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Inventory Allocation in 2024 (Definition, Methods, Tips)

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Mostly empty shelves in a grocery store.

Effective inventory allocation has never been more important than it is today.

With recent supply chain shocks, unpredictable consumer demand swings, and economic uncertainties looming, retailers need to ensure their inventory is in the right place at the right time to meet customer demand.

Moreover, if you’re poorly balancing inventory across your sales channels and locations you risk:

  • Losing sales due to out-of-stocks
  • Losing customers to competitors
  • Reduced margins due to unnecessary markdowns
  • Packed shelves that prevent you from building a guest-relevant assortment

And, if these issues continue, you may face an overall loss of market share.

So, how do you avoid these landmines without breaking the bank?

Let’s look at the top methods and strategies being used by retailers in 2023.

What is inventory allocation?

In the simplest terms, inventory allocation describes where in the supply chain an organization’s inventory is.

In other words, the number of units present in which stores, warehouses, and distribution centers.

However, when enterprise retailers talk about “planning and allocation” or “initial allocation,” they’re usually describing specific processes within their organizations.

“Planning and allocation” definition

Planning and allocation are pre-season planning processes retailers use to decide how much of each SKU needs to go to each location and sales channel in order to meet customer demand.

“Planning” is the keyword here. Planning where inventory will be allocated takes place in advance of a sales season.

Don’t let the simplicity of our planning and allocation definition fool you. Allocation will make or break a sales season. The right amount of each product needs to be in each location (and enough safety stock needs to be present in warehouses to buffer demand swings).

Customers can’t buy products that aren’t there and they won’t buy more than they need.

As such, retailers need to accurately forecast sales demand for a particular SKU at each sales location.

To avoid the costly consequences of poorly distributed inventory, retailers employ a variety of allocation methods.

“Initial allocation” definition

While “allocation” can refer to the location of any SKUs across your channels, when retailers use the term “initial allocation,” they’re typically referring to how new products will be distributed across their sales channels. Basically, how many units of a new SKU will go to which store, warehouse, or DC?

Allocation and planning vs. replenishment – what’s the difference?

While allocation and planning seek to map out the quantity of inventory at every location before the selling season starts – replenishment is the process of re-ordering inventory once the selling season has started.

Both are important processes (and most retailers will need to take advantage of both at different times). For example, a fashion retailer with new designs every season will need to plan the initial allocation of the new SKUs, and then rely on replenishment to keep popular items in stock during the season.

Most popular inventory allocation methods

The way a retailer allocates inventory is dependent on the scale of its operations. Small retailers with limited assortments often use traditional manual allocation methods. But when faced with thousands of SKUs across multiple sales channels retailers need more advanced allocation techniques.

Let’s discuss some of the ways retailers can achieve this:

1. Manual allocation

Manual allocation involves reviewing past sales data from spreadsheets or ledgers to figure out potential buyer behaviour in the coming sales season. An allocation plan can then be made on Excel (or comparable software) – with planners either using (a) statistical calculations, (b) intuition and expertise from veteran planners, or (c) some combination of both – to predict the number of units required for each SKU at each location.

This is fine for a small retailer with a handful of locations. A single planner can have a standardized allocation plan (and have a handle on all of the inventory across the company).

However, as the number of stores and SKUs increases, this manual Excel-based approach becomes much more time-consuming and error-prone. So, instead of one planner making these calculations for the entire organization, larger retailers are forced to break planning up into departments or categories. And this comes with its own challenges.

For example, when dealing with multiple category-level plans, someone still has to reconcile all of the information and translate it to high-level financial / budget plans. And someone else has to create purchase orders (and combine orders from multiple plans when ordering from the same vendors). This quickly becomes either (a) a highly inefficient, siloed approach where every department is doing their own thing or (b) a confusing, unauditable mess – with dozens of different spreadsheets with different formats and styles and versions passing through dozens of hands.

This is not only incredibly labour-intensive to deal with, it is also very error-prone.

2. Algorithmic and rule-based allocation

Once a retailer grows beyond the point where they can manually manage and plan their allocation one SKU at a time, they often turn to use a series of rules and formulas (“algorithms”) to simplify the process.

A hardware retailer, for example, might have rules based on historical sales patterns like this:

  • Seasonal air conditioner sales in Arizona start in March and end in October.
  • Seasonal air conditioner sales in Minnesota start in June and end in August.
  • Suburban stores sell more central air conditioners than window air conditioners.
  • Urban stores sell more window air conditioners than central air conditioners.

These patterns can be turned into rules, formulas, and macros. So instead of manually cross-referencing the historical performance of every SKU at every location, planners can run an algorithm to automatically allocate inventory based on these rules.

Moreover, retailers can build rules around everything from SKU variations (colours, sizes, etc.) and categories to store clusters (assortments in A-stores vs. outlet stores). Each level of the hierarchy can have its own set of rules – allowing planners to eliminate a considerable amount of manual work.

But although rule-based allocation is better than doing everything by hand, it has some major limits.

Firstly, analyzing the performance of complex assortments to come up with foolproof rules is a very difficult task. Doing this well requires looking at dozens of variables – not just historical sales.

For example, if you made allocation rules by looking at last year’s sales alone – you’d miss every instance of out-of-stocks. Perhaps Store X only sold 50 units last year because you only allocated 50 units. What you don’t know by looking at just the sales figures, is that Store X would have sold 100 units – if only it had enough inventory allocated in the first place.

Secondly, it’s unreasonable for retailers to have a rule for every SKU (especially if they introduce new SKUs every season). More likely than not, retailers have rules for their A-products (best-sellers), and category-level (and perhaps attribute-level) rules for the rest of their assortment.

While this definitely simplifies the process of allocation, the low-resolution approach to B and C products often leads to everything from out-of-stocks and lost sales to overstocks and unnecessary markdowns.

Lastly, allocation algorithms are only as smart as their rules. Analysts can’t possibly turn every possible factor impacting sales into a rule. Our hardware retailer, for instance, may not have a rule for El Niño weather patterns. Without that rule, the algorithm will mistime the summer sales season and allocate inventory to the wrong stores.

3. AI-based inventory allocation

The most sophisticated retailers plan their inventory allocations using AI-based demand forecasting.

Instead of merely looking backward through sales histories, AI-based demand forecasting uses real-time analysis to determine true customer demand – accounting for factors like the seasonality of each individual SKU at each individual store, cross-product cannibalization, lost sales, etc.

Software like Retalon’s AI-powered Smart Allocation solution can evaluate hundreds of such interrelated variables at the same time and calculate the optimal allocation store-by-store, SKU-by-SKU.

Because AI-based methods rely on forecasting demand, retailers’ allocations are more accurate. Stores get enough products to maximize revenue while minimizing overstocks and understocks.

Inventory allocation strategies

Retailers have several allocation strategies to choose from. The simplest strategies are easy to execute. At the same time, they create the most risk because stores do not get the optimal allocations for every SKU. More refined strategies will bring allocations closer to meeting customer demand.

Here are the 5 most common strategies retailers use to allocate inventory:

1. Equal or universal allocation

Retailers that rely on manual or algorithmic allocation methods have to prioritize. Only the most important SKUs will get detailed allocations. The least important SKUs will just get distributed equally across the retailer’s sales channels. That means thousands of SKUs get shipped in the wrong quantities. Retailers that accept the lost sales and extra markdowns may not have a sustainable business.

2. Tier-based allocation

Rather than treating every location the same, retailers may group stores into tiers based on sales volume. “A” stores get more inventory than “B” stores which get more inventory than “C” stores. The biggest problem with tiered allocation is that it creates a self-fulfilling prophecy: C stores don’t sell as much so they don’t get as much – which means they can’t sell as much. On top of that, tiering ignores the possibility that some C stores may sell certain products better than some A stores.

3. Cluster-based allocation

Store clustering is a more refined approach than tiering. Retailers allocate products to stores with shared characteristics. For example, format clustering puts flagships, outlets, and popups into their own groups. Climate clustering, on the other hand, would treat southern stores differently from northern stores. The trouble is, you can’t define a store by a single property. Northern flagship stores may have more in common with local outlets than with southern flagships.

4. Demographic-based allocation

Another nuanced strategy uses each store’s demographic trends. Apparel retailers, for example, can skew allocations to reflect the younger demographics at stores near universities. Retailers have to keep their fingers on the pulse of demographic trends for this strategy to work. Getting caught off guard by a change — like the shift away from on-campus learning — will throw off their allocation decisions.

5. Demand-based allocation

The trouble with these strategies is their reliance on top-down classifications that don’t reflect how each store’s customers actually buy. Retailers make the best allocations when customer demand drives their decisions. The right allocation is the amount of product each store needs to meet its unique customer demand — no more and no less. Out of these allocation strategies, demand-based allocation is the most accurate. But it is also the most complicated.

Next steps for creating an inventory allocation system

By now, it should be clear that relying on blunt instruments like historical sales analysis and rule-based allocation leads to sub-par results. These tools simply can’t account for all the critical factors impacting demand across your channels and stores. Factors like:

  • Store size
  • Store format
  • Store location
  • Regional climate
  • Regional demographics
  • Individual SKU seasonality
  • Product families
  • Substitutive products
  • Product cannibalization
  • Etc.

While it’s possible (but unlikely) for a small retailer with a handful of locations to account for all of these variables in their planning and allocation – the chances of success decrease dramatically as the number of stores and SKUs go up. This is because each unique SKU at each unique store will have its own demand profile. 10 SKUs at 10 stores means that analysts only need to account for 100 different demand profiles. But if you have 1,000 stores with 1,000 SKUs, your analysts will need to contend with 1,000,000 unique demand profiles.

No retailer can hire enough analysts to make these calculations. Furthermore, developing a demand-based allocation system requires data science expertise, machine learning technology, and other resources that retailers won’t often find in-house.

But this is an important problem to solve because a well-balanced allocation will have a myriad of benefits, including:

  • Improved customer loyalty because your customers always find what they’re looking for
  • Increased revenue from a reduction of lost sales
  • Improved margins with a lower rate of overstock and unnecessary markdowns

This is why the most sophisticated retailers turn to solutions like Retalon’s smart allocation software. Powered by retail’s most accurate forecast, Retalon’s allocation solution measures true customer demand for every SKU in every location and automatically generates the optimal allocation.

With experience in more than a dozen retail verticals, Retalon’s retail allocation experts can configure a smart allocation solution for your company’s unique business needs. See it for yourself with a demo.

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