AI’s role in food and beverage yield optimisation

Yield optimisation is the most powerful lever Australian food and beverage manufacturers have to improve profitability while reducing food loss and waste. Even small percentage gains, turning more raw material into sellable product, compound rapidly, because raw materials are typically the highest cost in production.

What makes this challenge uniquely complex is that food manufacturing starts with products of nature. Raw materials vary in quality, grade and potency with every harvest and animal. When yield is lost, it isn’t just margin that disappears — it’s food that never reaches the table.

Yield is often described as a simple mass balance — how much weight/volume of finished product you get from the weight/volume of consumed material. But yield is so much more than that when it comes to food and beverage production: it’s also about the value of what goes in versus the value of what comes out. Lower‑grade crops purchased at a lower price may deliver less yield by weight. But they can still make economic sense when the value of the finished products is taken into account.

Seen through this lens, improving yield is not just about efficiency. It’s about making better use of what nature provides and directing as much material as possible into the highest value food.

How AI helps to optimise yield

Yield losses rarely have a single cause. They emerge from interactions between material quality, process settings, equipment behaviour and environmental conditions.

Global trade dynamics are changing rapidly, potentially shifting demand for a particular crop overnight. Extreme weather events may create volatility in crop supply and demand. Issues often vary from batch to batch, meaning employees rarely have the capacity to track, let alone act on, the information generated from these interactions.

Accelerating technology adoption is critical for this. Organisations need robust digital strategies that incorporate AI, process intelligence, process automation, IoT and Industry 4.0 technologies. Investing in automation also addresses the shortage and high cost of labour.

By bringing together data from enterprise resource planning (ERP), manufacturing execution systems (MES), laboratory, scales and Internet of Things (IoT) sensors, manufacturers gain end-to-end visibility into what truly drives yield. ERP provides context on raw material sourcing, lots and cost. Quality control systems add insight into composition and quality attributes. MES captures how each batch is processed across temperatures, times, setpoints and equipment states, while IoT sensors enrich this view with insight into real-time environmental conditions, energy stability and throughput variation.

When data is connected at the batch and loT level, patterns emerge. Machine learning reveals which raw material attributes, process settings and environmental conditions consistently drive yield up or down. Manufacturers can turn large volumes of data into actionable insights and give AI agents a foundation that delivers value-driving recommendations to the process operators.

This shifts yield from after-the-fact analysis to predictive insights and prescriptive maximisation.

As an example, operations teams can quickly review the negative impact of a lower process temperature on yield. AI agents can simulate changes to processing parameters, project the impact, and help operators make real-world adjustments to get the process back on track, so teams can focus on the actions that matter most. And over time, this creates a learning loop where each batch improves the next, reducing waste and converting more of what nature provides into food.

Nuances per sector in food and beverage processing

Not all food products are the same, and neither are the factors that influence yield. While the underlying machine learning model may be similar, what matters in practice differs by product. That’s why effective use of AI to optimise yield combines domain expertise with data science. Process technologists bring the understanding of how products behave, while machine learning handles what humans cannot: real-time analyses of dozens of interacting variables. Where teams once reviewed a handful of parameters in spreadsheets after the fact, machine learning models can now evaluate more than 60 contributors per batch as production happens, enabling continuous adjustment and sustained yield improvement.

Yield loss is often cited as the largest area of value leakage for manufacturers, but that is not the case for all sectors. In bakery, where freshness and forecasting are paramount, waste optimisation is of greater concern when the clock is ruthless. Once again, the machine learning model will be similar, but the data, processes and levers will be slightly different.

How to quantify the financial impact of yield improvements

To translate yield improvements into business value, the simplest calculation model is based on raw material savings. If the same production volume can be achieved with less raw material, estimated savings can be calculated as: Reduced raw material spend = spend on critical raw materials x reduction percentage.

AI‑driven yield optimisation helps convert more raw materials into sellable food. By combining ERP, MES, quality and IoT data, machine learning exposes the true drivers of yield at the batch level. At the same time, AI agents help process operators quickly adjust parameters to maximise yield from costly raw materials.

And when data, systems and intelligence across your entire operation converge into a single connected layer, they stop being separate tools and become something greater. This is the agentic enterprise. It’s where insight and action are no longer separated by time, capacity or human bandwidth. Collecting and integrating high-context data can unearth otherwise hidden contributors and remove the guesswork from yield improvement.

Now is the time to combine the experience of your process technologists and data with purpose-built AI solutions for food and beverage. Now is the time to pull the lever of increased profitability.

*Marcel Koks serves as the Director of Industry & Solution Strategy for the food and beverage sector at Infor. With a professional background spanning over three decades, he has been deeply involved in the deployment, solution consultation, sales, marketing and product management of enterprise applications. Throughout his extensive career, he has helped many food and beverage businesses by defining and implementing fit-for-purpose solutions. In his current role, he is charged with shaping and guiding the strategic direction for Infor's engagements in the food and beverage market.

Top image credit: iStock.com/Organic Media