by Jim Malone, VP of Business Development for Retalon
Every year, retailers put fresh spins on evergreen holiday décor, remixing such classics as the Christmas tree and jack-o-lanterns to tempt seasonal shoppers. Though we have yet to see this year’s big trend, it’s probably safe to say it will not be the Christmas Turkey or Thanksgiving Skeleton.
Inventiveness is not so much the issue for retailers as time. With Thanksgiving, Halloween, and Christmas occurring over a period of two months, it’s critical that retailers practice efficient inventory management to move seasonal items at the appropriate moments. Retailers gearing up for big sales days may incur costs if they don’t strategize inventory properly.
Here are three prime examples of where retailers go wrong with seasonal inventory, as well as solutions for better management.
Too often, chain retailers fail to balance inventory between locations:
It’s common for an item to be sold out in a particular colour or size (SKU) in one store, while it’s in abundance at another location. This imbalance is a double whammy for retailers trying to clear inventory.
The balancing act is especially challenging with holiday inventory, which becomes outdated in the blink of an eye. The fluctuating demand for this nonessential inventory – along with the fact that what’s “hot” can vary by location – makes it extremely difficult to forecast sales using historical data.
Out of stock translates to lost revenue and may push customers toward the competitor. Surplus results in painful markdowns. Worse, by the time businesses realize the imbalance, it’s too late to avoid the associated costs. The sale and the consumer are out the door and bargain basement pricing is inevitable.
It is possible to get ahead of the problem. Retail predictive analytics is an increasingly popular solution for retailers struggling to manage inventory. A Gartner report states that by 2016, 70 percent of the most profitable businesses will manage their processes using real-time predictive technology.
Predictive analytics systems differ from past metrics in that they anticipate the necessary transport of an item before the transfer becomes tedious and overwhelmingly expensive. Inter-store inventory balancing solutions takes into consideration all costs associated with the switch, including logistics, store capacity, demographic diversity and the sizes and colours most likely to sell at individual locations.
The quick succession of fall holidays demands quick turnover. Predictive analytics is especially useful for clearing the bottleneck and selling out seasonal inventory at maximum prices.
Retailers misjudge inventory needs during promotions:
Retailers often use promotions to generate traffic in their brick-and-mortar stores. After all, what customer can stay away from a great deal, or twenty?
The trouble is most retailers don’t execute promotions profitably. Lacking an integrated inventory management solution, they many times fail to connect pricing and promotions with ordering and replenishment.
By setting needlessly low markdown prices, businesses deplete inventory before demand. This mismatch negatively affects profits and customer relations, since shoppers who arrive a day or two later will not expect items to be out of stock.
Another step in avoiding this dilemma is to consider how media pushes like television ads, Facebook promotions and billboards increase inventory needs. Retail predictive analytics pulls from demand forecasts, replenishment, current inventory levels, promotional media types, price optimization and product cannibalization to determine optimal inventory at the SKU/store level.
Add stock only when it adds value:
Retailers’ hands are tied when it comes to inventory shipping, a frustrating reality since premature or delayed shipments can have major financial ramifications for businesses. Early shipments bring higher carrying costs, while late or incomplete shipments may ruin promotions by causing items to go out of stock.
In order to protect against shipment problems outside their control, retailers typically add safety stock to inventory. But safety stock also carries a cost, and thus requires optimization. Unfortunately, optimal safety stock proves fairly difficult to calculate.
Overdone safety stock defeats its purpose by making it harder for retailers to optimize their supply chains. The idea of investing in additional inventory with the hope of additional sales is flawed – it creates an endless imbalance between what is needed and what is stocked. Leveraging retail predictive analytics, retailers can better optimize safety stock and keep on-hand only what is needed.
About the Author:
Jim Malone is the Vice President of Business Development at Retalon, the world’s leading provider of predictive analytics for the retail industry. Since 2002, Retalon has optimized pricing, inventory management, merchandising, planning and marketing operations for retail organizations in a variety of industries. Retalon products range from task-oriented solutions to a common analytic platform, resulting in tangible optimization of the supply chain and significant measurable benefits for the entire organization.