Ep. 175: Greg Hoggard - IT & OT. The Accuracy of Technology for Change

Greg Hoggard, CMA, CFO and VP of Finance and IT at Rembrandt Foods, joins Count Me In to talk about information technology (IT) and operational technology (OT) coming together to speak the same language and help the organization realize more value. Change management is an important are for this alignment, and Greg talks about how he has been able to shift his team's thinking and processes to better understand each other. He co-authored an article in Strategic Finance to show how computerized vision and AI adoption can create significant value for any organization and, in this episode, he shares some key considerations when adapting to or implementing technology. Download and listen now!
Contact Greg Hoggard: https://www.linkedin.com/in/greg-hoggard-cma-9a201b9/

Counting Eggs With AI: https://sfmagazine.com/post-entry/february-2022-counting-eggs-with-ai/

Full Transcript:
Adam: (00:04)
Welcome back to Count Me In, IMA's podcast about all things affecting the accounting and finance world. Kicking things off for you again is your host Adam Larson and I'm excited to introduce our featured guest for today's episode, Greg Hoggard. Greg is CFO and VP of finance and IT at Rembrandt Foods where he is responsible for all aspects of finance and accounting, IT, and grain purchasing. Greg co-authored an article in Strategic Finance to show how computerized vision and AI adoption can create significant value for an organization. In this episode, he discusses the value of aligning IT and OT and getting everyone in the company speaking the same language. Keep listening to hear how the intersection of AI and technology with finance leads to improved strategic performance. 
 
Mitch: (00:57)
So Greg, in your opinion, what is the value of, or really maybe even the need for information technology or IT and operational technology or OT to report into the same group. 
 
Greg: (01:11)
So for those that may not work in manufacturing, operational technology, so operations and technology, which are usually called a controls group, for those who work in manufacturing, they're the ones who deal with the hardware that collects the data. They have a little bit of a different talent than the IT group. They're data. They're not data miners necessarily, but they they're the ones who collect the data from the machines. They're a little bit of electrician, a little bit of, I hate saying maintenance. They're not maintenance, but they understand all those things. They understand how the operation is supposed to work and where the data's coming from. Think of those screens that you see in those old buttons and the really big, old manufacturing steel mill or just old manufacturing. When you see it on a television show, something, all of those buttons, all those controls, that's all done by operational technology groups. 
 
Greg: (02:14)
Whereas on the IT side, they really are the ones that control the data and the servers and the data bases, they make that data consumable for end users, and govern that data so that it's trustworthy and reliable. I think what we're seeing, these days in the last few years, especially, is that this whole internet of things push where everything is communicating with everything, those lines are starting to cross much more often than they used to, between like the operational technology groups and the information technology groups and what I've seen especially here at Rembrandt is that when those two teams work together, it becomes an unstoppable force. when those two teams are working separately and disparately, it can become a little confusing and unmanageable. So the need to have them report together, I think really it just drives change a lot faster and it gets you to the right answer much quicker. 
 
Mitch: (03:26)
So it sounds like, I would assume you mentioned this, you know, really being in manufacturing, but majority of businesses, they most likely require some additional change, right? Some new alignment to really implement this kind of strategy. So as far as change management goes, what are some of the key considerations for making this shift with the technology that we're talking about? The reporting. And really, I think it comes down to getting everybody from maybe two different sides to speak the same language. So, you know, what kind of steps would you recommend for that? 
 
Greg: (04:01)
So on the change management side, it's crucial that everybody understands their KPIs and their OKRs. So objectives, key results, and key performance indicators, really the goals that they're operating under, or goal posts, I guess you could say, I like to be a little more open-ended sometimes, but I think if you don't have that, then good luck with change. You gotta have that good starting point where everybody's working towards the same goal. I think that clear communication cross-functionally and just within the singular departments is necessary. And then I think that once you establish those clear metrics, you have, well at first you have to have something that, that can be measured, right? I mean, you can't have a metric that's not measurable. So having something that's accurate and objective to measure that goal is needed. And then really, I think these data visualizations, are much better tools to report feedback and then to measure against these goals than traditional scorecards and numbers. 
 
Greg: (05:19)
Most people don't like to look at numbers, I don't know if I should quote a book here, but I'm actually reading a book right now called Making Numbers Count, which, is a really good book by Chip Heath. And he's talking a lot about how most people really aren't wired to speak the numbers language. And I think as a accountants, we are wired to speak that way. At least we've learned that language and we wanna share the numbers down to the 10th decimal point to show that, to prove that we did the work and to prove that this is a real number, but other people get lost. so these visualizations that we're showing now with these tools like Tableau and power BI, those speak so much louder than the numbers that we wanna share. So I think this leads to accountability and acceptance of change. I don't think you can have change without that accountability and that doesn't come without that clear feedback. And then the objective measurements of clear objectives and key results and KPIs, 
 
Mitch: (06:25)
You know, with these metrics and, you know, the communication, everything you just mentioned with change management. I think, you know, the underlying theme here for both sides, you know, both languages, if you will, is the technology itself. I think that's causing the need for change and where people need to potentially, you know, learn a bit more or improve a bit more in their, you know, speaking capabilities, I guess you could say. So if we could just kind of shift from the human side of things and focus on the technological side of things and those resources for a moment, when adapting or implementing technology and, you know, increasing the need for this change management, what should our listeners really be most aware of when it comes to technology implementation? 
 
Greg: (07:14)
So I'm reminded of an experience during this, that I've been on with the company Rembrandt that I work for now and the team that we're working with. We, so I'll take a little bit dive into the story that was published as well at this point, but we have these analog counters. It's just an example. And these analog counters are just old technology. I mean, analog counting has been around since what the probably thirties, forties, maybe even before that, but anytime anything passes under this analog counter, it's counted as an egg in our case. So we have these counters on every single row of every single column of every single barn in our facility. And these counters, like I said, count anything that passes under that, their measure, their scope there and what we found, at the very beginning of this journey, we wanted to know if the measurement was accurate. 
 
Greg: (08:18)
So we performed an audit of the counters and we lined a hundred eggs up behind each of these counters before the start of the day. And then we turned them on and then after the a hundred eggs were counted on each counter, we turned off the system and then went and looked at the results. Some of the counters were off by 2% positive. Some of them were off by 2% negative. Some of them were off by 20% positive. Some of them were off by 20% negative in the worst case, some of them were off by 40%, both ways, but in the end, those errors offset each other. And so our operations group said, oh, well, we're within 2% overall. So we're good with these counters. Now, statistically speaking, you have to use absolute values and that would tell you that those counters are pretty much worthless, but that was one of the hardest things to talk about, with our group, because they had been operating for so long using that technology. 
 
Greg: (09:20)
And they had to trust it because that's what was available. I think now with improving technology and really AI at our fingertips, you don't have to rely on analog or on gut instinct or there's data there to be had. And I think really AI was definitely the path for us, but it's probably the path for a lot of other people with complex and really high quantity, high volume of data. Really. I think that harnessing all of that data or things that maybe we haven't thought really are data that's really what I believe will make companies more profitable over time. We're really all just data and supply chain companies. If you think about it, if when it boils down to it, you don't really have to work the hardest. You don't really have to make the best product, although those things are very important. If you wanna be a really sustainable ongoing concern type company, but really if you can make your product at the right time efficiently and get it to your customers at the right time when they want it, then really that's the value. I believe that many companies and most companies need to really tap into and really data is the way to do that. 
 
Mitch: (10:44)
Now I know our conversation today really initiated around an article that you wrote recently. And I know you, you said you kind of dove into some of the background of your company and some of your experiences, but I do wanna address the article and the significant value of AI adoption. So are you able to give us a little bit more of a history behind some of the initiatives that you implemented and, what the article is all about before we wrap up this conversation? 
 
Greg: (11:12)
Yeah, I would love to, actually, this has been really the high point of my time at Rembrandt so far. I'll take it back to the very beginning. in 2019, I wasn't working for Rembrandt at the time. I was working for a company called Oregon Freeze Dry in Oregon in Albany, Oregon. And, I had signed up and attended the San Diego IMA conference. And that's where I met, a gentleman named Daniel Smith, who I've been working with on this project, really, really great guy. If you ever get a chance to work with him, you should do it. But he sat next to me during a lunch. I believe it was a lunch and learn or a, just, we had a table full of full of experts and accountants and IMA members where we were just talking through different issues. 
 
Greg: (12:09)
And I was seated next to him. And we started talking about Python. We started talking about technology. There were a lot of other CMAs from the Philippines at our table as well, really good conversation convinced most of us at that table. Daniel did to go to his class, that he was teaching, the seminar that he had on Python and just making Python understandable for people like me, who aren't really programmers, aren't really data scientists. And really just got me thinking about technology. I switched companies and really came across these problems that I just talked about with the counters. We had a myriad of other problems as well, but it really occurred to me. I watched, I followed Daniel on LinkedIn and he was demystifying facial recognition and AI. And, I thought to myself at that point, well, if AI can recognize faces people, individuals, and all the different details that it takes to really identify who somebody is, then it should be able to identify an egg and eggs generally look the same they're different sizes. 
 
Greg: (13:21)
And generally the same shape. And so went down that path, contacted Daniel through LinkedIn, and that's where it started. I think it took us about two months to really get a proof of concept and a quote together to say, this is what it will take to do the project. And then we started really down that path of building a software system and developing that software to count these eggs and just the scale of these eggs. And I think I mentioned this in the article, but we've got 6 million birds on our site. And each of those birds, they lay eggs a little over six times a week on average. So it's about 92% of the time, of days they're laying eggs. So it's about 500, or sorry, about five, five point, I'll just say 5.5 million eggs a day, and the are all different sizes. 
 
Greg: (14:17)
So we have a big problem of trying to understand how efficient we are at converting the feed into egg, and then breaking those eggs into liquid, and then trying to identify where the loss is happening. So it seemed like a very complex problem that AI could solve. And what we found is that it works now, the hardest part has been, convincing people that this isn't Skynet. I think that, in the Ag side of the world, there's a lot of technology, a lot of AI, actually, if you read up on the Ag side of just Ag industry, AI is really taking hold. I haven't seen it yet in the egg industry, but, or I hope we're pioneering here and I hope it catches on, but it's, it's definitely an industry that, that requires something like this. There's just so much volume. When you think about feeding a nation and feeding the world, the Ag industry really is in need of really in depth complex models, which I think AI is really the answer to, to that problem of feeding the world efficiently. 
 
Mitch: (15:34)
Now, just real quick for our listeners. This was an article that was published in the February issue of the Strategic Finance magazine. And, we have a link to this article in the show notes here of the episode. And, you know, I just reiterating what you said. The article does a great job outlining the different phases and the different considerations that you went through. So as far as technology implementation and, you know, a real case on how it works and the benefits of it, you know, I think, it's a great example. And, you know, we're certainly appreciative of you writing the article for IMA and sharing your story here with us on the podcast, right before we wrap up, I just want kind of close out the conversation. At the end of the day, when it comes to technology and change management, you've had, you know, different experiences now and success with it. You know, some of the other considerations with technology that you, you briefly mentioned as far as the data and the governance, and, you know, really making sure that, you have your own policies in place. I'm just curious if there are any major lessons learned that really stand out, or maybe even, you know, more importantly, some key takeaways that our listeners can, you know, think about in their own technology implementation projects, as far, you know, things to remember. 
 
Greg: (16:50)
I really think the first thing that comes to my, for me here is, is so the president of my company now, he preaches people process tools, his name's Paul Hardy, great president. I think for me, what I'll take it back a second. So I love to build I'm a, I love to create things and we had so many things to fix. When I first go out here at Rembrandt I always ask for three years of financial statements before I accept a job offer. I think that's a good, a good habit to form for all of the accountants out there. You should know what you're getting into. And I knew I was getting into something that needed to be fixed. And that's kind of what that's my M.O. I really like to, to build. I like to fix, I like to, I think I understand manufacturing enough that I figured out the algorithm of how to make manufacturing work, but there were so many things, there were so many things to change, so many things to, to improve. 
 
Greg: (17:50)
I think I, I was thinking about this the other day, and there were 15 major projects that I undertook in less than two years. And we were successful in all of them when it came to implementing technology, when it came to building process and building the tools, what I wasn't as successful at, and only looking back, this was about in February last year, I realized that I had done all of these things and I left everybody else in the dust. I left the people out of it, and that was devastating for me. It was devastating for me to, to know that I did all of this work. I built all of these tools. I built all of the, the process, but in the end of the day, if the people don't come with you, then you're just left with a tool on the shelf. 
 
Greg: (18:40)
And that's what if I had to go back and change it? I would, I think, taking on less projects and spending more time communicating with the people, helping them understand why it's so important, maybe modeling and showing why it's so important before going off and, and just getting it done. I think that would've been helpful for me and something that I will definitely take with me going forward. That being said, we did a lot of projects, and I think we're getting the people caught up with us. So it's not like we wasted all of our time on the 15 different things. But yeah, I just have to say people, perceptions. Those are the most important part of change management, because a tool won't do the work for you, a process won't of the work for you in the end, there are people that are the most important thing that we have, the most important resources that we have. And if we leave them in the dust, then what good is the tool that we've put in place. So that's the lesson I've learned in the last two years. 
 
Speaker 4: (19:51)
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