- 1 Technology in the Food Industry
- 2 Using AI in Food Industry: Machine Learning applications in Food Manufacturing
- 3 Data Science in the Food Industry
- 4 Machine Learning Applications in the Restaurant Business
- 5 AI in Food Safety
- 6 Artificial Intelligence in Food Waste
- 7 The Future Application of AI in Food Industry
- 8 AI in Food and Beverage Industry Statistics
- 9 The Benefits of AI in the Food Industry
- 10 Conclusion
Technology in the Food Industry
When talking about the food industry, technology isn’t usually the first thing that comes to mind. But nowadays, technology in the food industry is an essential part of food production and delivery processes. We find food through applications, and manufacturers produce it with the help of robotics and data processing. Tech could significantly improve packaging, increasing shelf life and food safety. The quality of food is also improving while production costs are lower. Robotics, machines, drones, and 3D printing are the reality, prompting the topic we will discuss in this article.
Artificial Intelligence and Machine Learning solutions offer many possibilities to optimize and automate processes, save money, and reduce human error for many industries. AI and ML can benefit restaurants, bars, and cafe businesses as well as in food manufacturing. These two segments have common use cases where AI in the food industry can be applied.
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Let’s take a look at the common cases where Machine Learning can be applied in both manufacturing and restaurant businesses:
Start from food market analysis
Knowing what goods to manufacture in large numbers or what dishes are the best choice to include on your restaurant menu is the key to increase revenue. Customer and market demands are changing very fast, so it is even more important to be one step ahead of the competition. Defining the most common tastes and preferences is the most valuable thing for a food business owner as well as for a food manufacturer. For example, the newest trends in food tech are linked to a stream of healthy lifestyle followers. In order to detect them, Machine Learning uses the Data Collection and Classification methods to deduce which food tech solutions are going to be the most preferred in the near future. A similar solution is given by Castrograph AI – it predicts the flavors and preferences of customers at the pre-production stage. AI for food understands the human perception of flavor and preferences, dividing users into different demographic groups and modeling their preference behavior or predicting what they want — even before they do.
Cleaning equipment that does not need disassembling (CIP)
Both manufacturers and large restaurants need expensive and complicated machines to clean and process many foods every day. So, significant amounts of substances of a different kind go through the cleaning equipment. Thus, as long as it is very costly to disassemble it every time, there must be a better method. Such equipment demands a lot of time and resources such as water. Developers from the University of Nottingham have developed a system that is able to economize resources by 20%-40%.
This system is called SOCIP or Self-Optimizing-Clean-In-Place. It uses ultrasonic sensing and optical fluorescence imaging to assess food remains and microbial debris inside of food processing equipment. But there is one disadvantage to this system – it is operated blind; thus, it is built for the worst-case scenario, tending to result in overcleaning. Still, the system will allegedly save around 100 million pounds in the UK food industry.
Better Hygiene – The KanKan AI in the Food and Beverage Industry Solution
Every food factory needs to make sure their workers keep hands and other things clean, as this is the Number One factor that influences food safety. Also, it is very important to monitor if the cooking crew keeps everything clean and orderly in the restaurant kitchen. Surveillance systems with the ability to detect and track people, as well as their movements and attire, are able to cope with this task. Solutions such as KanKan AI can be used by food tech companies in manufacturing or in restaurants and cafes. The embedded camera monitors workers by recognizing their faces and detects whether they are wearing masks or hats as demanded by food safety laws. This technology detects violations and turns them into images. KanKan AI has an estimated accuracy rate of 95%.
Food and beverage supply chain optimization
Algorithms based on Artificial Neural Networks can monitor and check the process of AI food delivery and goods tracking at every step, making it safer and providing transparency. Also, it makes pricing and inventory forecasts, which prevents extra costs.
Now, let’s see how machine learning can be applied to manufacturing and restaurant businesses in different cases.
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Using AI in Food Industry: Machine Learning applications in Food Manufacturing
Supply chain optimization – less waste and more transparency
As long as food manufacturers are concerned with food safety regulations, they need to appear more transparent about the path of food in the supply chain. Here, AI in food manufacturing helps to monitor every stage of this process — it makes price and inventory management predictions and tracks the path of goods from where they are grown to the place where consumers receive it, ensuring transparency. A solution such as Symphony Retail AI enables us to estimate the demand for transportation, pricing, and inventory to avoid getting an abundance of goods that end up wasted.
Sorting food: optical sorting solutions
Previously, a manufacturer had to hire many people to perform the monotonous and routine actions linked to food selection. Now, instead of manually sorting large amounts of food by size and shape (so that it can be canned or bagged), you can use AI-based solutions to easily recognize which plants should be potato chips and which are better to use for French fries. Vegetables of an inappropriate color will also be sorted out by the same system, decreasing the chance that they are discarded by buyers. Food Sorters and Peelers developed by TORMA show better processing capacity and availability, which increased food quality and safety. This is achieved by using core sensor technologies and a camera that recognizes material based on color, biological characteristics, and shape (length, width, diameter); the camera has an adaptive spectrum that is well suited for optical food sorting.
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Predictive maintenance, remote monitoring, and condition monitoring
It is obvious that manufacturing a lot of goods demands large, complicated, and intricately constructed mechanisms. The maintenance of such machines can be rather costly without predictive maintenance – figuring out the time-to-repair and cost-to-repair indicators through categorizing issues and making predictive alerts. Timely repairs can save up to 50% maintenance time and reduce the costs needed for it by almost 10%. To perform remote monitoring on complicated mechanisms, you can make a Digital Twin of a machine that will show you the performance data on parameters and manufacturing processes and boost the throughput. Machine Learning also allows the identifications of factors that affect the quality of the manufacturing process with Root Cause Analysis (eliminating the problem at its very source). With condition monitoring, you are able to monitor the equipment’s health in real-time to reach high overall equipment effectiveness (OEE).
Data Science in the Food Industry
The food industry is filled with big brands and highly successful restaurants. It’s easy to get lost among the competition, which makes it a less attractive market for new businesses. However, there are some ways that you can stay ahead of the competition. One of those ways is technology, so let’s find out how Data Science in the Food Industry can benefit you.
Matching customer tastes with your business strategy
Ooshma Garg, the founder of Gobble, considers a modern food company to be a tech company. It is definitely an arguable statement, but there is some truth to the fact that data science can help optimize different processes and the way the business operates.
Gobble is a service, aimed at providing young families with fresh 10-minute dinner kits. They have over 1,000 regular customers, with changing tastes and menu choices. The company is relying heavily on data science to predict demand and correctly manage correctly the acquisition of supplies. Basically, Gobble is connecting information on current food preferences in their menus, customer behavior, and purchasing history to their production. This is a very exciting, demand-driven example of data science in the food industry that could become the blueprint for similar businesses.
Introducing new recipes
The number of ways we can combine ingredients in recipes is limitless. Adding the fact that you can cook those ingredients in multiple ways, makes cooking dishes an area of endless possibilities. Today we have huge online databases for recipes, which allows the analysis of ingredients in different cuisines. Researchers are able to determine what similarities particular cuisines are sharing. For example, Western European and North American dishes are based on ingredients that contain similar flavor compounds that Southern European and East Asian dishes tend to avoid.
As a result, scientists can determine which food components drive taste and make a dish popular in certain regions. This fundamental understanding also allows intelligent algorithms to advise chefs of new ingredient combinations, which will ultimately result in a wider variety of menu offerings.
Reinventing Food Delivery
Every online food ordering platform contains a huge amount of information on ordering patterns and client preferences. Machine Learning algorithms can help facilitate more effective, cost-efficient, and time-efficient dispatching of drivers for food delivery. Data Science in this industry is still nascent, but it is already providing companies some valid chances for market domination. And we can hopefully get the food we want fast and delicious!
Machine Learning Applications in the Restaurant Business
Analytical solutions for a better customer experience
Currently, there are several applications in the food service space that may help predict visitor traffic, food orders, and relevant inventory needs to predict the number of orders needed for a certain period/date. Such applications and solutions collect previous data to engage customers more through examining their habits and preferences: it brings more repeat visits and orders as a result. These are Cloud Big Data solutions, restaurant management platforms to make the paying process easier, and applications that allow connecting and pre-ordering a table in advance.
Food-selling sites and applications
Once you have defined what to produce, the next step is to make the best online service system for your food and beverage business for people who have discovered your existence through the Internet or decided to examine your menu online. Perhaps it will be an online site that gives the best recommendations and makes the order process really fast or it will be a mobile application with a convenient and smart AI foods system. E-commerce is getting more popular in the digital world, so it is a bad thing to forget the promotion of your goods on the Internet. Automated customer service and customer segmentation can significantly increase the accuracy and efficiency of administrative functions such as creating reports, placing orders, dispatching crews, and formulating new tasks.
AI for online restaurant search
Restaurants, cafes, and bars depend on their ratings and feedback on the Internet. Nowadays, many customers get to know about their existence through Google Maps/searches. In these cases, an AI in food service solution offers to unite the data from various food delivery programs to give the user a hint for a café or a restaurant that might appeal to his tastes and is relevant to his location. There are also AI agents that notify clients about any sales and events in their favorite restaurants via their favorite platforms such as Twitter or Slack.
As people begin to prefer voice searching over typing anything into Google (around 27% of the population), voice commerce is gaining more significance. Restaurants can utilize tools such as Amazon Alexa to allow their customers to make an immediate order without even an ordinary “click.” In this method, you can place orders quickly and hands-free.
Self-service (point-of-sale systems) that enable customers to control the ordering process, carefully examining their choices, and sometimes even check the number of flavors and spices in a dish are being widely adopted by restaurants. It is believed that this technology should be available for all sizes of restaurants, not only for big ones. Applications and terminals for self-service ordering reduce customer wait times, make orders more accurate, and improves the quality of the customer experience.
Innovations in Robotics for the Food Industry
Some of the most complicated and brightest AI-based solutions like robotics have popped up recently, but are only a privilege affordable for large food businesses. These innovations include drones to deliver orders and robotic hands that can manage many processes in food manufacturing and even cooking. However, these devices can become more popular due to the exponential rise of the wages for human workers; thus, robotics can save restaurants more money in the long run. The international chain of convenience stores 7-Eleven already uses drones and street bots in its delivery service, while Walmart claims that it will soon use drones in warehouses. Another curious robotics implementation is the “Flippy” robot, which actually consists of two mechanical hands that are able to take and turn over fried burger patties and put them into buns along with the other ingredients needed to make burgers.
Restaurant Revenue Prediction Using Machine Learning
Food and service quality are very important but in the long term, restaurant sales prediction is just as valuable. Knowing what to expect, a restaurant owner can make viable plans for future operations. What if you need to create a sales forecast for the next five months? Even finding the most fitting algorithm to do that could take time and effort. Imagine just feeding data to your website or app and getting your sales prediction. Today’s Machine Learning technology allows you to find the best algorithm for your particular case and deploy it wherever you want. With the right dedicated development team, you can easily achieve that!
AI Culinary Uses in the Real World
Artificial Intelligence is closer to your kitchen than you think. IBM introduced Chef Watson, an AI-enabled digital culinary research assistant. The chef has access to a database of flavor profiles and various recipe ratios, helping create fresh dish combinations like a pro with tech knowledge. Just choose the ingredients you like in the program, pick the style of cooking, and look at the combinations the algorithm presents to you. Watson simulates the preferences of the chef and provides instructions. Machine Learning, in this case, provides real chefs the opportunity to step out of their usual cooking routines and get ideas that will lead to cooking something unique. This assistant uses a quantitative cooking methodology and is able to analyze a user’s taste preferences and suggest ingredients. Also, it has a user-friendly interface. Chef Watson is a really cool example of AI in culinary, and there will be plenty of solutions like this in the future.
ML in Food Delivery
Machine Learning is a perfect technology for planning more efficient delivery routes and logistics strategies. Every delivery agent can get optimization that will ensure the best routes for them, providing constant orders and planned break times. Speaking of ML in food delivery, artificial intelligence is definitely increasing the scale for possible data analysis. With modern algorithms, analyses could be performed much faster. Methodologies and strategies for entire companies could change faster, resulting in advantages against competitors.
AI in Food Safety
Probably, one of the most valid points of using AI and robots in food production is that the robots are sterile. This significant benefit is a huge factor in lowering the number of foodborne diseases.
The FSMA (Food Safety Modernization Act) has made sanitary requirements stricter, considering the entire supply chain. The reason is that now cereals, spices, and other foods that don’t require refrigerators are in danger of contamination. Previously such foods weren’t prone to contamination, but now that’s changed. Robots could definitely help here. There is no way that they could transfer illnesses, as humans do. Moreover, robots are very simple to clean. Technavio forecast that by the end of 2019 robot implementation in the food industry will be up to 30%. Government demands and regulations are a major influence here.
There are also two other groundbreaking AI in food safety technologies that are expected to become widespread soon. They are also aimed at reducing the incidence of foodborne diseases.
- Next Generation Sequencing a.k.a. NGS could replace DNA methods in the food safety testing area very soon. Automated processes and workflows make data capturing and the preparation of lab samples much faster and more precise than ever. The reason for implementing NGS in organizations like the CDC and the FDA lies in finding out about dangerous trends more quickly. NGS can even prevent some disease outbreaks before they harm masses of people.
- Electric Noses – these are basically the replacement for human noses in production settings. Electronic chemical sensors can accurately distinguish a variety of odors. Imagine these sensors in the food production environment. Powered by an AI/ML algorithm that has access to a database of dangerous odors that are the signals of pollutions or infection, Electric Noses could be the future of Food Safety and Quality Control.
While in other areas there are doubts about the implementation of AI, in the Food Safety area there are no two ways about it. AI, Robotics, and ML in Food Safety have already proven their worth, and will only evolve and get better in the future.
Artificial Intelligence in Food Waste
The United States Department of Agriculture claims that: “In the United States, food waste is estimated at between 30-40 percent of the food supply. This estimate, based on estimates from USDA’s Economic Research Service of 31 percent food loss at the retail and consumer levels, corresponded to approximately 133 billion pounds and $161 billion worth of food in 2010. This amount of waste has far-reaching impacts on society.”
According to McKinsey, Artificial Intelligence can solve this problem and unlock a $127 billion opportunity by reducing food waste in 2030! Such astonishing numbers could be achieved by introducing more regenerative recreational agricultural practices. What does that mean? It means that humans currently don’t use their resources wisely and mono-cropping, the blanket application of synthetic chemical fertilizers and intensive land use, can be replaced with “smarter” methods. Information gathered from sensors, drones, and satellites, as well as other equipment, could help farmers make better decisions faster. Here are some ways to reduce food waste with AI:
- While some solutions analyze the ripeness of the fruits, others figure out what microbes could increase crop growth without the involvement of synthetic fertilizers.
- Farmers could get rid of field trials, benefiting from advantages of the AI, which will save significant amounts of money.
- If farm-based food supply chains use visual imagery technology, the food inspection process will be much easier.
- AI food tracking will enable us to sell food before it becomes waste, through more efficiently connecting farmers with restaurants or people buying food.
The main challenge to make these ideas a reality can’t be delivered by one company. The whole industry needs to be changed. An entire network of partners is required to help these changes make a significant impact on the world.
This problem is getting more attention worldwide. Since 2011, more people have been searching for “Food Waste” in Google.
The Future Application of AI in Food Industry
We already know that among the investments in AI technology, there are significant investments in the Food Manufacturing sector. For example, AI can more easily predict many issues in agriculture than people can, and investors are beginning to notice it.
The Switzerland-based agricultural tech firm Gayama raised over $3.2 million in funding for an AI project. They use drones with hyperspectral cameras that detect changes in water, fertilizer, pests, and crop yields. Then the AI algorithms can find potential threats and alert farmers. AI algorithms can also suggest certain actions the human must take to best use their resources. An interesting case of the usage of Machine Learning in harvesting is through the analysis of satellite data on the Earth’s surface. The purpose is to find places that could use some help from investors or the government for improvement, which allows the providing of more food as a result.
If we talk about the agricultural industry in the context of the food industry, there is a lot of room to grow. Farming is still outdated in many parts of our planet. Britain’s Institution of Mechanical Engineers claims there are 550 billion liters of water wasted annually in the crop production process. Artificial Intelligence has a chance to solve this problem somehow in the future and reduce this number. Successfully solving this problem could raise the production of food by 60% or even more. Machine Learning and AI are nascent, but there will be plenty of solutions to eliminate waste in food production.
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The 77 Lab, for example, has already introduced smart bots that can pick food straight from the plant, eliminating the ineffectiveness of human farm labor. There were automated pickers in the past, but these robots are using Machine Learning and can determine the level of ripeness of fruit, distinguish fruits from other plants in a better manner, and handle fruits more carefully. What will happen if this is more widely adopted? This is just one way for the Food Industry to benefit from ML. Iron Ox has built entire farms based on robotics that aren’t even supposed to be handled with the help of humans. How fast will we see such innovations worldwide? Time will only tell.
AI in Food and Beverage Industry Statistics
During the 2019-2024 period, the Food and Beverage market is expected to register a CAGR of over 65.3%. The leaders of the industry are already transforming their businesses by enabling cutting-edge technologies in their processes. North America is expected to be a significant part of this explosion. Take a look:
- The United States of America is a leading region in its area. In 2017, the USA was the second-largest region of the AI food industry market in the world, with a 29.1% market share.
- North America has a really high level of readiness for AI adoption; that’s why it has a high automation potential that is expected to occur at the regional level in the period between 2019 and 2030.
- According to the United States Department of Agriculture, 16% of the shipment value in the USA is coming from food processing plants.
Increasing the application of Artificial Intelligence in the Food Industry in this region is inevitable because is a low-margin and high-volume industry. Even the slightest increase in efficiency can make a significant impact on the success of companies.
The Benefits of AI in the Food Industry
- Recently, more and more companies are trusting Artificial Intelligence to improve supply chain management thorough logistics and predictive analytics as well as to add transparency.
- Digitization of the supply chain ultimately drives revenue and provides a better understanding of the situation. AI can analyze enormous amounts of data that are beyond human capability.
- Artificial Intelligence helps businesses to reduce time to market and better deal with uncertainties.
- Automated sorting will definitely reduce labor costs, increase the speed of the process, and improve the quality of yields.
With AI, the food industry will ultimately be better in the area of safety standards.
The implementation of AI and ML in food manufacturing and restaurant businesses is already moving the industry to a new level, enabling fewer human errors and less waste of abundant products; lowering costs for storage/delivery and transportation; and creating happier customers, quicker service, voice searching, and more personalized orders. Robotics is still quite a subtle thing to introduce, even for big factories and restaurant businesses, but it will occupy its niche very soon, bringing an obvious benefit in the long run.
If you have any questions or need a consultation on Machine Learning in the Food industry, contact us to learn more from our Data Scientists. In the meantime, boost your awareness by reading articles on Machine Learning and AI on our blog.
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