Chain Store Inventory - CiteSeerX

Chain Store Inventory: A Cross Sectional Evaluation

Chris Dubelaar School of Marketing University of New South Wales Sydney, NSW, 2052 Australia e-mail: [email protected] phone: 61 2 9385 3739 Fax: 61 2 9663 1985 Garland Chow Faculty of Commerce University of British Columbia Vancouver, BC, Canada. Paul D. Larson COBA, Mail Stop 028 University of Nevada Reno, NV 89557 e-mail: [email protected]

submitted to Journal of Retailing

February 1999

The authors thank the Social Sciences and Humanities Research Council of Canada (SSHRC) and the Canadian Institute for Retail and Services Studies (CIRASS) for supporting this study. 0

Chain Store Inventory: A Cross Sectional Evaluation Abstract Effective inventory management is critical to retailing and supply chain management success. Surprisingly, there is little published empirical research examining fundamental relationships between retail inventory, customer service and sales. Based on a survey of 101 chain store units, this paper develops and tests hypotheses about retail inventory. Seventy-five percent of the store owners/managers responded to the mail survey. As expected, significant relationships are found between inventory, service and sales.

1

Supply chain management (SCM) is concerned with flows of products and information between supply chain organizations (Handfield and Nichols 1999), for the purpose of serving consumer demand at a profit. According to Nevill, et al. (1998), SCM works to get stock to the right place at the right time, at lower cost. The “right place” is often a retail store, where goods are available for consumer inspection and purchase. Inventory management is at the heart of SCM, since inventory must be available to serve customers. Moreover, the ability to service demand with lower inventory levels improves return on investment (ROI) both above and below the line. Inventory reduction increases return, by reducing the cost of carrying inventory--and reduces investment in inventory (Copacino 1997). This paper quantifies relationships between inventory levels, service (availability) and sales. Interfirm variations in assortment, retail sector and other characteristics are controlled by analyzing observations from a single chain. Stores in this chain make buying decisions independently and receive stock directly from suppliers, rather than through a corporate distribution center. Still, between store variation in the variables allows for investigation of hypothesized relationships. The control variables include product variety, environmental uncertainty and competitive strategy. Among the research questions are the following: (1) Do inventory levels increase at a decreasing rate (relative to sales) as inventory theory predicts? (2) Does product variety, competition and demand uncertainty determine inventory levels? (3) What is the relationship between inventory and customer service? The Retail Inventory Literature Inventory management has significant influence on retail profitability. Nevill, et al. (1998) note 2

that “on nearly every merchant’s balance sheet, inventory tops the list of valuable physical assets.” After cost of goods sold, the major costs incurred by retailers involve the resource trinity--space, labor and stock (Lusch 1986). Thus, important measures of retail efficiency are sales per square foot, sales per employee, and stock turnover. Inventory provides customer service in the form of product availability, at the right time and place. Availability is consistently rated an important dimension of customer service (LaLonde and Zinszer 1976; Copacino 1997). The cost of stockouts (lack of availability) includes immediate lost sales, backorder expense, loss of good will, delayed cash flow and worst of all, lost customers. The purpose of retail inventory management is to provide availability at the lowest possible cost. A large body of normative inventory theory offers optimal order quantities, safety stock levels and related inventory control procedures (Gill, Soma and Sutherland 1985; Tersine 1988; Sherbrooke 1992). For instance, a customer service objective, e.g. 95 percent availability, can be achieved by holding a calculated amount of safety stock, when demand and lead time distributions are known. A literature search found most work to be theoretical, rather than empirical, with hypothetical examples or single practical applications. A recent example is Urban’s (1998) “generalization of the inventory-level-dependent demand models,” and merging of these with product assortment/shelf-space allocation models. However, there is very little empirical verification of relationships between factors that impact inventory levels--and relationships between inventory, customer service and sales. The next three paragraphs briefly review published empirical findings on retail inventory. Baumol and Ide (1962) found significant, positive links between sales, the dependent variable, and both inventory levels and merchandise variety. Morey (1980) gathered sales and service level data 3

from 61 United States Government-run Navy Commissaries. Using nonlinear regression, he reported a positive relationship between sales and “service” (number of employees and hours of operation). Morey called for further research using range of items and in-stock rate (a.k.a. variety and availability) as service measures. Hise and his colleagues (1983) studied 132 chain store units offering non-clothing items in shopping malls. Using linear regression, they found inventory levels and number of employees to be positive predictors of sales. The impact of number of competitors on sales was not significant. Hise, et al. suggested that future research should use nonlinear regression, and focus on other retail sectors, e.g. grocery, department and drug stores. In the context of grocery retailing, Good (1984) conceptualized a productivity (output/ input) model. Outputs included sales and number of transactions; inputs included store size and number of employees. Finally, Larson and Sijbrands (1991) used aggregate Statistics Canada data, covering all Canadian chain stores, to assess the impact of quick response (QR) retail strategies on stock/sales ratios. They reported a significant, negative link between QR and stock/sales, but no relationship between interest rates and stock/sales ratios. The Inventory Decision Making Environment Inventory is held largely to smooth differences in supply and demand. Retailers can also carry stock to stimulate sales. Cycle stock services demand under conditions of certainty, when it is inefficient to ship products “just in time,” i.e. one at a time. Products are ordered in batches to service anticipated demand. Inventory carrying costs are balanced against the costs of more frequent ordering of smaller quantities. Seasonal, forward buy and quantity discount stocks are all special cases of cycle stocks, 4

where known benefits (e.g. lower ordering costs, volume price breaks) are balanced against the costs of holding larger inventories. Safety stock compensates for uncertain supply or demand, which may cause a stock-out. The cost of carrying safety stock is balanced against stock-out costs. Larson and DeMarais (1990) describe various categories of stock carried at retail, in detail. In addition to cycle and safety stock, they discuss pipeline and psychic stock. Pipeline stock is inventory en route from supplier (factory, wholesaler, distribution center) to the store. Psychic stock is in the store and on the shelf. It is display inventory carried to stimulate sales. Many factors influence store inventory levels that are beyond store control. For instance, an unreliable supplier makes a retailer hold higher safety stock to compensate for stock-outs due to late deliveries. A retail store is the final participant, before the consumer, in a larger supply chain that begins with the manufacturer of a product. Supply chain possibilities in which a retailer may participate are illustrated in Figure 1. Between manufacturer and retailer, there may be wholesalers who take ownership of products, and warehouses or distribution centers where products are stored, handled, repackaged or even processed further. Figure 2 illustrates the typical retail supply chain. {Figures 1 and 2 here} One strategic decision is the level of service the firm plans to provide its customers. Service level is often measured by stock availability, but could also be measured by order cycle time or consistency of cycle time. Customer service becomes an "effectiveness" objective (or constraint) over other decisions. For example a store could have a customer service objective of 99 percent stock availability and a maximum backorder time of one week. The number, size, location and function of facilities have a direct influence on a firm’s ability to meet 5

customer service objectives at minimum cost. More stores imply greater accessibility of customers to a retail location where the product is available. However, this increases total inventory held by all stores. The square root principle (Maister 1976) states that the maximum inventory held in a larger number of locations (n2) relative to a smaller number of locations (n1) serving the same overall demand is equal to the square root of n2/n1. Thus:

In2 =

n2 In 1 n1

Equation 1

In a distribution center (DC), products are received in bulk, stored and sent to retail stores further down the supply chain. The DC influences inventory levels in three ways. First, it could be the site of stock centralization, reducing the number of stock points. For example, safety stock for multiple stores could be held at a DC rather than at each store. This takes advantage of the square root principle and reduces overall inventory in the system. Second, the DC could be a point of postponement where inventory is held until needed at each store. This reduces the risk that the wrong product is shipped to the wrong market area. Store inventory can be reduced by keeping more inventory at the DC. The DC should be located so that cycle time to replenish retail stock will reduce stock-outs at reasonable cost. Third, the DC facilitates distribution of volume purchases for a group of stores, at discounted prices. A single store may have to purchase large quantities, resulting in excessive inventories--or small quantities at high prices. Consolidation of multiple store orders yields quantity discounts for a large purchase, delivered in bulk to a DC, then broken into smaller store orders. Push and pull systems determine how inventory decisions are made. In a push system, sales are forecast by corporate merchandising/buying personnel, bulk orders are made, and products are pushed 6

into stores. On the other hand, in a pull system, consumer demand triggers store orders. The pull approach is embodied in Quick Response (QR) strategy used by retailers to replenish store stock “just in time.” Store inventories are reduced since less stock is ordered in anticipation of sales. Gill and Abend (1997) describe Wal-Mart’s pull-through system, which is “based on the logic of replenishing products that consumers are buying, rather than ‘pushing’ products into the stores with no accurate forecast that they will sell.” Larson and Lusch (1990) further discuss the impact of quick response on retail inventory performance. QR involves technology--such as point-of-sale (POS) scanners, universal product code (UPC) symbology and electronic data interchange (EDI)--and closer retailer/supplier relationships. More generally, information technology and cooperative relationships are key aspects of SCM. QR impacts retail inventory in several ways. Since it reduces lead times, safety and pipeline stock should decline. Further, QR reduces ordering costs, allowing for cycle stock reduction. Store inventory decisions include: how much to order, when to order and how much safety stock to hold. The inventory ordering process is illustrated in Figure 3. The typical retail store ordering process is based on economic order quantity (EOQ) principles and desired customer service levels. Often called the model stock, it is the quantity of store inventory desired at any given time. One application of the EOQ is the reorder point (ROP) model. Once the stock level reaches ROP, the item is ordered based on EOQ. Another variation is an economic order interval (EOI) system where stock is ordered at specific time intervals. The difference between existing stock and the amount required for coverage to the next order is the amount ordered. Order frequency and quantity are determined by balancing costs of ordering, carrying inventory and stocking out. 7

{Figure 3 here} This study focuses on a set of stores from the same retail chain. Thus, strategic decisions affecting the inventory of each store are quite similar. At the time of data collection, each store dealt individually with suppliers, choosing items (from the chain's selection) to carry and order quantities. Each store is an individually owned and operated “franchise,” where managers are given extensive training and periodic briefings on store performance compared to other stores in the region. Similar vendors and distribution systems supply each store. The stores are part of a chain, all selling similar products, although assortments vary to please local tastes. In summary, the performance of each store compared to others should be highly dependent on store decisions and local competitive environments. Data and Survey Methodology Data were collected in two stages. In the first stage, a region of the chain, covering 101 stores, provided access to management information system (MIS) data for store-level items such as store size, sales, and stock levels. The second stage of data collection was a mail survey addressed to store managers asking a number of questions on retail operations (including questions covered by the MIS). The survey, sent to all 101 stores in the region, was mailed along with a two-dollar incentive and covering letters from the first author and the chain’s regional manager. The instrument was developed in consultation with the regional manager, and was pre-tested with several store management experts. After editing, the final version was mailed to store managers, and the regional manager sent an e-mail message urging response to the survey. A total of 72 complete and usable surveys were returned. No follow-ups were done, although store size, location and sales data were compared to known distributions and no patterns were found in 8

observations with missing data. The level of support provided by the chain was instrumental in gaining a high response rate from store managers. In addition, the instrument was administered immediately after the financial year-end, when managers had time to participate and the information requested was close at hand. Measures of the constructs were developed from existing scales where possible (Achrol and Stern 1988; Dwyer and Welsh 1985; John and Weitz 1989) although each scale had to be modified for this application. Modifications were made in consultation with the regional manager. Additional measures were developed specifically for this study, e.g. measures of sales, inventory, service level and product variety. The unit of analysis was the store, and respondents were store owners/managers. Thus, the Fink and Kayande (1997) argument advocating use of generalizability measures is irrelevant. Data were collected from a single respondent for each store, since these stores are small and only the owner/manager had knowledge to answer all questions. The survey and archival data provided measures of the constructs shown in Table 1. {Table 1 here}

Survey data were compared to MIS data, where both were available, to assess the validity of the instrument. Pearson correlations of store size, sales and inventory as reported on the survey with those from the MIS show significant relationships (see Table 2). {Table 2 here} These results suggest that survey items are a good reflection of in-store retail reality. One can feel confident using data from either source. Thus, MIS data will be used where available, and survey data will be used where no equivalent MIS data exist. 9

Factors Influencing Inventory at the Store Level We hypothesize relationships between inventory levels, local competitive environment, and store management decisions, as illustrated in Figure 4. Each causal path is discussed below. {Figure 4 here} 1. Competitive Environment The importance of customer service and the intensity of competition determine the level of product availability (customer service) a store seeks to provide. Respondents were asked the following questions: “Competition on service is characteristic of the industry” (IA9), on a scale from very little (1) to very great (7). Competitive intensity (IE1), as the number of competing stores within 1 km. Greater competition on service and intensity of competition are expected to bring higher service level objectives--and higher inventory levels, to provide this service. This leads to the first two hypotheses-H1: Importance of competition on service is positively related to inventory levels. H2: Intensity of competition is positively related to inventory levels. 2. Service Level Respondents were asked several questions on their perceptions of service provided. Specifically, they were asked how frequently customers comment on the availability of: new/extended products (IG01), house brands (IG02), items not carried (IG03), advertised items (IG04), environmentally friendly items.(IG05), and sale price items (IG06). 10

Assuming customers comment only on lack of availability, more frequent comments imply lower service level. IG01, IG02, IG03 and IG05 are likely to reflect merchandising decisions (what to carry) rather than inventory decisions of how much to carry. In a factor analysis of these six variables, IG04 and IG06 converged to form one factor, while the other four items converged to form another factor, as shown in Table 3. Not unexpectedly, “items not carried” (IG03) has the weakest loading. It is a broad category encompassing all the specific dimensions of product availability measured by IG01, IG02 and IG05. {Table 3 here} The two factors account for 42.9 and 18.3 percent of variance. Factor 2 is expected to best represent service level, and to be negatively related to inventory level (recall that it represents lack of availability). As customers comment on lack of availability, the retailer raises service level objectives-and inventory levels. Factor 2 scores were saved in the data set as FAC2_1. The next hypothesis to be tested is-H3: Higher service level objectives imply higher inventory levels. 3. Product variety Product variety increases overall inventory levels when different items are close substitutes for each other. The square root rule again applies. For example, if two products were perfect substitutes, merchandising both could split sales evenly between the two. In theory, this is the same as stocking one product at two locations. In reality, product variety also increases sales, since products are seldom perfect substitutes. Still, some sales for the original items are diverted to the new variations. Thus, the store makes inventory decisions for more individual products, each with a smaller demand, though 11

overall demand is increased. Two measures of product variety were included on the survey. Each manager was asked to indicate how his/her store compared with other stores on number of different stock-keeping units in cosmetics (IIIA03) and in front store (IIIA04). The scale ranged from much less than other stores (1) to the same as the average store (4) to much more than other stores (7). Inventory is expected to increase with product variety. While the variety index for general items (IIIA04) is applicable to every store, it is expected that the variety index for cosmetics (IIIA03) would be significant in stores where the proportion of total sales derived from cosmetics is relatively large. The hypotheses derived from this are-H4: More front store SKUs results in higher inventory levels. H5: More cosmetics SKUs results in higher inventory level. 4. Demand uncertainty High demand variability causes a store to increase safety stock levels, to achieve a given level of service. Three questions focused on demand uncertainty. Respondents were asked whether in the business climate they faced: volume was stable or volatile (IC1), projections (forecasts) were accurate or inaccurate (IC2), and the climate was predictable or unpredictable (IC3). A factor analysis of these three variables extracted a single factor (see Table 4), which accounts for 71.2 percent of the total variance. {Table 4 here} A relationship between uncertainty and inventory is hypothesized, as follows-H6: Greater uncertainty results in higher inventory levels. 12

5. Sales Level The distinction between cycle and safety stock was noted above. Service level and demand uncertainty affect safety stock level, and sales at the margin, i.e. a shortage results in a lost sale. Cycle stock supports anticipated sales that are largely dependent on product quality, price and promotion. These links are illustrated in Figure 5. {Figure 5 here} The square root principle suggests there are economies of scale in holding inventory, relative to demand. This can be demonstrated by examining the EOQ model, upon which many retail inventory ordering policies are based. Assume demand for an item = D. Average cycle stock can be approximated by 1/2 the square root of D, or (D)1/2/2. If demand doubles, average cycle stock is 1/2 the square root of 2D, or (2D)1/2/2. The ratio of inventory for double the demand to the base demand is (2D)1/2/2 over (D)1/2/2, or the square root of 2 or 1.41. Thus, cycle stock only has to increase by 41 percent to service a 100 percent increase in demand. This suggests that stock level differences between stores are a function of store size or sales, leading to a final hypothesis-H7: The relationship between sales and inventory should approximate a square root function when composition of sales is accounted for. Analysis To test the hypotheses, a series of regression equations were set up. To avoid problems of model mis-specification, the analysis started with the relationship between sales and inventory (H7), since sales is expected to explain much of the variation in inventory. The regression equation estimated was: log (inventory) = 13

2.92 + 0.48 * log (sales) + 0.39 * log (% front store) + 0.14 * log (% cosmetics)

Equation 2

All coefficients are significant at alpha < .01, and R2 for this model is .81. The sales coefficient (.48) is nearly 1/2, suggesting the square root relationship is reasonable. Remaining variables from the hypotheses were then added to the equation, to test whether they had any additional explanatory power. Table 5 shows the results. {Table 5 here} Hypotheses 1, 2 and 6 were not supported by the data, while the remainder were. Note that coefficients for sales, service levels, cosmetics variety and front store variety were all significant, at alpha < 0.05. This indicates the importance of taking into account store assortment. In addition, the coefficient for sales remains near 1/2. Discussion H1 and H2 It was expected that greater importance of service and greater competitive intensity would lead to higher inventory levels. However, these hypotheses were not supported by the data. It could be that competition in this retail sector is based largely on attributes other than product availability. If customers are most concerned about fast, friendly service, competitive intensity and service-based competition would not necessarily imply higher inventory levels. H3 FAC2_1 (lack of availability) was expected to be negatively related to inventory level. The higher the service level objective, the greater the amount of inventory. The coefficient was negative and significant at alpha < .05, confirming the inventory/service relationship.

14

H4 Front store variety behaves as expected--inventory increases with product variety. H5 The negative coefficient for IIIA03 (SKU's in cosmetics) was not expected. It would be logical for more variety in SKUs to imply larger inventory. Field observations suggest two possible explanations for this anomaly. The cosmetics area in these stores is a relatively fixed sized island, replacing two regular aisles. Thus, as total store size changes, percent of sales in cosmetics changes. For a medium-sized store, percent of sales due to cosmetics may be 10 percent. But smaller stores, having the same amount of space allocated to cosmetics, have a larger percent of the store allocated to it. Therefore, they have a larger percent of sales in cosmetics, despite a decline in total store inventory (due to smaller store size). Within the dedicated cosmetics section, trained personnel sell and manage the SKUs. Unlike other products, there is close management of cosmetics, including constant updating of stock levels, frequent revisions of demand forecasts and re-ordering of stock as needed. At the time of the survey, most of the stores utilized a manual process for updating inventory records, which meant products were ordered at regular intervals. The closer management accorded cosmetic SKUs may have resulted in lower inventories. Thus, as the percent of store business accounted for by cosmetics goes up, overall inventory goes down. H6 It was expected that higher demand uncertainty would lead to higher inventory levels. However, this relationship was not significant. This may be the result of measuring both safety stock and cycle 15

stock levels together, rather than partitioning them. Demand uncertainty influences safety stock, not cycle stock. However, safety stock tends to be small relative to cycle stock, so cycle stock may have masked the impact of demand uncertainty. In addition, uncertainty in the environment raises the risk of carrying certain SKUs. Conclusions Based on a survey of retail chain store units, this paper reported positive, significant links between inventory levels and the following variables: sales, service level and product variety. In the case of cosmetics, a negative variety/inventory relationship was found. This was explained in terms of special merchandising/inventory management attention the chain devoted to cosmetics. In part, SCM works to defy traditional inventory management trade-offs, e.g. high inventory as the price of wide variety. Handfield and Nichols (1999) present the case of a computer software merchant that reduced inventory levels, while increasing variety, using SCM technology and relationships. Surprisingly, no relationship was found between demand uncertainty and inventory levels. Theoretically, demand variability induces the retailer to avoid stock-outs by increasing safety stock levels. The lack of significance for demand uncertainty suggests the need to partition or distinguish between types of inventory. While safety stock services demand under conditions of uncertainty, cycle stock services demand under conditions of certainty. Survey and MIS data combined these two types of inventory, confounding effects on individual inventory types. Future research is needed to partition types of stock--and empirically test their determinants. Support was found for the “square-root law,” which posits that a doubling of demand can be serviced with the square root of two or 1.41 times the base inventory level. Note that inventory is the 16

dependent variable here, it services demand and is determined by anticipated demand. An alternative perspective models retail inventory as an independent variable, a predictor of sales (Larson and DeMarais 1990; Urban 1998). Again, further research is needed to partition types of retail inventories and assess their inter-relationships with consumer demand. At the time these stores were surveyed, the chain had yet to embrace SCM. Little technology was used to support merchandising and inventory management decisions. Nevill, et al. (1998) argue that information technology is critical to effective SCM. Retail buying decisions were largely decentralized to the store level. Thus, this (several hundred store) retail chain was not exploiting its volume buying power with suppliers. Since the survey, point-of-sale (POS) scanners have been installed in all stores. Buying decisions are becoming more centralized, and closer relationships are being sought with key suppliers. The stores are collectively moving toward participation in a “demand-driven supply chain” (Anonymous 1998), i.e. a true pull system. In short, these stores are only beginning to understand and use their power; driven by technology, consumers, and size of the chain (Handfield and Nichols 1999). The authors hope to return to the chain, and examine how SCM has changed inventory management practices since survey administration.

17

References Achrol, Ravi S. and Louis W. Stern (1988), “Environmental Determinants of Decision Making Uncertainty in Marketing Channels,” Journal of Marketing Research, 25(1): 36-50. Anonymous (1998), “Demand Driven Supply Chains Reduce Inventory and Lead Time,” Managing Logistics (September), 1, 10-11. Baumol, William J. and Edward A. Ide (1962), “Variety in Retailing,” in Mathematical Models and Methods in Marketing, Frank M. Bass, et al., eds., Homewood, IL: Richard D. Irwin, 128-144. Brown, Robert G. (1982), Advanced Service Parts Inventory Control, Norwich, VT: Materials Management System. Copacino, William C. (1997), Supply Chain Management: The Basics and Beyond, Boca Raton, FL: St. Lucie Press. Coyle, John J., Edward J. Bardi and C. John Langley (1996), The Management of Business Logistics, 6th ed., St. Paul: West Publishing. Dear, Anthony (1990), Inventory Management Demystified, London: Chapman and Hall. Dvorak, Robert E. and Frits van Paasschen (1996), “Retail Logistics: One Size Doesn’t Fit All,” The McKinsey Quarterly, 2: 120-129. Dwyer, F. R. and M. A. Welsh (1985), “Environmental Determinants of the Internal Political Economy of Marketing Channels,” Journal of Marketing Research, 22(4): 397-414. Fink, A. and U. Kayande (1997), “Reliability Assessment and Optimization of Marketing Measurement,” Journal of Marketing Research, 34(1): 262-275.

18

Gill, Lynn, George Isoma and Joel Sutherland (1985), “Inventory and Physical Distribution Management,” in Robeson, J. and R. House (eds.) The Distribution Handbook, New York: The Free Press, 615-736. Gill, Penny and Jules Abend (1997), “Wal-Mart: The Supply Chain Heavyweight Champ,” Supply Chain Management Review, 1(1), 12-20. Good, W. S. (1984), “Productivity in the Retail Grocery Trade,” Journal of Retailing, 60(3): 81-106. Handfield, Robert B. and Ernest L. Nichols (1999), Introduction to Supply Chain Management, Upper Saddle River, NJ: Prentice Hall. Hise, Richard T., J. Patrick Kelly, Myron Gable and James B. McDonald (1983), “Factors Affecting the Performance of Individual Chain Store Units: An Empirical Analysis,” Journal of Retailing, 59(2): 22-39. John, George and Barton A. Weitz (1989), “Salesforce Compensation—An Empirical Investigation of Factors Related to Use of Salary Versus Incentive Compensation,” Journal of Marketing Research, 26(1): 1-14. LaLonde, Bernard J. and Paul H. Zinszer (1976), Customer Service Meaning and Measurement, Chicago: National Council of Physical Distribution Management. Larson, Paul D. and Robert A. DeMarais (1990), “Psychic Stock: An Independent Variable Category of Inventory,” International Journal of Physical Distribution & Logistics Management, 20(7): 28-34.

19

Larson, Paul D. and Robert F. Lusch (1990), “Quick Response Retail Technology: Integration and Performance Measurement,” The International Review of Retail, Distribution and Consumer Research, 1(1): 17-35. Larson, Paul D. and Margret J. C. Sijbrands (1991), “Quick Response Retailing in Canada and the Netherlands,” International Journal of Retail and Distribution Management, 19(7): 10-17. Lawrence, James (1986), “Effective Spares Management,” International Journal of Physical Distribution &Materials Management, 16(5): 1-10. Lusch, Robert F. (1986), “The New Algebra of High Performance Retail Management,” Retail Control (September): 15-35. Magad, Eugene L. and John M. Amos (1989), Total Materials Management: The Frontier for Maximizing Profits in the 1990s, New York: Van Nostrand Reinhold. Maister, David (1976), “Centralization of Inventories and the 'Square Root Law'” International Journal of Physical Distribution & Materials Management, 6(3): 124-134. Morey, Richard C. (1980), “Measuring the Impact of Service Level on Retail Sales,” Journal of Retailing, 56(2): 81-90. Nevill, Steven J., David G. Rush and Dorothy W. Sadd (1998), “Real-World Examples of Inventory Effectiveness,” Supply Chain Management Review, 2(3), 39-46. Sherbrooke, Craig C. (1992), Optimal Inventory Modeling of Systems: Multi-echelon Techniques, New York: John Wiley & Sons. Tersine, Richard J. (1988), Principles of Inventory and Materials Management, 3rd ed., New York: North-Holland. 20

Urban, Timothy L. (1998), “An Inventory-Theoretic Approach to Product Assortment and Shelf-Space Allocation,” Journal of Retailing, 74(1): 15-35. Young, Jan B. (1991), Modern Inventory Operations, New York: Van Norstrand Reinhold.

21

Tables

Table 1 Constructs and Indicators Construct Size

Survey Item IIIB02 (total selling space)

Sales

IV01 (cosmetics sales)

Inventory

Service Level (availability)

Competition

Variety

Demand Uncertainty

IV02 (pharmacy sales) IV03 (total front store sales1) IIIA07 (dollar value of front store inventory) IIIA08 (dollar value of cosmetics inventory2) IG01 (new/ extended products) IG02 (house brands) IG03 (items not carried) IG04 (advertised items) IG05 (“green” items) IG06 (sale priced items) IA9 (competition on service) IE1 (number of competing stores within 1 km) IIIA03 (number of SKUs in cosmetics) IIIA04 (number of SKUs in total front store) IC1 (stable vs volatile volume) IC2 (inaccurate vs accurate projections) IC3 (unpredictable vs predictable climate)

MIS Item Total_se (total selling space in square feet) Total_sa (sum of sales: cosmetics, pharmacy and front store)

Total_in (total dollar value of inventory in the store)

N/A

N/A

N/A

N/A

1 "Total Front Store" refers to any items in the store not considered cosmetics, pharmacy or tobacco. 2 Note that due to an administrative error, the corresponding questions for pharmacy were not included in the survey. 22

Table 2 Instrument to MIS Correlations Construct

Correlation

Store Size (IIIB02/Total_se)

0.92

Store Sales (IV01-03/Total_sa)

0.86

Inventory (IIIA07,08/Total_in)

0.70

Table 3 Service Level (Availability) Factor Analysis Rotated Factor Matrix Item

Factor 1

Factor 2

Label

IG01

.88

.10

new/extended products

IG02

.75

.14

house brands

IG05

.55

.20

environmentally friendly items

IG03

.47

.11

items not carried

IG06

.08

.93

sale price items

IG04

.35

.86

advertised items

23

Table 4: Demand Uncertainty Factor Analysis Item

Factor

Label

IC03

.94

Business climate: unpredictable or predictable

IC02

.88

Projections: inaccurate or accurate

IC1REV

.70

Volume: volatile or stable

Table 5: Regression results for Hypotheses 1 through 7 Variable

Hypothesis

Coefficient

t-value

P

Service Competition

H1

-0.006

-0.57

.285

Intensity of Competition

H2

-0.007

-1.10

.139

Service Level

H3

-0.036

-2.35

.011

Cosmetics Variety

H4

-0.067

-3.63

.001

Front Store Variety

H5

0.038

2.17

.017

Demand Uncertainty

H6

-0.007

-0.48

.318

Sales

H7

0.524

12.04

.000

24