STUDY: AI RETAIL QUEUE MANAGEMENT UNLOCKS 5 MAN HOURS PER DAY IN LOST CHECKOUT PRODUCTIVITY
The platform helped to reduce cashier idle time by 57.66% when measuring against the baseline, equaling to more than 2.5 man hours per store per day. The software also prevented 237 queue forming incidents, saving an average of 2.25 hours in customer wait time per day. The experiment was conducted for two months at two store locations of one of the largest retailers in Central and Eastern Europe with 18 manned checkout counters at both trial locations.
“The retailer already had highly efficient processes for in-store staff resource allocation and queue management practices. The trial was aimed at discovering whether AI software can tune up the efficiency from ‘very good’ to ‘perfect” – said CEO of EasyFlow – Real World Analytics Simas Jokubauskas. The company develops Computer Vision powered applications for retail, manufacturing and construction.
Simas Jokubauskas, CEO of EasyFlow
EasyFlow Queue Management platform utilizes video footage from in-store security cameras. During the trial, EasyFlow measured the visitor flow at the store entrance. It also monitored the checkout area for how many checkout counters are open and whether there is a risk of queue forming. No additional data points were fed into the EasyFlow platform – all the data was derived from security camera video footage.
“Say the software detects that 50 shoppers entered the store in the last few minutes. Yet only two checkout counters are opened. It is very likely that the queues will begin to form in a couple of minutes. The platform then sends an notification to head cashier and employees on duty that additional checkout counters should be opened, giving staff enough time to react before the shoppers head to the checkout” – said Simas Jokubauskas.
Similar logic is applied to overstaffing situations. When detecting a decrease in customer flow with too many checkout counters open, the platform notifies the employees that their resources could be better used elsewhere in the store.
“The software helps to balance out overstaffing and understaffing situations allocating the available staff resources throughout the store. As the trial was conducted during April and May when additional safety measures were in-place due to COVID-19, this helped to ensure smooth operations with lower employee headcount” – noted Simas Jokubauskas.
The platform sent a total of 2048 notifications reacting to overstaffing and understaffing situations. It also helped to measure irregularities in visitor footfall during the COVID-19 pandemic. Contrary to other retail queue management platforms, EasyFlow software not only measures the real-time fact, but also provides future visitor traffic and staffing requirement forecasts.
EasyFlow – Real World Analytics also develops Computer Vision applications for real-time shelf stock level monitoring. The platform notifies employees when a particular product (e.g. milk) stock is running low and restocking is required. The software is also employed to monitor planogram compliance, as well as automated product identification at self-checkout counters, reducing the number of mis-scanned items and preventing checkout fraud.
“Retailers have abundant video data from in-store security cameras. This information can be harnessed to drive operational efficiency. Most importantly, Computer Vision solutions are simple to integrate, do not require any additional hardware investment, they do not rely on additional data from ERPs or other business software, and can quantify real world in-store intelligence in similar fashion as Google Analytics does for the digital realm” – said Simas Jokubauskas.
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