Retail inventory is a statistic that is closely watched by retailers as well as their investors, lenders, and suppliers. Retailers not only benefit from inventory, but also bear the cost of excess inventory. Investors, lenders, and suppliers interpret this statistic for signs of the retailer's health, future sales prospects, and impending costs. This article synthesizes the perspectives of investors, lenders, and suppliers on inventory. Moreover, the article shows that inventory turns, a commonly used metric to identify excess inventory, has important limitations that reduce its utility for all these stakeholders. It then presents a new metric, adjusted inventory turns, which can be effectively utilized by all stakeholders to assess whether a retailer is carrying too much or too little inventory.
Retailing is a large and important sector of most economies. In the U.S., retailing accounted for 8% of GDP and roughly 20% of non-agriculture related jobs in 2011. Moreover, retailers carry considerable inventory on their balance sheets. In September 2012, U.S. retailers carried a seasonally adjusted $503 billion dollars of inventory in their balance sheets.
Managing retail inventory has become more complicated during the past few decades. Witness department store markdowns that grew from 7% in the 1970s to more than 20% by the late 1990s,1 surveys that find stockouts rampant in most retail formats,2 and inventory turns that have not increased substantially despite massive investments in technology. Among many reasons for the increased complexity of retail inventory management is an explosion in product variety and a sharp reduction in the length of product lifecycles in most categories.
In both academic and practitioner literatures on retailing and supply chain management, considerable attention has been given to the problem of how much inventory a retailer should carry. Increasing the amount of inventory reduces stockouts, improves customer satisfaction, fuels sales, and expands a retailer's product offerings. However, what is overlooked is that these benefits to customers and the retailer's performance often represent a source of concern to others, such as equity investors and lenders, who tend to view rapid inventory growth like the canary in the coal mine, as a harbinger of trouble. For example, as observed by highly respected investor Peter Lynch: “When I research a stock, I always check to see if inventories are piling up …. With a manufacturer or a retailer, an inventory buildup is usually a bad sign. When inventories grow faster than sales, it is a red flag.”3 Lenders also view rapid inventory growth cautiously since it increases a retailer's risk of bankruptcy. Even relatively small increases in inventory or modest reductions in sales can substantially diminish a retailer's cash flow.
The metrics most commonly used—not just by retailers—to assess inventory productivity have been inventory turns and the algebraic equivalent, days of inventory. That they are easily calculated and understood, and rely only on data available to all stakeholders through public financial statements, probably explains the longevity and popularity of these metrics.
Inventory turns, however, is a generally coarse metric against which to benchmark inventory productivity. An overarching challenge to its use for benchmarking is that inventory turns vary widely across retailers and even over time for a given retailer. For example, among retailers within the same segment, inventory turns can vary widely, as evidenced within the consumer electronics segment by Best Buy Company Inc. (“Best Buy”), which turns its inventory over six times per year, and Radio Shack Corporation (“Radio Shack”), which turns its inventory fewer than four times per year. It would be wrong to conclude, based on inventory turns alone, that Best Buy is doing a better job than Radio Shack at managing its inventory since there are several alternate explanations such as differences in product mix, store sizes, and location strategies that might explain the differences in inventory turns. Even a given retailer can experience substantial variation, as evidenced for example by inventory turns at Family Dollar Stores Inc. (“Family Dollar”), which, over the two decades that spanned 1990-2010, fluctuated between 2.2 and 4.9 times per year.
This article integrates the perspectives of retailers and their investors, lenders, and suppliers on the amount of inventory that a retailer should carry. We show that retailers, investors, and lenders can resolve this dilemma by identifying a target or benchmark inventory level that enables them to perform apples-to-apples comparison across retailers after taking into account the differences across the businesses. Using historical data from a large number of public retailers, we identify two drivers of inventory turns, gross margin and capital intensity, and derive a method for benchmarking the amount of inventory a retailer should carry. Our method adjusts inventory turns for its correlation with gross margin and capital intensity and thus addresses the drawbacks of inventory turns. We call this benchmarking metric, adjusted inventory turns (AIT). Our research shows that AIT is predictive of future sales, earnings, and stock prices of retailers. For example, stock portfolios formed by ranking retailers on AIT show that the top 20% of the U.S. public retailers on AIT outperformed the bottom 20% of the retailers by 12.6% per year during 1985-2010, and had statistically significant abnormal returns benchmarked to the Fama-French-Carhart risk factors. We suggest that managers, investors, and lenders ignore this metric at their peril.
Contrasting the Retailer, Equity Investor, and Lender Perspectives
Retailers seek to determine the optimal amount of inventory to stock in a challenging and dynamic environment. Investors and lenders face a problem of information asymmetry when they seek to assess retailers' performance from limited and aggregated public information. They often cannot distinguish between a retailer who strives to make optimal inventory stocking decisions and one who sub-optimally varies inventory levels to influence future cash flow projections. Investors use inventory as a signal of future performance, and they are wary of retailers manipulating their sales, gross margins, and sales growth rates using inventory. Lenders worry about the quality and quantity of inventory carried by a firm due to its effect on cash flows and liquidation value. Thus, an increase or decrease in inventory that a retailer knows to be optimal for its business may be viewed with consternation by its partially informed external stakeholders. Retailers must manage and integrate these differences in perspectives and expectations.
The Retailer Perspective
Every retailer understands that one usually can't sell what one doesn't have in stock, and that the availability of inventory is a significant driver of customers' in-store experience and satisfaction. Not having the right product at the right location, time, and price typically costs a potential sale and its associated profit. However, inventory also represents substantial investment. For the average U.S. public retailer in 2011, inventory was 21% of total assets, one of the largest items on most retailers' balance sheets. A retailer unable to sell what it buys in one season will not only have less cash to buy fresh inventory for the next season, but also have to exert effort and incur expenses to dispose of leftover inventory.
To help them better manage inventory, retailers have invested in developing their organization (e.g., investing in departments called buying, merchandising, and merchandise planning) as well as in a variety of technological tools. However, determining optimal inventory level, as textbooks in operations research have long noted, can be complicated. Optimal inventory level is affected by numerous variables including a product's expected demand, demand uncertainty, gross margins, obsolescence, inventory carrying costs, lead-time for replenishment, and periodicity of review.
Increased variety and shorter lifecycles have made it more difficult for retailers to have the right product available in the right quantity at the right place and time. As we have shown in prior research, greater variety has also resulted in a variety of execution problems at the store level (inventory records, for example, become less accurate as inventory levels goes up), and retail consolidation has complicated the task of tracking sales at the individual store and SKU level and stocking accordingly (the typical buyer might now be responsible for planning inventory for hundreds of SKUs at thousands of locations). With the growth of alternate channels (e.g., the internet)—often with their own rules governing pricing, taxes, and returns—retailers have been faced with the need to balance inventory across channels. The longer lead times often associated with the search for production sources with cheaper labor costs in many categories has further complicated inventory planning.4
Due to these complexities, retailers find it hard to match supply with demand. They face large risks of excess inventory and shortage. Thus, they must pay closer attention to inventory.
The Investor Perspective
Equity investors, although they care about retail inventory for the very same reasons retailers do, also use inventory as a signal of future performance. They worry that growth in inventory might conceal weaknesses in a retailer's ability to generate future sales and earnings. In other words, investors view inventory as the proverbial canary in the coal mine. Savvy professional investors we interviewed routinely track whether inventory growth exceeds sales growth, and challenge management to offer an explanation when it does. For example, according to David Berman,5 a hedge fund manager who focuses on retail stocks, “This relationship [between inventory and stock price] is astoundingly powerful, but surprisingly few understand why. Most think it's just a function of inventory risk. It's not. It's primarily a function of how the operating margins can be manipulated by management in the short term by playing around with inventories.”
Investors examine a retailer's inventory level carefully in hopes of mitigating the problem of information asymmetry they experience in assessing a retailer's performance. Unlike retailers, investors have access only to aggregate information about a retailer's performance from public announcements and SEC filings. They cannot observe the actual decisions on pricing and sourcing taken by the retailer. Moreover, as we show below, investors cannot verify the quality of sales and earnings of a retailer or the quality and actual value of its inventory. Determining a retailer's risk and return profile from such limited data is one of their primary concerns.
Investor consideration of inventory is especially relevant because frequent booms and busts characterize retail investment. Consider, for example, Circuit City Stores, Inc. (“Circuit City”), a U.S.-based retailer that sold consumer electronics primarily through big-box stores until it went out of business in March 2009. Investing $1,000 in 1968, when Circuit City went public as Wards Company, would have returned $515,000 by 2000. The same investment in Wal-Mart Stores, Inc. (“Wal-Mart”) in 1973, and in The Gap Inc. (“Gap”) in 1976, would have returned $1,116,000 and $572,000, respectively, by 2000. Downside changes in retail valuation can be equally abrupt, as evidenced by Circuit City's experience from 2000 to 2008, by which time the company had been liquidated and investment in its stock was valueless. Circuit City's saga is a common story in retail. During the twenty-year period spanning 1992-2011, more than 15% of public retailers have entered bankruptcy, and an additional 3.4% been liquidated.
Given the risks inherent in investing in retail stocks, savvy investors watch carefully for decisions that can be used to overstate performance in the short term. One such decision relates to markdowns. To understand how inventory gives signals about markdowns, consider the following example of a retailer that purchases and sells a single product. Assume that the retailer purchases 10 units of the product at $1 per unit and sells six units at $2 per unit, yielding a revenue of $12 and leaving four units of unsold inventory, the latter to be written off (i.e., to have zero value) at the end of the selling season. The retailer's income statement would look like scenario A, below. Note that gross margin is $2, or 17% of revenue. (See Table 1.)
In scenario B, in which the retailer values the leftover inventory at cost (i.e., at $4), the cost of sales drops to $6 (from $10) and the gross margin increases to $6 (from $2) and 50% (from 17%) of revenue. The retailer has overstated its gross margins in the current year by overvaluing its inventory, but future earnings will be depressed because the retailer will likely have to write off the excess inventory at some future date.
The foregoing example probably exaggerates retailer discretion relative to inventory valuation. It would be difficult for a retailer to value its entire obsolete inventory at cost, and such inventory might not be entirely worthless (i.e., it may have some residual value). Yet retailers enjoy considerable discretion in the value they assign to inventory. According to David Berman,6 “Managements sign off on the inventories as being fairly valued, and the auditors pretty much rely on their word.” More important, management can choose when to write down the value of inventory, which substantially determines the timing of profits. Thus, investors are wary of retailers manipulating their operating margins using inventory. They worry because they cannot verify if a retailer made good decisions or used inventory to “play games” with profits.
Inventory, and a retailer's valuation thereof, has a substantial impact on profit because, for most retailers, pre-tax earnings are a small fraction of sales and can be impacted considerably by inventory valuation. For example, consider a retailer with sales of $10 billion, inventory of $2 billion, and pre-tax income of $500 million. A 5% difference in the valuation of its inventory could overstate its pre-tax income by $100 million, or 20%. Moreover, in some sectors—such as fashion apparel—there is considerable subjectivity in the valuation of inventory.
Another reason for investors to track inventory closely is because inventory can be used to fuel sales growth, albeit temporarily. Growth rate—specifically, “comp-store sales growth,” a metric that tracks year-on-year sales growth after controlling for store opening and closing—can affect a retailer's valuation significantly. Savvy investors watch inventory levels closely to distinguish between two types of sales growth: persistent growth that stems from an increase in consumer demand for a retailer's products or services, and transient growth attributable to increase in inventory and reduction in stockouts. This is because the market valuation would differ considerably in these two cases. To illustrate, consider two retailers who grew sales by 3% in the current year. Both retailers are identical in all respects; they had sales of $500 million, a gross margin of 40%, fixed expenses of $180 million, a pre-tax profit of $20 million (or 4% of sales), and they turn their inventory twice a year. The only difference between the retailers is that one retailer's growth is “persistent” (i.e., the growth is driven by increase in consumer demand, which is expected to continue growing in the near future), while the other retailer's growth is “transient” (i.e., the growth rate is one-time-only because it is fueled by increase in inventory level). In other words, the first retailer's sales are expected to grow at 3% for the foreseeable future, say 25 years, and the second retailer's sales are expected to be flat during this time period. Using a standard discounted cash flow model and discounting rate of 10% per year with zero taxes, the first retailer would be valued at $625 million while the second retailer's valuation would $182 million; the NPV of the first retailer is higher by 243% because of the change in growth rate.7 Sales growth generated by increases in inventory levels, because it is one-time-only, should thus not be rewarded as handsomely as sales growth generated by heightened consumer demand.
For the above reasons, investors view rapid inventory growth suspiciously as it could mask weaknesses in reported sales, earnings, and sales growth. Such weaknesses would have large impact on the market valuations of retailers. Therefore, investors punish retailers that have unexpected inventory growth, especially if a retailer exhibits other signs of trouble. For example, we found in a study that the stock prices of retailers that miss analysts' consensus earnings estimates drop by 8% in the nine-month period following the earnings announcement. The stock prices of retailers that miss their earnings estimates and report unexpected inventory growth, however, drop by 13% over the same period, whereas those that miss earnings without any unexpected inventory growth observe only a 2.6% decline in stock price. Clearly, investors see bad news in inventory growth as well as in missed earnings. It is important for retailers to manage investor expectations given their focus on inventory.
The Lender and Supplier Perspectives
Inventory matters to lenders and suppliers because inventory growth can often predict future cash-flow problems and, in some cases, bankruptcy and liquidation. In the event of liquidation, inventory is often the primary vehicle by which lenders recover money from retailers. They face similar information asymmetry about the performance of a retailer as investors do, but to a lesser extent.
To understand the relationship between inventory growth and cash flow, consider a retailer, shown in Table 2, with annual sales of $100, a gross margin of 50%, fixed costs of $46 per year, and a net profit 4% of sales (or $4, currently). Ignoring, for simplicity, taxes and non-cash expenses, the retailer's cash flow will be $4. Inventory, which turns twice per year, at half the cost of sales, is valued at $25.
Anticipating 20% growth in aggregate sales in the subsequent year, the retailer increases its inventory by 20% to $30. It projects a net income of $14, and a cash flow of $9 net of the cash used up by increase in inventory. However, what will happen to the retailer's cash flow if the anticipated sales growth does not materialize?
Assume, conservatively, that the extra inventory carried by the retailer does not lose value (i.e., can be valued at cost). (It will, of course, incur some inventory carrying cost, which we ignore for convenience.) In this case, the retailer's profit will be $4, but its cash flow will be −$1 as opposed to $4 in the previous year. This adverse impact of carrying excess inventory would be exacerbated if the additional inventory increased the obsolescence cost or we were to explicitly incorporate inventory carrying cost in our example.
Note that, in this example, cash flow declined precipitously even though the retailer's business held steady. The retailer incurred negative cash flow because it increased inventory in anticipation of forecasted sales growth that did not materialize. A retailer takes a risk every time it plans inventory in line with forecasted growth. Interestingly, retailers with higher inventory turns are less prone to this risk. A retailer with five inventory turns per year in the example above would have started with $10 in inventory, and 20% growth in inventory would have reduced cash flow by only $2 (from $4 to $2).
Lenders also pay close attention to the quality of inventory. Both quality and quantity of inventory largely determine the extent to which lenders are able to recover loans made to retailers that run into cash flow problems and are forced to liquidate. A lender that is too conservative may give too small a loan and miss a profitable opportunity for both itself and the retailer. A lender that gives too large a loan may be unable to recover its money if the retailer suffers bankruptcy. In many recent retail liquidations (e.g., the 2012 liquidations of book retailer Borders Group and women's apparel retail chain Betsey Johnson), banks with substantial loans have managed to recover most (if not all) of the money due to them.8
So What is the Appropriate Level of Inventory for a Retailer?
Retailers and external stakeholders need a benchmarking tool that can be used to determine appropriate levels of inventory. It is important that such a benchmark be transparent so that it can mitigate the information asymmetry between retailers and their external stakeholders. Therefore, the metric should be derived from publicly available data so that it can be calculated as easily by equity investors and other external stakeholders as by managers.
Our past research9 has identified multiple drivers that explain the variation in inventory turns and address its drawbacks. Of these, we have found two drivers to be most salient, gross margins and capital intensity.
Ceteris paribus, an increase in gross margin should be associated with a decrease in inventory turns, and vice versa. There are several reasons to expect this negative correlation between inventory turns and gross margin. An increase in gross margin is associated with improved product availability or fill rate, increased variety, a reduction in markdowns, or a shift towards higher-quality, slower-moving products. With each such change, inventory turns generally decreases. Indeed, retailers often recognize an “earns versus turns tradeoff,” whereby, in order to be profitable, a high gross margin must be earned on each sale or inventory turned very fast. Retailers with low earns and low turns would be under pressure to improve their operations or eventually shut down. Retailers with high earns and high turns tend to be rare because of the difficulty of sustaining this advantage over a long period of time in a competitive marketplace. Figure 1 depicts this correlation between gross margin and inventory turns.
Capital intensity, defined as the ratio of capital assets to total assets, should be positively correlated with inventory turns. Decisions that lead to an increase in capital intensity include setting up warehouses and investing in supply chain infrastructure and logistics. Such decisions should enable a retailer to use inventory more productively because warehouses can be used to pool inventory upstream in the supply chain and better supply chain infrastructure can be used to replenish stores faster and in smaller quantities. All these activities would lead to an increase in inventory turns. Note that the growth of online retailing business will also be generally associated with increases in capital intensity and inventory turns because of investments in fulfillment capability and technology.
To investigate the correlation of inventory turns with gross margin and capital intensity, we examined and quantified (using regression analysis) historical annual inventory turns of U.S. retailers against their gross margin and capital intensity. Our methodology and detailed definitions of variables are presented in the Appendix. We found that a 1% increase in gross margin leads to a 1.48% average decrease in inventory turns, and a 1% increase in capital intensity leads to a 1.05% increase in inventory turns. In other words, inventory turns have an elasticity of −1.48 with respect to gross margin and 1.05 with respect to capital intensity. Thus, we define the adjusted inventory turns (AIT) of a firm as its inventory turns multiplied with adjustment factors for gross margin and capital intensity. Our method for adjusting inventory turns for the effects of gross margin and capital intensity is similar to the method used in economics to adjust GDP for inflation and the method in finance to adjust financial returns for risk premium.
Figures 2a-2b illustrate the concept of AIT. In Figure 2a, the solid-line curve shows combinations of inventory turns and gross margin that achieve an AIT of 2. The dotted-line curve shows respective combinations for an AIT of 4, which is a higher tradeoff frontier. In order to focus on the inventory turns/gross margin relationship, the capital intensity is fixed at 25% on both curves. Consider firms A and B. Firm A has inventory turns of 6, a gross margin of 18%, and a capital intensity of 25%. Its adjusted inventory turns are 2.0. Firm B achieves the same inventory turns as firm A with the same capital intensity but a higher gross margin of 28%. Thus, firm B has a higher AIT of 4.0, and is on a higher tradeoff frontier than firm A. Firm C achieves a higher gross margin than firm A but with lower inventory turns. Thus, firms A and C have the same value of AIT and are on the same tradeoff frontier.
Figure 2b illustrates the effect of capital intensity. The solid and the dotted lines show combinations of inventory turns and capital intensity that achieve AIT of 2 and 4, respectively. Firm D achieves inventory turns of 10 with a capital intensity of 67% and gross margin of 25%, whereas firm E achieves the same inventory turns with a capital intensity of 34% and gross margin of 25%. Thus, firm E is on a higher tradeoff frontier than firm D. This is reflected in the values of AIT, which are 2 and 4 for firms D and E, respectively. Firm F achieves a higher inventory turns than firm E but with higher capital intensity. Our computations show that firms E and F have the same value of AIT and are on the same tradeoff frontier.
We may also illustrate the logic behind and the working of our methodology by examining the contemporaneous variation of inventory turns across different retailers. Consider the plot of inventory turns of all U.S. public retailers in 2011, shown in Figure 3a. Note the substantial range, from retailers like Men's Wearhouse Inc. that turn inventory less than once, to retailers like Alimentation Couche-Tard Inc. that turn inventory as many as 10 times, per year. To understand the causes of this variation, look at Figure 3b, which plots inventory turns against the gross margins realized in 2011. Note the strong (non-linear) relationship between the two variables. Figure 3c plots inventory turns against a weighted combination of gross margin and capital intensity, the weight chosen to maximize the strength of the relationship. Figure 3d plots the AIT adjusted for the variation in gross margin and capital intensity. Note that retailers with the same inventory turns may have different AIT values because of differences in their gross margin and capital intensity.
AIT is similar in concept to a commonly used performance metric termed as gross margin return on inventory (GMROI), which is defined as the gross margin earned per dollar invested in a firm's inventory, and measured as the ratio of gross margin to inventory. Although it controls for the correlation of inventory turns with gross margin, GMROI assumes that a 1% increase in gross margin must be compensated by a 1% decrease in inventory turns. Such a fixed relationship does not have an economic basis. In fact, retail data analyzed by us show that a 1% increase in gross margin is associated with a 1.48% decrease in inventory turns on average, which we use in constructing AIT. Moreover, GMROI does not adjust for the correlation of inventory turns with capital intensity. AIT overcomes these drawbacks as it is derived from historical data on retailer performance.
To illustrate how inventory turns and adjusted inventory turns can offer different insights, we evaluate, using both metrics, the performance of two well-known retailers, Wal-Mart and Target, over the years that spanned 1985-2007. Figure 4a shows the change in inventory turns for both firms during this period. As shown in this figure, Wal-Mart's inventory turns grew steadily, from fewer than four to nearly eight turns per year. Stated alternatively, the company went from carrying roughly 90 days to carrying roughly 45 days of inventory. Target's inventory turns during the period changed less, rising from 4.5 to 6. Looking at that metric alone would suggest that during this period inventory management was improved substantially more by Wal-Mart than by Target.
Now compare how adjusted inventory turns evolved at each of these firms during this period. Both increased their adjusted inventory turns substantially, from around 11 in 1985 to roughly 16 in 2007. It is hard from the data to say if either firm outperformed the other on AIT during the entire period under discussion; a numerical count reveals that Wal-Mart's AIT exceeded Target's AIT for 11 of the 23 years that are represented in Figure 4b while Target's AIT exceeded Wal-Mart's in the other 12. Looking at adjusted inventory turns would lead us to conclude that there was essentially no difference in the firms' inventory productivity. In contrast to AIT, GMROI fails to explain the difference in inventory turns between Target and Wal-Mart during this time period.
Why do the insights derived from inventory turns and adjusted inventory turns differ so? It is useful to examine the 22-year period in three phases: 1985-1995, 1996-2002, and 2003-2007. During 1985-1995, both firms' inventory turns held reasonably steady; Target's inventory turns exceeded Wal-Mart's during the period but AIT was similar for the two firms. Why were inventory turns at Target higher than at Wal-Mart even though AIT was roughly tied at the two firms? During this period Wal-Mart's capital intensity was usually lower than Target's. In 1985, for example, capital intensity was roughly 60% at Wal-Mart, and roughly 70% at Target. A plausible reason for the difference in capital intensity may be the difference in store sizes and formats between Target and Wal-Mart during this period; Target operated smaller stores under several labels, whereas Wal-Mart operated larger stores including Wal-Mart supercenters and Sam's Club. Another plausible reason for the difference in capital intensity could be the difference in location strategy for the two retailers. This difference in capital intensity compensates the gap in inventory turns between the two firms, and results in similar AIT values for them.
From 1996-2002, Wal-Mart's inventory turns grew rapidly from 4.5 (1996) to 7.5 (2002). In 2002, Wal-Mart's inventory turns were roughly 24% higher than Target's. AIT however told a different story; Wal-Mart's AIT was only 3% higher than Target's. Why were the AIT metrics for Target and Wal-Mart much closer than inventory turns? The answer lies in Target's gross margins, which grew substantially during this period. By 2002 for example, Target's gross margins had grown to 33%, significantly higher than Target's gross margins of 23-25% in the early 1990s and also substantially higher than Wal-Mart's gross margins of 23% in 2002.
From 2003-2007, inventory turns at Wal-Mart and Target stayed largely flat. Wal-Mart averaged 7.5 inventory turns a year while Target averaged roughly 6 turns a year. Yet the average AIT was essentially identical. The answer once again lay in Target's superior gross margins, which averaged 34%, as opposed to 25% for Wal-Mart.
Other examples show instances where the relative ranking of firms on inventory turns is reversed when the same firms are ranked on AIT, or where IT and AIT show different time trends. As shown in Table 3, rankings reversal is illustrated by a comparison of auto parts stores, Autozone (Stock ticker: AZO), Advance Auto Parts (Stock ticker: AAP), and Pep Boys (PBY). PBY has the highest inventory turns followed by AZO, then by AAP. However, their rankings on AIT are reversed because Pep Boys has the lowest gross margin of these three and Advance Auto Parts has the lowest capital intensity. A difference in time trends between IT and AIT is illustrated by Harris Teeter Supermarket Stores over the period 1985-2010. During this period, Harris Teeter's inventory turns were flat but its AIT increased by 40% because its gross margin increased steadily over this period. In other words, Harris Teeter was achieving the same inventory turns with higher gross margins by shifting to private label products, thus improving its inventory productivity. Due to such contrasting inferences between inventory turns and AIT, retailers must consider the sum total of their actions when evaluating their inventory productivity.
Adjusted inventory turns thus provides, by adjusting, on the basis of a statistical model, changes in inventory turns to reflect simultaneous changes in gross margin and capital intensity, a method of benchmarking inventory productivity. This gives us a metric that supports comparisons, whether across firms in a retail segment or within a given firm over time.
What can be Learned from a Retailer's Adjusted Inventory Turns?
Our research shows that AIT is more effective than inventory turns at gauging sales, earnings, and stock prices of retailers. Thus, it is a useful metric that is a reliable indicator of changes in retailers' performance.
Does AIT predict sales and earnings?
In a word, yes. If a retailer has a high AIT in a particular year, it means that the retailer has lower inventories than its benchmark. Such a retailer, which we call “under-inventoried,” would have realized its sales and gross margin with low inventories, and thus, would be likely to achieve higher sales in the future. On the other hand, a retailer with a low AIT, which we call “over-inventoried,” would have realized its sales and gross margin with high inventories. Such a retailer would have lower sales potential in the future. For instance, a retailer whose demand exceeded its forecast and inventory plan is likely to be under-inventoried. Sales will not be a full indicator of the strength of demand in such a situation. On the other hand, a retailer whose demand fell short of its forecast and inventory plan is likely to be over-inventoried. Sales may overstate the strength of demand in this situation.
Our analysis of this phenomenon begins with an examination of bias in equity analysts' forecasts for over- and under-inventoried retailers. We show a powerful result that not only is AIT predictive of sales and earnings, but also equity analysts fail to make sufficient adjustments for inventory levels so that their forecasts are overly optimistic for over-inventoried retailers and overly pessimistic for under-inventoried retailers. We identify over- and under-inventoried retailers by their AIT rankings in a given year. We classify as under-inventoried retailers with an AIT in the top third of all retailers, and as over-inventoried retailers with an AIT in the bottom 33rd percentile. We expect that under-inventoried retailers will exceed sales forecasts made by equity analysts in the subsequent year because analysts would fail to take the effect of inventory on sales into account in issuing their forecasts. In other words, analysts would rely too much on prior sales history and too little on prior inventory history. The opposite should hold for over-inventoried retailers. Applying the formula for AIT stated earlier to Bombay Company, which in 2006 had inventory turns of 0.77, gross margins of 0.24, and capital intensity of 0.77, we arrive at an AIT of 1.52. This places Bombay Co. in the 1st percentile and, hence, over-inventoried classification.
Figure 5a plots the bias in equity analysts' sales forecasts for the years 2002-2010 for retailers that were over- and under-inventoried at the end of the prior fiscal year. Analysts' sales forecasts are seen to be overly optimistic for firms that were over-inventoried, and overly conservative for firms that were under-inventoried in the previous year. Examine first the forecasts made about a month after release of the previous fiscal year's financial statements. Our method uses data from released financial statements to identify whether a retailer is over- or under-inventoried. In this time frame, analysts tend to over-estimate sales for over-inventoried retailers by 0.8%, on average. For a retailer with annual sales of $10 billion, this amounts to over-estimating sales by $80 million. Average sales growth during this period (2002-2010) across all retailers being 5%, over-estimating sales by 0.8% is equivalent to over-estimating sales growth by 16% for the average retailer. Sales of retailers that are under-inventoried tend to be under-estimated by roughly 1.2%, which translates into under-estimating the growth rate for the average retailer by 24%. Although it declines over time, this bias persists well into the financial year, with analysts continuing to over-estimate sales for retailers that were over-inventoried, and under-estimate sales for retailers that were under-inventoried.
Figure 5b plots for the same period the bias in analysts' earnings forecasts, which, like their sales forecasts, tend to be overly optimistic for retailers that were over-inventoried and overly pessimistic for retailers that were under-inventoried in the previous year. A month after the release of the prior fiscal year's financial statement, analysts over-estimated earnings for over-inventoried retailers by 10 cents per share; this bias persists several months into the fiscal year, with analysts over-estimating earnings for over-inventoried retailers by 4 cents per share after six months into the fiscal year. For under-inventoried retailers, analysts under-estimated earnings by 6 cents per share one month after the release of financial statements for the previous fiscal year. Given that the average change in annual EPS for retailers is only 10 cents per share, forecast errors of 6 cents per share for under-inventoried and 10 cents per share for over-inventoried retailers are economically salient.
Consistent with the shortcomings of inventory turns as a performance metric, weaker predictive power is found if we apply the above methods to (unadjusted) inventory turns. That is, if we rank retailers by inventory turns in a given year and use those rankings to classify retailers as over- or under-inventoried, then we do not find that classification to meaningfully explain bias in analysts' forecasts of sales and earnings.
Does AIT predict changes in stock price?
Again, the answer is, in a single word, yes. We tested if a portfolio based on AIT performs better than retail stocks in general by studying stock prices during 1983-2010. We formed investment portfolios on July 31st of each year using financial statements for fiscal year end dates up to January 31st. Providing a minimum six-month lag between fiscal year end dates and portfolio formation dates is a standard practice that allows information contained in financial statements, which are typically filed with the SEC within one to three months after the fiscal year end date, to disseminate into the market. Thus, our method only uses information that is readily available to investors on the portfolio formation date. The AIT method of portfolio formation is conservative in also not using any metrics that might have been available before July 31st, such as earnings announcements for the first quarter of the current fiscal year.
Our methodology ranks retailers within each segment based on the adjusted inventory turns derived from their financial statements. On the basis of these AIT rankings, the retailers are then classified into five portfolios, from those in the lowest 20% to those in the highest 20%. For example, in the apparel and footwear segment in 2010, 43 retailers were classified into five portfolios based on their AIT, as can be seen in Table 4 below.
During the 25 years that this study spanned, the lowest portfolio (which consists of retailers with the lowest AIT) had an annual excess return of 2.28%, the highest portfolio an annual excess return of 14.88%; excess return is calculated as the portfolio return minus the risk-free rate over the same period. The 12.6% per year difference between the two portfolios is statistically significant and managerially substantial. To understand the impact of such a difference, consider that a $100 investment over 25 years would, with an annual return of 2.28%, amount to roughly $176, and with an annual return of 14.88%, amount to roughly $3,207. We further found that the predictive power of AIT is more robust than that of inventory turns. For example, in Fama-MacBeth regressions, we found that AIT is a significant predictor of the cross-section of stock returns, but inventory turns are not.
The returns from AIT-based stock portfolios are not limited to specific sub-periods in our dataset. Instead, the high AIT portfolio outperforms the low AIT portfolio in 22 out of 25 years. These returns are also not driven by specific retail segments. When we divided our dataset into smaller segment-focused portfolios according to the two-digit Standard Industry Classification (SIC) system, we found that high AIT portfolios outperform low AIT portfolios by similar and statistically significant magnitudes in almost every segment despite the smaller portfolio sizes and higher statistical errors.
Addressing the Retailer's Choice
What can retailers do to manage investors' and lenders' as well as their own concerns about inventory? One, they should benchmark their adjusted inventory turns over time and against those of other retailers. As we have argued and shown above, this metric is a leading indicator of sales, earnings, and stock price so it would help retailers proactively manage their financial performance. Moreover, it is derived from retailers' past historical performance and reflects the operational relationships that retailers have always been aware of. Two, they should be wary of significant changes in their inventory turns. Like it or not, many investors and lenders continue to rely on this metric. Hence, retailers should manage the potential signals that might be communicated by changes in inventory turns.
Because improving AIT is equivalent to improving inventory productivity, retailers stand to benefit significantly from carefully tracking their adjusted inventory turns and taking appropriate actions. AIT is improved by having the right product in the right place at the right time. As we10 and others have argued elsewhere, retailers have at their disposal multiple levers for improving inventory productivity. These can be broadly categorized as levers for improving forecasting, responsiveness, and planning.
Retailers that improve their forecasting ability are better able to match supply with demand; more accurate forecasts that reduce the need for safety stock translate into improvements in inventory productivity. Forecast accuracy can be improved via a mix of technology and processes. Better forecasts are possible owing to the availability of large volumes of data11 and superior analytical technologies. Moreover, our past research has shown accuracy to improve dramatically when forecasts are based at least in part on early sales data.12 Dramatic improvements in inventory productivity have accrued to companies that have invested in capabilities that enable them to read early signals from the market and respond quickly with additional supplies (e.g., Zara)13 or well-planned price changes14 or other means of stimulating demand. Inventory planning is complicated by the numbers of stock-keeping units (SKUs) and stores that compose a typical retail chain today. Even a medium-sized retailer will have tens of thousands of SKUs in each of hundreds of stores, yielding more than a million store-SKU inventory locations for which inventory needs to be planned. Recent advances in information technology are nevertheless enabling retailers to substantially improve their planning approaches, and, in some cases, demonstrably improve gross margin and inventory turns concurrently.
A focus on managing AIT needs to be supplemented with attention to inventory turns. Even though inventory turns can be a noisy indicator of a retailer's inventory management capability, many investors still regard it in the manner of a canary in a coal mine. For example, Peter Lynch's red flag of inventory growing faster than sales is mathematically equivalent to tracking inventory turns.
Retailers might have compelling reasons to slow their inventory turns, that is, to increase inventory at a faster rate than sales growth. However, to allay investor concerns, such compelling reasons need to be communicated, ideally before inventory is increased. Consider the following example shared with us by a manager at a leading publically traded retailer with more than $1 billion in annual sales. “We were planning to announce new merchandise categories coming to our stores, and a significant SKU expansion,” the manager explained. The expanded assortment would, according to the manager, require “a significant inventory investment and a decline in turns for [a] period of time.” The company was convinced that increasing inventory and reducing inventory turns was the right call. Observed the manager: “We were making a substantial planned investment in inventory to drive sales, broaden our assortment, and become more relevant to our customer. We needed higher inventory levels to drive higher sales per square foot … While inventories were going to be up a material amount by the end of the year, it was a fully planned and thought-through strategy by our teams.”
The manager advocated communicating the planned increase in inventory levels to investors at the upcoming earnings call. Other managers, although initially hesitant, came to view this as a worthwhile step, and in the subsequent earnings call, according to the manager, it was “loudly stated [that] inventory per store will be up between 15%-20% by the end of our fiscal year.” Given a clear and compelling explanation before inventory was increased seemed to allay investor concern; “questions about inventory,” the manager observed, “have been at an absolute minimum from investors.”
This retailer's experience illustrates the benefits and importance of managing investor perceptions of inventory levels. Retailers should, of course, make choices about what and how much to inventory with an eye towards long-term performance. AIT is a useful metric in this regard. However, retailers also, recognizing that many investors still attend closely to inventory turns, need to ensure that any changes therein are explained to, before they are observed by, investors.
Although the costs associated with carrying inventory, especially inventory that becomes obsolete, can be substantial, inventory is vital to sales, absence of the right inventory at the right location often translating into lost sales. Moreover, investors and lenders should and do view inventory levels as an indicator of future earnings, sales, and stock price. To get inventory levels “right” is thus a challenging task for retailers. This article offers retailers guidance in balancing the different perspectives that are brought to bear on inventory, and, perhaps most important, develops a metric, Adjusted Inventory Turns, that, when used to establish appropriate inventory levels, constitutes a superior indicator of sales, earnings, and stock price.
APPENDIX Research Methodology
The research presented here builds on prior research15 that the authors have conducted either together or with other co-authors. We have simplified the research methodology and constructed a new benchmarking metric that can be easily used in practice. The methods presented in our earlier research are technically more sophisticated but also a little harder to understand and explain. Readers interested in exploring technical and methodological details and more sophisticated econometric models should look at those papers.
We obtained data for all public listed U.S. retailers (SIC codes ranging from 5200 to 5990) for the period 1984 to 2010. We excluded automotive dealers and service stations (all firms with two digit SIC 55) and eating and drinking places (firms with three digit SIC 581) from our analysis.
We collected data for the following variables, which are included in the annual financial statements of firms: Ending Inventory, Last-In-First-Out (LIFO) Reserve, Property, Plant and Equipment Gross, Rental commitments for the next five years, Total Assets, Sales Revenue, and Cost of Goods Sold. LIFO reserve is the difference between LIFO and First-In-First-Out (FIFO) valuations of inventory. It is zero for retailers that use FIFO valuation. For retailers that use LIFO valuation, their inventory is converted to FIFO by adding back LIFO reserves, and their cost of goods sold is correspondingly adjusted by subtracting the current year's LIFO reserve and adding back the previous year's LIFO reserve. This allows us to conduct an apples-to-apples analysis of inventory across retailers that use different valuation methods. We compute the net present value of rental commitments to capitalize operational leases. Next we add capitalized leases to Property, Plant, and Equipment Gross and to Total Assets. Next we construct the key variables using these data. Inventory turns are measured as the ratio of Cost of Goods Sold to Ending Inventory; Gross Margin is the ratio of Gross Profit to Sales Revenue; and Capital Intensity is the ratio of Property, Plant, and Equipment Gross to Total Assets.
We convert inventory turns, gross margin, and capital intensity into logarithms. Then we regress the log of inventory turns on the log of gross margin and capital intensity across all firm-year observations. The coefficients estimated from this regression represent the elasticity of inventory turns with respect to gross margin and capital intensity. We use them to construct the AIT metric. The regression is conducted for the years 1985-2010 because data for the first year, 1984, is used to obtain lagged LIFO reserves for 1985.
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Vishal Gaur is Professor of Operations Management at the Samuel Curtis Johnson Graduate School of Business at Cornell University.
Saravanan Kesavan is Assistant Professor of Operations at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill.
Ananth Raman is the UPS Foundation Professor of Business Logistics at the Harvard Business School.