The retail environment is experiencing a great shift in part due to the emergence of AI-based computer vision technology. Retailers across the globe are tapping into Artificial Intelligence to extract actionable information from immense amounts of visual data from their existing in-store cameras. With the increases in Vision AI, retailers are now able to detect stockouts, verify shelf compliance, and trigger real-time alerts to store managers so they can be responsive.
This technology was truly impactful in 2021 when the U.S. retail industry faced widespread supply chain interruptions that led to empty shelves and incensed customers. In response, researchers and industry experts began looking for ways to automate on-shelf availability strategies. AI-powered computer vision has proven to be a transformational shift for retailers who are able to leverage this approach to bolster inventory management, improve customer experience, and take an analytical approach to operational decision-making.
The Impact of Computer Vision on Retail Shelf Monitoring
Computer Vision on Retail Shelf Monitoring is one of the most significant innovations in retail today. AI-based computer vision technology enables retailers to monitor and analyze shelf conditions in live time and gather vital data for inventory management.
Data and Insights are Available in Real-Time
Tracking data from edge devices enables retailers to recognize low stock, misplaced products, or shelves in disarray in real-time. This leads to informed decisions and ultimately helps keep shelves full, which translates to happy customers.
Personalized Shopping Experience
Retail Shelf Monitoring with Computer Vision helps to understand patterns of shopping behavior, enabling retailers to more effectively create product placement and costing strategies that provide a personalized shopping experience for every customer.
Data Sharing and Transparency in the Supply Chain
Vision AI aids in eliminating data sharing barriers throughout the supply chain, allowing retailers to provide data and insights to suppliers, distributors, and partner retailers to improve visibility and coordination.
Forecast Changes in Demand
AI-enhanced data analytics enables retailers to plan for the expected change in demand by planning inventory accordingly. This establishes data-backed sensitivity on inventory positioning and reduces lost opportunities or losses from over- or low-stocking popular products.
Leverage AI for Shelf and Stockout Tracking
Computer vision provides retailers with real-time visibility into building operations, creating smarter shelf and stock tracking for retailers. Retailers now can have AI-driven data to track product inventory and movement to trigger automatic stock replenishment alerts. All of this creates a continued operational efficiency and cost savings in retail while easing operational performance and user experiences.
The Importance of Smart Shelf Monitoring
Smart shelf management systems use artificial intelligence (AI) cameras and image recognition software to determine if stock is low, if products are misplaced, and to identify consumer behavior patterns. Since consumer expectations will continue to rise, retailers will have to upgrade their technological capabilities to remain competitive.
Improving the Customer Experience
Several negative consumer shopping experiences arise when customers are unable to find the products they are looking for in stores. A smart shelf monitoring system can provide real-time information about inventory levels, so that products will always be available, located exactly where they should be, thus creating potential for better customer service and building customer loyalty.
Identifying Consumer Behavior Trends
Monitoring shelf inventory and shopper data can reveal shopping patterns to retailers. Information regarding what products are frequently out of stock, which products are selling faster than others, and how shoppers behave with specific arrangements of products determines how and when retailers plan new marketing activities and promotional offers.
Inventory Management Optimization
Using insights from artificial intelligence, retailers can also optimize inventory levels for more informed decisions about restocking inventory. Doing this can reduce the frequency of overstocking or stockouts, reduce operations costs, and increase utilization of storage capacity.
Increasing Sales and Revenue
For customers that find the value they expect in-store is easy to access, they will make more in-store purchasing decisions instead of going to a competitor. When shelves are fully stocked, products are easy to access, and customers can easily find the products they need, computer vision driven shelf monitoring can directly add to retailer discipline to sell more, increase sales, and raise revenue.
Practical AI Implementations in Shelf Management
Restocking Inventory
With the observation of inventory data in real-time, retailers can make better choices in the areas of pricing, promotions and reallocating inventory. AI-enabled systems can determine which products are not moving, which ones will sell quickly and where there may be overstock situations, thus improving profitability and efficiency of operations.
Contractual Compliance
Compliance audits which include looking at on-shelf availability, share of shelf, and product placement can be extensive and time-consuming. The rise of AI technologies will simplify these audits by consistently monitoring compliance and ensuring the retailer and supplier obligations are satisfied.
Planogram Improvements
AI can provide insights related to improving your product placement. The analysis of shopping behavior and shopper dwell times can increase the efficiency of shelf layouts by placing trending products in visible and easily-accessible places. Computer vision can create heat maps that will improve planogram designs that increase product visibility and therefore, higher sales.
Maximizing Vision AI Data for Retail Optimization
It is typical for a grocery store to have over 10,000 products on its shelves. Tracking those items is improbable without large amounts of time and effort. Retailers are building AI models using synthetic datasets to improve the process of monitoring shelves. Using synthetic datasets, retailers can provide "realism" to various combinations of product contexts, (including lighting, occlusions, noise, and more), and train computer vision models to accurately identify products in real-world scenarios.
When testing out different computer vision models and deploying analytics, combining real and synthetic datasets provides a robust AI model that reports on inventory management level and provides (real time) monitoring for all types of store products. Automating alerts for low-stock and misplaced items, allows Staff to minimize response times, reducing (operational) errors and costs. This level of monitoring forms the foundation for the high speed of data automation for retail analysis to manage out of stocks and provide customer satisfaction.
Creating a Seamless Shopping Experience with AI
For traditional retail stores, it is often difficult to attract and keep customers. With AI-driven shelf monitoring, retailers can improve the shopping experience to make it easier and more enjoyable. Retailers will be able to make better management decisions with real-time insight into inventory levels, product locations, and shopper behavior, enabling stores to better manage inventory and performance.
The future of retail is linked to new technological developments involving AI and computer vision. With solutions like Nextbrain's AI Video Surveillance Software, retailers can maximize efficiencies, decrease costs, and offer superior customer experiences. Our software solves the specific problems retail stores face to ensure products are readily available to customers, resulting in better inventory and operational efficiency.
If your retail store is ready to modernize shelf monitoring and inventory management, contact Nextbrain today to see how AI-driven solutions can better your retail business.
Frequently Asked Questions (FAQs)
1.What does computer vision for retail shelf monitoring involve?
Computer vision for retail shelf monitoring utilizes artificial intelligence and image recognition technology to monitor products' stock status and availability, identify misplaced products, and maintain real-time stock levels on the shelf. This technology allows for better inventory management on retailers' parts while improving customer satisfaction.
2.How does AI-powered computer vision aid in inventory management?
AI can analyze shelf-level data on an ongoing basis to flag low stock, provide insight into trending demand changes, and alert retailers before they run out-of-stock limits. This helps retailers manage and eliminate overstocks, protect against stockouts, and optimize operational efficiencies.
3.Does computer vision increase sales in a retail operation?
Absolutely. Retailers can reduce 'lost sale days' due to stock availability while making it easy for items to be located in the store or online. Effectively creating a better customer experience will in-turn, create customer loyalty while increasing sales.
4.What are some benefits of using AI for shelf monitoring?
AI will provide real-time inventory insights, product placement optimization, invisible insights on consumer behavior, improved customer experience, reduced operational costs while ensuring compliance with planogram execution and supplier agreements.
5.How does computer vision help with planogram compliance?
AI-powered technology will automatically monitor shelf placements based on a planned schematic. The AI will evaluate products by considering placement, proportionate share of shelf, and stock level to ensure suppliers contractual compliance.
Top comments (0)