How Out of Stock Solution Transforms Retail Stores?

The out-of-stock solution works to balance stock levels (amount of a product kept in storage) with customer demand. This blog post shows how retailers can lower their Out of stock (OOS) rates by using computer vision and machine learning. You’ll learn how technology works and see some of its applications and benefits.
out of stock solution
How Out of Stock Solution Transforms Retail Stores

Many retailers find that about 8% of their merchandise is out of stock at any given time. Some retailers are even seeing 15% of promoted items go out of stock. So when customers place an order to be shipped, retailers need to ensure that they don’t run out of what their customers ordered. Instead customers will be satisfied with knowing that they got what they wanted on time.

Manufacturers experienced most distortions in retail in 2020. They have seen a loss of 677 million U.S. dollars due to pandemic. However demand for consumer goods created both overstock and shortages. The former led to a 580-million-dollar loss for stores.

This blog post shows how retailers can lower their Out of stock (OOS) rates by using computer vision and machine learning. You’ll learn how technology works and see some of its applications and benefits.

artificial intelligence in retail

source : Fortune Business Insights

Out of stock solution is a standard for retailers.

The out-of-stock solution works to balance stock levels (amount of a product kept in storage) with customer demand.

Out of stock is a complex measurement that considers many factors, including:

1. Product location data.

2. Quantity of every product.

3. Demand for goods.

4. Seasonal shifts

What’s the challenge?

OSS means customers can find right product on right shelf at right time in stores. It’s a percentage of all products available to customers. OSS mismanagement results in a loss for manufacturers and distributors. When a customer leaves a store without purchasing, customer is unlikely to come back. And if customers don’t go back, stores lose sales.

What’s the solution?

Machine learning models have proven increasingly capable of handling object detection and recognition. Computer vision has become a viable alternative to human vision in healthcare and self-driving cars.

Allied Market Research says value of global computer vision industry is expected to grow from $9.45bn in 2020 to $41.11bn by 2030.

OSS with computer vision and machine learning helps retailers reduce out-of-stock rates. By monitoring inventory levels and alerting you to when and where items will soon be available.

How can retailers optimize their OSS tracking to take advantage of computer vision technology?

Computer vision solution enables real-time, image-based data collection via cameras on shelves and in stores. So you can monitor movement of products within your supply chain. This helps you save money by identifying peak times and off-peak times and allowing you to identify damaged or mislabeled items.

How can retailers integrate computer vision?

Let’s look at some companies that integrated computer vision with their OSS tracking systems.


Amazon made a change to its inventory tracking process in 2019. In 20 of their 175 warehouses, they added computer vision to keep up with demand for its products. Instead of having employees place bar codes on bins, a computer vision-powered camera and scanner reads bar codes as inventory travels through a warehouse. Amazon’s vice-president of robotics Brad Porter said that this technology had significantly improved efficiency of their warehouses.


Unilever turned to computer vision to audit their stores. They used a “crowd-sourcing” strategy to gather digital images of shelves in European stores that sell their goods. After that a computer vision-enabled platform processed images. With this managers were able to utilize a snapshot of in-store shelf conditions provided by company. Computer vision shelf image analytics allowed Unilever to identify problems in their stores and respond quickly. This technology allowed merchandisers to replace sales reps and focus on tasks that directly impact increasing sales.

How does OOS solve real-time challenges?

Retailers can eliminate following challenges by using out of stock solutions for tracking in-store product inventory:


Stockouts occur when inventory is inadequate, inaccurate records lead to poor forecasting, and customer experience decreases. Machine learning can figure out when demand is getting higher and send alerts to managers and other supply chain members to stock up on more inventory.


Overstock results when a business has too much inventory. Having more products in storage may cost you more money and your inventory may expire or go bad. Deploying a machine learning model that relies on historical sales data for each product can help you solve this problem.

Misplaced inventory

When inventory is misplaced on store shelves or in warehouses, employees waste time looking for products, increasing overhead and slowing down deliveries to customers. The current solution for tracking goods does not involve ML and relies on manual stock placement. Yet companies are increasingly using barcode scanning and robots to more efficiently locate misplaced products. OOS also helps them in detecting misplaced inventory.

Human error

Although no technology is perfect technology can improve with feedback from others in its early testing stages. An ML pipeline that handles OSS tasks can drastically reduce the human error rate.

What benefits do you get?

1.When you integrate computer vision into OSS monitoring, benefit you get is 

you can provide retailers with real-time insights. This is important for keeping customer satisfaction high.

2.Reduced manual labor real-time shelf data for inventory management help retailers improve productivity. 

3.Computer vision offers many advantages in inventory management, including accurate forecasting, quick ordering of goods through online systems, and fast notification of a surplus or shortage.

4.Smart video analytics can help companies retain customers and boost sales through improved marketing strategies.

5.Computer vision and ML can help retailers reduce waste in stores. Enables you to know when to restock your product displays so that your products are always fresh and enticing.

Leverage computer vision to maximize your business. 

Computer vision continues to evolve bringing new and exciting applications for businesses.

Here at visionify we create custom Out of Stock Solutions using computer vision and machine learning. If you’re looking for a client to help you develop computer vision for your retail store or business, we’re here to help you in solving your retails store issues call us today for a demo and you will know how our solution can deployed in your store.

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