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Industrial Manufacturing and Machinery

Artificial Intelligence In The Food And Beverage Industry

Artificial Intelligence (AI) is redefining industries by offering personalization, automating processes, and disrupting how we work. It is playing a predominant role in the world of food safety and beverage industry like never before.


What is Artificial Intelligence?

Artificial Intelligence (AI) is a way of making intelligent machines that work and react like humans. The aim is to teach the machines to think intelligently just the way humans do. Till now, the machines have been doing what they were told to do. But with AI machines will think and behave like a human being.

The study focuses on observing the thinking and learning a pattern of humans and then the outcome is used to develop intelligent software and systems.

Today tech giants like Google, Microsoft, and IBM are highly involved in studying developing the technology which has already started bringing a revolutionary change.

Although it is going to shape our future, yet we need to know how it’s affecting our present life. So, in order to give you a glimpse of the same, we focus today on the food and beverage industry and the impact of Artificial Intelligence.

The food industry isn’t known for its early adoption of technology. But, spurred by innovative startups, artificial intelligence (AI) may prove the exception. To meet fickle consumer tastes, food and beverage (F&B) companies are looking to artificial intelligence to help them scale new products and stay profitable.

Here are six examples of how the food industry is already using AI or could be using it in the very near future.

Sorting Food

One of the most time-consuming processes in any facility that receives fresh produce is sorting. For example, sorting potatoes by size can help manufacturers decide which ones should be made into French fries versus potato chips or hash browns.

Sorting out off-color tomatoes will help decrease rejection by the retailer or consumer. And, of course, all foreign matter needs to be sorted out as well.

That’s why TOMRA Sorting Food develops sensor-based optical sorting solutions with machine learning capabilities. The systems use various technologies, including cameras and near-infrared sensors, to “view food in the same way that consumers do” and sort it based on that perception.

The result is fewer hours spent on manual sorting, higher yields and less waste, and better quality.

Managing The Supply Chain

With new food safety regulations and the increasing need for transparency, supply chain management is a top priority for all food companies. A 2017 article in Food Online described several ways the food industry is using Artificial Intelligence to improve supply chains:

  • Food safety monitoring and testing product at every step of the supply chain
  • More accurate forecasting to manage pricing and inventory
  • Tracking products from farm to consumers to provide transparency
Artificial Intelligence In Supply Chain.
Artificial Intelligence In Supply Chain.

Ensuring Employees Follow Hygiene Procedures

In a food plant just like in a kitchen, good personal hygiene is necessary to ensure food is safe — and the facility is compliant.

Last year, technology company KanKan signed a huge deal to provide an AI-powered solution for improving personal hygiene among food workers in China.

The system, which can be used in restaurants as well as manufacturing facilities, uses cameras to monitor workers and then employs facial-recognition and object-recognition software to determine whether workers are wearing hats and masks as required by food safety law.

If it finds a violation, it extracts the screen images for review. According to the company’s press release, the accuracy of this technology is more than 96%.

Developing New Products

Wouldn’t it be great if food manufacturers could know their products would be a home run before they even hit the shelves?

Gastrograph AI purports to help food companies do exactly that. Their technology uses machine learning and predictive algorithms to model consumer flavor preferences and predicts how well they will respond to new tastes.

The data can be segmented into demographic groups to help companies develop new products that match the preferences of their target audience.

Cleaning Processing Equipment

This one goes into the near-future category.

As everyone in the industry knows, cleaning processing equipment requires a lot of time and resources, including water. Researchers at the University of Nottingham are developing a system that uses AI to reduce cleaning time and resources by 20-40%.

The system, which they call Self-Optimising-Clean-In-Place, or SOCIP, uses ultrasonic sensing and optical fluorescence imaging to measure food residue and microbial debris in a piece of equipment and then optimize the cleaning process.

Team leader Dr. Nik Watson explains: “To prevent product contamination, many food and drink manufacturers use a non-invasive, Clean-in-Place (CIP) system to wash inside food processing equipment without disassembling it.

As CIP has to operate ‘blind,’ it has a worst case scenario design. In daily use, this often results in the over-cleaning of production lines.”

The researchers predict that the system could save the UK food industry £100 million per year.

Growing Better Food

Here’s another one for the future category — what if AI could help farmers actually grow better food by creating optimal growing conditions?

That’s the goal of Sentient, a company that’s using AI to monitor the effects of variables like UV light, salinity, heat, and water stress on basil. With the data, they’re developing “recipes” for the perfect crops.

Moving this one from lab to field might take some time, but if that means tastier pesto, then let’s hurry it along! the farming level, AI is also being used to detect plant diseases and pests, improve soil health, and more.

The grocery industry is also using AI to offer customers targeted offers, manage inventory, and reduce waste. And many food-focused AI platforms are available for consumers.

For example, Wellio uses machine learning and behavioral science to provide personalized recipe recommendations and then allows customers to order their groceries online, and Habit develops personalized nutrition plans based on the results of a nutrition test.

These are only a few of the applications of Artificial Intelligence in the food industry. As technology gets better, we expect to see many more!

Consumer Packaged Goods

Artificial intelligence and machine learning fundamentally change the consumer packaged goods (CPG) industry.

Aside from the challenge of mounting consumer expectations, established food and beverage companies also face challenges. They are shifts in customer trends away from global conglomerates toward local, artisanal providers.

Consumers demonstrate a willingness to shovel out more money for a “handcrafted” experience, and DIY preparation trend of home cooking and craft brewing.

“CPG, in general, face this perfect storm, where activist investors expect a lot in margin while consumers expect more high-quality tailored products … along with better service,” explains Ben Stiller in an interview with TOPBOTS. (Stiller heads digital transformation and analytics for Deloitte’s Consumer Products Business.)

No wonder many players in the CPG (or FMCG) space are going beyond automation to the more esoteric fields of big data, machine learning, and other aspects of artificial intelligence.

A Taste For Trouble

Consumers judge food based on its impact on their palate and their wallet. But successful food brands with staying power require more than just a killer recipe. Any of the following challenges regularly plague CPG companies trying to speed up and maintain innovation:

  • Product design and specifications (or the recipe)
  • Raw materials (or the ingredients) to create the product
  • Equipment, tools, and machinery to scale production
  • Venue (processing plant, factory floor, etc) where a company assembles/processes goods
  • Safety and quality control implementation
  • Compliance with government/international regulatory standards (health, environmental, safety, financial, zoning, etc.)
  • Product packaging and tracking system
  • Inventory management for storage and distribution
  • Logistics and transport for distribution
  • Marketing and public relations
  • Long-term engagement with partners and intermediaries for sale
  • Back office operations
  • Sales and order tracking that follows the brand’s supply chain, manufacturing, and logistics processes

This is a long list of problems, isn’t it? In addition to minding all the possible points of failure mentioned above, food and beverage companies need to mitigate significant risks. For example, contamination and spoilage, even when the products in question have been passed along to retailers and are no longer within their control.

AI The Magic Elixir

The infrastructure behind the production and consumption of CPG products is complicated than one imagines.

Machines were designed back in the day to run a certain way. If anything doesn’t meet the exact standard to run that way (e.g., materials don’t show up in time or are out of spec) they just won’t run.

Then when it stops, you have to manually stop and fix it. Another challenge is that older factories lack sensors and tracking equipment. Hence, logging these abnormalities is a challenge. Therefore, they continue to plague the food production process.


“Once the ingredients and materials get into the building or assembly line to build the product, that’s where the challenge begins,” reveals Leading2Lean CEO Keith Barr.

Leading2Lean is a developer and provider of streamlined manufacturing software and cloud-based solutions. It helps businesses achieve sustainable process improvements through data analytics.

Using data analytics to detect and eliminate inefficiencies, the company helped Ohio-based specialty food maker Lakeview Farms achieve significant improvements.

For example, reduction in line downtime (34 percent), equipment repair costs (15 percent), and worker overtime ratio (17 percent).

Factors Behind AI Solutions

The pressure to seek out similar providers of automation and AI-driven solutions, hinges on several factors:

  1. There are more marketing and distribution channels to engage.
  2. Competition has gone from brisk to brutal.
  3. Unified, synchronized data across all departments reduces errors, downtime, and costs.
  4. Visibility across all stages of the business process serves as a key competitive advantage.
  5. Real-time data on customer behavior and market trends helps future-proof businesses.

Dr. Tom Bradicich, vice president and general manager for Servers and IoT Systems at Hewlett Packard Enterprise, puts it in another way: “Customers can’t stop their businesses, so they are challenged with how to keep it going while improving operations all at the same time.”

Technology Integration

Currently working with a major F&B CPG company to integrate new technologies in production, Bradicich believes automation, edge computing, and artificial intelligence are set to dramatically reduce human errors, hike quality, and increase sales.

His team currently rolls out a new product class called Converged Edged Systems. It aims to establish more reliable production environments that cut costs and require less energy and space.

A Buffet Of Automation Options

The enterprise has used AI to tackle challenges ranging from gaming and dating to banking and health care. Despite the wide range of applications, F&B companies tend to stick to specific use cases, according to Lori Mitchell-Keller, global general manager of Consumer Industries at SAP.

Artificial Intelligence In Food Industry Automation
Artificial Intelligence In Food and Beverage Industry

Describing how clients are using the capabilities of SAP’s new Leonardo Machine Learning Foundation, Mitchell-Keller cited key AI applications that positively impact the front- and back-end processes of F&B companies:

Shelf Management

F&B retailers use AI to automate inventory management. One use case is to have staff take photos of store shelves to initiate a machine learning process. This automatically detects missing or misplaced items and notifies stakeholders to restock or make corrections.

Image-based Procurement

AI and image-recognition technologies can ease the procurement process and reduce the time it takes to send an order. Employees can take a photo of an item. This will, in turn, activate an automated database search for the exact item or an equivalent product.

Personalized Customer Service

Chatbots or Voice Assistants with natural language processing and machine learning help companies tap consumer shopping data and history. This helps them provide hyper-personalized and automated customer service experiences.

Heightened Consumer Engagement

CPG players can use AI to maintain strong empathy with their audience. By closely monitoring conversations on social media, companies can use AI to analyze consumer data. It also helps identify sentiments or behaviors crucial in building positive experiences, development, and design of new product lines.

Some CPG businesses are already implementing AI in many areas. For example, financial and sales planning, chemical/contaminant monitoring, and back office paperwork automation.

Choking on change: Challenges of AI adoption


Having a full plate of options may seem tantalizing, but potential adopters face numerous challenges, chief among them cost. With margins already thin, F&B companies simply don’t have the deep pockets of companies like Google or Amazon when it comes to investing in AI.


In-House and Outsourcing

Whether to build or buy is another critical decision. In an ideal world, F&B brands would build tightly integrated in-house technology that reflects the unique needs of their company. In the real world, the battle for AI talent is so severe that leading technology companies spend over $650 million annually to woo desirable candidates.

Companies with established data analytics capabilities and a team of competent in-house developers may safely build their own AI platform. Those without such resources must instead seek out solutions and providers based on clearly defined needs, goals, and budgets.

AI System Integration

Even for F&B companies that have found the perfect vendors, integrating a new AI system into existing technology stacks can be a headache, especially for large conglomerates with fragmented systems. Ken Wood, executive vice president of product management at logistics technology company Descartes, warns: “It’s painful to wire systems together — our customers tell us that consistently.

The more vendors, the harder the project. The more systems you have to cobble together, the more expensive and longer time it takes.”

AI Models

The final challenge remains the Artificial Intelligence technology itself, which presents at least two issues for the industry. Without the right proprietary data, an F&B company may not be able to build machine learning models that perform.

Matt Talbot, CEO of GoSpotCheck, describes this as “a huge obstacle without a cost-effective solution.” PepsiCo, Dannon, and Anheuser-Busch use GoSpotCheck’s AI-powered inventory software to maximize supply chain efficiency and provide business insights to sales reps.

Food and beverage companies are infamous for guarding their secret recipes fiercely. But machine learning models should not be a mystery. Unfortunately, even with the right data, many AI solutions on the market work like black boxes.

Without clarity and transparency into how algorithms are making decisions, F&B executives have a hard time determining whether a technology is truly adding value or how sustainable that value-add is.



Complications of Artificial Intelligence adoption aside, one fact is clear: F&B companies must invest in new innovations to cut costs, grow revenue, and stay current with consumer trends. Those who do may live to thrive another day. Those who don’t may find themselves replaced by tech-forward giants like Amazon.