To address out-of-stocks, retailers need to assure that there is adequate inventory on the shelf to meet shopper demand. In any given category, there are items that have high demand, and sell several units a day, while others have lower demand and are purchased less frequently.
All of our research has shown that lost sales due to out-of-stocks correlate with product sales velocity. Fast sellers sell out more frequently, and when they do sell out, the impact is felt almost right away. Conversely, slower-moving items sell out less frequently, and their absence may not impact sales for a long period. In our examination of several categories across several retail chains, we found that on average, 86 percent of the items had enough inventory on the shelf to last more than 7 days (days of supply, or DOS). This means that only 14 percent of the items need to be stocked more than one time per week.
To deal with this disparity, retailers must adjust the amount of shelf space allocated to items, giving more space to the faster-moving items, which requires additional shelf facings. A shelf facing refers to the number of times a particular item (or SKU) is visible from the front of the shelf. When the shelf space is designed through a category management system, it seeks to optimize the number of facings for each item. Needless to say, this presents a challenge to category managers, wrestling with the tension of providing enough facings to keep the faster-moving items in stock while providing the variety that shoppers seek.
When variety wins out, retailers frequently institute processes where the top movers or “never-outs” are identified with a specially marked shelf tag to alert store personnel to watch for potential stock out situations with those items. This is a workaround, acting as a medication to soothe the problem but is not a cure. The cure is a redistribution of shelf space based on actual shopper demand, and this requires reliable and accurate data. Traditionally point of sale (POS) data gathered from check-outs has been used in category allocation decisions. But this only tells part of the story. Newer technology, like that provided by Pensa Systems, that continuously monitors the shelf, can provide a much better picture to make decisions on shelf facings.
The starting point for allocating the number of facings on the shelf has traditionally been the case pack size. Retailers prefer to stock an entire case on the shelf, and this takes up space on the shelf. Since labor is expensive and tracking partial cases increases labor costs, stocking a full case on the shelf increases labor efficiency. Keep in mind that, regardless of sales frequency, even the slowest moving SKU requires one facing, and this occupies the available space.
Like with any budget, most of the space is fixed, and there isn’t a lot of room for discretionary facings. This places a greater need for reliable and accurate data to make the hard decisions of how to allocate that discretionary space as well as knowing which items might be “rationalized” or entirely removed from the shelf to make room for additional facings of faster moving items. Using algorithms like the ones behind Pensa’s Shelf Facings Optimization provides the information needed to make the best decisions.
We know from multiple tests in category management that shoppers respond favorably to minor reductions in choice within a category, especially when there are reasonable substitutes. Thus, an option worth examining is finding additional space for fast moving items at the expense of removing the slowest moving items. The question becomes, just how deep can a retailer make this adjustment until the negative effects outweigh the sales gained from fewer OOS events? Again, these decisions can only be made with reliable and accurate data.
These are not easy decisions, and they also must include the overall strategy of the store. In my years of researching shopper reactions to stock outs, it is clear that the total cost of having items out-of-stock when the shopper wants to purchase them is much larger than the immediate lost sale. Costs are incurred by the retailer, the supplier of the item, and by the shopper.
I am encouraged to see the focus on shelf-facing entering the discussion. The “real estate” on the store shelf is a valuable asset, and out-of-stocks reduce the return on that asset. Using computer vision and AI to measure out-of-stocks regularly and accurately is an important new technology that enhances our previous reliance on the less reliable point-of-sale data,
We (myself along with co-author Daniel Corsten) address the topic of shelf-facings in more detail in our new book, Shelf Confidence: A Practical Guide to Reducing Out-of-Stocks and Improving Product Availability in Retail, which is available on Amazon.
This guest blog is by Tom Gruen, Professor of Marketing at University of New Hampshire. Tom is a retail industry expert and an advisor to Pensa Systems.