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An Analysis of Working Capital Management Results Across Industries

Greg Filbeck, Schweser Study Program
Thomas M. Krueger, University of Wisconsin-La Crosse

Introduction

The importance of efficient working capital management (WCM) is indisputable. Working capital is the difference between resources in cash or readily convertible into cash (Current Assets) and organizational commitments for which cash will soon be required (Current Liabilities). The objective of working capital management is to maintain the optimum balance of each of the working capital components. Business viability relies on the ability to effectively manage receivables, inventory, and payables. Firms are able to reduce financing costs and/or increase the funds available for expansion by minimizing the amount of funds tied up in current assets. Much managerial effort is expended in bringing non-optimal levels of current assets and liabilities back toward optimal levels. An optimal level would be one in which a balance is achieved between risk and efficiency.

A recent example of business attempting to maximize working capital management is the recurrent attention being given to the application of Six Sigma methodology. Six Sigma methodologies help companies measure and ensure quality in all areas of the enterprise. When used to identify and rectify discrepancies, inefficiencies and erroneous transactions in the financial supply chain, Six Sigma reduces Days Sales Outstanding (DSO), accelerates the payment cycle, improves customer satisfaction and reduces the necessary amount and cost of working capital needs. There appear to be many success stories, including Jennifer Townes (2002) report of a 15 percent decrease in days that sales are outstanding, resulting in an increased cash flow of approximately $2 million at Thibodaux Regional Medical Center. Furthermore, bad debts declined from $3.4 million to $600,000. However, Waxers (2003) study of multiple firms employing Six Sigma finds that it is really a get rich slow technique with a rate of return hovering in the 1.2 4.5 percent range.

Even in a business using Six Sigma methodology, an optimal level of working capital management needs to be identified. Industry factors may impact firm credit policy, inventory management, and bill-paying activities. Some firms may be better suited to minimize receivables and inventory, while others maximize payables. Another aspect of optimal is the extent to which poor financial results can be tied to sub-optimal performance. Fortunately, these issues are testable with data published by CFO magazine (Mintz and Lazere 1997; Corman 1998; Mintz 1999; Myers 2000; Fink 2001), which claims to be the source of tools and information for the financial executive, and are the subject of this research.

In addition to providing mean and variance values for the working capital measures and the overall metric, two issues will be addressed in this research. One research question is, are firms within a particular industry clustered together at consistent levels of working capital measures? For instance, are firms in one industry able to quickly transfer sales into cash (i.e., have low accounts receivable levels), while firms from another industry tend to have high sales levels for the particular level of inventory (i.e., a high inventory turnover). The other research question is, does working capital management performance for firms within a given industry change from year-to-year?

The following section presents a brief literature review. Next, the research method is described, including some information about the annual Working Capital Management Survey published by CFO magazine. Findings are then presented and conclusions are drawn.

Related Literature

The importance of working capital management is not new to the finance literature. Over twenty years ago, Largay and Stickney (1980) reported that the then-recent bankruptcy of W.T. Grant, a nationwide chain of department stores, should have been anticipated because the corporation had been running a deficit cash flow from operations for eight of the last ten years of its corporate life. As part of a study of the Fortune 500s financial management practices, Gilbert and Reichert (1995) find that accounts receivable management models are used in 59 percent of these firms to improve working capital projects, while inventory management models were used in 60 percent of the companies. More recently, Farragher, Kleiman and Sahu (1999) find that 55 percent of firms in the S&P Industrial index complete some form of a cash flow assessment, but did not present insights regarding accounts receivable and inventory management, or the variations of any current asset accounts or liability accounts across industries. Thus, mixed evidence exists concerning the use of working capital management techniques.

Theoretical determination of optimal trade credit limits are the subject of many articles over the years (e.g., Schwartz 1974; Scherr 1996), with scant attention paid to actual accounts receivable management. Across a limited sample, Weinraub and Visscher (1998) observe a tendency of firms with low levels of current ratios to also have low levels of current liabilities. Simultaneously investigating accounts receivable and payable issues, Hill, Sartoris, and Ferguson (1984) find differences in the way payment dates are defined. Payees define the date of payment as the date payment is received, while payors view payment as the postmark date. Additional WCM insight across firms, industries, and time can add to this body of research.

Maness and Zietlow (2002, 51, 496) presents two models of value creation that incorporate effective short-term financial management activities. However, these models are generic models and do not consider unique firm or industry influences. Maness and Zietlow discuss industry influences in a short paragraph that includes the observation that, An industry a company is located in may have more influence on that companys fortunes than overall GNP (2002, 507). In fact, a careful review of this 627-page textbook finds only sporadic information on actual firm levels of WCM dimensions, virtually nothing on industry factors except for some boxed items with titles such as, Should a Retailer Offer an In-House Credit Card (128) and nothing on WCM stability over time. This research will attempt to fill this void by investigating patterns related to working capital measures within industries and illustrate differences between industries across time.

An extensive survey of library and Internet resources provided very few recent reports about working capital management. The most relevant set of articles was Weisel and Bradleys (2003) article on cash flow management and one of inventory control as a result of effective supply chain management by Hadley (2004).

Research Method

The CFO Rankings

The first annual CFO Working Capital Survey, a joint project with REL Consultancy Group, was published in the June 1997 issue of CFO (Mintz and Lezere 1997). REL is a London, England-based management consulting firm specializing in working capital issues for its global list of clients. The original survey reports several working capital benchmarks for public companies using data for 1996. Each company is ranked against its peers and also against the entire field of 1,000 companies. REL continues to update the original information on an annual basis. The industries that include at least eight companies with complete information over the 1996-2000 period are listed in Table 1.

REL uses the cash flow from operations value located on firm cash flow statements to estimate cash conversion efficiency (CCE). This value indicates how well a company transforms revenues into cash flow. A days of working capital (DWC) value is based on the dollar amount in each of the aggregate, equally-weighted receivables, inventory, and payables accounts. The days of working capital (DNC) represents the time period between purchase of inventory on acccount from vendor until the sale to the customer, the collection of the receivables, and payment receipt. Thus, it reflects the companys ability to finance its core operations with vendor credit. A detailed investigation of WCM is possible because CFO also provides firm and industry values for days sales outstanding (A/R), inventory turnover, and days payables outstanding (A/P). More information on how these values are calculated is presented in Table 2. Prior to 2002, CFO also provided an overall WCM management ranking based on an equally-weighted combination of CCE and DWC.

Statistical Techniques

Our first hypothesis is that statistically significant differences exist among industries with respect to the measures of working capital efficiency identified by CFO magazine. Classical analysis of variance is used to address issues of industry rank differences within years. Thus,

H 1: Differences exist among industries with respect to the measures of working capital efficiency identified by CFO magazine.

Our second hypothesis is that working capital measures for firms within an industry change across time. Since the complete data set includes only four years (1996-1999), there is the potential for degrees of freedom issues when using sophisticated models. Assessment of WCM performance across years is conducted using the Kendalls Coefficient of Concordance. Thus,

H 2: Working capital measures for firms within an industry change across time.

Research Findings

Average and Annual Working Capital Management Performance

Working capital management component definitions and average values for the entire 1996 2000 period are given in Table 3. Across the nearly 1,000 firms in the survey, cash flow from operations, defined as cash flow from operations divided by sales and referred to as cash conversion efficiency (CCE), averages 9.0 percent. Incorporating a 95 percent confidence interval, CCE ranges from 5.6 percent to 12.4 percent. The days working capital (DWC), defined as the sum of receivables and inventories less payables divided by daily sales, averages 51.8 days and is very similar to the days that sales are outstanding (50.6), because the inventory turnover rate (once every 32.0 days) is similar to the number of days that payables are outstanding (32.4 days). In all instances, the standard deviation is relatively small, suggesting that these working capital management variables are consistent across CFO reports.

The low standard deviations reported in Table 2 are accentuated by the individual year values presented in Table 3. As one might expect, given a gross domestic product growth rate range of only 5.6 percent to 6.5 percent, there is relatively little difference in the CCE and DWC values. In 1996, CCE was at a low of 6.0 percent. Otherwise, the CCE ratio was between 9 and 10 percent. DWC reached a high of fifty-nine days in 2000, mostly due to the slower inventory turnover in 2000. Otherwise, DWC values ranged from forty-six to fifty-two days. The best year for working capital management, as measured by a low days working capital figure was 1999, when days payables outstanding reached a high of thirty-four days and inventory turnover reached a high of twelve times per year (otherwise days payable outstanding ranged between twenty-seven and thirty-three days, with inventory turns between ten and eleven times per year).

Industry Rankings on Overall Working Capital Management Performance

CFO magazine provides an overall working capital ranking for firms in its survey, using the following equation:

Overall Ranking1=(Highest overall CCE - Company CCE) / (Highest overall CCE - Lowest overall CCE) x (Lowest overall DWC - Company DWC) / Lowest overall DWC - Highest overall DWC)

Industry-based differences in overall working capital management are presented in Table 4 for the twenty-six industries that had at least eight companies included in the rankings each year. In the typical year, CFO magazine ranks 970 companies during this period. Industries are listed in order of the mean overall CFO ranking of working capital performance. Since the best average ranking possible for an eight-company industry is 4.5 (this assumes that the eight companies are ranked one through eight for the entire survey), it is quite obvious that all firms in the petroleum industry must have been receiving very high overall working capital management rankings. In fact, the petroleum industry is ranked first in CCE and third in DWC (as illustrated in Table 5 and discussed later in this paper). Furthermore, the petroleum industry had the lowest standard deviation of working capital rankings and range of working capital rankings. The only other industry with a mean overall ranking less than 100 was the Electric & Gas Utility industry, which ranked second in CCE and fourth in DWC. The two industries with the worst working capital rankings were Textiles and Apparel. Textiles rank twenty-second in CCE and twenty-sixth in DWC. The apparel industry ranks twenty-third and twenty-fourth in the two working capital measures, respectively (also in Table 5).

The second column of Table 4 exhibits the standard deviation in overall working capital performance rankings. The industries with the greatest variation on the overall working capital performance measure, as measured by standard deviation, are the telecommunications industry and the beverage industry. If one only examines the extremes, the furniture industry is the industry with the greatest extremes in rank as it has at least one company whose rank varied from another firm in the same industry by 904 places. Variations in profit margins and turnover rates are worthy explanations for the wide disparity of rankings within the furniture industry. In general, the stability of firm rankings on WCM measures suggests that although a given level of current asset or current liability management impacts share price, one does not have to be overly concerned with changes in working capital management style.

Industry Rankings Across Individual Working Capital Management Characteristics

Table 5 breaks the overall working capital management rank in Table 4 into rankings of particular working capital measures (including the two components, CCE and DWC, which make up the overall rank). For instance, the petroleum industry, ranked first for overall performance, only ranks first in one of the five specific working capital measures, CCE measure. In fact, as shown in the center column of Table 5, petroleums DSO performance is second worst among all industries. However, DSO is not included in the compilation of the overall rank. While Table 5 provides the relative rankings of industries across the five working capital management measures, one may still wonder about the variation of these rankings over time.

All of the instances wherein the standard deviation of firm rankings exceeded 5.0 are exhibited in Table 6. There were only eleven instances wherein the standard deviation exceeded 5.0. Both inventory turnover and days payables outstanding had a higher standard deviation in four instances. Only one industryTelecommunicationshad over two instances where the standard deviation of the industry ranking on a given working capital measure exceeded 5.0. One reason for this variation is the lack of stability in industry members, with over 60 percent of the firms in 1996 no longer in the study in 2000. Some of the other significant changes include a dramatic drop in inventory turnover within the Petroleum industry and slower payment of accounts payable in the wholesale trade industry. In the other 93 instances (26×4 11), the variation in industry rankings for a working capital management variable is relatively stable.

The number of days of working capital is relatively low in both the food services and food stores industries. Food stores, which are primarily cash-and-carry businesses, exhibit the shortest days sales outstanding ranking (with food services coming in second). However, food services have quicker inventory turnover, with the publishing industry squeezing in between it and food stores. As one might imagine, food services, which like food stores tend to get payment upon purchase for merchandise, also need to make payments rapidly. In fact, these industries have the shortest days payables outstanding ranking, resulting in being at the bottom of the DPO column. Another factor hurting the performance of the food stores industry is its poor cash flow from operations per dollar of sales, resulting in it being ranked twenty-sixth in the Cash Conversion Efficiency, the first column of Table 5. Most industries were slower in collecting on sales than paying bills. In fact, only the food services, food stores, and specialty retailers had an average days payables outstanding value that exceeded their average days receivables outstanding. In addition, the beverage industry had a higher DPO than DSO value in three years, while the same relationship was true of the Apparel industry in only one instance. In all other eighty-eight (26×4 16) instances, the industrys average DSO value was higher that year.

Since CFO magazine only provides annual information, we are unable to assess the seasonal variation in WCM. All of the standard deviation data supplied illustrates the lack of much variation in WCM. Looking at the data, the most significant trends existed in the Inventory Turnover measure, with the Beverage industry rising from eighteenth to eighth place, and the Telecommunications industry dropping from the second to twenty-first position. Telecommunications also has a slower average collection period and quicker payment to suppliers, resulting in their DWC ranking dropping from first to twenty-fifth place. The only other trend in the data was the improvement (slowing) of payments to suppliers in the wholesale trade industry.

Six industriesfood service, food and drug stores, forest products, petroleum, pharmaceuticals, and publishing rank in both the highest three and lowest three levels for at least one of their working capital performance rank measures. Table 5 illustrates that three industries (aerospace, building materials, and furniture) show the five individual working capital performance rankings are within six places of each other. Of course, not having extremely different levels of performance across individual working capital measures is not necessarily good. The aerospace industry has the worst performance in Days of Working Capital (ranked twenty-third), but is only worthy of the nineteenth ranking for days sales outstanding, its best performance.
Statistical Significance of Raw Numbers

Table 4 and Table 5 report ordinal rankings of industries across working capital management variables. The ordinal rankings might be creating differences across industries that are, in reality, quite minute. Given the wide range of industry performance rankings, one might wonder whether there is a significant difference in industry performance within individual aspects of working capital management. Table 7 shows the tests related to our two hypotheses. In the first row (industry significance), we find support for our first hypothesis that significant differences exist between industries across time with respect to measures of working capital measures. The greatest differences occur in the days sales outstanding ranking, which has a statistically significant ANOVA F-value of 35.47. Table 7 shows persistent statistical significance, which suggests that there are significant differences in the industry working capital management rankings.

The second row in Table 7 (period consistency) shows the results related to our second hypothesis regarding the consistency of working capital measures within industries through time. This answers the question, are the firms cash conversion values consistent from period to period? Table 3 shows that despite the consistency in average values presented, there are significant changes in individual firm values from year to year, based on the significance of each of the values in the second row of Table 7. In other words, working capital measures for a given firm are not static, and significant differences in these measures exist across time. With only four years of observations, the critical Kendalls Coefficient of Concordance value is 44.31. Yet, each of the Kendall Coefficient of Concordance values tends to be about twice this level. These results indicate that working capital measures vary across time. Taken together, our results in Table 7 indicate that while working capital management ratios are changing over time for the firms sampled, these changes are consistent enough across industries to preserve the industry ordering across time.

Conclusions

The research presented here is based on the annual ratings of working capital management published in CFO magazine. Our findings indicate a consistency in how industries stack up against each other over time with respect to the working capital measures. However, the working capital measures themselves are not static (i.e., averages of working capital measures across all firms change annually); our results indicate significant movements across our entire sample over time. Our findings are important because they provide insight to working capital performance across time, and on working capital management across industries. These changes may be in explained in part by macroeconomic factors. Changes in interest rates, rate of innovation, and competition are likely to impact working capital management. As interest rates rise, there would be less desire to make payments early, which would stretch accounts payable, accounts receivable, and cash accounts.

The ramifications of this study include the finding of distinct levels of WCM measures for different industries, which tend to be stable over time. Many factors help to explain this discovery. The improving economy during the period of the study may have resulted in improved turnover in some industries, while slowing turnover may have been a signal of troubles ahead. Our results should be interpreted cautiously. Our study takes places over a short time frame during a generally improving market. In addition, the survey suffers from survivorship bias only the top firms within each industry are ranked each year and the composition of those firms within the industry can change annually.

Further research may take one of two lines. First, there could be a study of whether stock prices respond to CFO magazines publication of working capital management ratings. Second, there could be a study of which, if any, of the working capital management components relate to share price performance. Given our results, these studies need to take industry membership into consideration when estimating stock price reaction to working capital management performance.

Note

1. This ranking was not published in CFO magazine in 2002 or available at its Web site.

References

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Farragher, E., R. Kleiman, and A. Sahu. 1999. Current capital investment practices. Engineering Economist 44 (2): 137-150.

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Gilbert, E. and A. Reichert. 1995. The practice of financialmanagement among large United States corporations. Financial Practice and Education 5 (1): 16-23.

Hadley, S. 2004. Making the business case: Supply chain management. Strategic Management (April):28-34.

Hill, N., W. Sartoris, and D. Ferguson. 1984. Corporate credit and payables policies: Two surveys. Journal of Cash Management 559-576.

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Mintz, S. 1999. The 1999 working capital survey: Dollars in the details. CFO 15 (7):55-68.

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Scherr, F. 1996. Optimal trade credit limits. Financial Management 25 (1):71-85.

Schwartz, R. 1974. An economic model of trade credit. Journal of Financial and Quantitative Analysis 9:643-657.

Towne, J. 2002. Black inkSix Sigma archives, case study #5Thibodaux Regional Medial Center. www.hfma.org/resource.

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Weinraub, H. and S. Visscher. 1998. Industry practice related to aggressive/conservative working capital policies. Journal of Financial and Strategic Decisions 11 (2):39-46.

Weisel, J., N. Harm, and C. Bradley. 2003. The cash factor. Strategic Management (Sept.):29-33.

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