How AI can optimise yield in food and beverage manufacturing

Yield optimisation is the most powerful lever food and beverage manufacturers have to simultaneously improve profitability and reduce food loss and waste. Even small percentage gains, turning more raw material into sellable product, compound rapidly, because raw materials are typically the largest 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.

This is where artificial intelligence (AI), the agentic enterprise, and Infor™, are changing the game. They give manufacturers the ability to sense variability, learn from every batch, and act on recommendations that continuously close the gap between actual and optimal yield. Read on to discover how we’re putting these capabilities to work.

Mass balance and financial valorisation

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, such as crops, milk, and livestock. 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 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.

Processing yield versus end-to-end yield from a manufacturer’s perspective

It also helps to distinguish between processing yield and end to end yield. Processing yield looks at yield in production processes such as extraction, cooking, or trimming. End to end yield takes a broader view, following raw materials from intake through processing, packaging, shelf life, and ultimately to retail and food service. This wider perspective reveals forms of food waste such as products expiring due to demand–inventory mismatches. The point in the supply chain where most food waste and loss occur varies by product type. Fresh fruits often experience greater losses upstream, while fresh bakery products generate more waste downstream in the distribution network. For other products, the greatest potential for reducing waste lies in increasing manufacturing yield.

Globally 13.2% of food is lost in the supply chain after harvest on farms and before the retail stages, according to 2024 United Nations Environment Program statistics. Not everything is within the manufacturer’s control, but still the potential for yield improvement remains very large. To make this actionable, let’s take a deeper look at how processing yield can be optimised.

How AI helps to optimize yield

Yield losses rarely have a single cause. They emerge from the interaction between material quality, process settings, equipment behaviour, and environmental conditions. They often vary from batch to batch, meaning human employees rarely have the capacity to track, let alone act on the information generated from these interactions. Without integrated high-context data, these contributors remain hidden, and yield improvement becomes guesswork. But with machine learning, manufacturers can turn large volumes of data into actionable insights and give AI agents the foundation to deliver value-driving recommendations to the process operators.

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 such as fat, protein, moisture, or grade. MES captures how each batch is processed across temperatures, times, setpoints, and equipment states. While IoT and environmental sensors enrich this view with real time conditions like humidity, energy stability, or 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.

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 practise differs by product. A juice producer looks at Brix and optimises blends from multiple tanks and drums, for example, while a cheese maker focuses on butterfat and protein and adjusts coagulation temperature and time, among many other parameters.

That’s why effective AI yield optimisation combines domain expertise with data science. Process technologists bring the understanding of how products behave, while machine learning handles what humans cannot: Analysing dozens of interacting variables in real time. 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.

Also, there is a big difference between processing batches, such as blending, cooking and baking, versus optimising yield from carcasses in the meat industry, because the latter requires smart equipment with real-time imaging and edge AI to optimise cutting and slicing of the individual piece of meat at hand.

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 optimization 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 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 = spendon critical raw materials x reduction percentage

Case studies show the power of this math. Amalthea, a Dutch cheesemaker, reported that a 1% increase in milk yield represents €500k in annual savings.

Conclusion

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, while 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.

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

Learn more about the Infor difference or check out some practical AI applications in food and beverage manufacturing across demand forecasting to predictive maintenance in this eBook.