The new digital landscape in retail has provided companies with a host of emerging opportunities to create and meet demand, but despite all the exciting changes, one thing has unfortunately remained the same: out-of-stocks (OOS). Vendors and retailers are all too familiar with the recurring problem of OOS, one of the oldest and most pernicious challenges faced by the industry. A 2007 report funded by Procter & Gamble (A Comprehensive Guide To Retail Out-of-Stock Reduction In the Fast-Moving Consumer Goods Industry by Thomas W. Gruen and Dr. Daniel Corsten) reveals seven reasons why OOS situations persist, despite the best efforts of managers and decision-makers. Thankfully, new tools for collecting, sharing, and managing Point-of-Sale data can dramatically reduce incidences of absentee inventory.

The True Cost of Out-of-Stock

Even with new touchpoints multiplying along the path-to-purchase, studies indicate that brick-and-mortars will continue to be the foundation of the shopper experience for most sectors of the retail industry. And so long as customers are scanning shelves for the products they want and the brands they know, a small but vital percentage of those SKUs will be missing in action.

Retail out-of-stocks cost the industry billions every year. Studies into the issue consistently reveal an average OOS rate of 8%. In other words, one out of every thirteen items that a customer wants to buy won’t be on the shelf when they’re ready to buy it. The odds of a shopper delaying purchase after encountering an OOS are only 15%, P&G reports; shoppers are much more likely to simply move to a different brand. P&G estimates the direct sales loss from such a rate of OOS is about 4%. But apart from lost sales, the added costs of dealing with OOS are numerous: extra ordering and auditing eats up time and resources; forecasting accuracy plummets; brand loyalty is eroded; promotions lose impact.

Better Metrics Make Better Solutions

No company can afford to let their customers hunt fruitlessly for products that aren’t even there. But solving this problem has baffled vendors and retailers for decades. The first step to rolling back the number of OOS incidences is understanding how they occur. Here are the seven root causes of OOS and how Big Data analytics can alleviate them.

1) Poor Data Sync: As new products are introduced and old ones are changed or discontinued, database inaccuracies inevitably occur, with obvious consequences for the supply chain. The best solution for data inaccuracy is continually ensuring that vendor and retailer records match. Aligning data between the two partners has a proven impact on lowering OSS. Unfortunately, transferring data between retailers and vendors has traditionally required a lot of manual labour. Apart from the cost of collecting, managing and transmitting that data, the hands-on approach has also meant that the communication is very slow moving. P&G recommends “collaborative synchronization of data between suppliers and retailers using a third-party vendor.”

2) Perpetual Inventory (PI) Failure: P&G found that the level of accuracy for PI systems is extremely low, as little as 32% to 45% of the time. These system failures inevitably lead to phantom inventory: the product is listed, but it’s nowhere to be found. Phantom inventory arises from shrinkage due to theft or damage, or from more benign causes, such as when products are sold at multiple locations in one store. These absentee products may only have a ghostly presence on the shelf, but their effect on ordering is all too real. Not only may replenishments come late, but managers may decide that products aren’t selling well and cut back on further orders. Improving inventory accuracy can have a dramatic impact on reducing OOS. Stores with accurate inventory records have an average OOS rate of 4.1%, cutting the usual rate by half. Integrating data streams from various points on the supply chain can move companies toward this goal.

3) Distorted Forecasting: 47% of OOS arises from poor demand forecasting. According to the P&G report: “Whenever a shopper shifts their buying pattern due to an OOS, it adjusts the demand history away from the sales history and no one can see the true demand history.” Thus, demand equals sales plus lost sales. But lost sales are exactly that: lost. Shoppers don’t usually register their altered purchasing decision with the store, and the demand becomes impossible to measure accurately. But by properly managing POS data, companies can better assess the speed at which items move. In other words, comparing historical data to data visualized in real-time allows savvy vendors and retailers to estimate the impact of lost sales from unobserved OOS and integrate those figures into their demand forecasting.

4) Excessive backroom inventory: One of the most interesting findings of the P&G report shows that there is a positive relationship between the size of backroom inventory and OOS. Amazingly, the larger the backroom, the higher the rate of OOS. Inadequate shelf replenishment accounts for 25% of all incidences of OOS, and often that needed inventory is simply languishing in the stockroom. Retailers and vendors can confront this issue head-on by creating delivery schedules that meet the demand on the shelf. Delivering directly to the shelf takes true collaboration at all levels of the supply chain. Retailers and vendors that are already committed to real-time data sharing can use this information to better calibrate their deliveries and ultimately make a huge dent in their rate of OOS.

5) Faulty Shelf-Space Allocation: As may be expected, fast-moving CPGs are more likely to experience out-of-stocks much more than their slower-moving counterparts. In fact, the lost sales are six times greater, but the allocation of products at most retailers doesn’t reflect this disparity. Of course, competition for shelf space is intense. But retailers using historical POS data can easily identify the products with the highest OOS rates and start generating demand-based planograms to reflect that information. Increased shelf space for fast-moving items may only lead to a small improvement in sales, but the returns from lowering the rate of OOS can make up the difference.

6) Low Planogram Compliance: Creating demand-based planograms is impossible if store managers aren’t complying with planograms in the first place. The P&G report shows that planogram compliance is relatively high in most categories, but for those areas where it’s low, there is substantial room for lowering OOS.  Real time collaborative analytics with suppliers gives additional ‘eyes in the aisle’ for retailers that can identify problem areas like planogram non-compliance sooner so they can be fixed faster.

7) Poor Stocking Practices: After all that planning and preparation to get the right product into the right place at the right time, many OOS situations occur as a result of things that happen after the SKU is on the shelf. For example, products that have gone out-of-stock will leave a hole on the shelf, which can be tempting to fill quickly with other products. Or in the hurry to keep pace with stocking schedules, some products can end up hidden behind other products. For issues like these, the best solution is for vendors and retailers to maintain vigilant store-level awareness of products through collaborative retail intelligence platforms that show inventory in real-time.

By centralizing data from across the supply chain into integrated visualizations, companies can quickly recognize the irregularities that lead to out-of-stocks. To see what real-time product management looks like on any device, try a demo of Askuity’s Retail Intelligence platform.