A highlight from Using Data to Untangle the Sticky Problems of Manufacturing Procurement - with David Schultz of Westfall

AI in Business


Inventory management has come up in manufacturing, so many other use cases, procurement specifically, not exactly the hottest topic we've covered, but it's definitely an area where there's a lot of room for improvement. There's a lot of clunky guessing games and procurement and they are extremely costly if we get them wrong, whether we're ordering too much or too little of something or overpaying or taking too long to get something, all of these have downstream consequences in the manufacturing domain. Our guest this week is an expert in this space. David Schultz is the VP and chief supply chain executive at westfall, westfall is a manufacturing firm based in Las Vegas, Nevada. David studied chemical engineering before getting his MBA at Bentley. Westfall is a contract manufacturer. They do a lot of different things, but they work a lot in plastics and resins. David himself has studied chemical engineering before getting his MBA at Bentley university and then serving a number of leadership roles in the supply chain. Today, we break up this interview into two sections. The first of which is articulating what the specific challenges are in procurement in manufacturing. Why is this as consequential as it is? And what kind of rules of thumb guessing games do we have to play today and manufacturing to make business decisions? We have to guess how much our customers are going to do business with us. We have to guess which of them are being overly optimistic about the orders that they say they're going to do this year, which of them we think are being a little bit more truthful or have a better understanding of reality. We have to factor all of that in to how much we're going to spend for parts and materials for our manufacturing operations. The second part of the interview, we focus on where data and artificial intelligence fit into the mix, westfall is a client of orchestral, orchestral is the sponsor of this series. So we previously had an episode with Edmund Zachary, who's the CEO of orchestral. his perspective on the kind of data that is becoming increasingly important in manufacturing when it comes to decision making, and also where AI is fitting into the mix to be able to help make smarter, faster decisions. There's a little bit of talk at the end about the future. You can stick around to the end of the episode for that. Again, this episode is brought to you by our kestrel. Without further ado, let's fly right in. This is David Schultz with westfall. Here on the AI and business podcast. So David, welcome to the program. Yes, thank you very much, Dan. Thanks for having me. Glad to have you here. We're diving in on manufacturing and David over the years we've covered so many use cases in manufacturing from inventory prediction to predictive maintenance, et cetera haven't focused that much on procurement, but that's the topic of our interview today. Before we get into where AI and data come to life, I want to get an insider's look at some of these big challenges of manufacturing procurement, ordering parts, dealing with inventory, et cetera, and kind of tee up for the folks at home. What makes this such a hard problem? Could you help us out with that? Sure, yeah, be happy to do so. You know, the whole supply chain environment has really risen to a different level. Obviously, through the pandemic, people hear the word supply chain and they understand maybe what it means now or at least they're exposed to that. It starts and ends really really, the customer, right? So really what it comes down to is, you know, what kind of forecast, what kind of demand predictability can you get on that end? And then really cascading that all the way back through the operation that goes all the way back through to your suppliers so that you can take that demand and satisfy that with the parts and the operation that you bring in. Historically, that's talked about as S and OP in the industry, sales and operations planning. So it truly does encompass all the way from your customer, your commercial side of the business, all the way through to the manufacturing side. Got it. And I can imagine this has been a clunky and complicated process for as long as it's been around because if I know anything about customers, you can't necessarily predict everything they're going to do, everything they're going to want all the time. There's likely some best practices that you folks have to operate with today or that the industry has to operate with today about looking at historical kind of forecasting kind of quarter over quarter a month over month, looking at maybe the activity of different customers and estimating, okay, based on what they ordered last year, what do we think they're going to order this year? What are some of the factors that go into these, you know, guesstimates hate to say it? What are the factors that go into these guesstimates today that allow manufacturing to operate? Well, I think Dan, you said that perfectly. They are guesstimates. And the day that you issue a forecast, it's wrong. But I think what you have to make sure that you do, as you mentioned, is, is it 90 plus 95% of the way there that's going to get you to your end goal. And basically what you're looking at is historical, as you mentioned. But I think what's made it difficult, you know, in the last 18 months or so, is people talk about in many ways, you know, what are we going to get back to normal? There is no back to normal ever in my opinion. It's the next normal. And I think what you have to realize is when you look at historicals, there's a lot of noise in the data over the last 18 months, let's say. And what I mean by noise is, for instance, we're in the contract manufacturing business, which means that we

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