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QSR Innovation Labs Using Computer Vision to Accelerate Innovation

Over half of the top 25 quick-service restaurant companies have innovation labs or kitchens being used to simulate and innovate new products or services, processes, and operations.

Chipotle, Sweet Green, Panda Express, McDonald's, Chick Fil A, Shake Shack, Arby’s, Dunkin Donuts, Dominos, Sonic, Jack in the Box, Tim Hortons, and Taco Bell, all have these innovation environments and they are looking to computer vision and artificial intelligence to accelerate growth and operational efficiencies for their organizations.

Computer vision can help, for example, with back-of-the-house operations improvements by measuring any visual product, activity, or process and offering insights into the entire production process. Specifically, these counts can include production times by product, product journeys (or product paths), employee utilization per product, product count, and detection. Product data points are measured, analyzed, and explored for modifications and enhancements to improve product development, production processes, and server efficiencies.

Foodservices is a labor-intensive business and employees are at the heart of every food service operation. Restaurant staff work extremely hard whether at front of the house or back of the house and protecting employees, minimizing burn-out while increasing efficiencies is the key to long-term success for companies in the restaurant business. Particularly as labor shortages continue to challenge the industry, companies are looking for new ways to solve server efficiencies and automation is a must.

Employee productivity, utilization, and the production processes can be tracked by measuring dwell time, staff movement, and activity. This data can be analyzed and explored for modifications and enhancements to improve the overall operational efficiencies and employee productivity for existing processes, whether for existing products or as part of new product development, new services, or delivery models.

David Greschler, CEO and Co-Founder of Nomad Go explains how it works; “automated, computer vision-based, visual intelligence is being used for collecting test data in innovation labs during test runs which are utilized to measure, analyze and improve for key customer, employee and product data points. Improving customer experience, or speed of service is a common goal with dwell time, customer counts, customer movements and activity as well as customer flow data and insights all being captured, analyzed, and explored for modifications and enhancements to improve the overall guest service experience.”

A large chain’s innovation lab team is confidently working with Nomad Go. These large brands are looking for new ways to see test results in real-time and compare results across different tests with a desire for rapid ideation and comparison of variables. Having a flexible system that could move and adapt to different tests, accommodating different formats, layouts, experiments is critical for success.

Not only did this large global organization accelerate their innovation cycle and improve their learnings for a faster go-to-market process, but they also saved money with less labor needed to run tests. The in-store, in-lab environment was digitized accelerating digital transformation for their operations. Key highlights included: (1) reduced data collection time by nearly 800 hours per month and (2) are now running experiments and getting results four times faster than before using computer vision. Read the full success story.

For foodservice companies to be innovative they need to be adaptive and agile and having a system that can easily be deployed beyond the lab for field testing in real store environments and that doesn’t require major retrofit of existing stores is key.

A visual intelligence solution must be able to collect accurate, reliable data from the field without deploying significant human resources and it needs to scale the innovation process beyond one isolated lab, capturing a 360-degree view of field stores so that companies can learn about hidden process efficiencies, best practices, and areas for improvement.

As food services companies evolve to be more adaptive and innovative, the majority will be joining these leading brands and tapping into computer vision and artificial intelligence to improve their operational efficiencies, guest experience and support the need to innovate and grow.

For more information on how computer vision helped this large coffee chain accelerate innovation, read the full success story blog post.

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