AI and the cloud serve up sizzling food

“If you look at the Brinker business, every day is a new challenge, especially in the restaurant industry,” says Pankaj Patra, CIO of Brinker International. “My business is running restaurants, and we have to support our business in many dimensions. One is operating the restaurants and another is managing the guest experience.”

Ensuring each restaurant is properly staffed, has enough ingredients on hand for the day’s forecasted sales, and maintains a positive guest dining and to-go experience relies on drawing insights to make better decisions.

Innovating with AI leads to sizzling, on-time orders

“Leadership is asking, ‘What’s going on with AI and generative AI?’ It’s been a hot topic for everybody. There is a use of AI and machine learning, but it has to be in the context of what business drivers or goals it’s trying to achieve,” explains Patra, whose organization leverages AI for cook-time accuracy.

Understanding there is a direct correlation between on-time orders and guest experience, Brinker relies on Teradata ClearScape Analytics™ in VantageCloud on AWS for AI models with Amazon SageMaker machine learning (ML).

“When you order online from Chili’s, we’ll tell you how long it’s going to take to prepare the order,” says Patra. “That’s completely driven through an AI model, which takes into account how busy the kitchen and to-go are, how many orders are from third-party apps, and historical insights to drive an accurate forecast of your order’s cook time.”

Whether the order will take 25 minutes or 45 minutes, accurately predicting order-availability times leads to the sizzling hot food, like fajitas, that Brinker and Chili’s are best known for and keeps guests returning. If a restaurant is not able to deliver a good guest experience, Brinker knows that guest may not return.

“What if that guest doesn’t return?” asks Patra. “What’s the impact to sales? We use analytical models that we’ve built with Teradata ClearScape Analytics. This gives us a huge advantage of changing the guest experience to improve or increase guest visit frequency.”

Amazing guest experiences start with data

Guest experience goes beyond sizzling food. Brinker’s Chili’s restaurants collect upwards of 25 million guest surveys yearly. By integrating data and accelerating data preparation, Brinker is able to improve business performance through democratized insights.

The guest experience and customer surveys are often the tip of the spear that points to improvement opportunities. Customer 360 analytics deepen understanding of customer needs and expectations for Brinker to improve engagement and loyalty. Restaurant operations’ improvements to food quality, service and staff friendliness, cleanliness, employee training, staffing demand, and pricing and promotion lead to better customer experiences. 

“Every data point serves a purpose,” says Patra. “Identifying who our most loyal guests are, their lifetime value, and how we get guests back into the restaurant are all driven by data.” 

AI/ML in ClearScape Analytics manages ingredient inventory, prevents food waste, and guides restaurant cleanliness

Even available inventory of ingredients greatly impacts guest dining experiences. “We manage through just-in-time inventory,” Patra says. “So, we don’t have a lot of inventory in the restaurant. Looking up the chain from restaurants to distribution centers and suppliers, having an analytical system to understand our inventory levels allows us to make decisions based on models built in Teradata VantageCloud.”

Since Brinker holds minimal inventory, preventing food waste is critical. “Forecasting ingredient demands is a critical challenge so that food waste is minimal,” explains Patra. “We also don’t want to over-index on the side of not having enough, because then the guest experience suffers.” 

ClearScape Analytics allows Brinker to “accurately forecast how much inventory we need based on current situations in that restaurant and certain events that are happening, to be able to accurately predict how much inventory we need in every restaurant,” Patra says. 

Patra shares that even restaurant cleanliness is guided by an AI/ML model that predicts cleanliness based on variables that are proactively monitored and focused on the individual restaurant, all in an effort to improve restaurant operations by frontline restaurant managers. Brinker takes proactive steps to maintain clean and healthy eating environments using analytics and data within Teradata.