Tackling the multi-billion dollar out-of-stocks problem
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 make up a $634 billion problem.
Many studies, guides, and tools have been conducted, written and created, to address this problem. It’s a complex issue and unlikely to be completely eliminated anytime soon. However, there is one way retailers and manufacturers can approach this problem: taking advantage of a broader range of data to better understand the root causes of out-of-stocks.
Scanning errors, delivery mishaps, missing shelf tags, and shrink are just some of the culprits behind empty shelf space. In addition to lost sales, out-of-stocks lead to dissatisfied customers and brand erosion. These challenges are due to the fact that retailers and their suppliers often rely on inventory data alone. But using only inventory tracking systems often leads to information gaps about on-shelf availability (OSA) issues at the SKU or store level.
By expanding the range of analyzed data, retailers and suppliers can minimize data gaps and gain a more comprehensive view of out-of-stocks so they can more readily address OSA. Leveraging historical sales patterns, for example, is one way to circumvent less-than-accurate inventory systems. But why is using sales data a good alternative to standard inventory tracking systems? 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 tracks and signals 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 a quick, efficient, and automated way across top SKUs, all stores or divisions, all distributors and suppliers. Providing business users the ability to navigate across and between the different levels of information is arguably even more important. 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 is necessary to understanding product availability in detail. Armed with this level of analytics, retailers are better equipped to institute corrective actions that prevent lost sales due to out-of-stocks. By approaching OSA in this manner, it’s easier for retailers to decide what corrective actions to take at the right point in the supply chain.
Helping retail supply chain organizations tackle out-of-stocks is just one example of how 1010data’s powerful analytics platform can help retailers make the most use of their supply chain data. Beyond understanding out-of-stock drivers, retailers can improve total supply chain visibility with tools as turnkey as a suite of pre-built, easy-to-use reports or custom solutions designed to address specific challenges. Whatever the approach, the key to a good analytics platform for the retail supply chain is to incorporate as much historical data and visibility as possible. This is where the future of retail is headed: the retail winners will be using advanced, scalable analytics to improve every aspect of their supply chain.
To learn more about how 1010data can help retailers automate their supply chain analytics, check out our deep dive into this topic, Spotlight on Supply Chain Increase visibility and Drive Greater Profitability
*IHL Group Research Study: Retailers and the Ghost Economy $1.75 Trillion Reasons to be Afraid, 2015