Relationship of Working Capital Management with ...

International Journal of Trends in Finance Volume 1 issue 2 Feb 2013

© ISSN:0976-9736 Chaklader, Sharma, Khatun & Goel

Relationship of Working Capital Management with FMCG Sector Firm’s Profitability Dr. Barnali Chaklader, Associate professor (Finance and Accounting) International Management Institute New Delhi Dr. R. K. Sharma, Professor (Finance) Bharati Vidyapeeth University Institute of Management and esearch New Delhi Ms. Rabia Khatun Research Fellow (FPM programme) International Management Institute New Delhi Ms. Pooja Goel PGDM Student Institute of Management Technology Ghaziabad

Abstract Purpose: The purpose of this study is to find out the relationship of working capital management policies on the profitability of FMCG sector’s firms listed in Bombay Stock Exchange (BSE) FMCG sector index. The period of study was from 1991 till 2011 Design/methodology/approach: We have taken all 7 firms that are listed on BSE’s FMCG sector for the purpose of our study. We had taken annual data from 1991 till 2011from CMIE, prowess data base. We took return on total assets as a measure of profitability and average inventory turnover days, average collection period, average payable period and cash conversion cycle as various exogenous variables. After conducting multicollinearity check and Hausman test, we ran panel regression through random effect method. Findings: The study indicates that all the variables were significant when average collection period factor was dropped and all factors except average inventory turnover days were significant when cash conversion cycle was dropped. This proves that aggressive and conservative working capital management policies affect profitability. Practical implications: The different analyses have identified critical management practices that will assist the managers in improving corporate profitability by improving their operations in working capital management in FMCG sector. Research Gap: Literature review shows that researchers have conducted a number of studies in manufacturing sector and telecom sector. We could not find a study on working capital management policy of FMCG sector which was one of the reasons for motivating us to conduct a


© ISSN:0976-9736 Chaklader Goel, Khatun & Sharma

similar kind of study in this sector. Moreover, there is a huge demand for fast moving consumer goods in India. Key words: Working capital policies, FMCG sector, profitability of FMCG firms. I Introduction Reserve Bank of India hiked its repo rate to 8.5% on 25th of October, 2011. This was the 13th hike in repo rate since March 2011. It meant that loans were to be costlier and an efficient working capital management is extremely necessary for smooth running of the business. Working capital decisions are important to the organization as they affect the firm’s liquidity position. Accountants view working capital as the difference between the current assets and the current liabilities. Working capital is alternatively referred to the investment of the firm in the current assets. Working capital decisions affect the firm’s profits through their impact on sales, operating costs, and interest expense. They affect the firm’s risk through their impact on the volatility of cash flows, the probability of not receiving the cash flow and the ability of generating cash during crisis. (Srivastava and Mishra, 2010).The working capital policy touches upon almost every functional area of the business’s operation. A firm is required to maintain a balance between liquidity and profitability while conducting its day to day operations. Working capital management is important because it consumes a large portion of the financial manager’s time. Most of the financial managers’ time and efforts are consumed in identifying the nonoptimal levels of current assets and liabilities and bringing them to optimal levels (Lamberson, 1993). Working capital plays an important role in the firm’s profitability, risk and value (Smith, 1980). A firm may choose an aggressive working capital management policy with a low level of current assets as percentage of total assets, or it may also be used for the financing decisions of the firm in the form of high level of current liabilities as percentage of total liabilities (Afza and Nazir, 2009).Keeping an optimal balance among each of the working capital components is the main objective of working capital management. Kim et.al says that there is a close relationship between sales growth and level of current assets. Liquidity is a precondition to ensure that firms are able to meet its short-term obligations. The liquidity and profitability goals conflict in most decisions which the finance manager makes. For example, if higher inventories are kept in

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anticipation of increase in prices of raw materials, profitability goal is approached, but the liquidity of the firm is endangered. Similarly, the firm by following a liberal credit policy may be in a position to push up its sales, but its liquidity decreases. Company has to borrow less if it manages its working capital well. Even cash has to be invested in such a way that it generates proper return to the investors. 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. The paper proceeds as follows. In the next section we have done literature review. In section 3 we discuss objective of the study, sample and the variables used in the empirical analysis. We have presented the results of the empirical analysis in Section 4. Section 5 discusses the causality in the relation between working capital management and corporate profitability. Section 6 concludes. II Research on Working Capital Nunn dealt with strategic determinants of working capital on a product line basis and examined why firms have different levels of working capital. He used factor analysis to test 166 variables against the working capital policies of over 1700 businesses or product lines from 1971 to 1978. The study showed that small batch production, order backlog, capital intensity and relative breadth of product line were positively correlated to working capital. On the other hand, continuous process production, capacity utilization and made to order products were negatively associated with working capital levels. He also found that working capital divided by sales are positively correlated to industry concentration. Gupta and Huefer, in the year 1972 examined the differences in financial ratio between industries and found that differences exist between ratio means among industries. Frecka and Lee (1983), focused an area of research on the issue of using regression analysis verses financial ratios for analysis and prediction, Filbeck and Krueger (2005) discover that significant differences exist between industries in working capital measures across time. In addition, they discovered that these measures for working capital change significantly within industries across time. Maynard Rafuse, (1993) proposed that improvement of working capital by delaying payment to creditors is an inefficient and ultimately damaging practice, both to its practitioners and to the economy as a whole. Stock

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reduction strategies, drawing on some of the techniques of “lean production” are far more effective, and the article proposes that those seeking concentrated working capital reduction strategies should focus on stock reduction. The author’s experience says that a good proportion of the finance managers will take the view that these arguments “are good in theory, but not in the real world”. In the event, when cash flow pressures arise, suppliers (and even customers) are invariably the first to feel the draught. Apart from being ethically questionable, this reflects dangerous short-termism. Deloof (2003) investigated the relationship between working capital management and corporate profitability for a sample of 1,009 large Belgian non-financial firms for the 1992-1996 periods. The result from analysis showed that there was a negative correlation between profitability that was measured by gross operating income and cash conversion cycle as well number of day’s accounts receivable and inventories. He suggested that managers can increase corporate profitability by reducing the number of days of accounts receivable and inventories. Singh and Pandey (2008) studied the impact of working capital components on profitability of Hindalco Industries Limited in India for period from 1990 to 2007. Results of the study showed that current ratio, liquid ratio, receivables turnover ratio and working capital to total assets ratio had statistically significant impact on the profitability of Hindalco Industries Limited. Lazaridis and Tryfonidis (2006) have investigated relationship between working capital management and corporate profitability of listed company in the Athens Stock Exchange. They used a sample of 131 listed companies for period of 2001-2004 to examine this relationship. The result from regression analysis indicated that there was a statistical significance between profitability, measured through gross operating profit, and the cash conversion cycle. From those results, they claimed that the managers could create value for shareholders by handling correctly the cash conversion cycle and keeping each different component to an optimum level. Ganesan, (2007), analyzed impact of working capital management upon the performance of firms in Telecom industry. The variables used were, days sales outstanding, number of days for payment to vendors, average days inventory held, cash conversion efficiency, revenue to total assets, revenue to total sales, etc. Findings reveal negative & insignificant relationship between profitability and daily working capital requirement in the said industry. Amarjit Gill et.al (2010)

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explored the relation between Working Capital Management and the firm’s profitability by taking a sample of 88 American Manufacturing Companies which are listed on the New York Stock Exchange for the period of 3 years from 2005 -2007 and found out that there existed no statistically significance difference between Average Days of Accounts Payable and Corporate Profitability. In some of the studies, positive correlation exists between Average Days of Accounts Payable variable and the firm’s profitability. This can be explicated with the fact that lagging payments to suppliers ensures that the firm has some cash to buy more inventory for sale thus escalating its sales level hence also enhancing its profits. (Huynh Phuong et.al, 2010). Tahir and Anuar in their paper did a literature review of the related papers from 2008-2010 and found out that the firm’s Growth (in Sales) has significant and positive influences on the firm’s profitability. They also mentioned that some of the studies in literature showed that the ratio of fixed financial Assets to total Assets has a negative relation with the Dependent Variable. Reheman et.al (2010) conducted a study of working capital management policy of 204 firms from the manufacturing sector in Pakistan for the period from 1998 till 2007. Their results showed that the manufacturing firms in Pakistan follow a conservative working capital management and that the firms need to improve their collection and payment policy. II

Objective, Sample and Variables

Objective of the Study: To analyse the impact of management of working capital on the profitability of firms in BSE’s FMCG sector. For the purpose of our study, we have considered FMCG sector listed in BSE sectoral indices. Literature review shows that researchers have conducted a number of studies in manufacturing sector and telecom sector. We could not find a study on working capital management policy of FMCG sector which was one of the reasons for motivating us to conduct a similar kind of study in this sector. Moreover, there is a huge demand for fast moving consumer goods in India. An average Indian spends around 40 per cent of his income on grocery and 8 per cent on personal care products. The large share of fast moving consumer goods (FMCG) in total individual spending along with the large population base is another factor that makes India one of the largest FMCG markets. Even on an international scale, total consumer expenditure on food

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in India at US$ 120 billion is amongst the largest in the emerging markets, next only to China. (IBEF report, 2010) Moreover, with increasing raw material prices, gross margins continue to witness pressure despite price hikes taken by FMCG sector companies. Companies took selective price hikes so as to protect volume growth. These price hikes were not enough to mitigate the impact of increase in raw material prices. Due to increase in raw material cost, gross margins and consequently earnings before interest tax depreciation and amortisation margins contracted for the companies. Faced with rising costs and competition, Indian FMCG companies are increasingly betting on expanding their geographical footprint with overseas acquisitions, expecting higher returns from international operations to offset lower growth in India (CII Report, 2010). All these make working capital management in this sector extremely important. We expect that the results of this study will help the managers in this sector frame policies for efficiently managing the working capital of their company. We have adopted the research methodology adopted through the literature review. There are 7 firms listed in this sector. The data of all 7 firms from 1991 till 2011 is taken from CMIE prowess data base. The reason for taking this particular period is that, this period has been very crucial in development of industrial sector in India. Policies in India were liberalised in India and globalisation took place from 1991 onwards. Also, this period was challenged with phases of growth and decline due to global instability. We have done a panel data regression analysis. The advantage of panel data analysis over either time series or cross section modeling is that is that it captures the differences across individual cross sections much better. The panel data analysis is done to find out the impact of aggressive and conservative working capital policies on the profitability of the firm where we have measured profitability by Return on Total Assets (ROTA) taking as the dependent variable and average collection days (DTRD), inventory turnover period in days (ITRD), Cash conversion cycle (CCC) and average payment days (CTRD) the various independent variables. Average collection days (DTRD) are the average number of days of credit period extended to the debtors. Average payment days (CTRD) are the average number of days extended by the creditors to the firm. Inventory turnover days (ITRD) are the average number of days required to convert inventories into sales. Cash conversion cycle

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(CCC) is another measure of managerial effectiveness. CCC is the time lag between expenditure for the purchase of raw materials and the collection of sales of finished goods. It does this by following the cash as it is first converted into inventory and accounts payable (AP), through sales and accounts receivable (AR), and then back into cash. A longer cycle would mean a larger fund blocked in the working capital. We have used E Views 6 software for analysis of data. The following formulae are used for the dependent and different independent variables. ROTA = Profit after tax divided by total assets DTRD=

365/ (Sales/ average debtors)

ITRD=

365/ (Cost of sales/ average inventory)

CTRD=

365/ (Cost of productions/ average creditors)

CCC= DTRD+ITRD- CTRD III Data Presentation and Analysis We took data of 7 companies from the year 1991 till 2011. There were 7 cross sections of 21 years resulting into 140 firm year observations for FMCG sector. Table I shows the descriptive statistics. Correlation of the independent variables with the dependent variable as well as multi co linearity problem was checked (Table II). Table II shows that DTRD and CCC have a high correlation (> 5) with ROTA. Therefore we decided to frame two regression equations by dropping DTRD in one equation and dropping CCC in the other as independent variables. (Equation I and II) The cash conversion cycle (CCC) has an average of 47 days with a standard deviation of 52 days. Firms receive payment on an average of 20 days with a standard deviation of 15 days. The minimum number of days to collect debt is 2 days whereas the maximum number of days is 73. The average payment period to creditors is 61 days with a standard deviation of 21 days. The minimum period to pay is 26 days. Firms convert inventory into sales on an average of 89 days or 2 months. Data in Table II reflects that ROTA is negatively correlated with DTRD, ITRD and CCC thus indicating that the firm can increase its profitability by reducing the collection period, inventory turnover period and cash conversion cycle. Aggressive working capital management will lead to a higher profitability. Correlation between collection period (DTRD) and ROTA is -0.601 and is

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highly significant at 1 percent level. This shows that the firm can increase its return on total asset by reducing the credit period extended to the debtors. Inventory turnover period in days (ITRD) has a negative correlation of 0.331 and is significant at 1 per cent level. This indicates that the firm can increase its ROTA by reducing ITRD. The cash conversion cycle which is a comprehensive measure of working capital management also has a negative coefficient – 0.535 and the p value is (0.000). It is significant at ά. = 1%. It means that if the firm is able to decrease the time period of cash conversion cycle, it can increase its profitability. By analyzing the results we conclude that if the firm is able to reduce these time periods, then the firm is efficient in managing working capital. This efficiency will lead to higher profitability. Correlation between ROTA and average payment period to creditors (CTRD) is -.238 with p value of 0.004. It is significant at ά. = 5%. This means that profitability of firms in FMCG sector can be improved by increasing the payment period to creditors. REGRESSION ANALYSIS To investigate the impact of the various variables of working capital management on profitability, we have taken the following model for regression analysis. ROTA = β0 + β1 (ITRD) + β2 (DTRD) + β3 (CTRD) + ε ----------- Equation I ROTA = β0 + β1 (ITRD) + β2 (CTRD) + β 4 (CCC) + ε ----------- Equation II Where β0 is the intercept of the equation β1, β2, β3 are the coefficients of independent variables. i.e. ITRD, DTRD and CCC respectively. ε: The Error Term Unit root test was conducted for the dependent series to check for stationarity of the data. ROTA, DTRD, ITRD, CTRD and CCC were converted in stationarity series at first level. The new regression with stationary data were run as d(ROTA) = β0 + β1 d(ITRD)+ β2 d(DTRD)+ β3 d (CTRD) + ε ----------- Equation III d(ROTA) = β0 + β1 d(ITRD)+ β3 d (CTRD) + β 4 d(CCC) + ε ----------- Equation IV We ran panel data regressions for equation I. The determinants of corporate profitability were estimated using pooled least square method. The results of OLS pooled regression is reported in Table IV. The estimation procedure starts with the Hausman Specification Test, which

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essentially tests the hypothesis that Random Effect Model is true for panel data analysis.We conducted Hausman Specification Test and found out that Random Effect Model (REM) was suitable for this equation. The results are of Hausman Test and REM are shown in Table IV and V. Since p value of Hausman test is .8799 or is insignificant at 1% level of significance, it shows that REM is appropriate. Darwin- Watson result of 2.13 shows that there is no autocorrelation in the series. R2 is coming out to be11.11% which means that 11.11% variance in ROTA can be explained with the help of this regression. All the independent variables are significant except average inventory turnover in days. Similarly, we ran panel data regressions for equation II. The determinants of corporate profitability were estimated using pooled least square method. The results of OLS pooled regression is reported in Table VI. The results are of REM are shown in Table VII and VIII. Dropping DTRD and adding CCC further improved the results. R2 is coming out to be12.11% which means that 12.11% variance in ROTA can be explained with the help of this regression. All the independent variables are significant by both the methods, i.e. pooled OLS and REM. All these show that firms can increase or decrease its profitability by following a conservative or aggressive working capital management policy. Conclusion The different analyses have identified critical management practices that will assist the managers in improving corporate profitability by improving their operations in working capital management in FMCG sector. The results showed that all the variables except average inventory turnover days were found to be significant. We have found that all the variables were significant when average collection period factor was dropped. This goes with Deloof’s (2003) research that managers can increase corporate profitability by reducing the number of days of average receivable days and inventory. This was also in consistent with lazaridis and Tryfonidis’s (2006) study that there existed statistical significance between cash conversion cycle and profitability. This study was however inconsistent with Gill’s study whose results showed that there exists no statistical difference between corporate profitability and average payable period. The positive correlation of average days in accounts payable variable and corporate profitability can be explained by the fact that lagging payments to suppliers ensures that firm has some cash to buy more inventory for sales and hence enhance profits. Interestingly, our study shows that that the 7 111

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firms listed in FMCG sector follow an aggressive working capital management policy to enhance corporate profitability. However, we must not forget that the risk element is also high in having an aggressive working capital policy. There should be a point of tradeoff. REFERNCES Frecka, T.J. and Cheng F.L. (1983), “Generalised Financial Ratio Adjustment Processes And Their Implications,” Journal of Accounting Research, Vol. 21 No. 1 , pp. 308-316 Gupta, M. C. and Roland J.H. (1972), “A Cluster Analysis Study of Financial Ratio and Industry Characteristics,” Journal of Accounting Research, Vol. 10 No.1. pp. 77-95 Nunn, Kenneth P. Jr.

(1981), “The Strategic Determinants of Working Capital: A

Product Line Perspective,” Journal of Financial research Vol.4 No.3, pp. 207-219 Greg F. Thomas M.K, (2005) "An Analysis of Working Capital Management Results Across Industries", American Journal of Business, Vol. 20 No. 2, pp.11 – 20. Huynh P.D. and Jyh- tay S. (2010). “The Relationship between Working Capital Management and Profitability: A Vietnam Case”, International Research Journal of Finance and Economics, Issue 49, pp. 59-67 Lamberson, M. (1995). “Changes in working capital of small firms in relation to changes in economic activity”, Journal of Business, Vol. 10 No.2, pp. 45-50. Maynard Rafuse,(1996). “Working capital Management: an urgent need to refocus”, Management Decision, Vol.34 No. 2, pp. 59-63. Smith. (1980). “Profitability versus liquidity tradeoffs in working capital management, in readings on the management of working capital. New York,St. Paul: West Publishing Company. Afza, T., & Nazir, M. (2009). Impact of aggressive working capital management policy on firms' profitability. The IUP Journal of Applied Finance, Vol.15 No.8, pp. 20-30. Ganesan, V. (2007). “An analysis of working capital management efficiency in telecommunication equipment.” Industry Rivier Academic Journal, Vol.3, No. 2, pp 1-10. Tahir,M. Anuar and Melati Ahmed, B. (2011). “The Effect of Working Capital

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Management on Firm's Profitability: A Review Paper.” Interdisciplinary Journal of Contemporary Research in Business, Vol. 3 .No.4, pp.365-376 Singh, J. P.and Pandey, S. (2008). “Impact of working Capital Management in the Profitability of Hindalco Industries Limited”. Icfai University Journal of Financial Economics, Vol.6. No.4, pp. 62-72. Report Downloaded from website http://www.ibef.org/download/fmcg_sectoral.pdf TABLE I: DESCRIPTIVE STATISTICS 7 FMCG firms, 1991-2011, 147 firm year observation

ROTA DTRD CTRD ITRD CCC

N 147 147 147 147 147

MINIMUM .04 1.83 26.19 24.87 -62.00

MAXIMUM .47 73.21 120.15 210.48 177.12

MEAN .1608 19.7665 61.4904 88.6144 46.8150

STD. DEV 7.913 14.5227 20.8168 42.5161 51.6412

TABLE II: CORRELATION ANALYSIS Pearson’s Correlation Coefficient Correlations (FMCG SECTOR)

ROTA

DTRD

113

Pearson’s Correlation Sig (2-tailed) Pearson’s Correlation Sig (2-tailed)

ROTA 1.000

DTRD **-0.601

CTRD **0.238

ITRD **-0.331

CCC **-0.535

.000

.004

.000

.000

1.000

**-0.251

.110

**.437

.002

.187

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Pearson’s Correlation Sig (2-tailed) Pearson’s ITRD Correlation Sig (2-tailed) Pearson’s CCC Correlation Sig (2-tailed) ** Correlation is significant at the 0.05 level (2-tailed).

1.000

CTRD

.045

**-.434

.591 1.000

.000 **.839 .000 1.000

TABLE III: DETERMINATION OF RETURN ON TOTAL ASSETS ON WORKING CAPITAL VARIABLES IN FMCG SECTOR (1992-2011) (OLS AND PANEL DATA)

* **

Variable

Coefficient

t-Statistic

C D(ITRD) D(CTRD) D(DTRD)

0.004350 -0.000123 -0.000439 -0.001949

1.682059 -0.841097 -*2.091751 **-3.358247

significant at 5% level significant at 1% level

R-squared Adjusted R-squared F-statistic Prob (F-statistic) Durbin Watson Stat

0.111073 0.091464 5.664467 0.001097 2.13258

TABLE IV: RESULTS OF HAUSMAN TEST

Correlated Random Effects - Hausman Test Test cross-section random effects Test Summary Cross-section random

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Chi-Sq. Statistic Chi-Sq.d.f. 0.671498

3

Prob. 0.8799

114



TABLE V: DETERMINATION OF RETURN ON TOTAL ASSETS ON WORKING CAPITAL VARIABLES IN FMCG SECTOR (1992-2011) RANDOM EFFECTS

Variable

Coefficient

C D(DTRD) D(CTRD) D(ITRD)

t-Statistic

0.004350 1.664759 -0.001949 **-3.323707 -0.000439 *-2.070237 -0.000123 -0.832446

*

significant at 5% level ** significant at 1% level R-squared Adjusted R-squared F-statistic Prob(F-statistic) Durbin- Watson Stat

0.111073 0.091464 5.664467 0.001097 2.13258

TABLE VI: DETERMINATION OF RETURN ON TOTAL ASSETS ON WORKING CAPITAL VARIABLES IN FMCG SECTOR (1992-2011)

Variable C D(ITRD) D(CTRD) D(CCC)

Coefficient

t-Statistic

0.004478 1.744156 0.001926 3.319117 -0.002493 **-4.131739 -0.002052 **-3.60787

**

significant at 1% level

R-squared Adjusted R-squared F-statistic Prob(F-statistic)

115

0.121438 0.102058 6.266139 0.000514

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Durbin-Watson stat


2.124175

TABLE VII: DETERMINATION OF RETURN ON TOTAL ASSETS ON WORKING CAPITAL VARIABLES IN FMCG SECTOR (1992-2011) RANDOM EFFECTS

Variable C D(ITRD) D(CTRD) D(CCC) ** significant at 1% level

R-squared Adjusted R-squared F-statistic Prob(F-statistic) Durbin-Watson stat

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Coefficient

t-Statistic

0.004478 1.726887 0.001926 **3.286254 -0.002493 **-4.090830 -0.002052 **-3.571968

0.121438 0.102058 6.266139 0.000514 2.124175

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