Customers rely on Quick Service Restaurants (QSRs) for the service excellence, dependability, and repeatability they know and love. When customers queue up in a drive-thru or in front of a counter, they’re not looking for the kitchen crew to reinvent the meal, but there are a number of expectations customers have and QSRs strive to meet with every customer and every order.

Since QSRs often face stiff competition from nearby restaurants as well as high expectations, satisfying customers each and every time is essential. A disappointed patron won’t hesitate taking their business elsewhere if they have any reason to expect service that won’t be fast, friendly and accurate. 

 Vision AI offers solutions to a number of the challenges facing restaurants, presenting use cases for everything from keeping kitchens cleaner to ensuring each patty spends the exact right amount of time on the grill. 

Restaurants are Investing in AI-Powered Kitchen Analytics

Chains including McDonald’s, Chipotle, Wendy’s, and Pizza Hut (to name just a few) believe AI-powered data analytics hold the key to making the most of high demand and turning visual data into a source of insights for making kitchens run more effectively and ultimately achieving new levels of service excellence and efficiency.

Improving order speed, process compliance, and accuracy

It’s right there in the name – QSRs are supposed to take orders speedily and deliver food just as fast. 

Custom visual data analytics models can support QSRs with: 

• Analyzing food preparation for speed, accuracy, compliance, and efficiency. 
• Assessing items against quality standards both during and after preparation.

Cutting down on food waste

U.N. estimates suggest a third of all food produced, worth as much as $2.5 trillion, goes to waste each year and restaurants not only contribute to the problem, but feel its effects as well. Industry estimates suggest restaurants lose tens of billions each year when its ingredients don’t make it into dishes. 

Restaurants can introduce computer vision models for help: 

• Tracking “dwell times” for prepared food items to ensure food is served within a safe window of time, reducing the risk of foodborne illness and maximizing quality.
• Assessing food preparation methods to ensure the proper quantity of each ingredient is used every time.