Predictive Analytics Transforms Inventory Management in Retail

Predictive Analytics Transforms Inventory Management in Retail

Originally Published on PA Times

Every retailer wishes to have the right product at the right place at the right time. In order to achieve this there is a lot of thought put into inventory management, allocation, and replenishment processes. Most retailers begin by creating a plan based on the big picture of their business process, and then they purchase the inventory, and allocate it to stores from the DC. Once inventory begins to run low at a specific store, it is replenished from the DC. This strategy would be nice if real life actually worked that way. Unfortunately, this scenario raises a lot of important questions and concerns.

For Example:

  • When products don’t sell well at specific stores, retailers are forced to markdown inventory in order to clear the inventory.
  • When there is no more inventories in the DC, or even before inventory is replenished, retailers experience lost sales at stores where these products are still in demand.
  • Customers resolve to purchase from a local competitor in order to fulfill the sale.
  • Lost sales paint an inaccurate picture of lower demand, which in turns makes a retailer order and allocate less inventory to stores in the future. (This cycle repeats itself.)

IHL Group, a global research and advisory firm specializing in retail technologies describes the challenges mentioned above as “Inventory Distortion”. A recent study done by IHL Group revealed that Inventory Distortion costs retailers collectively nearly $800 Billion globally. Over-stocks and Out-of stocks is not a new retail problem, it has been challenging retailers as long as the retail industry has existed.

Fortunately the growing magnitude of this problem forced the industry to look at new ways to deal with inventory management. This has significantly contributed to the growing interest in retail technology and optimization solutions in the past few years. In turn, predictive analytics solutions have come to the forefront of the spotlight on retail technology, and has transformed the way leading retailers manage their inventory.

However, not all solutions are equal. A major downfall with the most of inventory management systems is that these systems don’t focus on the entire retail business process.Existing solutions for pricing, merchandising, planning, allocation, markdowns and event promotions are not taking each other into account, and often producing conflicting outcomes, and inaccurate forecasts.So how do you find the right retail predictive analytics solution?

Inter-Store Inventory Balancing & Predictive Analytics

In order to properly forecast demand, and build the optimal inventory management strategy a retail solution must be tailored to the specific business process of the retailer, which means that it must take all factors that affect demand into account. Predictive analytics technology will significantly improve demand forecast accuracy, and suggest better allocation and replenishment strategies. Moreover, there are specific tools that enable a retailer to further decrease inventory distortion. For instance, one powerful tool is called Inter-Store Inventory Balancing. How does Inter-Store Inventory Balancing work?

To illustrate how it works imagine this very simple situation:

You only have 2 stores, “Store A” and “Store B” and 1 product (a red hat). Each store has an initial allocation of 9 red hats:

However, after about 3 weeks of sales, the overall picture becomes a lot more uneven. “Store A” is already low in inventory for this product due to increase in demand, while “Store B” has low demand for the same product, and is now forced to markdown the hats to clear their shelves. Here is a snapshot of what the stores look like at this point:

Looking at the illustration above it quickly becomes apparent that if 4 hats are moved from “Store B” to “Store A” all the orders will be fulfilled. However, by this point, any action by a retailer will be retroactive, customers have already moved on to your competitor, and moving the inventory will not avoid the inventory costs this problem has already created. The only way to avoid these costs is to make these moves proactively. Business Specific Predictive Analytics allows retailers to do that. With the Inter-Store Balancing Solution, the system proactively analyzes all the influencing factors of a retail supply chain, and recommends the optimal inter-store transfer schedule to move slow-selling products to stores where there is a high demand for them. In our case, it would look like this:

This means that the retailer avoids unnecessary markdowns, and has the capability to sell the inventory at full price. The example used above is a very simple one, using only 2 stores and 1 SKU.

To put this strategy into perspective, a retailer with 100 stores and 1,000 SKU’s has 10 Million possible inter-store transfers. To make the decision even more complex the system must consider the different combinations of sizes, colors, the distance traveled between each store, merchandise pricing, the volume of merchandise available at each store, and the cost to transfer product to calculate optimal transfers. Predictive Analytics makes this happen.

Retailers that use Predictive Analytics for Inter-store Inventory Balancing report very tangible results such as:

  • A decrease in Inventory costs by 25 -40%
  • An increase in sales of 11 -20%
  • A boost in turnover by up to 3.5 times.

If you’re wondering how Inter-Store Inventory Balancing would benefit your business, you should ask us for an analytic assessment on your own data.

Learn more about Retalon’s Inter-Store Balancing Solution.

Retail + Predictive Analytics = a Match Made in Heaven
Learn more about Predictive Analytics for Retail.