How to Effectively Balance Inventory Across All Locations (2021)

How to Effectively Balance Inventory Across All Locations (2021)

Optimizing your inventory balancing process is key to increasing ROI

Even the best-executed assortment strategy can lead to imbalanced inventory levels halfway through the season.

An inaccurate forecast, unpredictable changes to consumer preferences, shifts in the competitive landscape, or major economic events can all completely throw your planning and purchasing off-track, leaving you with major inventory distortions despite your best plans. Some stores will be overrun with inventory, while others will struggle to keep up with demand.

And this is exactly what happened to many retailers within the last year. So it’s no surprise that retailers are beginning to take a serious look at their mid-season inventory balancing practices.

But before we dive into the solutions, let us first discuss what inventory balancing actually is, why it’s important, and what causes inventory imbalances in the first place.

What is inventory balancing?

Simply put, inventory balancing is the process of moving excess inventory from one retail location to a retail location where that inventory is in demand. When done right, this process will free up shelf-space and budget for stores with slow-moving inventory, while also quickly (and cost-effectively) restocking stores that can’t keep up with demand — thus increasing sales.

Why is unbalanced inventory such a big problem for retailers?

As consumer expectations evolved in the last several decades, many retailers have been forced to move past the traditional DC -> Warehouse -> Storefront model.

Today’s big retailers operate multiple store formats, from big-box flagship stores to minimal-assortment express locations. And since today’s customers are increasingly ordering online for in-store pickup or home delivery, stock is often picked from a regional warehouse or a local store’s inventory — wherever it’s available. This is further complicated by the fact that each channel and location has its own unique geographic and demographic profile that influences demand (and as such, its own unique assortment balance).

As the number of variables impacting each channel multiply, determining the optimal assortment for each store becomes overwhelming.

Inventory imbalances are the inevitable result of this expanding complexity, leading to ROI-sapping issues like:

  • Lost sales due to out-of-stocks
  • Inflated inventory costs due to excess inventory
  • Reduced customer service levels
  • Long-term negative impacts on brand image

While minimizing stock imbalances has always been essential to retail success, unfortunately, the standard tools that retailers use have not kept up with the growing need for stock balancing in modern retail. 

Why isn’t the current method working?

Most of today’s inventory management issues start at the level of forecasting.

Despite their statistical rigor, most retailers are using forecasting models and applications that rely heavily on historical sales data — mostly ignoring dynamic, ever-changing factors that have an impact on demand. 

For example, legacy forecasting methods fail to account for the fact that new products in an assortment will inevitably impact sales of older products. Or that new promotional strategies will lead to an uplift for discounted products, while likely reducing sales for similar, regular-priced items. And this is just the tip of the iceberg. Changes in seasonality, shifts in consumer preference, more frequent intense weather events, and dozens other unpredictable factors can completely change demand across the entire assortment — well after retailers bake in their forecasts.

These difficult-to-predict swings in demand can quickly lead to out-of-stocks for products at some stores and excess inventory in other stores. 

Scale is another area where these forecasting systems struggle.

Today’s retail assortments can stretch into the thousands or even hundreds of thousands of SKUs. At that scale, legacy forecasting methods cannot generate accurate forecasts of true demand — especially on a store-by-store, SKU-by-SKU level. In the isolated cases where retailers try to create such granular demand forecasts, the projects require unsustainable levels of manual labor to clean, correct, and consolidate data.

How can retailers effectively balance inventory?

The complexity of modern retailing had already led forward-thinking companies to shift from old spreadsheet-based tools to software suites designed for the retail industry. Forecasting software uses more sophisticated statistical methods to estimate the right quantities of product for their stores. 

For example, allocation applications use modeling tools to divide incoming inventory among stores, warehouses, and fulfillment centers based on a dynamic demand forecast. Other applications can help with purchasing, replenishment, and other inventory management processes. Moreover, after the initial forecast, many of these systems act in real-time, replenishing inventory in response to the previous day’s sales by pulling inventory from vendors, warehouses, and backrooms (when it is cost-efficient and profitable to do so). This allows modern inventory systems to proactively optimize and balance inventory as variables change and new factors impact demand.

Simply put, new, AI-powered retail analytical systems are less reliant on historical data and are better able to consider hundreds of variables influencing sales and inventory in real-time.

Retalon, for example, uses advanced analytics and AI to identify emerging inventory balance issues and recommend the optimal inter-store transfers. As a result, retailers can free up shelves where a product has slow sell-thru rates while replenishing the out-of-stocks in better-performing locations.

The benefits of implementing advanced analytics 

Advanced stock balancing tools complement retailers’ existing software suites while bringing significant benefits.

This inventory transfer solution rebuilds broken assortments across all channels and locations. It recognizes the demand variation between large-format and small-format stores, online fulfillment centers, and delivery-or-pick-up business models.

When the system recommends inventory balancing, it accounts for all opportunities as well as picking and transportation costs. The system will consolidate transfers to minimize the number of truck rolls and prioritize inventory transfers to locations that have a higher chance of selling the product at full price.

Using this sort of inventory balancing solution, retailers can reduce out-of-stocks and lost sales by getting product inventory to the locations where it can sell. At the same time, minimizing slower-selling inventory in other stores significantly reduces the end-of-life or end-of-season markdowns that erode profitability.

If you’re curious, you can run a proof of concept to see how an advanced Inventory Transfers and Assortment Balancing tool can boost ROI in your business.

Contact our team to get a personalized demonstration of the solution.

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