Fast-food chain Juici Patties, which operates more than 70 locations in Florida, New York, and Jamaica, started on the island nation as a family kitchen in 1978. When the chain expanded into the US last year, it experienced stockouts.
Executives knew they needed a different strategy — one with advanced technology to scale their business, manage franchises, and sell thousands of patties each day, Stuart Levy, the company’s chief technology officer, told Business Insider.
Today, Juici Patties uses AI’s predictive and proactive features to prevent disruptions before they occur.
“AI is helping to keep our distribution centers stocked with enough of our branded packaging to meet demand,” Levy said.
Indeed, AI technology is making its way into quick-service and fast-casual restaurant operations. AI can use data to form predictions about customer orders, then generate insights for leaders on how to manage inventory and operations.
Domino’s Pizza and Microsoft teamed up to create a generative-AI assistant that saves managers time on inventory management and ingredient ordering. Starbucks also inked a deal with Microsoft to use genAI in its product development. And Yum Brands, the parent company of KFC, Taco Bell, and others, partnered with Nvidia on AI for internal tasks such as labor management and analytics processing.
For many quick-service restaurants, “their entire brand is built on speed and efficiency,” said Spencer Michiel, the restaurant technology advisor at Back of House, a resource for restaurant tech solutions. “If there’s anything that can help them with speed, efficiency, and lower cost, they’re going to jump all over it.”
Data-rich restaurants layer on AI
Restaurants are “extremely data-rich,” Michiel said, which makes them well-suited to adopt AI. Major fast-food chains already have standard operating procedures to purchase based on demand, but AI takes that to the next level with forecasting abilities that more accurately predict demand and inform supply.
With AI’s forecasting capabilities, restaurants can predict what customers might order and use this data to buy ingredients, a notoriously tricky part of restaurant supply chain management.
“The biggest thing that restaurants do badly is purchase,” said Stephen Zagor, a consultant focused on restaurants and food businesses and an adjunct assistant professor of business at Columbia Business School.
AI draws from quick-service restaurants’ internal point-of-sale data, such as sales trends and which products customers tend to buy at the same time. Then, an AI algorithm combines this data with external factors like the weather or local events.
“The beauty of AI is it’s taking forecasted demand and turning that into a reaction all the way through the supply chain,” Zagor said.
For example, AI can deliver granular data by location. For a restaurant right off an interstate, AI could predict that travel will slow down on certain days. Seeing that prediction, restaurant managers could decide to drop their inventory levels and purchase fewer items, Zagor said.
He named McDonald’s as one quick-service restaurant that uses AI to maximize everything from its point-of-sale to its supply chain. The fast-food giant has partnered with Google Cloud and IBM on various AI solutions.
When it comes to data and AI, the level of standardization across major chains puts them at an advantage over smaller franchises and independent restaurants.
A mom-and-pop restaurant may not have “the time, the bandwidth, the skills, the knowledge” to gather data and create an action plan, Michiel said. Subscribing to software can cost hundreds of dollars each month, presenting financial barriers to small businesses. Any new back-of-house or supply chain software would need to integrate with existing point-of-sale systems. If done incorrectly, the result could be data loss or lag, “and it’s going to be frustrating,” Michiel said.
Serving up efficiency and financial gains
AI’s predictive power can also help minimize waste in restaurant supply chains. If a restaurant orders too much, it could have to discard unused or expired food. This could require the business to increase meal costs to compensate for the loss, according to Michiel.
“Food waste is just a killer,” Michiel said. “Over-ordering is straight loss. There’s no way you’re going to recover that cost.”
Controlling costs is especially critical for fast-food chains, which order at scale and sell low-priced products. Making just 5 cents more on an item, or making 5 cents fewer, “is a big deal,” Zagor said.
AI can also promote cost savings by flagging if a particular ingredient swap could result in higher profits without sacrificing taste or quality. The technology “smooths out” a restaurant’s ability to purchase inventory while still keeping customer satisfaction top of mind, Zagor said.
“You can get good profit, and the customer is going to be happy,” Zagor said. “It’s win-win.”
Levy said Juici Patties’ AI implementation into its point-of-sale system and supply chain was time-consuming, involved some growing pains, and sparked fears about replacing the workforce with AI. He acknowledged that “AI isn’t flawless.”
Now that the technology is in place, though, Juici Patties has seen a boost in operational efficiency, Levy said. In one instance, the AI revealed that customers wanted to purchase food earlier in the day, before Juici Patties locations were open.
“We were missing potential sales during earlier hours of the day,” Levy said. The restaurant chain acted upon that information and adjusted its opening times. The result: “a consistent increase in daily sales,” Levy said.
