Feed mills are sitting on untapped potential within their existing operations, according to Nick Malott, analytics architect with Interstates. While most facilities already collect crucial data about motor performance, voltage levels and equipment run hours, few are leveraging this information to move beyond reactive or time-based maintenance strategies. The problem isn't a lack of technology or data — it's integration.
Malott joined the Chat to explain how predictive maintenance approaches can extract more useful life from equipment, reduce premature replacements and create collaborative decision-making frameworks between maintenance, operations and engineering teams. The shift requires more than new software; it demands a cultural change that bridges the gap between operator experience and data-driven insights.
Interview with Nick Malott, analytics architect, Interstates Inc.
Jackie Roembke, editor-in-chief, WATT Feed Brands: Hi everyone, welcome to Feed Strategy Chat. I'm your host, Jackie Roembke, editor-in-chief of WATT's Feed Brands.
This edition of Feed Strategy Chat is being brought to you by the Feed Mill of the Future Conference. The Feed Mill of the Future Conference is a half-day event that will bring together leading feed industry experts to examine emerging feed mill technologies poised to impact animal feed manufacturing. The conference is produced by Feed Strategy and Feed & Grain, and organized in partnership with the American Feed Industry Association.
To learn more about the 2026 edition of the Feed Mill of the Future Conference, please visit www.FeedMilloftheFuture.com.
Today we're joined by Nick Malott, an analytics architect with Interstates. He's here to talk about how predictive maintenance is transforming feed mill operations.
Hi, Nick, how are you?
Malott: Hi, Jackie. Thank you for having me.
Roembke: Thank you for taking the time to be here. Well, let's get right into it. Where do you see the greatest gap between the maintenance capabilities mills have today versus what they'll need to stay competitive in the next, let's say, five years?
Malott: From what we've seen, most mills still rely on reactive or time-based maintenance instead of predictive or condition-based approaches. What this leads to quite often, they'll do things like run a motor until it's 80% of its rated run hours, and then they'll replace that motor to avoid downtime or any kind of events in the future. What we see is that using predictive maintenance, or condition-based maintenance, allows us to get a little bit more of that remaining useful life out of a motor or out of an asset.
This is very common throughout feed mills, where we're replacing motors early, instead of, you know, waiting until they're about to fail and then replacing them again just so that we can avoid downtime happening during a run.
The real gap that we see within feed mills is integration. So, the data already exists — they already are collecting things like motor currents, motor voltages, run hours, and those kind of information pieces, but it's not connected across systems to use to drive reliability decisions. So what we end up seeing is that, in the future, we really need to have data platforms that unify this data to allow operators and engineers to do things like predictive analytics and then collaborate between maintenance, operations and engineering at a site.
We've seen success when these areas align around shared metrics and digital tools that really close those feedback loops
Roembke: As feed mills face growing pressure to improve efficiency and reduce waste, what role should that data play, and how should operators make decisions about equipment and their operations?
Malott: That's a little bit of a shift in how we're using data, right? So traditionally, we've used data in more of a historical fashion, looking at root cause analysis when something goes wrong, when we really should shift to using data to be more real-time and actionable. What this means is giving operators clear visual insights, not just raw data for them to actually process through. Those insights should really guide them to make fast and informed decisions about their process. We've used dashboards quite frequently to reveal inefficiencies of the plan, and what that does is it really leads to projects that can improve uptime and throughput.
The key is turning that operational data, the raw data, into KPIs for operators to be able to act upon things like energy use, downtime and equipment health, and those kind of things can be used between different teams in order to organize and make a more effective maintenance strategy at the site.
Roembke: In your opinion, what is typically holding feed manufacturers back from making the leap from a more traditional maintenance approach versus a more technology-driven strategy?
Malott: Yeah, I think sometimes we get a little bit concerned that the biggest barrier is cost, but it's really not. One of the challenges that we actually see is the trust and expertise from clients to be able to bring in a product and actually take it through to integration with their site.
Most teams rely on experience over analytics. Typically, we have a lot of operators that have been in the plant for 10, 20, 30 years, and so they have these experiences, they know how the system runs. And so there's a little bit more trust in that kind of traditional approach to manufacturing.
Really, what we need to build are systems and tools that allow our operators to be able to understand and really dive into the analytics and trust those analytics alongside what they're seeing in real time.
One of the big things we've seen is there's a large skill and training gap, and that leads to slow adoption of some of these new technologies, especially when they require an operator to do some kind of analysis across the data, instead of just being given an answer or being given a directive to go act upon.
One of the ways we've kind of helped bridge this is really starting small. So building out small pilot projects that really prove the ROI of a maintenance strategy that builds confidence with customers, and then we can show the practical benefits beyond that. It helps because our leadership, they see those small projects as small wins, and they can see kind of the trajectory of doing larger and larger projects and building on that small piece.
Sometimes we refer to that as the LAER model: landing, adopting, expanding and renewing. The landing is that small project, getting them to adopt it and really make it into their maintenance strategy, and then expanding and renewing those type of projects.
Roembke: Based on your experience, is there any type of feed operation, either size or specialty, that most benefits from this approach?
Malott: Yeah, typically, a larger feed mill is already doing these kind of things, and we find this more beneficial with the smaller feed mills, specifically, because they're less interested in trying to dive into analytics and some of those kind of things, because they're so focused on producing product. They need to make sure that they're running and they're much more higher priorities.
The larger customers are already doing some of these things, so building on top of that is something that they're already in the process of doing well.
Roembke: Thank you so much for your thoughts, Nick. If you'd like to hear more from Nick about predictive maintenance, please consider attending the 2026 Feed Mill of the Future Conference, held at IPPE on January 27, 2026. For more information about the event, please visit www.FeedMilloftheFuture.com.
Thanks again, Nick, and thanks to you for tuning in.