Computer Vision has advanced from basic image processing in the 1960s to highly sophisticated systems that capture, analyze, and interpret real-time image and video data. With e-commerce gaining momentum, brick-and-mortar businesses striving to keep their share, and omnichannel retail experience becoming a new normal, retail stores must play on their strengths by providing thorough customer experience, personalized services, streamlining inventory management and using predictive analysis to forecast demand and make better product choices. 

Just like we look around things with our eyes and our brain detects, analyzes, interprets, and stores data, computer vision is the eye for the Internet of Things (IoT). SAS defines computer vision as “A field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they see.” 

As the world around sees more use of augmented reality and machine learning in various spheres of business and life, inventory management is also not untouched. With the use of Computer Vision, retail inventory management has greatly changed. For example, IKEA Kreativ's technology uses AI and computer vision to analyze panoramic photos from mobile phones, creating a 3D model of rooms that can virtually erase clutter and add products. Customers can view the detailed virtual model of the rooms and can add, remove, or replace existing furniture with IKEA catalog items. With the help of a 3D model, clients can see how the item would look before making a purchase. This is a win-win for both clients and the business. 

In the rapidly evolving retail industry, efficient inventory management is crucial for maintaining profitability and ensuring customer satisfaction. Traditional methods of stock monitoring are often labor-intensive and prone to human error. AI advancements, particularly computer vision, are transforming retailers' inventory management. By automating the process of stock tracking and replenishment, computer vision offers a more accurate, efficient, and scalable solution. This article examines how computer vision transforms retail inventory management, emphasizing its advantages and industry impact. 

Computer Vision Assists With Inventory Count

For any retail store, maintaining optimum inventory levels is crucial to achieving overall profit. Surplus in inventory takes space in the warehouse/store, and deficit leads to a negative customer experience and lost sales. 

Knowing the right levels of inventory is also crucial for the timely replenishment of stocks. Using cameras and advanced algorithms, shelf stock levels are continuously scanned and analyzed in real-time. This technology accurately detects and records the quantity and placement of products, reducing the need for manual counting and minimizing human error. By providing up-to-date inventory data, computer vision ensures timely restocking, prevents stockouts, and maintains optimal inventory levels. This leads to increased operational efficiency, reduced labor costs, and improved customer satisfaction through better product availability. 

When stock levels fall below a predefined threshold, computer vision systems can automatically trigger alerts for restocking. This proactive approach prevents stockouts and ensures that popular items are always available, enhancing customer satisfaction. Additionally, it can identify misplaced or mislabeled items, helping staff correct inventory issues promptly. Automated alerts and notifications help store managers focus their attention and resources where needed.

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Gather AI is a US-based company that provides autonomous drones to track warehouse inventory. According to their website “Gather AI automates your inventory monitoring, with autonomous data collection and automatic reconciliation with your Warehouse Management System. Quickly and easily find empty locations, fix inventory errors, and walk your aisles.” With a drone-powered inventory management solution, inventory counting is 15 times faster than traditional methods. Barrett distribution centers that uses the Gather AI solution report savings of $250,000 in one warehouse and reallocating the material handling equipment.

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Demand forecasting and inventory management go hand in hand. Computer vision gives a better predictive analysis of demand forecasts based on historical data, current market trends, and other factors. Customers receive a seamless omnichannel retail experience with a real-time computer-based inventory count. 

Using computer vision for inventory counting significantly enhances accuracy and efficiency in retail management. This technology employs cameras and AI to continuously monitor stock levels, automatically counting and tracking items in real-time. It reduces human errors, ensures precise inventory records, and provides instant updates on stock levels, helping to prevent stockouts and overstocking. Additionally, computer vision can analyze shelf conditions and product placements, optimizing inventory organization. This automation frees up staff time for other tasks, improves operational efficiency, and ensures better product availability, ultimately leading to a more streamlined and effective inventory management process.

Loss Due To Human Error Paves The Way For AI-enabled Computer Vision Inventory Management Systems

Traditional inventory counting methods are prone to human error due to miscounting, overlooking items, or data entry mistakes. Inefficiency in managing inventory impacts the bottom line. A business is estimated to lose 9-14% due to inefficient inventory management. This includes human errors like lost sales, inventory wastage, increased labor cost, inaccuracy, and tracking challenges. 

For example, computer vision systems use sophisticated algorithms to accurately identify and count stock on shelves. These algorithms can distinguish between different products and variations, minimizing the risk of misidentification that often happens with manual counting. By automating the counting process, computer vision removes the need for manual inventory checks, which are prone to human error. 

Unlike humans, computer vision systems do not suffer from fatigue or distractions, maintaining high accuracy in inventory tracking over extended periods. The ability to maintain focus for extended periods is not practical. Automation of mundane tasks allows employees to focus on things that require more complex thinking. Computer vision systems provide consistent performance regardless of the scale of operations. This consistency reduces errors that can occur in large-scale manual inventory management processes. According to a Retail Technology Study conducted by RIS, about 64% of retailers want to implement AI-driven solutions to manage inventory. This shows the growth potential the industry has for computer vision-based AI solutions for retail inventory management.

Computer vision can guide robots in tasks such as sorting, picking, and placing items, ensuring precise execution and minimizing errors that can arise from manual handling. Optimized pathfinding for robots and automated guided vehicles (AGVs) ensures efficient movement within storage areas, reducing the likelihood of misplaced items. 

Computer vision systems automate the collection and recording of data, eliminating errors associated with manual data entry, such as typos, incorrect entries, and missed records. Integration with other software systems (e.g., ERP, POS) ensures that data flows seamlessly between platforms, reducing errors due to data transfer and manual input. 

Computer vision systems improve over time through machine learning, becoming more accurate and efficient as they are exposed to more data. Feedback loops enable systems to learn from past errors, continually enhancing performance and reducing the likelihood of repeated mistakes. 

Automated systems can ensure adherence to regulatory requirements, reducing the risk of human errors in compliance documentation and reporting. 

Computer vision reduces human error by automating repetitive and precise tasks, providing real-time monitoring and alerts, ensuring consistent and unbiased inspections, integrating seamlessly with other automated systems, and enhancing data accuracy and analysis. These capabilities improve efficiency, accuracy, and reliability in various operations. 

Improved Utilization Of Storage Space With Computer Vision Solutions

Gather AI-deployed drone-powered inventory management solution for one of its clients. The customer had to store approximately 500,000 pairs of individually packed and serialized shoe boxes in narrow aisles. Traditional inventory management techniques posed the risk of inaccurate inventory counting and inefficient tracking methods. Using the AI-enabled drone-powered solution, the client achieved 99.95-100% accuracy, a 15-20% increase in inventory counts with fewer people, and increased customer satisfaction. 

AI systems with computer vision track the inventory movement with the help of images and videos. For example, when a product is sold, the system automatically detects empty space on the storage rack. It will update the same in the database and also alert the store manager when the inventory level drops below the threshold. This helps stores to keep track of goods, avoid theft, prevent stockouts, and reduce the carrying cost. Winco Foods, a US-based company, uses computer vision to detect theft. When the system detects any suspicious activities, it sends an alert to the security. This has helped the company reduce thefts by 60%. Walmart also uses AI-enabled security cameras to prevent stealing from the stores and warehouses. As computer vision (CV) can track the stock it is easier to detect any thefts and misplacements. 

Companies can manage storage space better with computer vision by leveraging its capabilities to enhance efficiency, accuracy, and optimization in various aspects of inventory and space management. Cameras and sensors installed in storage areas provide continuous monitoring, capturing real-time data on inventory levels and space utilization. Real-time data allows for immediate adjustments in storage practices, such as reallocating space or reorganizing items to maximize efficiency. 

Walmart’s in-house waste management AI is a proactive step to reduce waste. The tool scans the produce and products nearing expiration. Then, it gives recommendations to the staff on how it can be saved from going to waste, such as reducing the price, returning to the vendor, etc. AI is also used to determine which items contribute the most waste, considering if the item is in stock every day or if it’s a seasonal item. With these insights, associates are empowered with data and insights to make more informed decisions. This also allows real-time changes in layout; for example, keeping discounted products in the area where footfall is higher may boost the sale of these items. 

Computer vision systems can analyze the dimensions and characteristics of items to determine the most efficient placement, ensuring that space is used optimally. By creating detailed 3D maps of storage areas, computer vision can help plan and visualize the best layouts for storing items, taking full advantage of available vertical and horizontal space. 

In summary, computer vision enhances the utilization of storage space by providing real-time monitoring, optimizing placement and organization, improving stock management, integrating with automated systems, offering detailed analytics, and reducing manual errors. These capabilities lead to more efficient use of storage space, improved operational efficiency, and better overall inventory management. 

Seamless integration of computer vision with existing ERP for greater inventory visibility: 

For omnichannel retailers, real-time knowledge of the quantity of stock, its location, and its status impacts the overall customer experience. Computer vision solutions for inventory management are directly related to the company’s ROI, increased demand fulfillment, smart allocation, operational efficiency, accurate forecasting, and increased inventory control. The possibility of stock getting lost is greatly reduced as cameras can monitor stocks by reading barcodes.

AI-supported computer vision solutions utilize APIs and middleware to enable data flow between the computer vision and ERP systems. It ensures that the computer vision system's data is correctly mapped to the relevant fields in the ERP system. This integration allows for comprehensive data analytics, providing valuable insights for better decision-making. Improved visibility aids in more accurate demand forecasting and inventory planning. The integrated system can easily scale with the business, adapting to changes in inventory levels and storage needs without significant overhauls. 

SpartanNash is equipping associates with a real-time, comprehensive view of in-store stock. The grocery retailer and distributor is broadening its use of the Upshop Magic inventory and replenishment optimization application to streamline ordering systems, maintain accurate planograms, and support merchandising reset planning in both the center store and produce department. 

In 2022, Sam’s Club, a U.S.-based wholesale retailer, introduced Inventory Scan, an inventory counting system powered by computer vision and AI. This solution allows autonomous robotic floor scrubbers to capture real-time shelf images, which are then processed to analyze stock levels, product localization, planogram compliance, and pricing accuracy. It seamlessly integrates with the retailer’s existing inventory management system, automatically providing inventory insights to store managers. 

Computer vision inventory management solutions promote diversified product lines

Machine vision, the technology that enables machines to interpret and process visual data, plays a pivotal role in the diversification of inventory management. By

Leveraging advanced imaging and artificial intelligence, machine vision enhances the capability of retail operations to manage a wide variety of products efficiently and accurately. Machine vision systems analyze sales patterns and customer interactions, providing insights into which products are popular and when. This data helps retailers decide which products to diversify and expand. By analyzing historical sales data and current trends, machine vision can predict future demand, helping retailers plan and diversify their inventory to meet customer needs. Machine vision ensures products are displayed according to planograms, optimizing shelf space and enhancing the visibility of a diverse range of products. The ability to make real-time adjustments based on visual data and heat maps ensures that inventory levels are maintained and diversified product lines are properly managed. 

Predictive analytics combined with personalized suggestions are changing how inventory management is done. Using historical data, market trends, macroeconomic factors, customer shopping history, and preferences, companies can predict demands for seasonal goods more accurately and assist shoppers by giving personalized recommendations. The computer vision algorithms continuously learn from new data that becomes available. This involves retraining the model periodically with updated data to improve its performance over time. This iterative process ensures that AI systems can adapt and improve over time, providing accurate and reliable predictions. 

Zara’s Smart Dressing Rooms is a good example of how deep learning, along with computer vision, is revolutionizing customer service and inventory management. As customers enter the dressing room, the sensors and camera detect the items and display their information along with different variations on the interactive screen. Customers can request assistance and recommendations using the interactive display. This helps the company analyze customer behavior and preferences and make product and market decisions. 

Automated Retail Inventory Management with Computer Vision

Amazon’s “Just Walk Out” technology revolutionizes retail by allowing shoppers to enter a store, grab what they need, and leave without the need for traditional checkout. Using advanced computer vision, sensor fusion, and deep learning algorithms, the system automatically detects and charges customers for items they take, offering a seamless and efficient shopping experience. This innovation aims to eliminate queues and enhance convenience, setting a new standard for frictionless retail operations. 

Recently Amazon announced that they will close the frictionless cashier-less stores but will sell the technology to more than 120 third-party businesses in a year. O2, a UK indoor arena plans to deploy Just Walk Out technology to over 12 stores making them frictionless. This means the customers can walk into the store, tap their card or mobile, grab what they want, and return to their seats. The checkout process will be automated using computer vision, deep learning, and sensor fusion. 

Impact of Computer Vision On Retail Inventory Management And Its Future

Computer vision has revolutionized inventory management by enhancing accuracy, efficiency, and real-time monitoring capabilities. This technology uses cameras and AI to automatically track and count inventory, reducing human errors and ensuring precise stock records. Real-time data updates help prevent stockouts and

Overstocking while optimizing product placement and shelf organization. Computer vision also streamlines operations by automating routine tasks, freeing up staff for more strategic activities. Integrating with existing inventory systems provides valuable insights into sales trends and customer behaviors, enabling better decision-making and improving overall inventory management efficiency and effectiveness. 

The future of inventory management is poised for significant advancements driven by technology. Key trends include: 

• Automation and AI: Increased use of robotics and AI for automated inventory tracking, reducing human intervention and errors. 
• IoT Integration: Smart sensors and IoT devices will provide real-time inventory data, enhancing accuracy and visibility across the supply chain. 
• Blockchain Technology: Ensuring transparency, traceability, and security in inventory transactions and reducing fraud. 
• Advanced Analytics: Predictive analytics and machine learning will enable better demand forecasting, optimizing inventory levels, and minimizing waste. 
• Sustainable Practices: Emphasis on eco-friendly inventory management practices to reduce carbon footprint and promote sustainability. 
• Enhanced Customer Experience: Faster and more accurate fulfillment processes will lead to improved customer satisfaction. 

Overall, the future of inventory management will be characterized by greater efficiency, accuracy, and sustainability, driven by continuous technological innovation.

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