The Challenge

Have you been to a grocery store recently where you could self-checkout without any assistance from employees? If so, did you make sure you scanned everything properly? Have you ever accidentally mixed up product codes when identifying a specific product, like choosing a non-organic version when the actual product was a higher-priced organic? Perhaps you once put a bulk package of paper towels under your cart and accidentally forgot to scan it before you left. If this sounds familiar, you might have (unintentionally) contributed to the problem of retail loss, aka “shrink” or “shrinkage.”

But this isn’t just a grocery store problem, nor is it always an accidental problem…

When customers are responsible for self-checkout, mistakes are going to happen and those mistakes cost the retailer money – lots of money. The total cost of retail loss in the US attributed to self-checkout shrinkage is estimated to be around $100B. Nearly 7% of self-checkout transactions (6.7%) had at least some partial shrinkage compared to 0.3% with checkout using cashiers.

Self-checkout (SCO) systems are not only here to stay, but they are continuing to expand despite the shrinkage issue. Evidence suggests that as much as 80% of consumer transactions in supermarkets might be processed through variants of SCO technologies.

Retail loss affects every type of store when self-checkout is available. Retail loss doesn’t happen just because customers accidentally walk out and forget a payment. It also happens when customers intentionally steal, for example, by swapping barcodes for cheaper products or when outright shoplifting occurs by not scanning items.

The same holds for employees. Trained employees can also mix up a product code or miss an item when lines are long and they are trying to speed up the process. Unfortunately, they could also be stealing as well. Employees who steal items tend to be repeat offenders, becoming more brazen the longer they go uncaught. When they collude with customers, the losses begin to pile up!

The Solution

What can a store do to prevent this? Traditional camera systems are forensic in nature, only being used to investigate after the crime has already been committed. Additionally, they are only looked at when a loss is suspected to have occurred, which is too late. Thankfully, new technological advancements based on artificial intelligence (AI) can help solve this problem and reduce the financial impact of shrinkage.

Software using computing vision technology can interpret input from cameras in real time and identify mix-ups at the checkout as they occur, providing direct feedback to the customer. By doing this, recovery of self-checkout losses directly converts to more sales. The same technology can also be used to identify suspicious behavior, alerting staff, who can then act accordingly. Research indicates that the vast majority of unscanned items are due to errors that customers are willing to address once they have been pointed out to them.

Intelligent solutions like this rely on a robust platform, using complex software and optimized hardware components. These would require a lot of time and IT resources for a retailer to develop on its own, but thankfully, the latest advanced technologies from companies like NVIDIA make the development of software a lot easier. These include NVIDIA Metropolis microservices for the NVIDIA retail loss prevention AI workflow. Leveraging the Metropolis microservices and models pretrained on hundreds of the most frequently lost goods, this AI workflow provides few-shot, active learning to quickly scale to recognize hundreds of thousands of products and provide intelligent alerts with actionable information.

Software is just one piece of the complete solution. You also need robust and reliable hardware to support it. Supermicro’s line of enterprise edge products are purpose-built for retail deployments. These come in a myriad of form factors to cover nearly every deployment scenario. Supermicro works closely with AI software providers such as NVIDIA to offer systems supporting the latest AI acceleration technology as soon as it’s available.

Reliability is critical, since store staff need to focus on their day-to-day tasks. They don’t have time (nor, in most cases, the expertise) to troubleshoot hardware issues with the very systems that are supposed to be helping them. And when IT staff are stretched thin, outside vendors are often called in for technical issues. These “truck rolls” can adversely affect operational budgets and distort the financial value of loss prevention systems. Having reliable hardware to run your AI applications on is of paramount importance.

Interested in learning more about how AI-driven applications can help retailers with their loss prevention problem? Make sure to return for our next article in this series, where we’ll take a deep dive into the hardware that powers these solutions.

In the meantime, download our whitepaper to discover more about the potential across key use cases in the retail industry. https://www.retail-systems.com/rs/ai-powered-retail-whitepaper

ⁱ Source: Grabango
ⁱⁱ Source: Grabango
ⁱⁱⁱ Source: ECR Loss Study

---