Leadership Insights: Analyzing Verusen’s Manufacturing Survey with Jeremiah Woodford

Hello and welcome to our digital CXO Leadership Insights series. I'm Amanda Ani. I'm excited to have Jeremy Woodford here today.

He is the Chief Revenue Officer for Varus. How are you doing today? I'm doing well.

How are you? Doing well, thank you for coming on the show. I have to be here.

So can you share a little bit about Varus and what services do you provide? Yeah, so Verisin, we are a, uh, an ai, uh, software company. We're, uh, we've been in business for about eight years now, operating and, and working mainly with Fortune 100, fortune 500 big industrial asset companies.

Um, all the way from food and beverage to pharma to oil and gas, to mining power generation utilities. Our software is, uh, plugs in, uh, to their ERP and EAM software, and it pulls out all their procurement, their master data catalogs, and ultimately what we're doing is, is optimizing maintenance, spare part, stocking strategies. Um, so big customer may have a mid max on a bearing in a warehouse of 10 and 11.

Our, uh, AI models will look at all the historical, uh, movements, determine how critical that spare part is, and then make a, ultimately make a stocking recommendation. Wonderful. Well, our topic for today, we're gonna be discussing a recent survey that y'all put out.

It's called the Future Strategies for MRO Optimization. Uh, so with that, first of all, can you share a little bit about who did you survey? What key things were you trying to find out from that survey and a little bit of just overall information about it?

Um, we put it out to a number of big industrial, um, um, target accounts and customers and, uh, from procurement to materials management to supply chain, um, analysts who, maintenance and operations people, all of the sphere of people that touch and buy and stock and use the, uh, MRO inventory. Great. And so what were some key stats that you can share with our audience today that really stood out to you?

Um, I think the biggest one was that I think 71% of the respondents felt that the, uh, MRO procurement operation should be treated as strategic initiatives versus, and, and continuous improvement versus potential innovation source as an innovation source versus, um, it's kind of an afterthought in a lot of, uh, industries that we go into and a lot of the accounts we go into. Um, you know, a lot of these organizations that we come in that, that haven't bought a system like ours, um, are treating this like any other inventory or service that they would go buy. And, uh, they'll, they'll arbitrarily stick, have a MinMax or they won't have a MinMax at all.

And, uh, and in the maintenance it's a very emotional, um, review, meaning that the only time they look at how much inventory they have or what what their current MinMax is, is when they stock out, uh, and they don't want that to happen again. And typically they just double the number without any type of scientific or mathematical approach to it. Yeah, absolutely.

That's definitely something they wanna avoid. So from your experience in, in working with these different, uh, business leaders in this industry, what advice do you have, um, to help them with making that more of a, uh, first thought instead of a afterthought? Well, I think for the longest time a lot of people looked at this problem as too complicated a problem, meaning that data sets were disparate master data catalogs, poor naming convention.

So 90, probably 8% of people we demo our software to and meet with on the first case think this is fantastic, it's really cool technology, but our data's not ready for something like this. And, uh, so our go to market is give us your data and we'll put it into the solution and we'll show you what we can do. Um, there has been a few cases that where, you know, their data is so bad there's nothing we can do to it, but nine times outta 10 we do find that they actually do have, uh, we can make sense of their data.

Um, and that one of the neat things about the technology that we've built over the last eight years is the joint venture with, uh, Georgia Tech to build a large language model specifically around, um, maintenance spare parts and what it does, that large language model is really, really good. And it's built on vector graph database at normalizing poor data sets. So if you imagine you've got multiple plants, they're all buying the same thing, but you call it all something different.

So a bearing, uh, at this one plant may be a different abbreviation for the size of that bearing and the tolerance of that bearing. Um, and it may be misspelled, our large language model is that has been trained on 40 million plus individual skews and actually been trained on all the abbreviations, the misspellings, the, you know, point point, uh, five is a half inch versus one dash two, uh, is a half inch. It knows all those are the same thing.

So it has a, a, a really cool ability to normalize those data sets. So we're really getting to a point now where these problems are no longer real problems. How important do you think machine learning and AI is gonna be to the manufacturing industry?

I mean, we're seeing it advance quite rapidly. I, I would say, yeah, I would say it's huge. Um, it, you know, if we're going industries want to stay evolving, want to be, uh, profitable, um, a lot of the ways that we've been doing things for the last 20, 30 years using legacy systems, most of your ERP and EA provider, uh, providers and software, uh, companies out there, what I would say are legacy software providers.

And what I mean by that is, is that when they look at a problem, they're still looking at it from a legacy lens and how they can build software to collect data to, into a relationship database and then report that out. And that's why that's one of the biggest problems we're solving for most people hate those software systems hate using those software systems 'cause they're clunky and old, not user friendly. And, uh, what large language models are and AI is bringing to the table is the ability to normalize that data and then automate.

So anytime we look at, uh, an initiative or a problem a client comes to us with, we're looking at, we wanna look at it as, as an interaction with an AI agent that automates the process versus collecting more data so you can report on it. And then another thing you said earlier, you said it's, it's rare, but every once in a while there's a company whose data is just so boggled that you, you can't help them. Uh, for those companies, what advice do you have for them as far as what to do to get, to get in front of this data problem that they're struggling with?

Yeah, so in that, in those examples, it is implementing better processes. So just, you know, we do run into some companies that say that they, they may not have a, a master data catalog, they're just free text purchase orders or they're buying their materials off of PCard. And, uh, and the, and the recommendation, you know, we have partners, we're partners with most of the big sis from Accenture to PWCs of the world.

And uh, and then let them come in and implement a master data catalog so they can start locking down their procurement and not have rogue buying. Uh, and then that, and then we can help them. So one of the things we do with our partners is they can use our large language model to ingest all those free text purchase orders to tell the customer what are the, the common parts that should be cataloged and stored in a warehouse.

So we still do add value, it's just, you know, you don't need to buy our software for a multiple year subscription until you get that catalog created. Yeah, absolutely. Well, if there was one key takeaway you could leave our audience with today, what would that be?

Um, well there's a number of key takeaways that came out of it, but I would think that the biggest is that getting your organization aligned. One of the problems that we solve for and that we see time and time again is procurement knows that they probably are overbuying some of this inventory and, uh, supply chain is just serving the maintenance operations teams to make sure they have what they need when they need it. Uh, maintenance operations, their job is out turning riches and keeping the assets up and running.

They don't have the time to go do some complicated math to figure out what they should be stocking and how often it should be bought. So it getting those three groups to align and agree, that's what we do. So we, we ultimately, it's not a black box.

Our system is, is very straightforward, but ultimately helps you determine that you have the right amount. So we reduce surplus, but we also help identify the critical spare parts so you don't have enough of, so for better planning and execution for your maintenance organization. So getting them all trusted and bought into the system is, is is the crucial takeaway and getting them aligned.

And then is this survey available to the general audience if somebody wanted to go and look at the full survey results? It is, yes. And where could they find it?

O on our website. Okay, great. Thank you so much for coming on the show and sharing your insights.

Happy to be here. Alright. And thanks to our audience, stay tuned.

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