Machine learning solutions offer large possibilities to optimize and automate processes, save costs and make less human error possible for many industries. Food and Beverage is not an exception, here it can be beneficially applied in restaurants, bar and cafe businesses as well as in food manufacturing. These two segments of food industry have common use cases where Machine learning techniques can be applied, as well as different ones, what is linked to different problems that must be solved.
Let’s take a look at the common cases where Machine learning can be applied both in manufacturing and in restaurant business:
Start from food market analysis
Knowing what goods to manufacture in large amounts or what dishes are the best choice to include into your restaurant menu is the key to increase earnings. Often customers’ and market demands are changing very fast and so it is even more important to be one step ahead to take measures in time. Defining the most widespread 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 are linked to a stream of healthy lifestyle followers. In order to detect them, Machine learning uses Data Collection and Classification methods and deduces which food related trends are going to be on top very soon. A similar solution is given by Castrograph AI – it predicts flavors and preferences of customers at the pre-production stage, saving funds for Food and Beverage businesses that will avoid spending money on low-required production. AI for foods understands 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, manufacturing of large productions of goods or big 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 and, as long as it is very costly to disassemble it every time, there must be a better decision. Such equipment demands a lot of time and resources, like water. Developers from the University of Nottingham have developed a system able to economize resources by 20-40%.
This system is called SOCIP, or Self-Optimizing-Clean-In-Place and it uses ultrasonic sensing and optical fluorescence imaging to assess food remains and microbial debris inside the equipment for food processing. But there is one disadvantage to this system – it operated blind and so it is built for the worst-case scenario, tending to result in overcleaning. Still, the system will allegedly save around 100 million pounds for the country (UK).
Better hygiene – KanKan AI solution in Food and Beverage industry
Every food factory needs to make sure that their workers keep hands and things clean, as it is the factor number one to influence food safety. Also, it is very important to monitor if the cooking crew keeps everything nice and clean in the restaurant kitchen. Surveillance systems, able to detect and track people, their movements and attire items are able to cope with this task. There are such solutions as KanKan AI can be used in food manufacturing, or in restaurants and cafes. The embedded camera monitors workers by recognizing their faces meanwhile detecting if they are wearing masks or hats as demanded by the food safety law. This technology is estimated at around 95% in accuracy, detects violations and turns them into images.
Food and beverage supply chain optimization
Algorithms based on Artificial Neural Networks can monitor and check the process of delivery and goods tracking at every step, making it safer and providing transparency. Also, it makes forecasts as to pricing and inventory, which preserves extra costs.
Let’s now see how applicable machine learning can be in manufacturing and restaurant business in different cases.
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 that have become more firm, they need to appear more transparent about the path of food in the supply chain. Here AI helps to monitor every stage of this process: it makes predictions as to the price and inventory management and tracks the path of goods from where they are grown and, eventually, to the place where consumers get it, ensuring the transparency. A solution such as Symphony Retail AI enables to estimate the demands for transportations, pricing and inventory not to get an abundance of goods that can turn into waste as a result.
Sorting food: optical sorting solutions
Previously, a manufacturer had to hire many people for performing monotonous and routine actions linked to food selection. Now, instead of manually sorting large food amounts 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 gives food more quality and safety. This is achieved by using core sensor technologies and camera that recognizes material based on color, biological characteristics and shape(length, width, diameter), these cameras have an adaptive spectrum that is the best suit for optical food sorting.
Predictive maintenance, remote monitoring and condition monitoring
It is obvious that manufacturing a lot of goods demands big, 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 categorising issues and making predictive alerts. Timely repairs can save up to 50% maintenance time and reduce costs needed for it to 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 parametres and manufacturing processes and boost the throughput. Machine learning also allows to identify factors affecting quality and causing flows in the manufacturing process with Root Cause analysis (eliminating the problem in its very source). With condition monitoring you are able to monitor the equipment health in real-time to reach a high overall equipment effectiveness (OEE).
Machine learning applications in restaurant business
Analytical solutions for a better customer experience
Currently there are several applications in the food service space that may help to foresee visitor traffic on different seasons and events, food orders and relevant inventory needs to predict the amount of orders 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 repeated visits and orders in result. These are Cloud Big Data solutions, restaurant management platforms to make the paying process easier, applications that allow to connect and pre-order a table in advance.
Food-selling site and applications
Once you have defined what to produce, next step is to make the best online service system for your Food & Beverage business for people who discovered your existence through Internet or decided to examine your menu/order takeaway online. Say, it will be an online site that gives best recommendations/makes the order process really quick or a mobile application with a convenient and smart AI 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, formulating new tasks and etc.
AIs for online restaurant search
Restaurants, cafes and bars are also dependant on their ratings and feedbacks on the Internet. Nowadays many customers get to know about their existence through Google maps/searches. In these cases, an AI solution offers to unite the data from various food delivery programs to give the user a hint for a cafe or a restaurant that might appeal to his tastes and be relevant to location. There are also AI agents that notify clients about any sales and events in their favourite restaurants via their most used platforms like Twitter or Slack.
As people begin to prefer voice search over typing anything into the Google address (around 27% of the population), voice commerce seems to gain more significance. Restaurants can create tools such as Amazon Alexa to allow their customers make an immediate order without even an ordinary “click”. In such a way you can place orders quickly and hands-free.
Self-serving (point-of-sell systems) are being massively taken up by restaurants as long as they enable customers to control the ordering process, carefully examining their choice, sometimes even checking the amount of flavours and spices put into the dish. It is believed that this technology should be available for all size restaurant businesses, not only for big ones. Applications and terminals allowing to make a self-order reduce customer wait time, make orders more accurate and increases customer experience, being highly engaging.
Innovations in robotics for food industry
Some of the most complicated and brightest AI based solutions like robotics have popped up recently, but are only a privilege for big food businesses and fabrics, still unavailable for small-to-medium.These are drones to deliver orders or robotic hands that can manage many processes in food manufacturing and even cooking. However these devices can get popular due to exponential rise of human talent wages and can save more costs in a long-term run. The International chain of convenience stores 7-Eleven already uses drones and streetbots 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, able to take and turn over fried burger patties and put them into buns along with other ingredients for burgers.
The implementation of AI and ML in manufacturing and restaurant business is already moving the industry to a new level, enabling less human errors, less waste of abundant products, costs saved for storage/delivery and transportation and well as happier customers, quicker service, voice search and more personalized orders. Robotics is still a quite subtle thing to introduce, even for big factories and restaurant businesses, but it will occupy its niche very soon, bringing an obvious benefit in a long run.
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