March 19, 2019
Despite the many exciting advancements in retail technology, many companies are as plagued as ever by the problem of stock-outs.
Empty shelves are the last thing retailers need when they already have to contend with such perilously thin profit margins.
The solution to an overabundance of stock-outs lies in smart technology. By using AI and machine-learning software, retailers can time their orders and manage inventory with surgical precision, making sure customers will rarely, if ever, come face to face with an empty shelf.
Definition of stock-outs
A stock-out, sometimes referred to as out-of-stocks (OOS), is simply the circumstance of inventory for a particular product being totally exhausted.
The term “stock-out” is typically used to refer to a product being unavailable for purchase at the retail level, as opposed to supply being exhausted further back in the supply chain.
According to a 2015 study, 75 percent of all U.S. adults have been plagued by the problem of stock-outs within the past year.
Stock-outs stem from a variety of sources. Although some can be attributed to problems in the supply chain, more often than not stock-outs are a direct result of a retailer’s lack of foresight. The most common causes of stock-outs are retailers ordering too little or too late, poor stocking practices and faulty shelf-space allocation.
A high rate of OOS can also be attributed to an unseasonable spike in purchasing, inaccurate demand forecasting due to faulty or missing data, inaccurate inventory data, and poor employee training on stock level monitoring and replenishment. Researchers attribute 70-90 percent of stock-outs to inefficient shelf replenishment practices.
The negative effects of stock-outs for retailers
Customers facing a stock-out will typically go down one of the following paths: finding a substitute of the same brand, substituting a similar product of a different brand, going to a different store to shop, delaying their purchase or giving up and not buying at all.
Those instances when a customer throws up their hands, abandoning the purchase altogether, typically translate to sales losses of 4 percent for a retailer.
Retailers who suffer from frequent stock-outs experience a myriad of consequences: loss of customer loyalty, driving customers to competing brands, missing revenue goals, tarnishing brand image and damaging investor confidence.
Stock-outs are especially damaging to customer loyalty. The more a customer has to deal with the frustration of an empty shelf, the less likely they are to keep coming back to your store.
Brick-and-mortar stores aren’t the only businesses that suffer from the many downfalls of stock-outs. Online retailers suffer the same consequences — if a customer sees an item is unavailable, they will likely navigate to a different online retailer who does have what they need in stock. Patience is a virtue that many shoppers lack now that the Internet grants them access to thousands of retailers all over the world.
Stock-outs are ultimately a detriment to a retailer’s bottom line, because 58 percent of those shoppers disappointed by an empty shelf turn into lost sales, either buying from another retailer or not buying at all.
It is not enough to track inventory and reorder products when stock-outs occur — that lapse of time between a product running out and its replacement getting delivered is where profit is lost. AI can help retailers anticipate stock-outs before they occur.
With the right smart technology in place in their stores, retailers can accurately calculate and prevent stock-outs based on point-of-sale data.
How to prevent stock-outs with Evo Replenish
This is where Evo Replenish comes into the equation. Our distribution solution can offer range and replenishment proposals at the most granular level, with a revolutionary participative approach.
Armed with data and working closely together, suppliers, retailers and in-store sales staff can expertly coordinate shelf space, special sales and new product introductions.
By tracking historical inventory data and seasonal fluctuations, retailers can adjust their delivery cycles to anticipate and meet demand.
The key to minimizing stock-outs is finding the right smart technology for your retailer to collect, share and manage point-of-sale data.
About the author
Joel Boland is a content expert at Evo.
He studied accounting and journalism in Sacramento, California before moving to New York City to be a freelance writer.
When he isn’t writing about Big Data, machine learning and the retail industry, he enjoys walking dogs in Central Park.