Food industry automation

Food industry automation includes the use of software, sensors, advanced learning algorithms, and robotics to decrease the need for human labor in the food industry's supply chain, and to increase the efficiencies of the supply chain. This is a supply chain that begins with the growing of the food, to the processing and distribution of the food, to the warehousing and sale of food at grocery stores, and to the sale of food at restaurants. Many of these forms of automation can solve different challenges in the food industry, such as staffing, quality assurance, safety, and moving product, while providing SKU variety and related demand. However, the food industry, throughout the different points of the supply chain, is notoriously resistant to automation, although much of this has begun to change in the wake of the COVID-19 pandemic. Regardless of the sector of the food industry, automation offers a few generalized benefits:

The production of food, or agriculture, and the distribution of food is the crucial first step of the food industry. To help modernize the field, industry leaders have invested a significant amount of money into robotics and automated technologies to improve efficiency and better meet the demands of the industry. A few critical areas of development of automated systems include breeding, harvesting, fertilizing, and irrigation. These solutions often focus on minimizing production costs while conserving fuel, water, and fertilizers, and they often provide efficiencies over previous technology while also replacing labor. And as traditional farming techniques continue to be impacted by labor shortages, the ability to replace some of the labor with automated systems is crucial, especially in regions where the labor shortages have come as a result of regulations around the COVID-19 pandemic. These cases have further incentivized corporations to invest in and develop automated and robotic systems, such as driverless tractors and sprayers.

The management of food waste in the growing, distributing, processing, and selling of food is an important place where the food supply chain can increase efficiencies. In a report published by the U.S. Department of Agriculture, it was found that food waste in the United States alone accounts for between 30 to 40 percent of the food supply. This estimate, which was also based on estimates from the USDA's Economic Research Service of 31 percent food loss at the retail and consumer level, corresponded to the approximately 133 billion pounds and around $161 billion worth of food consumed in 2010. AI has been proposed as one possible solution to these problems, by helping to identify and find solutions for food waste and by creating efficiencies prior to food production to limit overall waste. In the case in which food waste occurs, AI can help identify uses for the food waste, such as regenerative farming practices. Other applications of AI to food waste include the following:

• Using AI to gain an understanding of which microorganisms can boost fruit and vegetable development, and discovering at which point and at what level of application fertilizers can help or hinder
• Manufacturers utilizing AI to acquire a ground examination to better understand what kind of soil deficiencies they may be dealing with
• A farm-based food supply chain using computer vision to manage and examine each process to discover and resolve food waste points
• Using AI-based food tracking to allow for the sale of food, instead of it becoming waste

Example of automation in food processing.

In food processing, automation can help reduce expenses in food processing plants, especially in an industry in which sacrificing quality in the product is not an answer for cutting costs or increasing margins. Similar to other process industries, food processing companies are finding ways to improve productivity through the plant with use of various forms of automation. This can include different types of automated machinery, such as automated ovens, cutting and forming machines, sortation equipment, mixers and blending machines, filling equipment, wrapping equipment, and robotics that be used in highly automated production lines. And automation has since become a necessity in the food industry to address the required levels of quality control, production speed, labor shortages, and overall profitability.

Other than physical forms of automation, food processing software offers a software layer capable of uniting machine and production planning. This can automatically process orders to the production equipment and material consumption, with the number of produced items reported back to a producer's enterprise resource planning (ERP) system. Benefits of such a software system can include the following:

• Order management and planning
• Process control
• Weighing and labelling
• Performance monitoring
• Logistics
• Traceability
• Integration
• Quality, flexibility, efficiency
• Data collection
• Warehouse management

The ERP software provides a management tool for integrated applications for business process optimization. This software can offer an all-encompassing look at how a company and its employees are performing in different components of the business, in real time. For food-specific purposes, an ERP is designed to streamline processes unique to food and beverage manufacturing, and offer an increased opportunity for automation, such as the monitoring and alerting to unique and perishable products, like shelf-life, safe ingredients, consistency, and product safety. These systems also help food and beverage companies adhere to labeling and packaging requirements, manage recipes, maintain product quality, and implement safety precautions.

Using IoT, there are a myriad of solutions for automating and documenting food processing operations to help users meet different compliance and food safety requirements. In a food processing plant, using IoT can offer pathogen and contamination prevention. There are various apps and technologies that can be used for warehouse inspection and maintenance; general inspection and compliance; quality, environment, and workplace safety management; control and visibility of critical operations; expiration date management software; non-toxic sanitizing for handheld devices.

Example of IoT in food processing for product quality assurance.

The use of various connected sensors can help increase visibility into the transportation of goods, to better understand where food is coming from and where it is going, and offers traceability to help improve emergency planning and food defense to give customers a more transparent product. This can further include serial tracking tools; inventory management tools; layered process audits; quality assurance; supplier quality management; traceability and HACCP solutions; traceability and inventory management software for the food industry; process management software for batch and recipe/formulation-based manufacturing; invisible barcoding software, embeddable in everything, and supply chain management and logistics elements; and transportation monitoring. This can all be used to ensure the quality of the product is maintained by tracing and promoting the safety of a product through its tracing.

IoT can also be used in a mitigation strategy against intentional adulteration. This can include different applications and systems to help keep facilities and food safe from intentional or widespread harm. Any strategy can include visitor management and data collection systems, cloud-based integrated security and access control for multi-site locations, mobile access with smartphone integration, identity credential and access control for smartphones, and food fraud databases.

For food processing, digital process automation (DPA) software offers an all-in-one system that can help a production facility to overhaul an operation and offer a single place through which such an operation can be managed. This can include software integrations for accounting, work orders, and multi-level production processes; multi-module, collaborative software for a range of needs; freemium error-tracking software for applications; cloud ERP software for manufacturing; comprehensive software solutions; paperless, cross-business management software for food facilities; food decision software distribution and analysis software; and recall management.

These systems can also tie into them audits, forms, and data management to help a food processing plant automate regulatory management and provide mobile forms and online databases to target needs and input and track data on devices that integrate across an operation. This could include the automation of mobile forms and surveys for audits and inspections; customized applications and forms for Food Safety and Process Control; streamlined audit applications; testing and analysis software; statistical analysis software; FSMA compliance applications and software; and branded food products.

AI and ML are both technologies that are almost buzzwords when it comes to any kind of automation. And accordingly, these technologies have been applied in various parts of the supply chain of the food industry. For food processing, AI can be used for the proper ordering and packaging of food products, where AI can handle the task and reduce the chance of error. There have been some challenges of using AI-based sorting and packaging in food processing, based on the irregularities in shapes, color, and sizes of vegetables and fruits, although most of the time this can be accounted for with large amounts of data and proper training of the AI models and system.

In food processing, AI can also be used to ensure proper guidelines are followed for the handling, sanitization, and packaging of food. This can include the use of separate guidelines based on the expected destination of a given food product and the local guidelines. And the use of AI can be used to monitor individuals who are or are not following guidelines and to resolve issues to monitor individuals not following guidelines. And AI allows food processing to monitor all parts of the food processing system to manage everything, from price control and inventory management, and forecasting and monitoring the pathway of possessions, from where ingredients are grown all the way to where customers collect ingredients.

Example of robotics in food processing.

The use of artificial intelligence and robotics in food processing offers a chance to decrease the number of foodstuff-related diseases. The Food Safety Modernization Act (FSMA) offers stricter hygiene rules applicable across a supply chan. Often the foods that do not require refrigeration, such as cereals, spices, and related food products, are the most prone to contamination. These had previously been one of the safer food products, but this has since changed, and AI has been suggested as a possible solution for the problem. Using robotics means a food processing plant can manage these foods without transferring possible illnesses carried by humans, and the maintenance of an AI system is easier and simpler than humans.

Two promising inventions in the food industry include next-generation sequencing (NGS) and electric noses (ENs). NGS is capable of replacing older generations of food security, with the introduction of AI-based systems and workflow helping formulate data acquisition and laboratory trials quickly and more accurately than traditional methods. The NGS can help find hazardous inclination quickly and work to prevent the possible spread of infectious epidemics before any group of people are impaired. While electric noses (ENs) are sensors that act as surrogates for human noses. These sensors can be used to identify a diversity of smells in the surroundings and send the data to a data center, where algorithms can access the data and signal alarms to the manufacturing units to ensure product safety.

Example of robotics in warehousing in a food processing application.

As food is processed, or as it is brought from a farm, the food is often kept in warehouses and distribution centers. These centers have already, in other industries, begun seeing the use of automated guided vehicles (AGVS) rather than traditional forklifts, and with AGVS capable of performing a range of tasks, including loading and unloading trucks, transporting large items across warehouses, and other related duties that previously could require multiple employees. AGVS also, in terms of reducing food waste, are capable of conducting operations in harsh conditions, such as in cold storage environments—meaning a warehouse could be almost entirely chilled without concern for the health and safety of workers, as autonomous and robotic systems are capable of taking over.

The food industry has many well-established brands and food outlets, and for some the long-established businesses in the industry takes the luster off the idea of establishing a new business, due to the competition. However, through data science and automated technologies, users can try and develop a competitive edge. One metric that can be utilized is customer satisfaction, which can be used to understand supply and demand metrics of customers. Companies with products such as ten-minute dinner kits, with more than tens of thousands of customers and different menu options, can utilize data on buying history, customer behavior, feedback, and food preferences to ensure the readiness to meet demands.

Understanding this data can be especially important in the case of a food processor developing a new recipe. As a single recipe can be cooked in numerous ways, and individual ingredients can be cooked to provide endless possibilities for various dishes, this technology can appraise which food components have a decent savor mark and mark a cuisine that is trendy by region. This basic understanding can permit an artificial intelligence-based algorithm to recommend different types of ingredient combinations for chefs that could result in a broadened menu and profits for either a food producer or for a restaurant.

For the restaurant industry, the level or capability to automate different systems is dependent on the type of restaurant. For example, front-end and back-end automation in a casual restaurant with a static menu and delivery options is often easier to automate on various levels, versus the level of automation for a more formal dining experience with a seasonally changing menu and with an increased focus on the quality and experience of the food and the restaurant. But even in the latter example, there are chances to automate, especially in time-saving ways that are not always immediately apparent.

Possibility of completely automated or robotic restaurant experiences.

There are various ways automation can assist the restaurant industry that are similar to the benefits offered by other forms of automation through the food industry. This can include a reduction in employee turnover. And in the case of employee turnover, automation can save time, energy, and reduce overall training across an organization. Further, automation can streamline various front-end and back-end systems, as well as automate back office tasks in order to eliminate mandatory and mundane tasks that operators deal with on a daily basis, such as forecasting, production, vendor ordering, receiving, and scheduling. This could include tasks such as plan purchasing, menu building, and staffing, and bringing greater efficiency to those functions.

As well, any automation promises to offer predictive ordering, automatic price menu adjustments based on demand, drone food delivery, automatic food preparation systems, and automated food or drink delivery on-site for restaurant operations.

Restaurants have toyed with and developed AI and voice recognition systems for helping streamline incoming orders and enabling fast-casual restaurants to process drive-thru, phone-in, and kiosk-based orders without need for human labor. And this kind of technology can connect restaurant POS systems to provide the kitchen with the orders without assistance. This type of automation lends itself to the fast-casual restaurants' use of ordering kiosks or the use of mobile applications for order and pay. These different ordering systems provide different types of automation that remove several steps and interactions from the ordering experience. Restaurants including McDonald's and Shake Shack have opened some stores with kiosk ordering only.

At the same time, fast-casual restaurants have offered customers with mobile options for ordering and paying with the promise of reducing wait times and long lines, and allowing those customers to choose for either in-store, curbside, or drive-thru pickup options. And in the case of these online ordering portals, fast-casual restaurants have an opportunity for using virtual assistants to help customers, giving them information about the menu, ingredients, store locations, and operating hours, if not offering further food suggestions and information about possible deals.

A Cafe X location offering automated coffee.

An example of this type of automation, paired with back-end automation, is Cafe X in San Francisco, which offers a fully-automated cashless specialty coffee experience with customers either paying ahead through a mobile application or paying on tablets at the kiosk. These kiosks pair the automated ordering system with automatic coffee machines capable of brewing americanos, espressos, cappuccinos, cortados, lattes, and flat whites while offering customers choices of beans and milk types.

These point-of-sale systems, when adopted, have proven in many cases to be popular with customers, or have been assumed to be popular among customers. These systems are capable of assisting customers with the ordering process, often by giving them details on the flavors or spices used in a dish, showing them promotional material, and offering newly added items. And when these systems are offered through mobile application, they can further collect information about customer preferences and make recommendations based on those preferences. Further, these ordering systems can help restaurants deal with manpower shortages, customer engagements, and order accuracy.

Artificial intelligence or machine learning in front-end systems, especially for fast-casual restaurants, provides a chance to embed the system for various purposes. This could include customer feedback systems, which can engage with the customers, their food preferences, habits, and complaints, and collect and analyze related information to help food-service-based applications to secure more food orders from old as well as new customers, and to help secure customer loyalty. This feedback can be further used to remove loopholes and complaints from the applications and help develop more fail-safe and reliable applications. This can include restaurant management frameworks, payment gateway applications, cloud-based big-data applications, and restaurant table booking platforms.

Example of a restaurant POS system with integrations for increased automation.

And for fine dining restaurants, where the use of kiosks or even table-side mobile applications or tablets could be considered to interfere with the dining experience, the POS system can have these AI systems built in. And, for using statistical analysis, these POS systems can be used to further predict the type of food being ordered, relevant inventories, and provide predictions for stock levels and staffing levels depending on the type of day to reduce the burden on management decisions.

Using a POS system with AI or ML can offer revenue prediction services as well. This can be important for a restaurant, as the prediction of sales output is often an essential part of the business, and a part of any owner needing to understand to best plant for future operations for future business growth and increased profit.

For front-of-house and reservation and table management, a table booking platform can provide automation through an online portal. This can free up waitstaff and restaurant hosts from answering phones and managing table bookings and can further reduce the possibility of human error. Often this software ties into the POS or reservation management software used by a restaurant. And the reservation and table management software can further provide integrations with a POS or restaurant management system to provide better data for staffing and food preparation predictions.

In the kitchen, or the back of most restaurants, automation is focused on robotics and their possible use in kitchens. With further advancements in AI as well, these robotic systems can be more capable of completing complex tasks. The use of robotics has been more successful in the fast-casual restaurant space, especially as more fine-dining establishments focus on more complex food and menu building. There has historically been reluctance to bring robotics and automated systems into a kitchen as the cost of these technologies have been higher than employing relevant or related manpower. As the capabilities of robotics continue to increase, they become a more attractive possibility, but will be unlikely to be adopted until their productivity and lifetime or initial cost are considered to be lower than that of training and maintaining employees.

Example of a Zume Pizza robot building a pizza.

One example of using robots to make food has been used in Zume Pizza, in northern California, which has used pizza-making robots that are capable of handling low-skill repetitive tasks such as pressing dough, spreading sauce, and placing pizzas into the oven. The restaurant uses predictive technology to guide production and indicate both the volume and types of pizzas that will satisfy demand. As well, Zume Pizza allows customers to order online or via mobile application, while the pizza finishes cooking in transit in a specially designed delivery vehicle.

Using automation or robotics in the back-end of the restaurant also provides a chance to increase food handling safety and improve compliance with safety standards, as well as offers a chance for increasing consistency in quality and the product delivered to the customer. However, even in scenarios such as Zume Pizza, human labor remains an important part of putting the final product together.

Back office automation can include various tasks that are undertaken on a daily basis, including forecasting, production, vendor ordering, receiving, and scheduling. All of these tasks offer different chances for artificial intelligence and automation to bring greater efficiency in these functions and even work to provide restaurants with better plan purchasing, menus, and staffing. Often these systems can be integrated into the POS system and reservation management software to create a more integrated software system environment in the restaurant. This software can also help smaller restaurants manage social media accounts and reduce the amount of redundant social media posting.

Most restaurant management software is capable of integrating product information management tools, data management tools, and delivery platforms to create efficiencies and automated workflows for restaurants and reducing friction for customers.

With the increase of delivery services, restaurants have become similar to retail outlets in the early 2000s and the push towards e-commerce. Except for restaurants, these delivery platforms operate as e-commerce marketplaces. Often a restaurant has its own website where it can include a recent menu, locations, and a phone number, but often will offer links for online food delivery to a participating platform, or platforms, and for bookings and reservations, similar instructions. This allows a restaurant to focus more on the product and less on the software needed to bring customers into the building or to bring the food to the customer. As well, a delivery platform offers a chance for customers to discover a restaurant they otherwise might not discover.

Besides delivering already made food, some restaurants also offer meal kits, providing a customer with the necessary ingredients to cook one of their recipes at home, or even provide a near-finished recipe that only requires the customer to finish, and the customer in turn receives a hot meal from the restaurant of their choice.

In any case of a restaurant offering delivery services, there are a variety of services and technologies that are automated to make the services work smoothly. One of the simplest of these technologies includes automated order processing, which means a restaurant does not have to check to see when or if a customer is placing an order, but are notified automatically. Further, this reduces possible mistakes of a manual ordering system. Furthermore, for some systems, this can capture sales order data where the order processing system can contain or refer to a businesses order processing system and prevents the need for a company to constantly reenter information such as customer information or delivery locations. As well, the historical order data can be used by businesses for demand forecasting and provide better business decisions.

These delivery platforms and their automated order processing tend to offer restaurants a chance to track an order in real time and automatic verification process for the ordering, and the data can be forwarded to the nearest applicable restaurant.

While delivery and restaurant aggregation platforms offer online ordering and delivery and these are often less expensive and less complicated for a restaurant to participate in, there are still some restaurants which prefer to develop their own order and delivery systems. And even in the case of using an online ordering and delivery platform, there are further integrations that can increase the amount of data and automation between the restaurant and the delivery platform. This is often done through the POS system, where the availability of menu items and nutritional information on different items, or available substitutions in the case of allergies or dietary specifics, can be managed by the restaurant. Further integrations could include into a kitchen display system where the order can be displayed without need for any manual processes to pass the order from an ordering system to the necessary parts of the kitchen. And while it is a simple automation, it can reduce the possibility of human-related error.

Part of these delivery platforms, and part of consumers searching for a restaurant, café, or bar, a search engine is an important feature, and featuring in a search is important for restaurants. To further increase their overall rating, it can behoove a restaurant to address bad experiences to help retain customers and attract new customers, while it can be equally important to understand what experiences give their establishment a good or poor rating. A search engine and an automated search can help a restaurant find those poor experiences and reach out to redress any wrongs. As well, as consumers begin to use more voice search features, in the case of a restaurant working to develop their own delivery service, integrating an AI-based search engine and a voice-capable search feature can be increasingly important.

Data management systems, such as automated inventory management, could provide an automated system for managing a restaurant's food catalog. This process has traditionally been a manual process, and for most food delivery platforms and restaurants, remains a manual process. However, automating this process for restaurants and grocery stores and convenience stores can help these businesses participate in online marketplaces. A data management system can integrate the merchant data lifecycle, which includes the steps of getting a merchant on-boarded onto a platform, keeping their data updated over time, such as when prices change or marking items out of stock, and offboarding a business should that business decide to change a particular solution.

Without automation, each step in the merchant lifecycle involves various manual processes for platform companies to receive data from a large variety of sources and cleanse and transform it into structured data capable of being used on the platform. For restaurants, especially, the data is unstructured, which makes it harder to automate and continues to require a lot of manual effort to bring it into a platform system. A data management solution that can take a granular approach to the data has a better chance of automating the service, and this system can communicate between the restaurant and the platform, and allow the platform to pick and use the data the platform prefers while offering enough data to be used by multiple platforms for marketplaces.

A company like Woflow has developed an API to help structure this unstructured data, allowing restaurants to improve connectivity, transparency, and trust in the industry, to help sync onboarding tickets from CRMs or to deliver structured catalog data to benefit the entire food ecosystem and improve efficiencies. The system also uses models that are consistently being developed to help automation within a system and remove humans from the data entry tasks to maintain such a platform.

Product information management (PIM) platforms work to handle product information in one source for all channels, which can help restaurants or grocery stores maintain the quality of the data and the amount of data in a single place. This could include to change the data for different changes in expectations, such as for digitization and digital innovations; changes in consumer behavior and increased customer expectations; globalization, laws, rules, and restrictions; CSR and sustainability; demographic changes; health, allergies, and diseases; and big data and smart data.

These systems help food producers and sellers develop a greater amount of data and attributes, such as new laws and regulations, changes in business models, and new innovations and technologies to help with the complex process of producing and selling food. And this system can bring the information to recipients by channel to meet customer expectations and demands.

Many existing PIM tools do not cover the specific needs of the food industry and are often found to lack the flexibility to handle regulatory standards and related key aspects of the industry. A lot of this data is typically unstructured and does not fit well into spreadsheets, which can often result in error-prone, time-wasting silos of departmental product data. However, these solutions can help food suppliers and restaurants maintain compliance with industry and government-mandated regulations for food ingredients, traceability, and allergen labeling with verified product information.

While grocers and grocery stores have various forms of possible automations, one of the earliest versions of that automation came in the development of e-commerce with delivery for grocery stores. Although it has existed for longer, the COVID-19 pandemic accelerated the adoption of purchasing groceries online and having them delivered. This was similar to the same rapid adoption experienced by retail outlets during the SARS outbreak in 2003. E-commerce offers grocery stores a chance to provide new services, such as offering greater food safety, larger item baskets, greater or more collaboration between grocers and brands, and more payment options for users—creating a more competitive landscape for grocers. And for most of those stores, the infrastructure for e-commerce already exists, allowing for more development for grocery e-commerce.

Similar to e-commerce, another way for food processors and grocers to differentiate themselves is to offer meal preparation kits delivered to a consumer's front door. This provides a consumer with a chance to either cook for themselves or purchase a partially prepared meal online and have it delivered, and allows grocery stores and food processors to develop a new sales channel. These systems often rely on heavily automated software infrastructures where stock levels, specific meal preparation kits, and online stores for kits are managed. And for those working to develop these services, there are several platforms that help or act as that automated infrastructure.

The point-of-sale (POS) systems for grocery stores act as all-in-one management software similar to the way the restaurants management software or POS act. These systems tie into data management and inventory management systems to keep inventory and sales levels up-to-date in real-time. These systems also offer grocers the ability to track purchasing history in a data management system tied into a POS which can give grocers a better insight into purchasing history.

As well, the POS system can handle tracking, scheduling, and managing employees; maintaining shopper loyalty programs, and related customer relationship features. And, with greater amounts of people ordering groceries online for delivery, a POS system can tie into platforms for online ordering and delivery, and some POS systems can offer those functions.

Similar to the POS system, one of the growing trends for grocers is the use of self-serve kiosks for customers to purchase their groceries. These are not completely automated, as they still require a customer to scan their items and make a traditional purchase, and have in some cases received criticism for increasing the incidences of theft, being too confusing for customers, and providing a less-friendly customer experience.

When talking about automation in grocery stores, one of the more talked about developments is in the B2C store space, where e-commerce businesses such as Amazon or Wayfair develop brick-and-mortar establishments, other than warehouse spaces, for shoppers to go and shop products. For example, Amazon has piloted cashier-less, and almost people-less supermarkets, where a user can scan in to the store with an Amazon account, pick out whatever items at the supermarket, and leave. And these stores, a concept also piloted with Amazon-owned Whole Foods, charge the user for the items, which are tracked using computer vision.

A similar experience has been the buy-online, pickup-in-store (BOPIS) model which has been used by supermarkets for longer than fully automated B2C experiences. This increases the responsibilities of workers in outlets, rather than to warehouse workers, as often happens in an e-commerce model. However, the BOPIS model has proven to also be more inefficient for stock reasons, especially if a store clogs up retail space with staff collecting BOPIS orders. However, increased automation to the model could provide stores with better fulfillment capabilities, as the use of robotics could increase the collection options and reduce the likelihood of customers seeing items listed as "out-of-stock" or "replaced" and allow warehouses to handle specific demands faster.

Another growing trend in the automation of grocery stores, especially for stores working towards full automation, is the use of robots in the customer areas, rather than just in the warehouses. Robots have already been trialed for warehousing and stock areas in larger stores such as Amazon and Walmart, where they have been used to pick items or to move items from shelves to another robotic system capable of moving goods from one area to another. But these systems still require a larger amount of human labor. However, these systems have also grown in popularity due to the move towards delivery and BOPIS models, where more employee hours are spent on fulfilling orders, packing groceries, and delivering orders, rather than on the front-end grocery work of talking to customers, cleaning possible spills, stocking retail shelves, and related duties.

For grocery stores that have seen an increase in online ordering, utilizing automated or increasing automation in inventory management systems, such as electronic shelf-edge labels, self-checkout terminals, shelf-scanning robots, and partially automated backroom unloading, have proven to increase productivity. When paired with computer vision and related tracking technologies, they can further the automation in a given store and bring it closer to the levels of the above fully automated grocery stores.

A more common use of robotics in grocery stores has been using robots for cleaning spills, cleaning floors, checking inventory, or directing customers. Most of these robotic systems are unable to perform multiple functions. Some examples include Marty, made by Badger Technologies, which is capable of alerting customers to spills in English and Spanish. Similarly, Tally, a robot manufactured by Simbe Robotics, has been used in Giant Eagle and Shnucks, to monitor shelves and alert workers to out-of-stock items or problems related to merchandise presentation or merchandise out of place. Tally takes a predefined route through a store and transmits the collected data to employees.

Similar to Marty, Millie is a robot capable of detecting hazards, such as spills, and was trialed in a Woolworths in Australia. However, beyond alerting customers and employees to spills, Millie can also clean the spills it detects. During the robots trial, it was met with mixed customer reviews, with some customers finding the robot unfriendly and others concerned the robot would replace human workers.