Artificial Intelligence (AI) has been a hot topic lately, with a lot of media coverage on how ChatGPT and similar technologies are impacting our daily lives. With all this attention, you might be forgiven for thinking of AI as a new technology, but in fact, its origins can be traced back to his 1950s. What we actually see today is the result of decades of research and technological development. However, they are all becoming mainstream now and seem to be making a big difference in the way we live and work.
When it comes to the food and beverage sector, the situation is the same, with more companies reaping the benefits of AI technology. And the market value of AI in the food and beverage sector is expected to reach a staggering $29.94 billion by 2028.1The number of food and beverage companies investing in AI is clearly projected to increase. But while many in the industry have heard of AI, there is still uncertainty about what it really is, how it works, and how it can help the food and beverage sector. Widespread.
What is AI/machine learning?
AI is the ability of a computer or machine to imitate or imitate human intellectual behavior and perform human-like tasks. It performs tasks that require human intelligence, such as thinking, reasoning, learning from experience, and most importantly, independent decision-making.
Machine learning is a subset of AI. It is a computer system that can learn and adapt without being explicitly programmed or assisted. Machine learning uses algorithms and statistical models to intelligently analyze data and draw inferences from data patterns to inform further action.
Where does AI fit in the food and beverage sector?
Simply put, AI (especially machine learning) has the potential to optimize all areas of food manufacturing, facilitating smart, industry-specific applications to transform every aspect of the supply chain from farm to fork. improve and help build and drive an agile supply chain. revenue growth.
With the ability to take into account vast numbers of data values, parameters, what-if scenarios, and other factors, machine learning can generate accurate and timely recommendations for nearly every aspect of the food supply chain. . Ultimately, this provides a competitive advantage that cannot be replicated without the application of AI technology.
Where Is Machine Learning Already Used?
The applications of machine learning in the food and beverage sector seem endless. Take precision agriculture, for example, an area where machine learning is providing new and deeper insights. This is an analysis of past harvests in terms of both quantity and quality, combined with weather forecasts to inform which fields need to be watered, when or when to fertilize. is.
In the aquaculture sector, Nutreco, a leading animal nutrition company, achieved an additional production cycle of healthier shrimp while using 30% less feed. Specifically, we use audio sensors in aquaculture to listen to shrimp and understand when they are hungry. Then machine learning decides when and how much the shrimp should be fed. This reduces the feed conversion rate and shortens the shrimp production cycle, doubling his production without significant enhancement.
Another example of machine learning in action is Zeelandia Group, a global bakery ingredient business. The business addresses the challenges of rising costs and shortages of bakery ingredients by deploying machine learning models that recommend products and prices to offer to bakery customers based on what similar customers are buying. I have dealt with it. With the implementation of applied AI, the group reduced the time to prepare product recommendations for customers by 83%, reducing the time from 30 minutes to 5 hours. As a result of the reduced time it takes to recommend products, Zeelandia Group’s employees not only increase revenue per transaction and share of wallet per customer, but are also able to deliver a better customer experience. Improves accuracy and speed of recommendations and pricing strategies.
Food and beverage companies are increasingly turning to AI to reduce waste and identify inefficiencies in their supply chains. Amalthea, a leading global provider of goat and organic cow cheese, uses machine learning to make cheese quality more predictable, maximize yields, build customer loyalty and increase sustainability. increase. Previously, Amalthea was only able to manually analyze milk production on a weekly basis, making it difficult to adjust process parameters to optimize yield. By leveraging machine learning, Amalthea can not only directly understand the causes of yield changes, but also see yields immediately. This has allowed Amalthea to reduce overall waste from manufacturing. The company can quickly identify problems and improve processes at the same time. These changes had a direct impact on the company’s profitability and bottom line. Amalthea expects to save around €500,000 (£439,937) for every 1% increase in yield.
Planning for all contingencies
The food industry these days might be forgiven for thinking that certainty is uncertainty itself. So what role does machine learning play when data patterns may not be found? For example, unpredictable changes in weather conditions.
In this example, machine learning can better understand the risk of changing weather conditions and how it impacts yields globally. It is this improved understanding that can inform the strategies needed to mitigate these risks. But even with all the latest machine learning techniques, consensus is needed to ensure these strategies are effective. As the United Nations Food and Agriculture Organization (FAO) points out,2All parties involved in the food supply chain should minimize their use of water, energy and other resources to increase their resilience. All of these changes are underpinned by machine learning.
As technology advances and more companies discover the benefits that AI applications can realize, the capabilities of AI will be further developed and refined to solve specific industry and business problems. As we have already seen, the considered applications of AI technology are helping businesses across the food and beverage industry and supply chain, and this is only going to increase in the next few years. AI has already proven not only to drive true efficiency, but also to help companies plan for any eventuality, providing the actionable insights they need to stay ahead of the curve. doing.
References
- Market Size and Share Analysis of AI in Food and Beverage – Industry Research Report – Growth Trends(no date) AI Market Size and Share Analysis in Food and Beverage – Industry Research Report – Growth Trendshttps://www.mordorintelligence.com/industry-reports/artificial-intelligence-in-food-and-beverages-market (accessed May 4, 2023).
- FAO’s Strategic Framework.na climate changehttps://www.fao.org/climate-change/action-areas/fao-strategic-framework/en (accessed 4 May 2023).
