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Revolutionising Food Processing with AI


Artificial Intelligence (AI) is transforming various industries, and the food processing sector is no exception. With its ability to analyse vast amounts of data quickly and accurately, AI is revolutionising maintenance, quality control, scheduling, and resource allocation during the conversion of raw materials into edible semi-finished or finished products or ingredients. This technological advancement not only enhances efficiency but also contributes to sustainability in the industry. Let’s delve into the ways AI is reshaping the future of food processing.



Manufacturing facilities use capital-intensive machinery and improving and optimising the use of these machines, their energy consumption and efficiency are critical for staying competitive in the industry.

Digital solutions including predictive quality analytics and/or predictive maintenance are already used to detect machine failures and anomalies, predict faults and abnormalities, and find factors that impede productivity.

One effective approach is leveraging Industrial Internet of Things (IIoT) devices and their digital twins – digital representations of physical objects, systems, or processes. When combined with AI, these solutions offer significant advantages for predictive maintenance. By utilising machine learning algorithms to analyse real-time data from sensors, potential equipment failures can be detected in advance. This enables companies to schedule repairs or replacements during planned downtime, thereby minimising disruptions in production and reducing maintenance costs.

Fluke Reliability, a division of Fortive corporation and a long-time player in predictive maintenance using its temperature and vibration monitoring system, recently purchased Azima DLI, a top player in AI-driven vibration analysis software and subscription-based remote condition monitoring. They are currently overseeing 15,000 assets for industrial giant Cargill and have managed to lower their maintenance needs by 10%, decrease their downtime, and extend the lifespan of their machines.


Quality control

AI plays a vital role in ensuring consistent product quality by automating quality control processes. Machine learning algorithms can analyse data from various sources, including visual inspections, sensory evaluations, and lab tests, to identify patterns and detect any deviations from desired quality standards. This enables real-time monitoring and immediate corrective actions, reducing the risk of compromised product quality reaching consumers.

For example, one of PepsiCo’s project at their Frito-Lay production facilities uses lasers to hit chips and then listen to the sounds coming off the chip to determine the texture. Algorithms have been developed to process the sound and determine the chip texture to automate the quality check for Frito-Lay’s chip processing systems. The company has also developed a machine-learning model that could be used with a vision system to be able to predict the weight of potatoes being processed (to replace expensive weighing elements). The vision-based system uses a camera and a machine learning model and are essentially just additional data points collected with no additional cost. A final project (still in development) would assess the “percent peel” of a potato post the peeling process. By understanding this data, it can help the Frito-Lay team to optimise the potato peeling system. The vision-based weight system led to considerable savings for the company because it no longer had to spend $300,000 per line (they had 35 in the U.S. alone) for weighing elements. The percent peel project is estimated to save the company more than $1 million annually in the United States once implemented across the business.


Scheduling and resource allocation

AI can also be used to optimise production schedules, predict demand patterns, and streamline resource allocation. At the same time automation reduces manual labour, speeds up production cycles, and minimises errors. This improves operational efficiency and can reduce costs as well as waste from manufacturing.

In Feb 2020, Tyson Foods announced its plan to bring computer vision to its chicken plants to track how much chicken moves through its plants as part of an effort to invest more in automation and artificial intelligence to cut costs and reduce waste.

Combining cameras, machine-learning algorithms and edge computing—where data is processed and analysed in near-real time without being sent to a data centre— the company, partnering with Amazon’s cloud services division – AWS,  records hundreds of thousands of pounds of packaged chicken every week. The system identifies the type of product, such as a package of chicken thighs, and the stock-keeping unit, or SKU, number for the batch of products, using computer vision. An automated scale records the weight of a batch of chicken packages in the cart. An operator looks at a nearby screen and confirms the weight and SKU number. The computer-vision systems could also help detect foreign objects such as pieces of conveyor belt at facilities that process products at a high volume, which could be useful for food safety.

The accuracy rate for identifying the product type and SKU number is in the high-90% range, using computer vision, an estimated 20% improvement over manual processes.



By analysing data from the entire supply chain, AI systems and IIoT can help identify areas of inefficiency and waste. For example, AI algorithms can optimise inventory management, reducing food waste and minimising the environmental impact of overproduction. AI-based computer vision and pattern recognition techniques combined with parameter measurements using sensors can also recognise variances, removing contaminants without wasting whole batches, and continually adjust water and energy usage according to the process requirements.

As part of its Digital Manufacturing Acceleration (DMA) program Danone has established a factory in Opole, Poland. The goal of this program is to test and implement new digital technologies before expanding them to other manufacturing sites worldwide. Some of the technologies that Danone has tried out and implemented include the use of AI in the factory’s drum dryer, which is used for preparing raw materials for cereals-based weaning food. By using machine learning, the equipment can optimise its settings based on real-time data. This has resulted in improved process stability, reduced waste, and a 40% decrease in energy consumption.

So far, the programme has introduced more than 20 digital solutions to 39 Danone factories across the business. At the Opole factory, Danone has achieved a 19% reduction in manufacturing costs (2019-2021) and a 12% improvement in efficiency. Additionally, GHG (Green House Gas) emissions were reduced by 50%, and energy consumption by 23%.



AI is revolutionising maintenance, quality control, scheduling, and resource allocation in the food processing industry. By harnessing the power of AI, companies can enhance efficiency, improve product quality, optimise resource utilisation, and contribute to sustainability.  As AI continues to advance, its applications in food processing are expected to expand further, leading to a more efficient and sustainable future for the industry.

AI can be intimidating at times, and that’s why it’s important to partner with someone who understands your business and has the ability to democratise the new technology adoption and hardwire it to critical business processes and key performance indicators (KPIs).

Utilising AI technologies can help optimise processes, improve decision-making, and ultimately boost productivity and profitability.  Strategic Allies Ltd aims to collaborate with you to identify a broad spectrum of  solutions for your business. Our approach involves delving deep into the core of an issue and gaining a thorough understanding of the intricacies of a particular technology or market, to provide you with a wide range of technologies and solutions for consideration.

If you are interested in exploring how we can assist you, please contact John Allies at john@strategicallies.co.uk for an initial discussion.