Queuing up to wait for services is a nearly universal experience. We routinely arrange ourselves in single file lines at the bank, at retail stores, and at the fast-food drive-through. Since the COVID pandemic hit, we've increasingly waited in lines to ensure our health – lining up to be seen at hospitals, to receive vaccines, and to get tested.
As businesses reduced their capacity and implemented social distancing, lines seemed to get longer and more challenging to manage, often snaking out of storefronts and onto sidewalks.
When the India-based 3K Technologies team learned about the SAS Global Hackathon, they saw an opportunity to combine their technical expertise and passion for app development with SAS analytics to improve the queuing experience.
3K Technologies knows that we all value our time, and they wanted to create an optimized queue management system to benefit both the customers who wait in lines and the businesses that serve them. In their own words, 3K strives "to create and maintain long-term, successful relationships with their customers," and they understand that providing efficient service without long wait times is key to improving customer experience and building loyalty.
A streamlined queue system can change the complete working infrastructure of every organization.
– 3K Technologies
Real-time business recommendations powered by AI
SAS Hackathon teams were given access to SAS® Viya® environments hosted on Microsoft Azure to build their applications. The 3K team used their environment to design an end-to-end solution that handles every step of the analytics process, from collecting data about queues to making data-driven decisions that improve business operations.
First, cameras capture video of service lines and send the data to SAS® Event Stream Processing (ESP) for real-time analysis. The data scientists at 3K embedded a Python computer vision model into ESP to determine how many lines are present in the camera feed, how long each line is, and the average wait time. Then, predictive models in SAS® Visual Data Mining and Machine Learning use the data gathered to make recommendations to customers and vendors.
For example, they guide customers to the best lines to join and predict how long their wait times will be. For vendors, the solution suggests opening additional service counters or closing unneeded ones to operate more efficiently. Finally, the results of the queue management system can be explored use interactive dashboards in SAS® Visual Analytics.
Analytics that work for every industry
When designing this queue management solution, 3K was excited to create an innovative product with many potential applications. The market for this technology is significant, from airport check-in counters to toll plazas and voting booths. Queues are integral to organizations in a vast number of industries and locations all around the globe. As a result, they are a necessary part of life for all the people who patronize these businesses.
The 3K team built this solution to work similarly for any use case. Whether you're analyzing lines of people in a grocery store, or vehicles waiting at the pump, the application analyzes the data in the same way. The result is evidence-backed predictions that improve customer experience for any type of organization.
The benefits of streamlining queue management extend beyond the time a single customer waits in a line. Organizations can also use their queue data to make better long-term staffing decisions, reducing operational costs and building employee satisfaction. 3K believes that the result will include not only reduced wait times for service, but also improved quality of the service provided.