What would you do with an extra $130 billion? If you are in the retail business, that’s how much more revenue the industry would earn if it weren’t for out-of-stocks. This figure represents the amount of lost sales in North America alone, according to recent studies; globally, out-of-stocks are a $634 billion problem.
Many studies, guides and tools have been conducted, written and created, respectively, to deal with this problem. It’s a complex issue and unlikely to be 100% eliminated anytime soon. However, there is one way in which retailers and manufacturers can start to think about addressing this problem: using a broader range of data to better understand the potential root causes of out-of-stocks.
Scanning errors, delivery mishaps, missing shelf tags, and shrink are just some of the culprits behind empty shelf spots. In addition to lost sales, out-of-stocks lead to unhappy customers and erosion of a brand’s equity. These challenges are due to the fact that retailers and their suppliers often rely on inventory data alone. But only using inventory tracking systems often fails to provide sufficient information about on-shelf availability (OSA) issues at the SKU or store level, due to data accuracy problems like phantom inventory (phantom inventory is the phenomenon under which an inventory tracking system says there’s product available when there really isn’t).
By expanding the range of data that is analyzed, retailers and their suppliers can gain a more comprehensive view of out-of-stocks and be better prepared to address OSA. Leveraging historical sales patterns, for example, is one way to get around less-than-accurate inventory systems to get a handle on OSA. But why is using sales data a good alternative? And more importantly, how does it work? The why is simple: the more actual data you have, the more consistent a picture you can build that is based on historical patterns, promotional lifts, seasonal effects, etc. This is especially the case with high velocity products. For example, if an item regularly sells an average of 30 units per store per week and you set up a system that keeps an eye out for significant deviations from that rate of sale, then you can investigate this as a potential out-of-stock situation.
The how is not difficult in theory: select the inputs, gather the data, build the algorithm, then design the right reporting mechanism to create signals when an item is not selling as expected (which may indicate an out-of-stock). The trick is to do this in an efficient, automated and, quite simply, fast way across top SKUs, all stores or divisions, all distributors and suppliers. Giving a business user the ability to navigate across and between the different levels of information is also key. This requires having the right tools and the right design.
Ultimately, having as much information as possible about how products are selling or how they are replenished (using receipts-based data) is key to better understanding product availability and, in turn, taking corrective actions to prevent lost sales due to out-of-stocks. By approaching OSA in this manner, it is that much easier for business owners to decide on what corrective actions to take at the right point in the supply chain.
1010data offers this capability via its Advanced Product Visibility suite of reports within the popular 1010data Consumer Insights Platform. With a proprietary algorithm that can use inputs such as historical sales patterns and DSD (direct store delivery) receipts or ASN (advanced ship notice), Advanced Product Visibility is designed to help retailers and manufacturers uncover potential out-of-stocks. With 1010data’s powerful platform, implementation of the Advanced Product Visibility reports is fast, scalable and provides the flexibility to account for promotions or seasonality so that your visibility into OSA is keeping up with the changes in your business and in the market.
To learn more about how 1010data can help you identify the potential causes of out-of-stock in your business, reach to us at firstname.lastname@example.org.