Focus on AI:
AHX.ai | 41
FORMATION OF
AHX.AI “So, we understood the wood-based panels manufacturing process, having worked with CF2P in France and got access to a lot of data. We saw raw material quality changes all the time in the line, changes to the recipe and constant deviations from the actual targets, so you generally use a lot more resources to make that same product – more adhesives, more energy, and more maintenance.”
Mr Rashid Amin knew that others had
investigated AI in the industry previously, with mixed results. “We have been using AI for a very long time and knew it had a good shot of being successful. Our approach was if you can access all available data across a factory from raw material inputs to the final product you could in theory build a good quality indicator. To do that effectively at a good commercial scale you need to be incredibly transparent in terms of the quality you predict versus what actually happens.” It’s a big characteristic of
AHX.ai’s approach that it looks at AI as a continuously evolving system, which can be counter- intuitive for manufactures to grasp. “We have this radical transparent approach
to whatever we predict, the customer sees and directly compares it to the ground truth – like lab results. You are able to improve the models significantly over time.” Mr Rashid Amin said
AHX.ai can predict production quality really well most of the time, but there are always some occasions where it can’t – and that is an opportunity to learn with investigating the cause of that mis-prediction. “Maybe there was a sensor failure, or the
raw material changed too dramatically, so that the model couldn’t catch up.” On occasions where there is no known cause for mis-predictions, it gives the user a sense of caution that AI doesn’t know everything. After quality predictions, the second
AHX.ai component is cost estimations, a real opportunity to estimate the real time cost of the factory. “You need to have a good indicator of quality, and you need to have a good estimation of how costly these processes are.”
AHX.ai has real-time intelligence apps – analytics for simulations and optimisation, a production scheduler, asset maintenance, production emission indicator, and autonomous decisions engine. It presents itself as an intelligence platform that turns live data streams quickly into predictions, 24/7.
When Mr Amin and Mr Hitz, a statistician and AI specialist, presented at the International Wood Based Panels Symposium
Above: Wood-based panels production
www.wbpionline.com | October/November 2025 | WBPI
in Hamburg in 2024. They posed questions such as: • “What is the current quality of my product? • “What is the current cost per cubic metre of the product I am producing?”
• “Which processing parameters can I adjust to reduce costs without compromising the quality of the product?”
They showed figures for financial savings which could be made from
AHX.ai – potentially €500,000 a line annually for a production output of approximate €70-80m turnover. You can see how savings can mount up with multiple production lines. “Over time if you really embrace AI the
savings compound. If you have the quality prediction, cost estimator, recommendations and real time chat intelligence, then the savings can really go up quite exponentially.” Mr Rashid Amin said due to production variables the savings could vary from one year to the next. “It’s more of an art than a hard science, but the clients that are paying us each month would not be paying us if they weren’t making savings.”
AHX.ai says the cost of poor-quality manufacturing can range from 5-30% of gross sales for manufacturing, while static scheduling can cause the loss of up to 15% of revenue, and the cost of unplanned downtime can cost between 5-20% of production capacity. “So, you need to have a good cost
estimator and a good quality indicator. Once you’ve got these two you can start to build a recommendation system on top. That is quite critical. If the quality is too high, you
may need to speed up the line or reduce the glue loading, but you don’t exactly know by how much the recipes needs to change. With a good AI, you can build a very good recommendation system, but it is technically a very difficult problem.” He encourages producers to embrace AI
to help staff become more data-driven, so they rely on data to make decisions. The information generated compounds the longer the approach is used. Mr Rashid Amin also said companies begin
to attract the right sort of people into their companies. “You attract the right type of people because you do not bring people in that say they’ve done a certain approach in another factory and that’s how were going to do this here.”
AHX.ai’s recommendation system tells the
operator what they should be doing, but it isn’t yet able to generate recommendations in all eventualities. Out of context issues can be a problem, for instance. “But if you have this radical loop of transparency and you get your people in your company acquainted with AI then the savings are crazy.” Operators can work with their intuition and also with the indicators generated by AI. By working with each other, you make much better decisions on the production line and it is more transparent. “The operator can make a decision, and we directly monitor it – what was the decision, what was the recommendation and what was the output. This way a company learns.” Mr Rashid Amin emphasises that more data does not mean more intelligence.
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