size optimization for retailers

WHITE PAPER

Size OptimizatiOn fOr retailerSHow to effectively stock sizes of each item in each store – into a profitable advantage

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Size OptimizatiOn fOr retailerS

Table of Contents

Executive summary .................................................................................... 1The right merchandise in the right sizes to meet demand ........................ 2Size is important – so why has it been so difficult to get it right? ........... 2SAS® solves this problem for retailers today ............................................. 4The pack optimization component – sending the right units to stores .... 5Size profiling in action ................................................................................ 6Conclusion ................................................................................................... 7


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The content providers for this white paper include: Elaine Markey and Greg Wilson, SAS Retail Solution Strategy; Elizabeth Dove, SAS U.S. Retail Business Unit; and Alexi Sarnevitz and Diana McHenry, SAS® Global Retail Practice and Darius Baer, Global Analytical Services. For more information, please contact Diana McHenry at [email protected].


Executive summary

In a perfect world, retailers would always have just the right assortment of merchandise – in the ideal mix of sizes – at each store. However, that has been difficult to achieve on a consistent basis for assortments with tens of thousands of stock keeping units (SKUs) across thousands of stores. Traditional methods that rely on the use of “top down” chain averages have been overwhelmed by this complexity. Even if detailed size profiles were available, there remained the question of how to integrate this knowledge into assortment and allocation decisions.

“No one needs to pinch retailers and tell them this is a problem,” writes Susan Reda, Executive Editor of STORES magazine. “They’ve heard the groans coming from the dressing room and the complaints from shoppers who wonder why there never seem to be enough sizes 0 and 2 and 14 and 16. The question is, what to do about it?”1

SAS, the leader in retail intelligence, has incorporated its analytical strength into scalable business solutions that allow retailers to address this question. SAS® Size Profiling identifies store-level selling patterns across size ranges within any classification of merchandise. SAS® Pack Optimization identifies the best pack or combination of packs to efficiently fill stores’ size demands. Through open interfaces, this information can be integrated into existing planning, allocation and replenishment systems to produce assortments that more accurately reflect consumer size demand at the store level.

The potential benefit is enormous. Reducing out-of-stocks by just 1 percent of revenues will add US$100 million in sales to a $10 billion-a-year retailer. That figure doesn’t even include the cost of marking down overstock items, or the labor cost of handling frequent allocations and ad hoc transfers of merchandise. Longer-term benefits from an improved customer experience can be expected.

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■ Maximize assortment productivity by

ensuring that store shelves have the

right merchandise in the right sizes

to meet customer demand. Reduce

stock-outs and end-of-season

markdowns due to size.

1Reda, Susan. “Sizing Up Sizing.” STORES (January 2006).


The right merchandise in the right sizes to meet demand

A customer, eager to buy the dress she saw in the window display, approaches a fixture full of the dresses. There’s the right style, the right color; but her size is missing.

The dress she wants – in her size – is on the rack in another store where that size isn’t selling well. Right merchandise, wrong store. A potential customer walks away disappointed, while the dress gets marked down later at the other store.

This scenario is frustrating for consumers and costly for retailers. For the retailer, it brings two negatives: a forfeited sale and a markdown on the same item. According to the National Retail Federation, stock-outs amount to losing 1.5 weeks of store volume each year. That’s nearly 3 percent of sales. For retailers, that translates into millions in lost revenue. For a $1 billion retailer, that can mean tens of millions in lost revenue.

But it’s also costly to make every sale if you can’t be sure which sizes will be in demand. You could overstock stores, but you’d pay too much for inventory and shelf space – and end up marking down more merchandise at season’s end. You could send allocations to stores more frequently or handpick individual sizes from bulk packs as needed, but the processing costs would erode profit margins. You could transfer the dress to the shopper’s preferred store, but that’s a time-consuming and expensive proposition.

Size is important – so why has it been so difficult to get it right?

Clearly, the answer is to have just the right assortment of merchandise – in the right mix of sizes – at each store. That has been difficult, for several reasons:

An inability to process all the data: If a retailer starts amassing information at the granular level of store and size, data volumes can quickly overwhelm the systems that are supposed to make sense of it. Many systems used today to perform rudimentary allocation by size simply cannot scale to manage the huge volumes of data required.

An inability to consider variables over time and across products: Even if the system could digest all the required data, does it have the analytic strength to optimize the answer – not just automate it? Determining how to allocate the right assortment – in the right sizes, in the right place, at the right time – is a complex optimization problem that must factor in many costs and constraints.

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There is no way to account for demand variances among stores: Even if the retailer knows how many S, M, L and XL items were sold of a given color/style, actual sales will not be uniform across all stores in the chain. Size demand will vary by store. For instance, one store might sell 10 percent small, 40 percent medium, 40 percent large and 10 percent extra-large of an item. For another store with different customer demographics, the distribution could be 30-40-20-10 or 5-30-50-15. If the retailer shipped the average ratio of sizes in pre-packs to all stores, very few stores would be appropriately served.

An inability to address multiple packs: Many retailers suffer from logistical limitations. Systems, as well as distribution centers, are unable to consider and process multiple pack configurations. Consequently retailers use generic pre-packs and ship too many or too few of a size to stores.

Figure 1. Percent unit sales by cluster: Each line represents the size demand of an item for a cluster of similar stores. The green line represents the average for all stores. If the retailer allocates sizes based on the average, stores would be either under- or overstocked on some sizes at some point.

There is no standardized methodology: Within many retail organizations, analysts use their own approaches to try to create demand curves and apply them to orders and allocations. They often use different data and processes, which are typically not shared across departments. It is common for two analysts, even within one department, to produce drastically different results for the same item. Which one is credible?

It is no longer enough to allocate merchandise based on past regional performance with one distribution ratio for size and color across all stores. Retailers must fully understand demand variance – store by store and merchandise to merchandise – to reduce stock-outs, markdowns and lost sales.

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How do you get the right fit – that is, the right sizes to stores?

Forward-thinking retailers are applying sophisticated analytics all the way to the level of size and accounting for variances between stores. In the process, they are generating, analyzing and acting on much more refined information than in the past.

These retailers are incorporating analytical methodologies more commonly seen in industrial and financial arenas, such as data mining to identify sales patterns and outlying anomalies, and complex algorithms to group (“cluster”) stores according to size demand. The result is a fairly sophisticated answer to the problem of distributing right-sized merchandise to stores that achieves the right balance between size profile accuracy and process complexity.

SAS® solves this problem for retailers today

SAS Size Profiling and SAS Pack Optimization give retailers solid answers to the perennial question of how to order and allocate just the right sizes of merchandise for every store.

The size profiling component: determining which sizes sell – store by store

The SAS solution creates very accurate profiles of size distributions by merchandise category by store (the foundation for more accurate ordering and allocations) due to its ability to process huge volumes of data and balance many variables simultaneously.

Size specifications can vary significantly even within a single department. A high degree of variability of consumer size demand can exist across local store trading areas. The SAS solution is flexible and customizable to reflect these realities. A retailer defines its own optimization framework – the merchandise and stores, variables, and constraints that reflect its unique operations. It is not constrained to industrywide assumptions or across-the-board practices.

The behind-the-scenes logic that optimizes these decisions is highly sophisticated, but the complexity is invisible to users. A quantitative analyst with business acumen would establish the data management processes, analytical models and critical relationships. Then, business users need only indicate the high-level variables – such as which stores and how many units of a color/style – and then point the size profiling system to that problem. The system returns optimized answers to support highly accurate purchase orders, allocation plans, replenishment plans or reorders.

The SAS solution detects and addresses conditions that would erode the accuracy of analysis, such as out-of-stock conditions or inaccurate/missing data from stores. So, in the real world, where data collection processes might be imperfect, retailers will have confidence in the accuracy of results with SAS.

■ The use of predictive analytics to

forecast shopper demand has grown

rapidly in the last five years and

incorporates more variables

across the supply chain, such

as outlet profiling, promotion

uptake propensity, and now:

size optimization.

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At the end of the day, every store has an accurate and realistic size distribution profile – either derived from actual sales data (accounting for all the above variables) or from correlations with stores that do have reliable sales data. This size profile can then be applied to create accurate purchase orders, or against allocation plans to determine the best size distributions to stores.

The pack optimization component – sending the right units to stores

SAS Pack Optimization identifies the best combinations of packing configurations (such as pre-packs, case packs and bulk packs) to best fill likely size demand in stores. This process draws on updated size profiles and factors in a host of variables and constraints, such as seasonality, price, promotions, inventory levels and minimum shipments.

Using the SAS Size Profiling solution, size profiles are created for each store for specific categories of merchandise. These size profiles are applied against assortment plans to generate accurate purchase orders, and now the merchandise arrives at the distribution center in prepackaged cartons. Let’s assume that each carton contains a standard ratio of sizes. Perhaps a pack of 100 men’s polo shirts contains 10 small, 20 medium, 50 large, and 20 extra-large, packaged in sets of 10.

Given the standard pack configurations – and knowing how many of each size you need at each store – what is the best way to distribute these packs to stores? The calculation can be complex. Aligning product pre-packs with demand requires an understanding of a broad set of logistical costs and constraints.

The SAS solution provides the serious analytics and processing power to answer this question with confidence, even in cases where the vendor ships multiple standard pack configurations, or where the retailer can choose to open packs or handpick individual items from bulk.

The process starts by running the latest size profile for each store against the new inventory. After all, it has probably been several months since the purchase order was placed. Updated sales information might reveal new patterns that could affect distribution. Or, the vendor may have shipped a different quantity than what was ordered.

Then, the SAS solution evaluates all the constraints and variables that have been incorporated into its logic and returns an optimized allocation plan for distributing packs to stores. Should whole packs be sent? How many to which stores? Is it worth it to break open cartons and send packs of 10? Is it worth it to break open bulk packs and ship individual units? The pack optimization component answers these questions while considering a host of interdependent variables.

■SAS® Size Profiling is built on the

SAS®9 architecture, a foundation

of data management and analytic

technology that can scale to handle

databases of any size, even those

containing massive amounts of

retail data. A powerful solution that

leverages the business intelligence

platform underlying SAS® Retail

Intelligence Solutions, SAS®9

provides an integrated, open and

extensible foundation for creating

and delivering accurate, in-depth

retail intelligence.

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In short, the SAS solution solves a complex optimization problem where the objective is to balance conflicting variables. The solution will minimize handling costs associated with flowing the product through the distribution center, maximize sales and minimize markdowns at the stores, and consider any number of costs and constraints.

Size profiling and pack optimization: not just for size

The SAS® solutions are called SAS Size Profiling and SAS Pack Optimization, but they are not limited to size.

“How many basketballs should we order in each color?” “How should we distribute cases of red and white wines among restaurants in the chain?” If the attribute can be quantified and characterized in a distribution, it can be optimized.

Similarly, size is not the only attribute for which you can optimize pack allocations. A wholesaler, factory or vendor – any organization that ships merchandise in multiples to multiple locations – could find the solution useful to optimize those shipments.

“If I can buy toasters in their own boxes, or in cartons with four toasters apiece, or in large cases of 12 toasters – and there’s a discount on the larger cases – which packaging choice is my best bet?” “The Chilean fruit comes in boxes of 10 packages, but only a few stores will sell that much. Should we break open boxes and send individual packages, or will the processing costs be higher than the cost of some spoilage?”

Wherever many interdependent or conflicting variables must be considered, the SAS optimization engine can deliver effective and profitable answers

Size profiling in action

Kohl’s uses the SAS® solution to develop at the size level

Kohl’s is a family-focused, value-oriented specialty department store with some 800 stores nationwide. The fast-growing retailer plans to open 500 more stores in the next five years. With billions in revenue at stake, Kohl’s wanted to take every measure to improve in-stocks by size for seasonal basics and fashion merchandise. A major obstacle to this was that existing systems didn’t provide sufficient visibility into the dynamics and details of demand at the size level. Analysis criteria were loaded into spreadsheets and manually executed – a cumbersome process. Kohl’s needed more accurate representations of what each store was likely to sell in each size for each type of merchandise.

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Working with SAS, Kohl’s selected high-volume, size-intensive departments to run as test cases in a size profiling solution. They created size profiles for representative stores based on sales history, and applied these size profiles to assortment and allocation plans. They then measured the impact on sales and margin over product lifecycle compared to a control group.

Their findings were remarkable. Size distribution varied more significantly than expected among stores, among departments within a single store, and even among classifications or categories within a single department. The opportunity associated with these differences was “financially significant,” according to Jon Nordeen, Executive Vice President for Planning and Allocation.

For example, one cluster of stores showed a propensity for selling small sizes for a particular category. These stores required 60 percent more small merchandise than stores in the “extra-large” cluster. Stores that had a propensity for selling extra-large sizes required 27 percent more extra-large items than stores in the “small” cluster. Imagine the lost-opportunity cost of so great a mismatch if all stores had received one standard distribution of sizes.

If Kohl’s had sent products to all stores based on the average, very few stores would get the right mix of sizes.

Convinced of the merits of size profiling, Kohl’s worked with SAS to establish clusters of stores that shared the same size-selling characteristics and to develop hundreds of merchandise-specific size profiles for each cluster of stores. These size profiles have been integrated into processes and systems to optimize assortment planning, ordering and allocation.

Kohl’s results are more satisfied customers, higher sell-through, and fewer markdowns.

Conclusion

The scenario seems so simple: Make sure consumers can find the size they want. If their sizes are out of stock, you forfeit the sale and disappoint (and potentially lose) the customer. Marking down overstocked merchandise to move it undermines profitability.

Here is where SAS analytical strength can be invaluable. SAS Size Profiling and SAS Pack Optimization effectively match size-level demand with pack-level supply – for each store and merchandise category – improving the accuracy of purchase and allocation decisions based on a multitude of defined variables. This type of analysis has the potential to transform a difficult issue – how to effectively stock sizes of each item in each store – into a profitable advantage.

SAS® Analytics will give Kohl’s

an accurate view of consumer

preferences by location – information

that can be integrated into business

systems and processes.

“SAS Size Optimization will be

used by Kohl’s to define our order

quantities by size, create multiple

pre-pack configurations and allocate

specific size quantities to each store.

Underlying each of those processes

is a customer demand profile by size

and category of business for each

store.”

Jon Nordeen

Executive Vice President, Planning

and Allocation

Kohl’s Department Stores

In “Sizing Up Sizing,” STORES

magazine, January 2006

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