Many retailers continue to face challenges at the checkout line, especially during peak hours when long waits, limited staffed checkout lanes, and rising labor constraints converge. These delays affect the overall shopping experience and place additional pressure on store staff who must balance payment assistance, customer questions, and other in-aisle tasks.
Self-service technology offers a practical way to reduce these bottlenecks by giving shoppers a controlled process they can complete at their own pace. Whether through stationary kiosks, self-scanning tools, or AI-based recognition systems, self-checkout options help distribute transaction volume more evenly across the store and allow routine purchases to move more efficiently.
Learn about different types of self-checkout systems, with real-life examples and explanations of how they work.
Types of self-checkout systems
Self-checkout systems fall into several categories, and each option suits different store formats and customer expectations.
1. Stationary kiosks
These self-checkout stations are the most visible form of the technology. They include a barcode scanner, a touchscreen, payment units, and a bagging area monitored by weight sensors. Shoppers pick up their groceries, scan each item, place it in the bag, and complete the transaction using a card or mobile payment. Some kiosks accept cash, especially in regions where equal access is a priority.
They are common in supermarkets and are often installed in groups to shorten the long lines that form during peak hours or the holiday season.
2. Self-scanning
Self-scanning systems give customers a handheld scanner or allow them to use their phone. Items are scanned as you move through the store and placed directly into a bag or cart. When finished, customers proceed to a payment point or a dedicated kiosk to finalize the transaction. This approach reduces the need to unload groceries onto a conveyor belt and speeds up the checkout process.
3. Hybrid formats
Some retailers combine self-scanning with stationary self-checkout stations. Customers can choose to scan items as they shop or at a kiosk. This setup is typical in supermarkets where certain items are easier to handle at a kiosk, while smaller or lighter goods are more convenient to scan on the move.
Real-life example:
Fujitsu U-Scan Genesis is a self-checkout system designed to offer configurable capacity, a compact footprint, and a customer interface featuring LED guidance and above-scanner cash-handling units. The system includes multi-item scanning and other functions intended to support higher transaction volumes in varied retail formats.
The platform is based on a virtual POS architecture and a messaging framework that allows integration with most existing POS systems. This design reduces the time and cost associated with POS upgrades and minimizes the operational impact on retailers’ release cycles.
Figure 1: An example from Fujitsu’s U-Scan Genesis self-checkout system.1
4. AI-based recognition
New systems use cameras and computer vision to identify items without requiring bar codes. Shoppers hold an item under a sensor, and the self-checkout machine recognizes it from shape, color, or texture. These systems aim to reduce scanning errors and prevent the common unexpected-item alert in the bagging area.
Real-life example:
The Toshiba MxP Vision Kiosk is a compact and modular self-checkout system designed to support efficient “grab and go” transactions in space-constrained retail environments. It incorporates an Intel 13th Generation processor, a 19.5-inch display, and responsive touch capabilities, and is tested for use across varied retail settings.
The system employs computer vision rather than bar codes to identify items, with integrated cameras, NFC, and AI-based Smartpad technology for secure payments and two-factor authentication. A TCx EDGEcam biometrics camera offers additional oversight by monitoring customer behavior for indicators of shrinkage.
The ODIE item-enrollment feature enables retailers to add new products directly from the store or office, and the kiosk is designed to operate with both new and legacy infrastructure to support long-term lifecycle management.
Figure 2: Toshiba Vision Kiosk example.2
How self-checkout works
- Scanning: Customers scan items with a barcode reader or place them under a camera for recognition. If the bar code is damaged, a manual lookup option can be used.
- Placement in the bagging area: Weight sensors check whether the scanned item matches the expected weight. If the difference is too large, the system may request assistance. Many customers encounter this situation when buying produce or certain items packaged differently.
- Payment: Shoppers choose a payment method and complete the process. Payment problems can arise with mobile wallets, expired cards, or insufficient funds, so staff remain available to help.
- Receipt and exit: Once payment is complete, the customer leaves the station so the next person can begin.
These steps resemble the traditional checkout in function but require the shopper to perform tasks that a cashier handles in a staffed checkout lane.
Self-checkout advantages
Self-checkout systems offer several benefits to shoppers, particularly those who prioritize convenience or speed.
- Customers can move at their own pace without feeling pressure from a long line behind them.
- Short purchases involving only a few items can be completed more quickly than waiting for a cashier lane to open.
- People who prefer low human contact, or simply wish to focus on their own shopping routine, often appreciate the independence.
- Self-checkout lanes provide a predictable process for customers familiar with the technology.
Retailers favor self-checkout systems for several reasons, including cost, efficiency, and space management.
- Self-checkout stations reduce the need for a large number of staffed checkout lanes, which frees space for product displays.
- Store staff can shift to tasks such as helping customers, handling produce, managing inventory, or addressing other issues in the aisle.
- Self-checkout machines can improve store flow during busy periods and reduce the total cost of running a brick-and-mortar store.
- Data gathered from the system, including item frequency and common transaction errors, helps improve store operations over time.
Challenges of self-checkout systems
Self-checkout systems introduce several challenges for both customers and retailers.
Learning curve and usability
Some shoppers avoid self-checkout because the interface feels unfamiliar. Others encounter repeated alerts, such as unexpected items in the bagging area or issues with weight sensors. These can slow down the process and create frustration.
Theft and fraud
Self-checkout creates opportunities for theft. Examples include scanning a cheaper item with the same price or placing an item directly into a bag without scanning. Large retailers, including Walmart, rely on cameras, computer vision, and analytics to limit these situations. Theft remains a concern across the industry.
Technical issues
Self-checkout machines require regular maintenance. Problems such as frozen screens, scanner failures, connection drops, or payment errors can interrupt service. Store staff are needed to reset devices, clear alerts, and assist customers in resuming their transactions.
Best practices for implementing a self-checkout system
Retailers considering new systems or upgrading existing ones usually evaluate several factors.
- Conduct a cost and benefit analysis to understand the total cost, including hardware, software, training, and maintenance.
- Ensure compatibility with the current POS and inventory system.
- Provide clear instructions and visible assistance for customers who need help.
- Perform regular maintenance to reduce downtime and address minor issues early.
- Train staff to handle errors efficiently, including transaction overrides and weight sensor mismatches.
- Maintain staffed checkout lanes to ensure equal access for people who cannot or prefer not to use self-service.
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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