The (New) Best Way to Optimize Inventory Performance in the New Normal
With in-store and online customers essentially shopping from the same set of shelves, inventory management is becoming harder than ever for brick-and-mortar retailers. But prescriptive analytics is proving useful in filling carts and preserving sales.
If you’re in retail, chances are you’ve heard that inventory accuracy is the key to success.
That advice counts double in a pandemic.
You see, in-store shopping is no longer as simple as it once was. Pre-pandemic, many customers came to stores just to browse and interact with product. Consumers could find substitutions easily, perhaps trying a new brand when needed, and a shopping visit not resulting in a purchase was nothing serious (for the customer, at least).
How things have changed! For a customer, a store is no longer a casual place to visit, browse and leave. That could be why we are seeing non-contact shopping options like buy online, pick up in store (BOPIS), click and collect, and curbside pickup grow exponentially in popularity. But sometimes, customers find the only way they can get what they need is by walking up and down the aisles themselves, especially when shopping for the best-looking produce or fresh meat for their evening guests. When that’s the case, we are seeing customers buy more items per trip to minimize the need for frequent repeat visits.
As such, the trips tend to have one other thing in common: they are well-researched. Customers want to make their trips as efficient as possible, so they tend to do their homework before leaving the house. They search their favorite retailers’ websites, checking pricing, availability and item location to ensure when they reach the store they know exactly where they’re going and what to buy. In and out quick.
That being said, the fact that more customers are shopping more online means that inventory accuracies are much, much more serious than they were before the pandemic. If a customer went into the store pre-pandemic and found a needed item out of stock, it wasn’t a huge deal. Substitutes were widely available, and the customer would likely go ahead and settle with one of the available options out of convenience – meaning that, despite the unfortunate inaccuracy, the retailer still made the sale.
With more customers essentially shopping online first, inventory inaccuracies have become riskier. When a retailer realizes it has an inventory accuracy problem for a specific universal product code (UPC), or inventory counts reach a preset, low-quantity threshold, the item is taken off the website until the issue is resolved. This means that even if the retailer still has the product on the shelf in the store, a customer searching for that same product online will see it listed as unavailable and assume that it is out of stock in both places. Because the online customer can easily find another retailer carrying the same product before leaving home, all it takes is a quick internet search and the retailer with the inventory inaccuracy has lost a sale (or worse, a once-loyal customer).
In other words, inventory optimization is critical for retailers who want to succeed in the new normal. If a customer is already braving the risk of COVID-19 to enter your store, the last thing you want to happen is for them to walk in and find the item they came for out of stock. If your website indicated the item was in fact in stock, prompting a trip to the store, the customer may never shop at your store again.
I can’t stress this enough: inventory is your most important asset and your greatest capital expense – and the time to optimize it is now!
But optimizing inventory performance is tough. Inventory assortments are larger and more complex to manage than ever before, making it hard to identify shrink, slow-selling products, cement products (that occupied shelves and are not being productive), inaccuracies and out of stocks. New channels of commerce create even more movement to keep track of. And there’s the risk of non-compliance by associates. A shipper who brings a carton to the wrong store, a buyer who enters the wrong price in the inventory master list or an employee who forgets to restock a single product can trigger a ripple of problems for retailers and their customers.
That’s why many of our customers are adopting the Zebra Prescriptive Analytics inventory module to keep a closer eye on their inventory.
The inventory module drives inventory performance by predicting out of stocks, ensuring planogram compliance, forecasting risk and flagging items with potentially high shrink, improving stock accuracy and more. When an issue occurs, the module’s advanced root-cause analysis can trace it right back to the source and direct an appropriate response, factoring in its financial impact and how it should be prioritized.
Here are some use cases showing how retailers have used Zebra Prescriptive Analytics to resolve inventory errors before they could greatly affect the customer experience:
A grocer had slightly higher waste than usual related to expired milk at two of its stores. Every month, the stores threw out many gallons of milk that had reached their expiration dates, totaling more than $15,000 per year. Operations insisted that the store wasn’t following proper procedure, but the store managers said that corporate was allocating too much milk via its planogram for the dairy case refrigerator. The grocer’s existing analytics system could not trace the root cause. The workers continued to throw away the milk, and out of stocks were frequent.
When the grocer adopted the Zebra Prescriptive Analytics inventory module, the problem became clear. In order to control shrink, the grocer required that unsold milk be marked down at 50% three days before its expiration as a “manager’s special.” At the stores with higher waste, however, the dairy department workers were not following this procedure due to a training gap. Compounding the problem at one store was the fact that the general manager was hired from another grocer that didn’t implement the practice of marking down near-expired dairy. With no incentive for customers to buy the near-expired milk, combined with poor rotation (another training gap), it spoiled on the shelf, and the workers threw it out. The grocer retrained the dairy staff at the two stores, eliminating the annual $15,000 in waste.
At the height of the COVID-19 pandemic’s panic buying, a general retailer adopted the Zebra Prescriptive Analytics inventory module to help ensure it was keeping its most critical products stocked. It quickly detected a store that showed plenty of liquid soap in its inventory but hadn’t sold any for several hours, resulting in an estimated $400 in missed sales. The manager for the store received a prescriptive action directing him to verify on-shelf availability for the soap.
The manager went to the body care aisle and found that there was soap on the shelf, but it was at the back. The springs of the shelf display were broken and not pushing the soap forward as expected. Since the shelf was at waist height, it was impossible for customers to see or even reach the remaining soap without contorting themselves awkwardly.
The manager moved the soap toward the front of the shelf where it could more easily be seen and issued instructions for employees to do the same when restocking. Sales immediately resumed following corrective action. A facilities team was also called in to fix the springs on the shelf display.
A hardware retailer knew that it was suffering millions of dollars per year in damages and wanted to tackle it at the store level. It deployed a pattern via the Zebra Prescriptive Analytics inventory module to monitor individual stores for high damage claims.
Shortly after deployment, the pattern alerted a regional manager to a store that had reported 256 units as damaged in a single day, amounting to more than $11,000. The regional manager went to the store to investigate and found that two new associates had improperly scanned the items to damages simply because they didn’t know how to process the right code otherwise. The perfectly sellable products were now on their way to a destruction facility. Thanks to the near real-time alert, the regional manager was able to intercept the shipment and return it to the store for sale. The associates were also retrained on unit scanning to avoid such problems in the future.
Many inventory inaccuracy problems can easily be solved and losses easily mitigated by looking at your operations through a different lens, rather than reports. Though reports theoretically give you all the data you need (and then some), it tends to be faster to see data-driven behavioral patterns and correlate certain actions (or inactions) related to large-scale inventory issues via prescriptive analytics’ simple tasking since it tells you what to do to fix the problem. Reports just send you on a fact-finding mission through mountains of data. They don’t tell you what happened or “prescribe” a solution.
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