The Application of Industrial Panel PC in Smart Retail: Deep Empowerment of Electronic Shelf Labels and Inventory Management
In the wave of smart retail, retailers are facing unprecedented challenges: consumer expectations for shopping experiences continue to rise, supply chain efficiency requirements become increasingly stringent, and operational costs keep climbing due to reliance on human labor and information lag. In traditional retail scenarios, updating paper price tags relies on manual labor, inventory counts are time-consuming and prone to errors, and data silos lead to delayed decision-making—these pain points are becoming core bottlenecks restricting the digital transformation of retail enterprises. The emergence of industrial panel PC, with their integrated capabilities of "data collection - edge computing - intelligent display - cloud collaboration," provides a solution for smart retail.
Updating traditional paper price tags relies on manual replacement one by one. A certain chain supermarket once reported that a single promotional event required the input of 200 person-times for price tag replacement, taking 48 hours, with a manual operation error rate as high as 3%. Although electronic shelf labels can solve the updating problem, early solutions required independent networking, with high deployment costs, and lacked real-time linkage with inventory systems, leading to frequent embarrassing scenarios where "the price tag shows stock available, but the item is actually out of stock."
Traditional inventory counts rely on manual barcode scanning or visual inspection. A clothing brand store needed to close for 2 days each month for inventory counts, with an error rate of 5%. The separation of inventory data from sales systems led to delayed replenishment decisions: a fast-moving consumer goods enterprise failed to promptly identify regional inventory differences, resulting in an overstock of 1,200 units of a certain product in Store A while Store B was out of stock for 3 weeks, directly causing losses exceeding 500,000 yuan.
Sales data, inventory data, and customer behavior data are scattered across different systems. The decision-making level of a certain home appliance retailer had to spend 6 hours per week integrating Excel spreadsheets to analyze the correlation between best-sellers and slow-moving products. This "lagged decision-making" often led to promotional activities failing due to insufficient or excessive inventory. During a certain "full-reduction promotion," the failure to predict the inventory shortage of a certain model of TV resulted in an 18% increase in customer churn rate.
By integrating sensors, edge computing modules, intelligent display terminals, and cloud management platforms, industrial panel PCs build a closed-loop system of "perception - analysis - decision-making - execution." Taking USR-SH800 as an example, its core capabilities can be broken down into three dimensions:
USR-SH800 achieves real-time binding of price tags and products through its built-in RFID reader and electronic shelf label communication module. When the inventory system detects that the stock of a certain product is below the safety threshold, the all-in-one screen can automatically trigger the price tag to display an "out of stock" sign and simultaneously push replenishment tasks to the warehouse management system. After a cosmetics store applied this solution, the response time for price tag updates was shortened from 2 hours to 30 seconds, and the accuracy rate of out-of-stock prompts increased to 99.2%.
More crucially, the all-in-one screen supports dynamic pricing strategies: combining historical sales data, competitor prices, and inventory turnover rates, AI algorithms can generate optimal price recommendations and update them in real-time through price tags. During the "Double 11" period, a 3C store increased the sales of a certain model of headphones by 40% and improved the gross profit margin by 3 percentage points through dynamic pricing.
The edge computing capability of USR-SH800 can process multi-source data: it identifies the display status of products on shelves through cameras and constructs equipment health models by combining sales data with supply chain plans. For example, when the sales speed of products in a certain area suddenly accelerates, the system can predict that the area will be out of stock within 2 hours and automatically trigger replenishment instructions to AGV trolleys or employee PDAs.
In terms of predictive maintenance, the all-in-one screen can analyze the operational data of inventory equipment (such as refrigerator temperatures and shelf load sensor data) to provide early warnings of equipment failures. After a fresh food supermarket applied this solution, the refrigerator failure rate decreased by 65%, and product losses due to equipment failures reduced by 80%.
The cloud platform of USR-SH800 can interface with multiple systems such as ERP, POS, and CRM, breaking down data barriers. For example, when a customer browses a certain product in front of the all-in-one screen, the system can instantly retrieve their historical purchase records, membership level, and preference data to generate personalized recommendations; at the same time, the inventory system automatically checks the inventory of nearby stores. If the local store is out of stock, it can guide the customer to another store within 3 kilometers or provide online ordering and in-store pickup services.
A clothing brand achieved "omnichannel inventory visualization" through the all-in-one screen's function, integrating online orders with offline inventory, shortening the fulfillment time for "online ordering and in-store delivery" from 48 hours to 6 hours, and increasing the inventory turnover rate by 25%.
Pain Points: The supermarket has 200 stores. Traditional price tag updates rely on manual labor, with an error rate as high as 15% during promotional periods; inventory counts require store closures for 48 hours, with an error rate exceeding 8%.
Solutions:
Deploy USR-SH800 all-in-one screens to connect electronic shelf labels with the inventory system;
Generate dynamic promotional prices through AI algorithms and update price tags in real-time;
The edge computing module automatically identifies shelf display status and triggers replenishment tasks.
Effects:
The price tag update error rate dropped to 0.5%, and labor costs reduced by 70%;
The inventory count time was shortened to 2 hours, with an error rate reduced to 0.3%;
During promotional periods, the sales of individual products increased by 35%, and the gross profit margin improved by 2 percentage points.
Pain Points: Online orders accounted for 40% of the store's sales, but due to the separation of online and offline inventory, phenomena such as "overselling" or "out of stock" often occurred, with a customer complaint rate as high as 12%.
Solutions:
The USR-SH800 all-in-one screen interfaces with the ERP and OMS systems to achieve real-time inventory synchronization;
When an online order is generated, the system automatically checks the inventory of nearby stores and prioritizes allocation to the nearest store for delivery;
If the local store is out of stock, it guides the customer to other stores or provides online purchasing options through the all-in-one screen.
Effects:
The overselling rate dropped to 0.2%, and the out-of-stock rate decreased by 60%;
Customer satisfaction increased to 92%, and the repurchase rate improved by 18%;
The inventory turnover rate increased by 30%, and warehousing costs reduced by 15%.
USR-SH800 integrates sensors, edge computing modules, a 10.1-inch touch screen, and a cloud management platform, eliminating the need to purchase multiple devices separately. Its built-in WukongEdge edge application platform supports over 100 industrial protocols and can seamlessly interface with existing ERP and POS systems, shortening the deployment cycle from 3 months in traditional solutions to 2 weeks.
USR-SH800 is equipped with 1.0 TOPS of AI computing power and supports the deployment of models from mainstream frameworks such as Caffe and TensorFlow. Aimed at retail scenarios, it comes pre-installed with over 10 AI models such as "product recognition," "customer flow statistics," and "out-of-stock detection," allowing enterprises to quickly implement intelligent applications without starting from scratch.
USR-SH800 provides OpenPLC programming interfaces and RESTful APIs, supporting deep integration with enterprises' proprietary systems. At the same time, its cloud platform regularly pushes algorithm updates and feature upgrades to ensure that the system always meets the latest business needs. For example, the newly added "Dynamic Pricing 2.0" model in 2025 improved the price optimization efficiency of a certain retail customer by 40%.
