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In the aftermath of the Covid-19 outbreak, the food manufacturing sector encounters significant challenges, including the successful integration of technologies like artificial intelligence (AI). Despite professionals' optimism about its capacity to enhance processes like quality control and production efficiency, many are hesitant to fully adopt AI due to uncertainties about whether it will pay off in the long run. Saar Yoskovitz, CEO of Augury, explores the current state of AI integration in the F&B sector.

The Covid-19 outbreak happened almost four years ago, and with it, the food supply chain slowed almost to a halt. This put more pressure on manufacturers but also prompted calls on governments to help bring things back to normal. Spiralling global inflation didn’t make it any easier for them, as calls for leaders to intervene only increased.

In the UK, Prime Minister Rishi Sunak recently addressed the food supply chain issues at the Farm to Fork summit, commenting on the country’s multi-layered food supply-chain issues. While it brought attention to and some investment toward the problems, inflation, soaring costs and food security problems remain unsolved. In the US meanwhile, interest rate hikes and price-gouging investigations provided mixed results, though manufacturing and supply chain investments did help the industry move toward becoming more competitive through advanced technology.

To be sure, government support and investment is necessary for the industry’s safety and its journey toward 4.0 maturity. But the problem needs to be approached from multiple angles, and an essential component of any strategy needs to include AI. With Industrial AI technology we’re beginning to see real and measurable impacts from food quality to plant-floor efficiencies, benefiting both manufacturers and consumers.

The state of AI in F&B manufacturing Research shows that food and beverage manufacturing professionals globally have a high confidence in AI’s capabilities. Over a third believe AI could help them achieve quality, yield and throughput goals; 32% say AI could help them optimise asset care; and one in four manufacturing professionals said AI assists them in controlling the cost of materials and energy.

The trend goes beyond confidence in AI – it’s driving adoption across the industry. The top three use cases within the food and beverage manufacturing sector are using AI for supply chain optimisation, tracking energy consumption and overall production health. Organisations that are embracing the technology should go even further by tapping into AI’s most impactful industrial use case: AI-driven machine health. Currently, only 9% of manufacturers say they try to improve machine health and reliability using AI tools, far below the average of 28% across other industries.

Given the confidence food and beverage manufacturers have when using AI, 9% is surprisingly low when the benefits are widely known. For example, one of the world’s largest food and beverage manufacturers is documenting less machine downtime and fewer unexpected breakdowns as a result of AI-driven machine health. Moreover, it is also helping them spend less on replacement parts and avoid the loss of more than one million pounds of product or the weight equivalent of the Mriya cargo plane, the previously heaviest cargo plane in the world.

Augury’s research holds some of the answers as to why food and beverage manufacturers may be hesitant to embrace AI to its fullest potential: while the use of AI is expanding, many companies can’t measure or can’t see how AI is helping them. When asked about their ability to quantify the impact of AI in meeting business objectives, the results were quite low: just 15% could quantify its impact on improving production health, 15% can see its impact on reducing loss, wastes and emissions, 14% for maximising yield and capacity, and 12% for reducing machine downtime.

While AI is being used across organisations, businesses still do not understand if there is any return on investment – they are either lost in the vast amounts of data or simply flying blind. These gaps need to be closed for the industry to find true and quantifiable success with AI. However, it is not all bad news. Investment is on the rise, with 74% of food manufacturers saying they plan to invest either slightly or significantly more in AI this year. Another ray of hope is found in the workforce data. While 73% of employers face hiring challenges, 78% say AI, IoT and machine learning will positively impact their workforce upskilling efforts, and 29% believe AI and advanced technologies will help create new jobs in the manufacturing industry.

Advancing the AI journey

Food and beverage manufacturers are doing some things right when it comes to implementing AI in their businesses – like using raw materials and energy more efficiently, optimising processes, and upskilling the workforce – but a lack of understanding when it comes to ROI means that their true production potential is not being reached. With the right guidance, they could be lowering costs, reducing more downtime and reaching Industry 4.0 standards quicker.

So how can they close that gap? First, manufacturers should seek to understand what AI can do for their specific pain points, not view AI as a one-stop-shop solution that can solve all issues. They should start by identifying their biggest production challenges, like machine downtime, food quality or energy tracking and seeking solutions that are purpose-built for these challenges.

Additionally, when choosing an AI solution, manufacturers need to find the ones that will work with and for the people using it. Most companies making the most of AI treat it as a co-pilot, enhancing their worker's capabilities while also advancing their skills at the same time. Last, but not least, AI solutions should be thought of as more than just a technology. It should come with end-to-end services, analysts, system integration managers, trainers and change management assistance to ensure adoption and value at scale.

Advances in AI are changing the manufacturing world, giving companies the information they need to improve their machine reliability, optimise processes and transform their operations. Realistically, this means manufacturers can save both time and money, passing on those savings to shoppers through lower-priced goods. The good news is that the sooner manufacturers start or expand the use of AI solutions, the sooner the benefits will show up for businesses and consumers alike.