QSRs Focus on AI and Computer Vision to Solve for Labor Shortages
Restaurants across the United States are experiencing severe labor shortages coming out of the pandemic and quick service and fast-casual restaurants are no exception. With digital transformation in full swing across most of the leading chains, the need for artificial intelligence and automation is critical for scaling improved processes and increasing operational efficiencies.
Not only are QSR and fast-casual brands experimenting with robots in the kitchen, but leading brands are also focusing on computer vision and visual intelligence to provide the real-time data and insights in their physical spaces to measure, analyze and act up key data points that are used to optimize production processes and server efficiencies.
Because they need accurate and reliable data collected in real-time with a 360-degree view of food preparation and server processes, computer vision is an ideal solution for digitizing the in-store operation. Easy-to-deploy and scalable, edged-based smart devices (for example iPhones and iPads) and an app is all that is needed for a brand to deploy this innovative technology to their innovation and testing environments.
Once the devices are in place, automated AI models are then used for collecting accurate and reliable data straight from the field, all without any human intervention. Data is processed in real-time, and results are used to compare across different tests and experiments while allowing for rapid ideation, all without the need for significant human resources.
One leading pizza chain needed to innovate faster, better and cheaper, by automating data collection and obtaining deeper insights from innovation tests. They required the ability to expand testing and innovation beyond one standalone lab environment and improve results around launching new products, processes, and services across their large portfolio of restaurants. This large QSR chain looked to computer vision and AI to accelerate innovation.
Having a flexible system that could move and adapt to different tests, accommodate different formats, layouts, and experiments was critical. Because computer vision can be easily deployed beyond the lab for field testing in real store environments and does not require major retrofit of existing stores, it became the obvious solution.
With the help of visual intelligence, this leading QSR pizza chain was able to improve throughput and increase server efficiencies within the new product and service processes being tested in the innovation lab. With rollouts to the market, results included improving customer satisfaction ultimately leading to increased customer retention, sales, and keeping their margins in check. Read the full success story.
The promise of computer vision and AI is a reality today for foodservice and the future is bright. Being used for a handful of use cases now, many more will inevitably be added.
Labor shortages are driving many of these initial use cases. For example, improving the speed of service and customer wait times, while at the same time optimizing food preparation and server efficiencies. Specific data points such as the following can be included:
Improving Customer Experience - speed of service, dwell time, customer counts, customer movements and activity as well as customer flow data and insights were captured, analyzed, and explored for modifications and enhancements to improve the overall guest service experience.
Enhancing Employee Efficiencies - employees' productivity and utilization, as well as dwell time, movement, and activity data and insights were captured, analyzed, and explored for modifications and enhancements to improve the overall staffing efficiencies and employee productivity.
Product and Production Efficiencies - production times per product, product journeys (product paths), employee utilization per product, product count and detection, and other product data points were measured, analyzed, and explored for modifications and enhancements to improve product and production efficiency.
For more information on how computer vision can help your restaurant organization innovate and grow, read: the full success story.