The robots are coming!
Actually, they have already arrived for many applications in the food industry. While physical robotics are commonly used in food processing to perform tasks such as butchering, picking and placing fruit into containers, decorating cakes, and more, a new category of robotics—robotics process automation (RPA)—is likely to be more popular in the food industry in the near future.
RPA employs artificial intelligence (AI) to automate processes via a non-physical robot. RPA can fill out forms, validate invoices, copy and paste data, check and track data, and follow any other rules it is programmed for. It is an ideal fit for repetitive tasks that can be automated with software. The application, which may be implemented from a cloud application or on-premise, is gaining ground in many back-office operations. While the food industry is a late adopter at best, RPA could become valuable technology.
The researcher Information Services Group (ISG) reports that RPA affords a 43-percent reduction in resources for order-to-cash processes: billing, credit, collections, and pricing. Savings ranged from 32 to 34 percent. ISG predicts that by the end of this year, 72 percent of all companies will use RPA to automate support functions and reduce costs, improve productivity, increase compliance, and shorten transaction times.
Mars, Inc., the global manufacturer of confectionery, pet food, and other food products, boasted about using RPA to consolidate back-office operations into one streamlined operation. A food producer in Europe used RPA to streamline its vendor procurement with AI to analyze vendor documents, perform a vendor credit check, and recommend a vendor to select. The European firm also applied RPA to answer customer order inquiries via their email system: A virtual AI robot logged into their shipping portal, replied to the customer, and moved to the next customer inquiry, with no human involvement. The company claimed to eliminate 40 to 60 percent of the manual effort that otherwise would have been required.
Robotics Process Automation for Food Safety?
While many back-office operations—such as human resources, finance, accounting, and routine processing—use RPA, the food industry is likely to adopt functions, including managing documentation and data surrounding product recall management and Food Safety Modernization Act (FSMA) compliance. Heather Larrabee, executive vice president of GoSpotCheck and a former executive at Whole Foods Market, comments that a single food safety-related event can create tremendous risk for brands, cause harm to customers, and generally erode long-term trust.
“The financial and relational impacts of events can be enormous,” she says. “The proactive nature of FSMA requirements lends itself to robotic process automation to create a dynamic ‘safety net’ for companies. Used in tandem with Internet of Things (IoT) sensors that feed data to machine learning (ML)/AI tools, it also reduces cost and labor. We’re seeing brands invest with Testo to monitor when oil should be changed in fryers, automate temperature measurements of food, and understand things like how many times a bathroom door has been opened. This helps them optimize cleaning schedules and gain real-time, trusted insights into food safety and sanitation conditions.” Larrabee adds that AI and ML can detect and predict potential safety issues before they occur, as well as assist in reporting—automated reports can proactively signal busy leaders to risks in their locations.
“As we see increased costs to serve across restaurants and food services, high rates of attrition, and an increasingly complex global food system that amplifies foodborne illness risks in supply chains, AI and ML can provide an essential protection to safeguard consumers and brands alike,” Larrabee notes. “We’re also strong advocates for companies striking a balance between humans and machines. AI and ML are great at forecasting based on past performance, but as the technology evolves, we believe there is an essential role for humans to continue to play in the system. Ultimately, AI and ML can’t necessarily make decisions. We still need people in businesses to analyze the data and make critical calls. In the case of a food safety crisis, it’s paramount to understand what role the machine plays and what role people play in solving problems and keeping consumers safe.”