural way and be able to get to information faster, ultimately, I think is a good thing. It's just, you know, there's that bigger topic of how our information's distributed across across the internet as a whole. Hmm.

Tom Raftery:

Yeah. Yeah. How are you know the likes of Microsoft and other tech providers? How are they going to handle the fact that, a, it got phenomenal potential, but B, there are also phenomenal potential downsides, like if it does hallucinate something incorrect and someone bases a decision on it. You know, that's a, a large tech headline waiting to happen. How do you, how do you avoid that?

Mike Sigler:

Yeah, absolutely. It's something that with any new technology or any new platform, there is inherent risk. And especially as you're incorporating it into processes that are as important as perhaps, you know, again, coming back to that procurement example of you're responsible for purchasing, you know, hundreds potentially, you know, millions of dollars worth of material for your business. That's a very important, you know, responsibility in the organization and with the risk of misinformation coming from a tool such as ChatGPT I think, you know, companies like Microsoft and any others that are rolling out these solutions will have to have a, a healthy balance of incorporating those tools into the overall processes, but somewhat within the bounds of the, you know, gatekeepers and safety checks of, you know, the, ChatGPT or AI is producing, maybe this purchasing list. You know, have that list, still go through a series of approvals and have, you know, kind of that human touch still on it to make sure that. It's not making these sweeping assumptions that are wrong or having purchase amounts be wildly inaccurate. But I think that level of gatekeeping and, you know, workflows for approvals, I think is still gonna be a, a very necessary component, at least in the immediate future. You know, at five, 10 years from now, you know, there may be enough confidence in that tool to let it, you know, just do its thing. But, um, there's still gonna be, I think, quite a bit of gatekeeping and making sure that the tool is saving everybody time and, and, you know, doing that analysis and the summary of that data, but not taking away the responsibility of just pushing through and, and making sweeping decisions without that human touch.

Tom Raftery:

Yeah. Yeah. I find when I'm working with it on the the podcast here, it means I can get far more work done in the same amount of time than I could do before I started working with it. In prep for today's podcast, I went to ChatGPT and I asked it a couple of questions. So, First question I asked, it was, what are some potential use cases for generative AI in supply chain slash manufacturing? And it gave me a long list of 10 points with explanations for each one, starting with demand forecasting, then inventory management, supply chain optimization, supplier selection and risk management, quality control, predictive maintenance, production, scheduling, product design and customization, new materials, discovery and workforce planning. Those are the 10, each of those. 10 as a paragraph about why it chose those as its, you know, potential use cases. So then, I asked it another question, what are some potential risks of using generative AI in supply chain? And it gave me another 10 paragraphs. You know, overreliance onAI, data quality inaccuracy, security and privacy, black box problem, ethical considerations, legal and regulatory compliance, integration challenges, misaligned expectations, skills gap, environmental impact was the last one, which I thought was an interesting one to throw in there. And finally, I asked it what first steps should supply chain manufacturing organizations take to embark on the use of generative AI? And it gave me this time, 11, a list of 11 points, each one of paragraph on different things organizations could do. I mean, just that alone is, is, is to my mind, fascinating. So what I'm gonna do is I'm gonna take those three questions and answers and put'em into a blog post, and I'll link the blog post in the notes of this podcast. So if anyone's interested, they can go and check out the, the questions and answers. But I mean, even that alone speaks to how, I mean, the answers were great. It, I mean, it, it, it blew my mind that it, it it was able to come up with all that stuff. And the cool thing is you can go in and say, okay, the, the. The first step it talks about here in what steps should organizations take? The first step it says is assess current capabilities. Conduct a thorough assessment of your organization's current supply chain and manufacturing processes, technologies and data management capabilities to identify areas where AI can add value. So, What you do then is you go back to ChatGPT and go in point 1 you said I should do an assessment. What would be the first few steps to take to undertake that assessment? And bang, it'll come out with a, you know, a sub, a subset of things you need to, it's just, you can go deeper and deeper and deeper and it's, it's, I'd say it's like going down a rabbit hole, but it's not because it's all fascinating and productive stuff. It's all things that, you know, stuff you might not have thought of. So it, it can make you absolutely, to my mind, incredibly productive about things that you might not have been able to do in the past.

Mike Sigler:

I completely agree. It, it's amazing the ability to summarize that information and especially you know, being able to progress off of previous answers and previous information that, to your comment on, you know, steps. Should we take, you know, in step one, what should we do here? We've done that. Now, what kind of being able to progress and understand previous responses and act on those answers and new information. Because the, just, yeah, it, it's mind boggling the application that this can have and especially as you're able to drill down and, and, you know, provide it more information about current situation that you're in and have it react and, and continue to, you know, compound on that data.

Tom Raftery:

Yeah, we're coming towards the end of the podcast now. Mike, is there any question that I haven't asked that you wish I had, or any aspect of this we haven't touched on that you think it's important for people to be aware of?

Mike Sigler:

I think it's important for folks to be aware of, just the user adoption elements, you know, in our, you know, line of work. I think often it's referred to as change management and something like ChatGPT and AI, to folks maybe unfamiliar with it can perhaps have a negative reaction to the thought of using it and thinking that this tool will replace my job. You know, this tool will put humanity out of work, you know, and, and some of these extreme reactions when in reality it is trending to be more the opposite. You know, it's not so much that it's eliminating jobs. It's changing jobs. Mm-hmm. You know, it's allowing you to spend, you know, instead of maybe four hours of your day being spent updating a spreadsheet, it's spending, you know, 30 seconds having ChatGPT analyze the data, get you the information you need, and allowing you to then spend the rest of your day working with other teams on in your company and being able to, you know, spread that information and more accurately being able to make decisions about the rest of your workday and how you want to spend that time be more productive with information you have. So I think the biggest component, coming back to the original idea of change management is, you know, working with your team and your company to work through those points that ChatGPT suggested on, you know, how to leverage this tool, but also working with the, the team to best display that value, you know, that it can provide and that it's not replacing everybody with you know, army of robots. It's empowering people to, you know, make better decisions. And coming back to the Microsoft idea, be your co-pilot, you know, in, in your day-to-day, you know, work lives.

Tom Raftery:

Yeah. Cool. Off topic, I should also say that ChatGPT is phenomenal, absolutely phenomenal at suggesting recipes and giving cooking instructions. I just know that from, from trying it out, you can tell it, I've got all this stuff in my fridge, what can I make from that? And it'll give you some incredible ideas and the full steps in preparation of them. So if, if anyone listening is interested in cooking and hasn't tried ChatGPT for that. I suggest you give it a shot. You'd be really pleasantly surprised, but okay, enough off topic stuff. Mike, if, if people would like to know more about yourself or any of the things we discussed on the podcast today, where would you have me direct them?

Mike Sigler:

They can find me on for myself. They can find me on LinkedIn directly. Um, there's also the Nexer group website that will, I think, link in the show notes, but both through Nexer and myself directly can find us.

Tom Raftery:

Perfect. Yep. I'll put those in the show notes for people to find them. Great. Mike, that's been fascinating. Thanks a million for coming on the podcast today.

Mike Sigler:

Thanks for having me Tom, it's been great.

Tom Raftery:

Okay, we've come to the end of the show. Thanks everyone for listening. If you'd like to know more about digital supply chains, simply drop me an email to TomRaftery@outlook.com If you like the show, please don't forget to click Follow on it in your podcast application of choice to be sure to get new episodes as soon as they're published Also, please don't forget to rate and review the podcast. It really does help new people to find a show. Thanks, catch you all next time.

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