During VICTAM Asia 2026, co-located with Animal Health and Nutrition Asia in Bangkok, Feed Strategy spoke with exhibitors about the technologies and strategies shaping feed production in the region. In this interview, Ian Mealey, product marketing director for formulation at Datacor, draws on more than 30 years in the feed industry to discuss how feed mills can establish quality control programs without major technology investments, determine ingredient sampling frequency, manage variability in locally sourced ingredients and close the data utilization gaps that stand in the way of more precise formulation.
Interview with Ian Mealey, product marketing director, Datacor
Jackie Roembke, editor-in-chief, WATT Feed Brands: Hi, everyone. Jackie Roembke here, reporting from VICTAM Asia, also co-located with Animal Health and Nutrition Asia. So today, we are going to share some of the conversations that we had with exhibitors here at the exhibition in Bangkok, Thailand.
Ian Mealey, product marketing director, Datacor: Hello, I'm Ian Mealey from Datacor. I'm product marketing director for formulation. I'm based in the U.K. I've been working in the feed industry for over 30 years. I started as a formulation manager with a large feed manufacturer in the U.K. and then joined what was Format International in the past, now Datacor. I've worked across the globe looking to help users get the most from formulation software. In my spare time, I enjoy being in nature, bird watching and walking. And I enjoy the travel, which is great. So I get to travel and see lots of customers.
Roembke: For a poultry nutritionist who may not have a sophisticated LIMS or automated batching system, what's the most practical first step they can take to get a handle on those production-side errors?
Mealey: I think on the LIMS side, you don't necessarily need to start out with a sophisticated system. I think we all have access to tools which can help us manage data. I think the fundamental thing is to have a quality control program in place and to utilize that information, store that information, but most of all, to use it. I think in the quality control program, the aim is often firstly to manage ingredient quality, basically to ensure that you're getting what you're expecting.
But I think what a practical step is to take with the quality control program further and aim to check finished product quality against what you thought you were going to be providing. Keep that information and make sure that you're checking the information. As I say, you don't need a sophisticated LIMS program to start on that.
Roembke: How much historical analytical data does a company typically need before a probability-constrained optimization approach becomes reliable, and is it realistic for smaller or midsize producers?
Mealey: On the production side, I would say why not find out what you can measure in the first place, even if you don't have an automated batching system? I think perhaps an audit process within your plant is a good place to start. Find out what the weak spots are and by understanding the quality of your finished product, and understanding more about your plant and how that performs through things like an audit process, you can start to identify places you can make improvements, and that might include the justification for a more sophisticated set of tools like a LIMS system or an automated batching system in the future because you can identify the benefit.
Roembke: How should nutritionists be thinking about sampling frequency and protocol to make their data useful for formulation decisions?
Mealey: I think everybody in the feed industry already has a lot of data about their ingredients, for example. And so I think that perhaps the first thing to think about is how can we actually utilize the data we have? In terms of the quantity of data, the amount of historical data, as you put it, very much depends on the ingredients. There are certain ingredients you use more of more frequently. There are certain ingredients you know are more variable. And so the judgment about that depends very much on the ingredient itself.
There's another aspect, which is that a limited time horizon is sometimes applicable. New season supplies of ingredients can significantly change. So having a very long-term set of data is not always the best. So a combination of historical, and obviously the more data you have, the better. But a combination of historical alongside a rolling set of relevant, more recent relevant data is a great way to understand what's actually happening with that material right now, but also as a trend analysis. So I don't think there's one particular number you can say. I think it's very much in your situation.
Roembke: The conference you spoke at the other day brought together nutritionists from across Asia and the broader global market. How much does regional ingredient sourcing variation — think locally sourced corn versus imported soybean meal — complicate the nutrient matrix management challenge you described?
Mealey: I think there's a link back to the previous question, which is what's the nature of that ingredient and how much do I use? How often are we getting deliveries? That will dictate, in many regards, your sampling policy.
Protocols, there are standard approaches to sampling itself, technique of sampling. And when you're satisfied with the frequency, you need not only the number of samples or the number of samples you take, it's how to sample within a load you're receiving. It also needs education of your staff. If you're relying on people to take samples, the staff need to be confident in how to do that, and you need to be confident they're doing that correctly.
Another aspect of the ingredients that you're using is the usage type, its format. Is it coming in bags? Is it in bulk? Aspects like that, you need to consider, and that will affect how you're going to sample. Again, also the variability, the nature of the variability in that ingredient will educate you or provide a guidance to whether or not you need to be sampling more frequently or otherwise. But as a fundamental aspect, it's a great policy just to be sampling on intake as a way of ensuring that the ingredient you get is what you're expecting. It shouldn't be rejected. But don't forget finished product sampling either.
I mean, fundamentally, it's the same problem the world over. You have to manage your ingredients and understand those ingredients. I think for local suppliers, as I see it and others may have other ideas, but as I see it, the problem is that there's often not the research on the local ingredients in terms of quality. And that's not just in terms of the major nutrients, but it's aspects such as digestibility, utilization of those ingredients, purity of the ingredients. And so perhaps a strategy that you can take on a local basis is ensure you have that quality control process in place, but also work with suppliers. Go and see where the materials are coming from, what their sources are, and ensure that you are happy with the supply.
Roembke: Your conclusions call for investing in laboratory information management systems (LIMS) and system integrations to streamline data acquisition into the formulation platform. Where do you see the biggest gap right now between what the technology can do and how most poultry feed operations are actually using it?
Mealey: Integration is perhaps the first gap, and that's in terms of the technology integrating through technology, moving data from one system to the next is a gap in itself. And that's why I mentioned it. But it's also integration in the sense of joining the dots in your business. The feedback loop is hugely important. So the feedback loop is hugely important, and as we've already discussed in our conversation today, we all have a lot of data already, and I think the gap has been in knowing how to use it to its best effect. I think that is the gap that we now have the opportunity to address. And perhaps aligned with that is understanding the value in the data, and that's a sort of mental gap rather than a system gap, I would say. Realize the value of the data you have and what you can achieve with it to improve the precision of what we're trying to do. After all, there's lots of variability in the system. The more we can create certainty and control things, the better the outcomes.