logistics and assortment depth in the retail supply chain: evidence from grocery categories

LOGISTICS AND ASSORTMENT DEPTH IN THE RETAIL SUPPLY CHAIN:EVIDENCE FROM GROCERY CATEGORIES

by

Robert E. StassenUniversity of Arkansas

and

Matthew A. WallerMercari Technologies

Effective logistics requires managing tradeoffs between various costs subject to achieving target customer service levels (Ballou 1998; Bowersox and Closs 1996; Coyle, Bardi, and Langley 1996;Lambert, Stock, and Ellram 1997; Wood et al. 1999). Specifically, retailers and their suppliers facemany different cost tradeoffs and issues when making assortment decisions. In spite of this, researchon the logistics of assortment in the retail supply chain is sparse. This is surprising given thatresearch on grocery retailers has found that as many as 39% of consumers do not find at least one itemthey are looking for in a typical shopping trip (Emmelhainz, Emmelhainz, and Stock 1991). Sincesupermarket sales are about $351 billion per year in the United States, the magnitude of this situa-tion is large (Economic Census 1997). When coupled with the fact that the average consumer shopsthree or four different grocery stores per week, it is easy to see that retailers who manage the logis-tics issues resulting from their assortment well have an opportunity to take share from their competitorsand increase their sales (Woolf 1994).

Simply increasing assortment to increase returns on investment is particularly difficult in thegrocery industry since the margin percentages are low and there are billions of dollars of excess inven-tory (Kurt Salmon Associates, Inc. 1993). Increasing assortment depth normally implies an additionalinvestment in inventory, subsequently leading to an excess inventory on some items and, within a fixedamount of display space, an increasing incidence of stockouts on others. This, in combination withthe frequent introduction of new branded items and the growth of private label products (Norek 1997),means that managing the logistics of retail assortment is difficult.

Morash, Dröge, and Vickery (1996) classified logistics capabilities as either supply chain-oriented or demand chain-oriented. Within that framework, the capability of managing the logistics ofretail assortment would be classified as demand chain-oriented. Of the logistics capabilities they ana-lyzed in their empirical study, demand chain-oriented logistics capabilities had the greatest impacton firm profitability. Managing the tradeoffs between inventory and assortment within a constrained

JOURNAL OF BUSINESS LOGISTICS, Vol.23, No.1, 2002 125

space, taking into account the effects of sales, is a logistics capability that is needed in the retail supply chain.

Assortment and depth of merchandise are terms commonly used for characterizing the numberof stock-keeping units (SKUs) carried by a retailer in a category of substitute items (Levy and Weitz2001). Assortment depth (or selection) influences consumer store choice; significant inventoryinvestment and subsequent allocation of display area by retailers are needed to accommodate thesepreferences (Kahn and McAlister 1997). For example, a shopper in a conventional supermarket mightlike to select from among 56 SKUs of tomato spaghetti sauce; however the costs and complexitiesof maintaining such depth would make it unprofitable for retailers. Clearly, such depth raises ques-tions concerning: (1) the existence of extreme differentiation in market preferences; (2) the incrementalgains in the category’s sales realized from going, for example, from 55 to 56 SKUs; (3) the effectson inventory investment; and (4) the impact on customer service levels.

The purpose of this paper is to provide an analysis of logistics factors affecting retailer profitabilityresulting from the depth of SKUs stocked from a given supplier. First, the paper presents the deduc-tive analysis of the logistics resulting from changes in assortment depth. Second, variables from acase study of a grocery chain are analyzed across categories with respect to the deductive analysisto illustrate a typical position faced by grocery stores. Third, the retailer-supplier relationship gov-erning depth is examined in an analysis of 30 brands across 27 stores. The final discussion bringstogether all of the analyses and results of this research.

THE LOGISTICS OFASSORTMENT DEPTH

The following analysis is presented as a simplified framework to describe the effect of assort-ment depth on gross margin and logistics costs, determining retailer profitability. The analysis seeksto examine a retailer under competitive conditions, managing depth from a supplier whose brand(s)has a low degree of substitution, and therefore, lower levels of competition among suppliers. Addi-tionally, the analysis presumes that the retailer allocates a fixed proportion of retail display space toa category. Subsequent to this, a decision is made concerning the number of SKUs stocked.

It is assumed that increasing the depth within a brand will increase the magnitude of gross margin within that category in two ways. First, increasing depth can bring in additional sales fromconsumers familiar with the variety within the market. For example, if a store carries only one of a supplier’sSKUs, there will be shoppers who prefer other SKUs (different sizes or flavors of a brand) found inthe market and make purchases when they are in a competitor’s store. Thus, stores carrying deeperassortments have a higher probability of satisfying a broader range of consumers and have increas-ing gross margins (through increased sales). Second, adding unique items, not found at competitors,can provide opportunities for higher margins due to the lack of price competition on that item.These unique items may also increase gross margins by initiating trial purchases on new SKUs, ormay bring in new consumers to the category.

126 STASSEN AND WALLER

From a logistics perspective, there are many variables that must be considered in managingassortment depth. Considerations include: space available for the category, inventory holding costs,gross margin, substitutability of the items, and expected cost of lost sales due to stockouts. As the assort-ment in a category with a fixed amount of space increases, the total annual gross margin dollars willincrease and so will the annual cost of holding inventory. Thus, when selecting the depth of a cate-gory, the tradeoff between gross margin and inventory holding costs must be considered. Also, thenature of the tradeoff between inventory holding cost and gross margin depends upon the substitutabilityof the items in the category. Items that are highly substitutable may not have as much of a positiveimpact on gross margin as unique items. Similarly, as the assortment is increased for a category withina fixed amount of space, the inventory holding capacity for each item is decreased. This increasesthe probability of a stockout therefore, increasing the cost of lost sales. So again there is another trade-off – namely, increasing assortment increases gross margin but also increases the expected cost oflost sales due to stockouts. If two items are highly substitutable and one is out of stock, the consumeris more likely to simply switch to the substitute. This model is described in more detail below, refer-ring to Figures 1 through 4.

FIGURE 1

OPTIMAL DEPTH OF UNIQUE VERSUS SUBSTITUTE ASSORTMENTS IN A CATEGORY WITH A FIXED AMOUNT OF SPACE

$

Assortment

AS AU

Unique Assortment

Substitute Assortment

AnnualInventory

Holding Cost

Total AnnualGross Margin

Dollars


Figure 1 illustrates the relationship between assortment depth, inventory holding costs, and totalgross margin dollars. Total gross margin dollars (GM) for a brand is the sum of each SKU’s gross mar-gin dollar contribution. Each SKU’s gross margin dollar contribution is the price the retailer sells itfor minus the price they pay the supplier multiplied by the unit volume of the SKU. In Figure 1 GMhas a positive slope emanating from the origin and is concave.

Figure 1 includes two gross margin functions to illustrate the effect of varying degrees ofassortment substitutability at the display space. For the sake of discussion we will refer to additionalitems with a low degree of substitutability as unique and those with a high degree of substitutabilityas substitute. Unique items increase gross margin in their ability to be less price sensitive. Uniqueitems can bring additional purchases to the category either through new or current customers. Sub-stitute items have less effect on gross margin because additional unit volume comes partly at the expenseof existing SKUs at the same percent gross margin. Figure 1 includes the relationship betweenassortment and GM and the moderating effect of substitutability. As can be seen in the figure,ceterus peribus, assortments of items with lower substitutability have a higher optimal level ofassortment.

While there are varying gains in gross margins resulting from increased depth, there is an optimal amount of depth with regard to its effect on inventory costs. For a given category with a fixedamount of space, if there were only one SKU – minimal assortment – then the expected annual inven-tory holding cost (HC) would be relatively high. In most cases, the inventory holding capacity forthe SKU would exceed the requirements for cycle stock plus safety stock. Increasing the assortment,to a point, would decrease HC because it would reduce the holding capacity per SKU due to that SKU’sability to increase unit sales. However, if the assortment continued to increase, HC would begin increas-ing as well because more slow movers would be added to the category and total category saleswould be increasing at a decreasing rate. For simplicity, Figure 1 shows a single HC function of assort-ment, assuming increasing depth of substitute and unique items have the same effect on unit volume. The figure illustrates the optimal level of assortment based on net profit maximization, showing that unique assortments have a higher optimal depth than substituteassortments.1

Lost sales due to stockouts is a key logistics cost that has been omitted from the analysis to thispoint. If nearly 40% of customers do not find an item on a shopping trip, the effect on gross marginmay not be immediately evident to the retailer, as shoppers may switch to other brands and sizes withinthe category. With regard to subsequent effects, however, the impact on future sales due to these stockouts would be significant.

1If separate HC functions were provided for substitute and unique assortments to account for differences incategory unit volume, there would be a greater difference between respective optimal assortment points,although these would be in the same relative positions.


FIGURE 2

FOR A CATEGORY WITH A FIXED AMOUNT OF SPACE COST OF LOST SALES DUE TO STOCKOUTS

For a category with a fixed amount of space, increasing the assortment depth decreases the inven-tory holding capacity for the existing items. As the amount of inventory of each item decreases, theprobability of a stockout during the replenishment cycle increases for all items. This increases theexpected number of stockouts annually and, therefore, increases the annual expected cost of lost sales.Figure 2 illustrates the increasing cost of lost sales with increasing depth for unique and substituteassortments. The optimal level of assortment is lower with unique assortments than what would beoptimal for substitute assortments.

$

Assortment

ASAU

Space ReducedCost of Lost Sales

Unique Assortment

Total AnnualGross Margin

Dollars

Space ReducedCost of Lost Sales

Substitute Assortment


FIGURE 3

FOR A CATEGORY WITH A FIXED SPACE COST OF LOST SALES DUE TO STOCKOUTS AND INVENTORY HOLDING COST

Figure 3 shows the optimal level of assortment from a cost minimization perspective when considering both the expected annual cost of lost sales and the expected annual inventory holding cost.As the figure illustrates, when the annual cost of lost sales is added to the annual inventory holdingcost curve, the total cost curve is convex to the origin indicating an optimal level of assortment.

Figure 4 describes the relationship between assortment and costs and the moderating effect ofsubstitutability. As can be seen in Figure 4, ceteris peribus, assortments of items with lower substi-tutability (more unique) have a lower optimal level of assortment. Items that are more substitutablehave a lower unit cost of a lost sale. Customers would be more likely to buy a different brand, size,or variety in a stockout situation with substitute items than going to another store as they would withitems that are not as substitutable. This makes the annual expected cost of lost sales higher forassortments of unique items, resulting in a lower optimal level of assortment.

$

Assortment

A*

Total Cost

Cost ofLost Sales

Due to Stockouts

AnnualInventory

Holding Cost


FIGURE 4

FOR A CATEGORY WITH A FIXED SPACEUNIQUE ADDITIONALASSORTMENT OVER THE MARKET

VERSUS SUBSTITUTE ADDITIONALASSORTMENT

Considering both Figures 1 and 2 it is apparent that the level of substitutability of an assortmenthas an ambiguous effect on the optimal level of assortment. In Figure 1 this is described as the GMeffect. In Figure 2, it is described as the Cost of Lost Sales (LS) effect. The GM effect causes higherlevels of substitutability to reduce the optimal level of assortment, but the LS effect has the reverseeffect. If the lower annual expected cost of lost sales associated with assortments of items withhigher substitutability is greater than the reduction in total annual gross margin that will be receivedas a result of the assortment of items with higher substitutability, then the optimal level of assortmentwill increase. Otherwise it will decrease.

The following two sections provide evidence concerning the importance of depth managementin grocery chains with respect to the model’s components. First, categories (and brands) are exam-ined within a single grocery chain to permit an observation of depth and inventory costs. Second, thepractices found across competing retailers within a market are examined with respect to within-brand

$

Assortment

ASAU

Cost ofLost Sales

Due to StockoutsUnique Assortment

Total CostUnique

Total CostSubstitute

AnnualInventory

Holding Cost

Cost ofLost Sales

Due to StockoutsSubstitute Assortment


depth and gross margins. Both studies illustrate opportunities for improved category profitabilitythrough managing the logistics of the assortment.

INVENTORY COSTS AND DEPTH IN THE SUPPLY CHAIN: THE MARSH STUDY

It is assumed that the depth within any brand is first determined by the importance of thebroader category wherein that brand faces competition. In the category of shelf-stable juices, for exam-ple, the number of linear feet of display for the category is determined by the importance of the cat-egory to the overall performance of the store. If the category accounts for roughly 4% of the grossmargin dollars in edible grocery products, a comparable proportionate amount of space would beassigned for display (as in the case of Marsh discussed below). Over time, changes in the total spaceassigned to these categories will be managed with respect to their financial performance restrictedby the physical limitations imposed by the fixtures and layout of the store. The results lead to “zerosum” allocation of space across categories, and as such, the constraints lead to similar limitation onthe assignment of space to brands within each category.

Given the storewide restrictions of space, some inferences regarding the management of depthwithin brands can be drawn by examining the management of depth across categories. Some of themost complete evidence of depth management can be found in the “Marsh Super Study” (Progres-sive Grocer 1992). Marsh, an Indianapolis supermarket chain, evaluated the performance of 118 gro-cery categories across five of the stores over a 65-week period. For the study, measures of depth werecalculated by dividing the number of SKUs in each category by three measures of their allocated space:linear shelf feet, exposure feet, and cubic feet. With respect to the overall allocation of space basedon category performance, the cubic feet measure had the highest correlation with each category’s grossmargin and sales dollars, .64 and .76, respectively.

Analysis was limited to categories containing complete information and excluded categorieswith ambiguous titles, i.e., “miscellaneous general merchandise.”2 This resulted in 100 categoriesfor study. Table 1 contains the correlation coefficients between the three measures of depth and mea-sures of inventory cost, gross margin return on display space, percent gross margin, and gross mar-gin return on inventory (GMROI). In addition, the table includes correlations with average unitcost for the categories. Average unit costs ranged from a low of $.33 for pet foods to a high of $9.87for disposable diapers illustrating the proportion of variance in these measures attributable to cate-gory differences.

2“Milk/dairy drinks” was omitted because its GMROI was almost three times the next closest observation and21 standard deviations above the mean. In addition to this, the category’s performance on this measure wasmost similar to others excluded due to incomplete data.


TABLE 1

EFFECT OF CATEGORY DEPTH ON INVENTORY COSTS AND GROSS MARGINSCORRELATION COEFFICIENTS

Inventory costs per Gross margin dollars per_________ _____________________Dollar of Dollar of

Cubic Exposure Linear Cubic Exposure Linear sales Inventoryft. ft. ft. ft. ft. ft. (GM%) SKU (GMROI)

SKUs per: .5233 .4963 -.2573 -.3043 -.1851

Cubic ft.

Exposure ft. .7753 -.1781 -.2272 -.3523 -.3473

Linear ft. .7043 -.087 -.1761 -.4133 -.4253

Category .2563 .2863 .3163 .004 -.010 -.025 -.2963 -.123 -.2392

averageunit cost

1p < 0.10 level (2-tailed). 2p < 0.05 level (2-tailed). 3p < 0.01 level (2-tailed). N=100

A cross-category analysis of depth and its relationship on inventory costs can provide an indication of an actual in-store experimental manipulation of depth and measurement of variablesdescribed in the model. If one views the varying depth found across categories as indicative ofchanges in depth within a category (and within the category’s brands), the correlation coefficientsillustrate that the majority of Marsh’s categories would lie within the upward sloping portion of themodel’s holding cost function. First, with respect to the inventory cost function, category depth hasa highly significant positive correlation with inventory investment. Stated differently, categories withan above average number of SKUs per display space have above average investments in inventory.The magnitude of these coefficients shows that roughly one-half of the variation in inventory invest-ment is attributable to depth far exceeding that attributable to the average cost per item.

Second, with regard to the gross margin function proposed in the model, the table shows vary-ing relationships between assortment depth and gross margin dollars per display space. While the resultis positive and significant for the relationship based on the cubic feet measure (.496), the relation-ship is not highly significant and not significantly different from zero for depth per exposure feet andlinear feet, respectively. In other words, results show categories with above average depth have above average returns; however, interpreting the other measures would indicate few, if any, returnsto higher depth in a category. The results are indicative of a situation where the majority of Marsh’scategories are in a flat portion of the model’s gross margin function.


The significant positive correlations across all three measures of depth and category percent grossmargin suggest that this, not overall impact on gross margin dollars for the category, may have beenthe prime consideration for Marsh in adding SKUs to a category. Additionally, the significant negative correlations of gross margin per SKU and depth are consistent with the decreasing returns todepth or a “flattening out” of the gross margin function that comes through addition of substitute SKUsto a category.

Finally, given the strong correlations of depth with inventory investment and the mixed corre-lations with depth and gross margins, the impact of depth on Marsh’s GMROI is predictably negative. All measures of depth show a relationship where increases/decreases in depth result indecreases/increases in GMROI. Clearly, Marsh has categories with assortment depth outside whatwould be optimal for the chain.

If the situation faced by Marsh is typical of other supermarket chains, applying the logistics concept to assortment management must become a priority. In some instances, it may be appropriate to:(1) reduce depth uniformly across all suppliers in a category or (2) target specific suppliers, allow-ing them to make the recommendation of items for elimination. Prior to consideration of this, how-ever, the competitive assumption of the model and the logistics implications of depth managementon category profitability should be examined.

COMPETITIVE ISSUES IN MANAGING ASSORTMENT DEPTH

The gross margin function in the model has been presented following an assumption of com-petitive conditions existing in the retail supply chain such that increasing depth increases sales.When all competing retailers within a market stock the same brands in their categories, however, depthmanagement is limited to a within-brand assessment. The gross margin function of the model shiftsfrom one of unique assortments to one closely resembling the substitute assortments, and as such,improving gross margins via depth becomes more problematic for the retailer. Depth managementthen becomes an issue of differentiation within the brands, specifically to those SKUs that can be offeredat a higher price to achieve a higher gross margin. The following provides a competitive analysis ofassortment depth within brands, providing some insight into the relationship between suppliers andretailers that affects depth management and the attention given to the gross margins in the category.

The data come from a study of six packaged goods categories (ready-to-eat cereal, cake andbrownie mix, liquid salad dressings, shelf-stable juicers, pancake/waffle syrups, and spaghettisauces) found in all 27 supermarkets in the urban portion of a Midwestern MSA(Stassen 1989). Fivesupermarket chains, two regional and three local, accounted for sixteen of the stores. The remainingnine supermarkets were single-store operations. Five of the stores (two chains) were “box stores” andfour were smaller-sized neighborhood supermarkets with the remainder being conventional supermarkets.


TABLE 2

STOCKING CHARACTERISTICS AND REPRODUCIBILITY OF GROCERY BRANDS

Number of SKUs in Store depthStores ________ _______with SKUs in every 85% of onebrand market store stores store Maximum Minimum Reproducibility

Cake & brownie mixBetty Crocker 27 57 4 26 3 46 14 .920Pillsbury 27 46 14 4 36 3 .926Duncan Hines 27 26 1 9 2 20 6 .920

CerealKellogg’s 27 80 5 43 5 64 30 .938General Mills 27 55 2 21 2 43 13 .911Post 27 36 2 17 30 14 .916Ralston 27 25 8 2 21 1 .921Nabisco 27 19 1 5 19 5 .906Quaker 27 17 3 11 17 4 .943Malt O’ Meal 24 8 2 6 1 .901

Salad dressingKraft 27 63 2 22 57 12 .915Wishbone 26 26 2 3 20 1 .944Libby’s 25 25 3 5 16 1 .922Seven Seas 26 20 2 4 13 3 .919Albert’s 25 13 2 5 10 1 .948Hidden Valley 27 12 1 4 10 2 .920Dorothy Lynch 27 6 1 5 6 3 .981

Juice and drinkOcean Spray 27 40 3 11 2 39 11 .917Hi C 27 30 2 7 29 6 .935Welch’s 27 30 2 9 19 3 .920Kool Aid 24 17 13 2 .885Tree Top 26 16 1 2 13 1 .911Del Monte 27 16 1 13 1 .880Hawaiian Punch 27 10 1 2 6 1 .915Sunsweet 27 6 4 1 6 3 .975

Spaghetti sauceRagu 27 30 3 15 1 27 3 .926Prego 26 19 7 3 13 7 .941Classico Sicilia 26 6 2 6 1 .981

SyrupKaro 26 8 5 8 4 .966Log Cabin 26 7 3 1 6 2 .962Mrs. Butterworth 26 6 2 6 2 .974Smucker’s 26 5 2 4 1 .946


Table 2 provides the descriptive statistics of depth within thirty-two brands frequently carriedin the market. The criterion for inclusion in the table was that all these brands were found in at least24 stores; 19 were found in all 27 stores. While the individual brands are all widely stocked, the tableillustrates: (1) a considerable number of SKUs within each brand available for differentiation; (2) inmost cases, less than one-third of the SKUs were found at 85% of the stores; and (3) wide ranges indepth offered by competitors.

While substantial differences in depth existed across the stores, the relationship between depthand the actual SKUs stocked followed a predictable pattern, indicating substantial supplier involve-ment. The table includes a coefficient of reproducibility for each of the brands (Stouffer et al. 1966).Applied to assortment depth, it describes the degree of accuracy in “reproducing” the exact assort-ment for each store in the market knowing their depth and the number of stores carrying each SKU.In practical terms, in a market with perfect reproducibility (1.0), every store in the market carries allitems found in stores with smaller assortments. In contrast, in a perfectly differentiated market, allSKUs would be found at only one store, the coefficient would approach the inverse of the numberof stores. In the table, 30 of the 32 brands exhibit reproducibility greater than .90, illustrating a mar-ket with predictable depth management on these frequently stocked brands and substantial influenceby suppliers.

The model states that under conditions with diminishing returns to depth, retailers would stockSKUs with a higher gross margin either by adding higher priced, larger-sized items or unique itemsproviding more latitude in pricing. The high degree of reproducibility found across assortments per-mits a comparison of items that would be considered “basic” to stocking a brand versus those lessfrequently stocked items used to incrementally increase depth. To examine this, the SKUs within eachof the 32 brands were split into two groups based on whether or not the items were found in 85%of stores carrying that brand.


TABLE 3

STOCKING RATES AND PRICE COMPARISONS DUPLICATED VERSUS DIFFERENTIATING SKUS

Market Price distribution comparisons

stocking Mean Coefficient of variation Measure of skewness Brand rate > 85% < 85% t-value > 85% < 85% t-value > 85% < 85% t-value

Depth through items with higher prices

Dorothy Lynch .815 1.46 5.96 -10.402 .126 .111 .45 .00 1.30 -2.581

Hi C .627 .88 1.58 -2.312 .108 .079 2.692 1.04 -.05 2.792

Betty Crocker .683 1.34 1.65 -2.192 .140 .111 2.592 .52 .13 1.27Pillsbury .595 1.34 1.72 -1.981 .162 .124 2.362 .34 -.23 2.001

Hidden Valley .688 1.62 2.40 -1.861 .078 .072 .76 -.44 -.70 1.14Karo .813 2.00 3.29 -1.58 .096 .094 .39 -.10 -.37 .88Prego .559 1.64 2.01 -1.55 .091 .106 -.54 -.69 -1.03 .73

Depth through items permitting greater latitude in pricing

Sunsweet .772 1.70 1.98 -.91 .091 .143 -4.752 -.34 -.03 -.43

Depth through items with less price-cutting

Ocean Spray .562 2.58 2.23 .88 .091 .084 .82 -.53 -.18 -1.53Ragu .728 1.80 1.68 .59 .085 .079 .79 .00 .43 -1.46Ralston .578 2.36 2.45 -.47 .076 .080 -.38 -.13 .30 -1.36

Depth through items with slightly higher prices

Kraft .617 1.51 1.60 -.80 .090 .088 .18 -.05 .12 -.60Log Cabin .577 2.43 2.82 -.67 .078 .080 -.20 -.02 -.68 10.982

Post .718 2.41 2.49 -.55 .085 .083 .10 .26 .40 -.45Nabisco .645 2.40 2.55 -.54 .075 .079 -.31 .18 .05 .33Hawaiian Punch .352 .91 1.22 -.45 .089 .086 .07 -.96 -.74 -.26Albert’s .308 1.88 2.01 -.39 .100 .091 .37 -.08 -.08 .00Malt O’ Meal .625 1.27 1.42 -.35 .119 .091 1.26 .00 .10 -.15Duncan Hines .607 1.47 1.53 -.25 .151 .131 1.01 -.37 -.65 1.14Mrs. Butterworth .788 2.63 2.71 -.09 .080 .092 -.86 -.32 -.97 1.66

Brands with opportunities through improved depth management

Smucker’s .500 1.72 1.72 -.05 .100 .112 -.71 -.78 -1.31 .52Seven Seas .419 1.57 1.54 .08 .079 .070 .18 .17 .59 -.45Tree Top .320 2.13 2.01 .12 .103 .088 .41 -.42 -.06 -.28Libby’s .453 1.62 1.55 .19 .097 .077 .93 -.12 -.47 .62Wishbone .354 1.52 1.40 .36 .097 .065 1.59 .03 .05 -.04General Mills .599 2.62 2.34 1.17 .077 .068 1.38 .40 .11 1.00Quaker .847 2.35 2.00 1.851 .074 .069 .59 .60 .69 -.38Kellogg’s .668 2.66 2.28 1.961 .081 .088 -1.00 .51 .18 2.312

Classico Sicilia .519 2.40 1.78 2.01 .083 .048 1.83 -.58 -1.26 2.551

Welch’s .296 2.41 1.46 2.212 .085 .106 -.58 -.55 -.27 -.47

1p <.102p <.05


Table 3 presents comparisons of price distribution parameters found in the market in additionto the stocking rate for each brand. In each of the three comparisons, depth management consistentwith improving gross margins would be indicated with a negative t-value for this difference betweengroups. First, differentiating SKUs would be expected to have a higher price, and this is shown to bethe case in 18 of the brands. Second, differentiating SKUs should offer greater latitude in pricing aswith a greater coefficient of variation (standard deviation divided by the mean), but was only foundin nine of the brands. Last, comparing the distributions’ skews indicates whether typical pricing on the SKUs was above or below the market. Anegatively skewed distribution would indicatemore “below market pricing.” The results show that differentiating SKUs had less negatively skeweddistributions in 12 of the brands.

The model includes comparisons on two characteristics of the price distributions of the respec-tive groups to indicate the extent to which the differentiating SKUs provided the potential forincreasing gross margins, evidenced by either more price variation or by an absence of price-cutting.First, the coefficient of variation (the standard deviation divided by the mean) illustrates the degreethat duplicated items may face more “matching” of price and as such, a smaller variation in price. Impor-tantly, this was shown not to be a typical condition in this market. Only four brands showed signif-icant differences in price. In three of these instances, this was opposite to that expected, where morevariation existed in the duplicated SKUs. Second, the skewness of the price distribution illustrateswhether “price-cutting” or “below market pricing” (a negatively skewed price distribution) is typical within the brand particularly in those duplicated SKUs. The table shows that negatively skewedprice distributions existed in half of the brands, but was not typical of any of the three groups. Thetable organizes the brands by relationship with price; however, ten of the brands indicate an oppor-tunity in the market for retailers to improve the performance of the brand (and category) with betterdepth management.

Table 3 also provides the stocking rates for the brands and price comparisons between theduplicated and differentiating SKUs.3 The “market stocking rate” can be interpreted as the averageproportion of a supplier’s SKUs in a brand that are stocked by retailers. This ratio is the summationof all individual stores’depths divided by the product of the number of SKUs in the market and thenumber of stores stocking the brand.

3Two brands of juices and drinks, Kool-Aid and Del Monte, had no SKUs that were found at 85% of thestores, resulting in 30 brands for analysis.


The number of SKUs found in a market and market stocking rate are indicators of the effectivenessof a supplier’s ability to increase distribution and display of its brands in the market. Table 4 showsthe relationship of these two measures with the characteristics of their price distributions in themarket. The table shows significant relationship of these measures with measures of average priceand the skew of price distribution; brands with better distribution have less negatively skewed pricedistributions as indicated by the significant correlations of .411 and .380 on duplicated items. In otherwords, retailers are more likely to increase depth in brands with higher prices and less price competition.Additionally, the highest correlations are between the duplicated and differentiated groups on theirmeasures of price variation and skewness (.620 and .613, respectively). This consistency in price distributions between groups indicates that retailers maintain similar gross margins within the brandwhether differentiating or duplicating.

TABLE 4

EFFECT OF PRICING CHARACTERISTICS OF DUPLICATED VERSUS DIFFERENTIATING SKUS ON STOCKING CORRELATION COEFFICIENTS

Stocking Average prices Price variation Measure of skewness________ ________ ________SKUs rate Duplicated Differentiating Duplicated Differentiating Duplicated Differentiating

SKUs in market 1.000

Market stocking rate .146 1.000

Average pricesDuplicated .3752 .189 1.000

Differentiating .158 -.047 .201 1.000

Price variationDuplicated .119 .016 -.107 .110 1.000

Differentiating .022 .170 -.3181 -.235 .6203 1.000

Measure of skewnessDuplicated .4112 .3802 .5423 .261 .126 -.176 1.000

Differentiating .256 .275 .4793 .3712 .036 -.054 .6133 1.000

1p < 0.10 level (2-tailed).2p < 0.05 level (2-tailed).3p < 0.01 level (2-tailed).N=30


DISCUSSION

The issues addressed are consistent with the current concerns in the grocery industry. ProgressiveGrocer’s annual survey of chain, independent, and wholesale executives indicates an increasing impor-tance regarding the issues of category management and depth (increasing the number of SKUs andbrands per category) and less emphasis on reducing store inventory levels. The model and analysispresented here have attempted to draw attention to the relationship of these issues – particularly theimportance of managing depth with respect to costs of inventory and lost sales.

The across-category analysis in the Marsh Super Study shows that depth is a significant driverof inventory costs. The correlation coefficient of .704 (Table 1) between SKUs per linear feet and inven-tory costs is highly significant and may be the first reported for this relationship. Expressed as a bivari-ate regression coefficient (36.92, t = 9.80), it suggests that with an increase in one SKU per linear footof display, an additional $37 investment for foot of display is required. The positive correlation betweendepth and gross margin indicates the gross margin may be the characteristic given preference for mak-ing item additions within the chain, however, it is not enough to result in an increase in GMROI. Clearly,if the conditions characterized by the Marsh Study and the market study exist across markets, oppor-tunities for improved profitability exist in managing the logistics of assortment.

The high degree of reproducibility found across the brands represents a significant finding fromthe examination of the 27 stores. This may be attributable to either of two approaches to managingdepth. First, retailers may be mirroring the popularity of SKUs found within their market, followingthe advice of their wholesaler, or examining preferences in the national market. More likely, they maybe following the recommendations of the manufacturers, whereas the retailer assigns a fixed amountof display space to the manufacturer, who in turn determines the depth within that space (Progres-sive Grocer 1999). These approaches indicate a practice of “assortment by brand” rather than“assortment by category,” the results of which being: (1) predictable assortments found across competitors; (2) a high degree of duplication; and (3) with this, lower category margins associatedwith more differentiated assortments.

MANAGERIAL IMPLICATIONS

This paper has provided a model to illustrate the importance of managing the logistics ofassortment depth and the significant opportunities for improved profitability that lie with retailerswho are better able to manage it. In contrast, there is an increasing trend to shifting responsibility ofdepth management to suppliers. Currently, more retailers are seeking assistance from major suppli-ers in managing depth not only in their brands, but in many cases across the entire category. Giventhe reproducibility found across retailers within brands, a combination of supplier management ofdepth with increased concentration in retailing may result in markets where suppliers control amajority of categories and, in effect, constrain the pricing and gross margin opportunities of retailers.The challenge facing retailers is to identify an optimal level of depth within brands and categories thatis likely less than that desired by suppliers while simultaneously seeking to differentiate their assortment


from competitors. With this, an opportunity exists for suppliers who can better address retailers’concerns for inventory costs and service levels.

The model and discussion presented in the paper presume consistent inventory replenishmentthroughout the depth management process. With regard to stockouts and demands for additional dis-play space, if the shelf is replenished more frequently, the annual expected cost of lost sales decreasesfor any level of assortment. The cost of these replenishment activities (and cost of additional “back-of-the-store” inventory) may offset shortages of display space which result from increases in depth.However, it is argued that judicious use of space (within the model) would be a lower cost alterna-tive for retailers faced with labor shortages. Although if the costs of replenishment are sharedbetween suppliers and retailers through adoption of automatic replenishment (ARP) systems, ahigher level of depth can be maintained that is beneficial to both parties. Specifically, suppliers, andretailers committed to ARP are realizing cost benefits with respect to smaller shipments and shorterproduction runs, thus reducing inventories and costs of depth for both parties (Myers, Daugherty, andAutry 2000).

LIMITATIONS

This analysis has presented a model and accompanying analysis of the costs and returns to depthon a category-by-category basis. Ideally, the best test of the model would employ a field study thatwould incorporate actual manipulation of assortment depth across a sample of stores to provide a bet-ter understanding of the gross margin function as well as confirmation of the inventory costs and stock-out functions in the model. The scale and risks of such manipulations would be substantial for a specificsupplier though clearly less risky for a retailer due to the inventory cost reductions. The evidence provided in this paper has shown significant opportunities for retailers to examine the costs and contributions of depth within their own chains as it is unlikely that few depth reduction incentiveswill originate with the supplier.

With respect to the cross-category analysis of the Marsh Super Study data, it assumes a single,overriding approach to the management of depth within the chain. As such, the 100 categories ana-lyzed can be combined and evaluated as though there was a single objective across categories. It shouldbe recognized that the profitability of a specific category may not be as important as using the cate-gory to drive volume. For that reason, the importance of gross margins to the category may be over-stated. A superior approach, without actual manipulation of depth, would be for a retailer to assessthe relationships of depth within a category across the stores in the chain.


NOTES

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Woolf, Brian (1994), “Measured Marketing: A Tool to Shape Food Store Strategy: Using Electronic Marketing to Create Loyal Customers,” Coca-Cola Retailing Research Council.

ABOUT THE AUTHORS

Robert Stassen is an Associate Professor in the Department of Marketing and Transportationin the Sam M. Walton College of Business at the University of Arkansas. His research covers retail-ing and channels of distribution. His articles have appeared in the Journal of Retailing, Journal ofBusiness Research, Journal of Macromarketing, and Journal of Marketing Channels. He receivedhis Ph.D. from the University of Nebraska.

Matthew A. Waller is a founder of Mercari Technologies, Fayetteville, Arkansas, and ResearchAssociate Professor at the Sam M. Walton College of Business, University of Arkansas. His currentresearch focuses on retail supply chain management. Dr. Waller’s articles have appeared in theJournal of Business Logistics, Transportation Journal, The Logistics and Transportation Review,The International Journal of Logistics Management, Decision Sciences, and other academic journals.He received his Ph.D. from Pennsylvania State University.


logistics and assortment depth in the retail supply chain: evidence from grocery categories

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