As feed mills become increasingly automated and face significant operator turnover — an estimated 15% attrition rate affecting nearly 29,000 positions annually in North America alone — the industry is turning to advanced technology solutions to address training and operational challenges.
In this conversation, Ranjit Maharajan from ANDRITZ Feed & Biofuel explores how operator training simulators provide safe, repeatable practice environments for managing complex automation systems, while AI-powered tools enable operators and managers to "chat with their data" for faster root cause analysis and predictive maintenance. From preventing unexpected downtime to leveling the playing field between experienced and new operators, these technologies are creating a cultural transformation in how feed mills approach training, decision-making and operational excellence.
Transcript of interview with Ranjit Maharajan, head of product group - automation solutions, ANDRITZ
Jackie Roembke, editor-in-chief, WATT's 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 brought to you by the Feed Mill of the Future Conference. The half-day conference, held January 27 at the International Production & Processing Expo (IPPE), brings together feed industry experts to examine emerging technologies poised to shape animal feed manufacturing. The event is produced by Feed Strategy and Feed & Grain and organized in partnership with the American Feed Industry Association. For additional details and to view the full agenda, visit www.FeedMilloftheFuture.com.
Today, we're joined by Ranjit Maharajan, head of product group, automation solutions at ANDRITZ, is here to talk about how feed mills are revolutionizing operator training and decision-making through simulation technology and AI-powered data tools.
Hi Ranjit. How are you today?
Ranjit Maharajan, head of product group - automation solutions, ANDRITZ: Hey, Jackie, I'm doing fantastic. How are you?
Roembke: I'm doing great. Thank you so much for taking the time to join us. So, let's get right into it. Feed mill operators are increasingly working with complex automation systems. What challenges does this create for training and how are operator training simulators being used to address safety and skill development?
Maharajan: As you know, as feed mills have become more and more automated, the role of operators has changed considerably. It's not just starting and stopping a machine. They must manage a lot of interconnected systems. They must understand the process behavior, and they must respond to the situation when that's outside of their normal conditions. There are always problem conditions that will be thrown at you — and now today's feed mill operators have to basically figure out how quickly they can solve this problem.
These challenges create the need for training, and many of these situations are rare, but critical. Like a pilot, very rarely is he going to land on water, but that training is necessary. You'll feel comfortable knowing that pilot has gone through that training although the chances are very slim.
Similarly, there are a lot of things happening in feed mills. There's a raw material variability, there's a process upset, or there is an equipment failure that just doesn't happen every single day, but when it happens, the operator needs to be prepared to really understand the problem and act on it quickly in a safe manner. This is almost impossible in a live environment because today most of the training happens by work shadowing or by trial-and-error methods, and there is a risk of safety, quality and throughput when you are operating like that.
So, here comes the simulation-based training that addresses this and gives the operator a realistic environment — much like your flight simulator where the pilots are getting trained. We do have an option for the operators to sit in front of their operator station, and they will not know that they are operating a digital plant. This gives them the practice in responding to different situations.
We can program different scenarios depending on typical challenges, what they have, or if there is something specific to your mill — we wanted to develop that. We could do that where then the operators are given these challenges and they keep basically practicing. And what this does is that it eventually gives them confidence without really risking the production or safety of the feed mill.
So what is powerful — what we have seen — is that the operators repeat multiple scenarios, learn from their mistakes and build confidence. Then when they are actually facing this situation in real time, they are very well prepared. Over time, this actually leads to more consistent decision-making and a much stronger safety culture. So, ultimately, there is a shifting in the training that happens from on-the-job training, or trial and error, to more of a simulation-based controlled environment, which is more structured, repeatable and safe, which is exactly what modern automated mills need today.
Roembke: That’s really interesting. You mentioned in a previous conversation that operators can now chat with their data using AI tools. Can you explain what that looks like in practice and how it changes the way mill managers can make operational decisions?
Maharajan: Absolutely. With the advancements of AI (artificial intelligence) and LLMs (large language models), there is a lot of changes happening very quickly, which is a good way that fundamentally changes the way we actually interact with the data at the mill. In practice, this means chatting with data. It's really how people interact with the information.
Traditionally, in a feed mill, you have a set of alarms or your production report or your daily report. And people just take that when there is an alarm or, at the end of the day, they look at the production and they are more actually being reactive to it. Now, what does AI chat with data does is really removes that friction where the operators and managers can simply ask in a plain language. I'm sure everybody is familiar with ChatGPT and Copilot. It's basically the same technology that you would actually have it in your feed mill. For example, a mill manager would say that, "Hey, I'm noticing that the pellet quality or the throughput is slightly lower than normal, but there are no active alarms."
So what is going on? Instead of having an operator going and looking at it or an engineer going and digging all the data and putting together the reports and trying to find the root cause, the system now does it automatically for you. So it actually looks at all the historic data. It analyzes the patterns and digs through tons of data from your mill.
As an example, your throughput has been decreasing by 4% over the last two shifts because it correlates with the higher conditioner temperature variability. You obviously have that upstream and then that is what is affecting your energy consumption is actually a little bit higher. We did, and it might actually even go further and say there's a similar pattern in the past. We were associating with die wear or some changes in the raw material of moisture.
This level of insight is readily available at your fingertips where you're just asking these questions in a very conversational manner. Instead of reacting late or guessing, what this does is it gives them the confidence that they know the exact root cause, and they might be able to attack it. It could be something like you wanted to schedule a maintenance that inspection and then you wanted to act early before the quality gets impacted. This fundamentally changes the decision-making at the mills. The teams can move much more agile, and it's not the data is not sitting in front of one or two people.
What's important is this: accessibility. What I love about it is that the same data is available to the operators, to the mill manager to the maintenance manager, and everybody is going to operate off of the same data. It also actually levels the playing field because let's say there is an experienced operator, who over years of experience can understand this pattern, but when there is a new operator, then they're going to be completely lost. Now he would have the same access to the same data and he would be able to take a much more informed decision.
So, what I have seen is that over time, this changes the culture. The teams actually starts asking better questions, even when asking questions on ChatGPT, my prompts are getting better. You know how to navigate through that. So similarly, you get better in terms of asking better questions, asking the right set of questions, and really, then the AI becomes your decision-making partner. Basically you're really having one of your best operators with all the data insights sitting next to you. And that's what really this you know chat with data is going to bring to the feed now.
Roembke: That's excellent. I know that I use AI for similar things. It's my assistant — or that's how I describe it. Then just to clarify, mills are doing this right now? Is this something that is accessible at this moment or is that something that's further down the road?
Maharajan: Yes, it is it is something that is available, and we have this technology and it has been there for other process industries for quite some time. And so this is a very proven and better technology. And for now, it is already available. If somebody wants to explore that and they want to experiment, we can help them out.
Roembke: Based on your experiences, what's the biggest misconception about AI technology and what results have you seen when companies do take that leap and trust the technology?
Maharajan: I think this is an excellent question because I get asked about this a lot of time from my customers because I think the word AI is being thrown at everything. So sometimes the customers do not really understand it. So, the biggest misconception is that they feel like either the system is too complex or it's on the other end of the spectrum. They might think that this is about just dashboarding. There is a lot of confusion and this is even like from my real-world experience, the operators are actually scared. There was a moment when I remember when I was commissioning the system and the operator asked me a point blank: "Will I be fired after this system is in place?" because they are scared about this technology as well. Many teams worry about introducing this AI and there is a lot of confusion about it. Also they worry about the effect of really good operators or experienced operator.
In reality, it's actually quite opposite because what happens is that the AI is actually enhancing the operator and knowledge and also helping them along the way to be able to make better decision-making. So when applied to real operation problems, it actually amplifies the human expertise. It doesn't replace the humans. It actually amplifies that and produces tangible results.
I can give one example where recently there was a mill, and we had vibration sensors put it in there. And in the feed industry, we always don't have the luxury to actually analyze all the vibration patterns. We don't have dedicated vibration experts and so on and so forth. So, we wait until there are alarms or something like that, which is already too late. When we implemented our system, the AI detected a pattern, which was kind of like what was happening before. It gave an early sign, even though it was well within the limits, and we were able to predict that if this continues, then you're going to have a bearing problem. And so the system actually alerted the operator saying that, "Hey, the AI has detected some patterns and then there's early signs of bearing looseness and you might want it to do some corrective action."
Then this let the operators to kind of like basically finish the production, slow down and maintain and do a maintenance check. And they were literally able to save two days of downtime because if it had failed, then you know that would have been a much bigger disaster. Another example comes from production throughput and quality. There was a subtle deviation and it's almost impossible to track that deviation. And then AI kind of looks at it and it looks at not only your current production, but it looks at all the other parameters, your minimum sustainable rate, your overall equipment efficiency. It looks like a snapshot of your current system because it's not always optimum. There are you know not optimal and it takes into effect all these things. And then it will say that, hey, there is a bottleneck in the conditioner, for example, and the operators proactively would adjust this team and then be able to optimize that rather than you know that batch becoming a scrap or something like that.
What I have seen is that the most significant impact is the decision quality and the team confidence. So once this team's actually sees the AI, it can help them with the real problems and solve the real questions in their head — then it actually becomes a partner and not anymore seen as a threat or a confusion.
So the skepticism drops and the adoption grows, but there is a curve that everybody has to go through. And, you know, the focus really shifts from fear or doubting this technology to be using this to tool to help making a faster and more safer and consistent decisions over time.
Roembke: I think that highlighting those practical applications within feed production is so important because it is maybe difficult to imagine without context. Thank you for those examples. Now, are there any challenges to implementation for feed operations that are interested in exploring this kind of integration?
Maharajan: Typically, what I always say is that this is a journey and there are a lot of several tools that are available. There are a lot of solutions available. Typically how we operate is that we go and listen to the customers, like what their challenges are, and what their main issues are. Then based on the experience and our feed expertise, knowledge and the tools and everything, what we have in our tool set, we would work with them and really put together a solution. This is not a product that you just go and install and disappear. It is something that you have to collaborate with the feed mills and you have to work with them and understand their pain points and bring the right technology and the right solution.
One of the risks is that if you are not understanding the pain points or if you were to actually put in the wrong solution, it just becomes an expensive venture and everybody's going to lose confidence. We have to be extremely careful in understanding what is the challenge that we are facing and how do we solve it? Do we have technology that can actually solve? So if you were to ask, like even just a year ago, there were like a lot of technology that has completely you know shifted the way we are working.
And so as the technology grows, the solution grows as well. What I would suggest is that even if somebody has looked into these technology before, it might be actually not a bad idea to look into it again, just because the technology has been growing very rapidly.
Roembke: And looking back to those feed mills that you have worked with, but what's the most surprising transformation that you've observed with operators really digging into the tech?
Maharajan: What I have personally seen is that it kind of starts with a very low confidence and even doubts to some extent, and then once they see the results and once they see the effects of it, they understand this is helping me and not trying to replace me. That transformation is what's surprising.
If you look at it, there are roughly 20,000 feed mills around the world, just in North America 5,000 to 6,000 and on average if you take about six or seven operators and to some estimates we have about a 15% attrition rate. Now you're talking about close to 28,000-29,000 people that are churning every year.
We must understand the challenge here. I mean it's the scale of the challenge is huge because you simply cannot just do on-the-job training or shadowing or just a trial and error with this amount of churn that is happening. In addition to that, it's also sometimes very challenging to get operators in the feed mills because sometimes the location and everything.
That's why it becomes important that we are offering this simulation-based training where you can practice in and in a virtual environment, in a very safe environment. And the AI system not only just helps you with the training, but it also makes you a better operator. And over time, what I've seen is that the operator gets complete confidence in it. People that were really detractors started supporting it, and they even want to expand into other areas of the plant. What's remarkable in one word would be the cultural transformation.
The operators can collaborate more effectively and share insights and trust both their training and the recommendations that the AI is giving. So, they see that their overall skill level is also coming up and what used to take years and years to get to that level of competence. Now you're able to achieve it much, much faster and even sometimes a few days or months, you would be able to get to that level.
So, it elevates the overall team performance and results in much smarter, safer feed mill.
Roembke: Excellent. Thank you so much for your time. If you'd like to hear more from Ranjit, consider joining us at the Feed Mill the Future Conference in Atlanta, Georgia, held on January 27, 2026.
For more information and to view the full agenda, please visit www.FeedMilloftheFuture.com.
Thank you again, Ranjit, and thanks to you for tuning in.