Introduction

Merchandise allocation is the process of determining how to distribute merchandise to individual store units for maximum sales and minimal markdowns.

Depending on the size and sophistication of the retail operation, this can range from a simple process to an extremely complex, algorithmic exercise.

Some retailers plan their season’s purchases from the ground up based on ideal store allocation, while others use the allocation process to break down merchandise receipts to allocate to online warehouses, regional distribution centers, or directly to stores.

In addition to the math involved in the process, there are also strategic and tactical considerations.

For example, stores in their grand opening phase—when they’re new—will receive maximum merchandise allocation to both make an impact on new customers and help determine the sales potential of the new store for future allocation accuracy.

Allocation can also be influenced by competitive strategy, where a retailer attempts to show strength with wide assortments and deep quantities, creating a more favorable impression when compared to their competition’s inventory position.

As you might imagine, commercial software applications have been developed to assist retailers with the computationally heavy process of merchandise allocation.

On one hand, technology has been a godsend for retailers—it’s easier to manage inventory, fulfill orders, and generally improve the customer experience.

On the other hand, technology has made consumers more impatient, and every year, the average shopper’s threshold for waiting grows shorter.

Consumers are demanding and not particularly keen on having to wait for what they want.

If a shopper visits a retailer who is supposed to have his or her desired merchandise and doesn’t, chances are good the shopper won’t give that retailer another chance.

This is why a strong retail allocation strategy is so important.

The right strategy ensures that you have the inventory on hand to meet customer needs while also ensuring you aren’t wasting space with merchandise shoppers won’t buy.

Good & Bad Ideas

Bad Idea #1: Distributing merchandise equally across every location.

A retailer that uses this technique sends the exact same quantities of the exact same products to each store and warehouse, regardless of location.

They don’t take into account any historical data, customer behavior, or market trends. Instead, they treat every location as if the shoppers it serves are identical.

As a result, some locations are overflowing with inventory, while others don’t have the most popular merchandise on hand.

Stockouts lead to dissatisfied customers and lost sales.

Overstocks mean the retailer must substantially mark down merchandise to clear it out and make room for new products.

Both hurt profits—and both are totally avoidable.

Bad Idea #2: Relying entirely on historical data.

This once-popular retail allocation strategy is a slight improvement over the first, but it’s still very much a bad idea.

In this approach, the retailer uses only historical data to build their strategy.

The brand doesn’t consider how their target customers may have changed since the previous season or year, nor do they factor in whether competing retailers have updated their tactics.

They rely solely on how they managed allocation in the past. The result is the same: stockouts, overstocks, and unhappy shoppers.

Now, let’s break down the basics of a smart retail allocation strategy.

Good Idea #1: Embracing consistency and flexibility.

Think about your strategy as if it were a boat. Stay with me on this one—I promise it will make sense. The hull, tiller, rudder, and sails are made from data.

Your building materials include historical information, current trends, and intelligent forecasting models.

If conditions stay as you predicted, your boat will sail smoothly through the selling season.

But what if a product is more popular with a certain audience segment than you anticipated? Or what if last year’s best seller flops this time around?

Then it’s time for a course correction.

A good retail allocation strategy is one you can quickly and easily adjust in response to real-world performance.

It’s a strategy that allows you to steer clear of unexpected storms or choppy waters, like stockouts and overstocks.

Good Idea #2: Supporting customer satisfaction without neglecting business goals.

A smart retail allocation strategy follows rule #1: it ensures the right products are available to the right audience at the right time.

It’s customer-centric, but not customer-obsessed. A retailer can’t make allocation decisions based solely on what customers want.

While a business can’t survive without customers, it also can’t survive without a positive revenue stream—at least not for long.

For the most part, your strategy will be based on educated guesses, but the more information you acquire, the more confident you can be in those guesses.

Your ultimate goal is to determine the most cost-effective and efficient ways to maximize your storage and selling space while satisfying your customers.

Use every data source you have.

Your customer relationship management software can help you identify shopping patterns and browsing behaviors of current customers.

Social media can show you what potential customers across all demographics care about.

At its core, retail allocation is about balance. It’s about finding the right medium.

A data-driven, customer-centric retail allocation strategy will enable you to grow both your profits and your customer base. You just have to know where to look.

That’s all for this lesson. In the next lesson, we’re going to explore some rules for success in allocation. Thanks for watching, and I’ll see you in the next lesson.

Rules for Success

Allocations are top-down, bottom-up, and, if you don’t get it right, you’ll end up upside down.

The merchandise plan needs to meet the demand of two distinct entities: financial plans (top-down) and customers (bottom-up).

Getting it right requires simultaneously keeping an eye on the big picture and the details, all while performing complex, iterative calculations.

Far too often, retailers approach this task using manual, disconnected spreadsheets, and a lot of shimmying, rejiggering, and refiguring.

This approach results in inconsistent and rigid plans, leaving the organization upside down, unable to balance cost and service.

The sheer volume of data—SKUs, calculations, and adjustments—is far beyond the capabilities of spreadsheets.

So, to stay right side up, leave the spreadsheets behind.

Next rule: The future is subject to change.

What do we mean by that? Every plan is perfect until it meets the real world. The world doesn’t stand still long enough for plans to work out exactly as we want.

As seasons progress, customer preferences change, competition alters course, and more. Flexibility becomes your best friend.

Rigid plans that can’t be easily revised in-season will leave you with margin-eroding markdowns and lost sales.

Unless you have a crystal ball, you’ll need a technology platform that lets you update the forecast periodically to account for changes and integrate planning with allocation.

This ensures that optimal plans can be easily executed in the real world.

Next rule: Automation leads to strategic thinking.

Planning and allocation teams can make a major impact on sales, profit, and customer satisfaction—if they’re enabled.

Far too often, these teams are stuck in the weeds, sifting through rows and rows of data, trying to hold the merchandising process together.

Best-in-class retailers have freed their planning and allocation teams from this burden by turning to automation to handle frequent and repetitive merchandising calculations.

By taking repetitive processes off their plate, these teams can focus on strategic initiatives, tackle projects that drive bottom-line results, and move the needle on sales and customer satisfaction.

Similar to this concept, rule #4 says: Embrace merchandising innovation and nimbleness.

Retail merchandising practices have been around for a long time.

The best practices of five or ten years ago have evolved to meet the changing needs of retail, driven by new channels, new forms of risk, nimble and low-cost competition, and expanding markets—the list goes on.

The challenge for today’s retailers is to get ahead of these challenges and turn them into a competitive advantage.

To be successful, you must be open to new ideas, new processes, and new technology.

It’s not always easy to become an agent of change, but it’s always a good idea to identify the best-in-class organizations in your space, foster a healthy awareness of best practices within your executive team, and, well, emulate the leaders.

I guarantee that’s what others are doing.

And finally, the customer is everywhere.

Whether in-store, online, or mobile, every customer is an individual with specific wants and needs.

Every customer expects you to have the exact product they want, at the right price. The customer wants it now and wants options to choose from.

The omnichannel experience is what matters.

Retailers must be able to plan, allocate, and reforecast at the finest level of detail, quickly and across all channels, to meet customer demand—no matter where that demand comes from.

No one is in a position to critique all the allocation systems on the market.

However, it’s possible to set down some underlying principles that should ideally be reflected in the algorithms. We’ll look at those in the next lesson.

Thanks for watching, and I’ll see you in the next lesson on allocation principles.

Allocation Principles

Principle #1: Allocate to where the warm bodies are shopping.

Sales are always going to be the main driver in any allocation, but when we talk about sales, we really mean a mixture of what’s happening now and what we predict is about to happen, based on historical data.

This is the so-called “sales need.”

Principle #2: Allocate to stores that are selling fast.

Stock drives sales. Advanced stock turn is great, but it can also indicate low sales opportunities.

While it’s valuable information, it should be carefully factored into the allocation algorithm.

Many algorithms simplistically determine stock needs based on refilling stores to a predetermined stock level, regardless of their sales potential.

They then add sales need to stock need to come up with a total need and allocate as closely as possible to that final number.

The problem with that method is that stock needs should be calculated based on relative speed.

If a store is selling out twice as fast as the company average, give it twice as much stock.

By doing this, low sales opportunities are inherently captured in the algorithm with much better results.

In conclusion, don’t dismiss sizing and allocating as simple exercises—they can be deceptively complex tasks.

First, make sure your allocation system factors in both sales and the rate of sale, not just sales and stock.

Secondly, ensure each and every order is given due attention early in the buying and supply chain process.

If you wait until it’s time to execute the allocation, it’s already too late.

Key Allocation Algorithm Capabilities

Merchandise allocation is a crucial competency for retailers across all retail business models, including specialty stores, department stores, discount and mass merchants, as well as vertically integrated retailers and manufacturers.

The process seeks to assign optimal individual item quantities to specific stores as well as direct-to-consumer channels like e-commerce and catalog sales.

Retailers today no longer think in terms of the store competing with online; instead, they think of the store integrating with online.

Allocation must place the same merchandise both online and in-store. That’s where a smart, automated allocation system comes in.

Mathematical algorithms can be used to tailor allocation store by store, channeling the flow of stock according to factors such as selling space and store sales performance.

This ensures the right blend of items, styles, colors, and sizes while avoiding overstock and out-of-stock conditions.

By reducing costs and aligning inventory with each store’s opportunity to sell, a retail allocation solution minimizes the time required to accurately allocate products and maximizes profit by dramatically reducing markdowns and inventory carrying costs.

The allocation system must provide methods for demand forecasting and distribution across a wide variety of product types, using multiple sets of allocation rules and logic.

The system should employ analytical methods that can automate the allocation effort based on available sales histories and potential performance at different individual stores and across direct-to-consumer channels.

So, what are the key allocation capabilities you need to increase your team’s productivity and merchandise profitability? Let’s take a look:

#1: Plan High, Allocate Low.

What’s the issue?

Retail organizations tend to perform merchandise planning at high levels, such as department or chain-wide, by month, for key variables like inventory dollars, sales dollars, and gross margin.

However, allocation occurs at a lower level across three dimensions:

• Product: down to item, style, subclass, classification, category, department, division, channel, or company.
• Location: store, district, region, division, or banner (if you have multiple banners within the same company), and channel, of course.
• Time dimension: week, month, quarter, season, or year.

What’s the challenge?

Manually planning down to the item-store-week level is challenging due to the sheer number of combinations requiring review and action to optimize results.

Your allocation effort must excel at the item-location level.

What should your allocation algorithm have?

It must automatically spread changes made at higher planning levels down to individual items, locations, and times.

The system must also aggregate lower-level changes back up through hierarchies.

Additionally, you need to incorporate store-specific transit projections and individual store size needs.

Building accurate store-level size curves is tricky, but advanced algorithms can factor in selling patterns and out-of-stocks to roll everything up to higher-level plans.

#2: Handle Uncertainty.

What’s the issue?

Retail merchandise allocation can be a downsizing process when items are marked by rapidly changing, seasonal, and fashion-driven demand, subject to localized variations.

This is especially difficult for new items with no prior sales history. Uncertainty reigns.

What’s the challenge?

Allocating items optimally despite no history for the item or store, unknown sales patterns, and rapid price or demand changes is difficult.

What should your allocation algorithm have?

It must be completely responsive to short-term trends at a micro level.

You need the ability to project each item’s performance by modeling it on existing items, groups, or categories.

You should be able to easily combine automation with manual input for customization.

The system must also allow you to manage stores at the group or individual level, using unlimited attributes and customizable hierarchy definitions.

#3: Automate, Automate, Automate.

What’s the issue?

Retail planning and allocation teams often find it hard to stay focused on where their expertise is needed most due to the repetitive nature of the allocation process.

What’s the challenge?

Develop an optimal allocation process step by step, then shift to a scheduled and automated mode, so that repeatable processes are executed reliably across the entire merchandise landscape.

What should your allocation algorithm have?

It should provide easy-to-configure automated workflows that eliminate repetitive tasks.

The key is working with a powerful rules engine that is flexible enough to automate your unique allocation process while freeing up time for strategic thinking.

#4: Manage by Exception.

What’s the issue?

Allocators often fail to focus on the highest-value tasks, leading to stockouts that disappoint customers and markdowns that erode margins.

What’s the challenge?

Focus only on conflicts or conditions that require expert attention, then navigate to the highest-priority issues first.

What should your allocation algorithm have?

Exception filters should draw attention to high-priority problem areas.

The system must allow allocators to set constraints and business rules to reflect exactly how the enterprise operates.

For example, an exception filter might display stores whose sales for a selected time period were 20% above average while their inventory was 10% below average.

#5: Use ‘What-If’ Scenarios.

What’s the issue?

It’s often impossible to anticipate the different outcomes of implementing one stock or sales plan versus another.

What’s the challenge?

Explore a range of alternative planning and allocation scenarios, quickly set parameters, and run calculations to hone in on the best option going forward.

What should your allocation algorithm have?

The system should offer tools for scenario analysis, allowing you to model different rules and generate alternative sales and stock plans.

#6: Store Smoothing.

What’s the issue?

New, renovated, or anomalous stores often suffer from a lack of solid data for stock or restock allocations.

Items with short life cycles or low sales patterns may also exhibit random data distortions.

What’s the challenge?

Generate optimal allocations when existing data relevant to a single store is limited.

What should your allocation algorithm have?

Store smoothing should be built into the system, allowing you to use average sales values across the plan level.

#7: Store Capacities and Virtual Warehousing.

What’s the issue?

Small, high-volume locations often have fixture and space constraints, making it impossible to stock enough merchandise at once.

What’s the challenge?

Divide allocations into phases and automate frequent stock shipments from distribution centers to maintain optimal levels.

What should your allocation algorithm have?

It must recognize store unit capacity as a constraint in calculations and allow for virtual warehousing to reserve store-specific merchandise in the distribution center.

#8: Store-Specific Lead Times and Daily Plans.

What’s the issue?

Different stores have varying picking schedules and delivery lead times, creating stock imbalances of up to 30%.

What’s the challenge?

Create allocations that project relative store needs based on future receipt dates.

What should your allocation algorithm have?

It must support daily sales and stock plans by store, allowing you to forecast and allocate items at the daily level.

#9: Date Range-Driven Store Eligibility.

What’s the issue?

Seasonality, holiday periods, and promotions alter how different stores should be planned across the year.

What’s the challenge?

Manage time windows for stores to align allocations with real-world eligibility constraints.

What should your allocation algorithm have?

The system must provide practical date range controls that let allocators designate when each store is eligible to carry certain merchandise.

#10: Weekly Percent Need Calculation.

What’s the issue?

High-volume stores demand more units, while low-volume stores may suffer from shortages.

What’s the challenge?

Allocate equitably between high-volume and low-volume stores, factoring in future receipt dates.

What should your allocation algorithm have?

It must perform initial allocations using percent need calculations based on weekly sales and stock plans.

Retail allocation strategies come in many shapes and sizes, but regardless of your approach, your allocation solution should handle planning and distribution across a wide variety of product types, using multiple sets of rules and logic.