A grocery store worker prints up a price tag for a loaf of bread using a mobile printer
By Guy Yehiav | April 27, 2020

It Is Possible for Retailers to Avoid Out of Stocks in Times of Increased Demand – If They Can Detect Surge Patterns

Don’t worry. It’s easier than it sounds, thanks to prescriptive analytics. (Yes, this is different than predictive analytics.)

Recent research by Zebra Technologies shows that the pressure for retailers to avoid out of stocks has never been higher. In Zebra’s 2020 Global Shopper Study, a remarkable 39 percent of retail shoppers reported leaving a store without purchasing anything. Why? At least one item they wanted or needed was out of stock – or at least not on the shelf. Retailers simply cannot afford to alienate such a huge portion of their customer base. The time to improve inventory management practices is now.

Though it has never been more challenging to accurately predict demand, it is possible – and essential. But accommodating heightened demand is about more than just accurate forecasting. It’s also about knowing how to use the inventory and other resources you have on hand to sustain the increase in sales by any means necessary.

So, how do you improve your ability to accommodate unexpected demand surges? Numerous Zebra retail customers have asked me this question since the COVID-19 outbreak started increasing operational pressure, and my answer has never changed.

It all starts with leveraging technology, such as prescriptive analytics, to analyze data and increase visibility across the business so that you can understand:

  1. When surges in demand are likely to happen
  2. What resources you have available at any given time
  3. When, where, and how to deploy those resources

Patterns for Managing Inventory

At a more granular level, Zebra’s retail customers are deploying “patterns” (algorithms) to identify very specific data behaviors that indicate opportunities for inventory optimization and inform appropriate stakeholders about how to take action. Here are some examples of those patterns:

  • Most popular out-of-stock items, and distribution

Sometimes even your best efforts can’t prevent an item from running out. When this happens, expediting replenishment is an option. But this can be a major expense. So, how do you figure out which products are worth the cost of expediting? Is it even feasible?

It comes down to balancing the cost of expedition, the popularity of the out-of-stock items, consumer behavior and how many stores are sold out of them. This pattern identifies the items that are popular enough to justify the added expenses, as well as the best volume to expedite to certain stores and warehouses for maximum distribution efficiency. It then sends this information to your buyers and/or allocators, directing them to take proper action.

There are other variations of this pattern, including one that identifies the most feasible orders for “drop shipment,” whereby a store receives product directly from the manufacturer, not a warehouse or other intermediary. It’s important to automate this process, as drop shipping can be costly and labor-intensive. You need to know exactly when they are appropriate. The pattern continually monitors the ratio of demand to item shipments, ensuring that new orders are in line with customer demand. It registers any inconsistencies and issues corrective actions for them in real time, minimizing waste and increasing return on inventory assets.

  • Stores with slower-than-average rates of sales

If a busy store is in danger of running out of popular merchandise, you need to be able to tap other stores’ inventories to relieve the pressure. This pattern is especially useful for orders placed online as it doesn’t matter where the order is fulfilled, as long as it is fulfilled accurately and sitting at the pickup location on time. By identifying stores with slower sales, you can tap their inventories to help fulfill online orders for the more heavily trafficked stores at which inventory shelf availability is a priority.

The pattern can also determine whether it is easier and more cost-effective for the slower stores to simply fulfill orders by shipping items directly to customers. Few retailers will find it effective to re-balance their inventories across the network (i.e. return the highest-demand items back to distribution centers from stores, then re-distribute them to stores based on need). It is easier and more cost-efficient for slow-selling stores with inventory to spare to just ship these items direct to consumers in other zones. This also ensures inventory can be consumed before it becomes out of date.

  • ·Most popular “buy online, pick up in store” (BOPIS) / curbside pickup items

In the current retail environment, it has become popular for customers to place orders online and then pick them up in person. It’s important for your stores to balance what inventory they have on hand between orders placed online and purchased during in-store shopping trips. This pattern identifies items that are especially popular for pickup orders and calculates how much each store should reserve for these types of orders versus “walk-in” purchases to satisfy consumers equally in both shopping scenarios. It then sends out a prescriptive action directing department managers to “tag” a certain number of each item as in-store inventory only.

  • Phantom inventory

Few things make inventory management harder than “phantom inventory syndrome.” This is when your inventory management system calculates that certain items are available when they are not. This is especially problematic for “buy online, pickup in store” (BOPIS)/curbside pickup customers. If your website says a product is in stock, but the employee fulfilling the order can’t find it on the shelf or stockroom and either substitutes an unwanted item or cancels the item altogether, the customer may never shop at your store (or online, for that matter) again. They’ll likely start shopping at another retailer who can get them what they need or want. This is especially true when customers are paying a personal shopper or delivery fee for the service. When they only receive a quarter of what they ordered, they may find themselves having to pay another fee for a follow up order to hopefully get their items. Over time, these additional customer costs could cost you loyal customers.

Fortunately, this family of prescriptive patterns helps your stores identify phantom inventory – before the customer gets upset – by flagging items that are marked “inventory on hand” in the system but sales of items suddenly decrease or stop altogether. Explanations could be misplacement, theft, unreported damages, shipping misalignment with no claims filed or just a simple out-of-stock that needs to be refilled from the back room. The pattern empowers employees to investigate and correct any issues.

  • Demand to ship item ratio

This type of pattern identifies a sudden change in consumption behavior that is significant enough to warrant a change in ordering behaviors. It leverages the solution’s demand sensing capabilities to analyze inventory model stock, safety levels and the supply chain’s ability to accommodate these changes in demand. Without this pattern, a demand change could create a major “bullwhip” effect that is tough to escape. For example, if the ratio suddenly changes, the prescriptive analytics solution will direct the retailer’s buyers to ask the manufacturer for a direct drop ship at each affected store, expediting the delivery and preventing the bullwhip effect from impacting the “regular” supply chain (i.e. all the different nodes between manufacturer and retailer).

You Can’t Stop the Surge, but You Can Stay Ahead of It

Many retailers have told me that, right now, it feels as though they are spending all day, every day, just trying to stay on top of demand. Even those who felt prepared for a situation like COVID-19 are finding themselves racing to restock store shelves to minimize online order substitutions or cancellations and in-store customer walkouts. Though it may not seem like the right time to implement new technology solutions, I recommend that you at least consider the almost instantaneous benefits you could gain – and pass along to shoppers – by implementing a prescriptive analytics solution to increase your inventory on hand and create an exceptional customer experience. If you can accurately assess your inventory situation in real time, you will be able to make proactive changes to your supply chain flow and reduce the high rate of out-of-stocks that is currently challenging customer satisfaction. It will also empower your people to take immediate corrective action to avoid missed sales opportunities and, when needed, communicate out-of-stocks to customers so that they can make more informed decisions about their selections and/or timing of online orders or in-person shopping trips.

If you’d be interested in learning more about how prescriptive analytics could be implemented into your current technology architecture with minimal resources needed, please reach out to my team. We’re happy to answer any questions or provide more detail about how this could work in the current retail environment.

Retail, Retail, Retail, Retail, Retail,
Guy Yehiav
Guy Yehiav

As General Manager of Zebra Analytics, Guy Yehiav is responsible for setting the organic and non-organic growth, leadership strategy, and customer success for the Zebra Analytics business unit.

Formerly CEO of Profitect, recently acquired by Zebra Technologies, and a 25-year veteran of the supply chain industry, Mr. Yehiav has held senior leadership positions at Oracle and was previously founder of Demantra US (acquired by Oracle in 2006).

Fluent in English, French, and Hebrew, Mr. Yehiav has a passion for teaching, which started with educating high-school students pro bono in his native country of Israel. He continues to teach pro bono, now as a guest lecturer on professional selling, entrepreneurship, and statistics for the Massachusetts Institute of Technology (MIT) and Babson College.

Mr. Yehiav holds a Bachelor’s degree in Computer Science & Industrial Management from Shenkar College of Israel and an MBA in Entrepreneurship from Babson College. He currently lives in Wellesley, Mass. with his wife, Maya, and their three daughters.

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