Working capital management profitability an empirical analysis

International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 –
INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)
6510(Online), Volume 4, Issue 1, January- February (2013)

ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
Volume 4, Issue 1, January- February (2013), pp. 121-129
IJM
© IAEME: www.iaeme.com/ijm.asp ©IAEME
Journal Impact Factor (2012): 3.5420 (Calculated by GISI)
www.jifactor.com

WORKING CAPITAL MANAGEMENT AND PROFITABILITY: AN
EMPIRICAL ANALYSIS OF INDIAN MANUFACTURING FIRMS

Arunkumar O. N1 and T. Radharamanan 2
1
Research Scholar, Department of Mechanical Engineering, National Institute of Technology
Calicut, Calicut 673601, Kerala, India
E-mail: arunkumar_pme09@nitc.ac.in;
2
Assistant Professor, Department of Mechanical Engineering, National Institute of
Technology Calicut, Calicut 673601, Kerala, India
E-mail: radha_ramaman@nitc.ac.in;

ABSTRACT

This paper analyzes the effect of working capital management on the profitability of
manufacturing firms. The study considers different variables affecting working capital
management and their effect on the profitability of manufacturing firms. The authors apply
correlation analysis and group wise weighted least squares regression analysis to identify the
effects of these variables on profitability. The results of correlation analysis show that there is
a negative relation between profitability and debtor’s days, inventory days, creditor’s days,
and cash conversion cycle. The data analysis was carried for 1198 manufacturing firms listed
in Centre for Monitoring Indian Economy for a period of 5 years. We find a significant
negative relationship between profitability and debtors’ days, inventory days, creditors’ days
and cash conversion cycle. We also find a significant positive relationship between the size of
the firm and profitability.

Keywords: working capital management, manufacturing firms, profitability, correlation
analysis, regression analysis.

1. INTRODUCTION

Well managed working capital is a barometer of operation efficiency, a source of
operational and strategic flexibility, and a basis for improved vendor and client relationships.
Yet many functions within the company are making decisions that directly and indirectly
impact working capital management. Typically organizational efforts that are limited to
squeezing suppliers, aggressive collections and drastic inventory reductions fail to address the

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root causes leading to unsatisfactory returns on working capital improvement initiatives.
Working capital management (WCM) due to its multifaceted nature is the most complex
business process to manage. It requires seamless integration of functional
interdependencies of sales, operations and finance and a flawless execution to unlock the
hidden working capital. Many existing research papers have found that managers spend a
considerable time on day to day working of capital decisions since current assets are short
lived investments that are continually being converted to other assets type (Rao 1989).
The chief financial officers of most companies spend most of their time and effort on day
to day WCM. Still, due to the inability of financial managers to properly plan and control
the current assets and current liabilities of their companies, likewise, the failure of a large
number of businesses can be attributed to the inefficient WCM (Smith 1973). Business
success heavily depends on the financial executives’ ability to effectively manage
receivables, inventory, and payables (Filbeck and Krueger 2005). Kumar and Tayyab
(1989) formulate and estimate for India an aggregate production function. The rationale
for the formulation is argued from the importance of working capital funds in organizing
production, and how the supply of money or the lack thereof, may constrain its
availability in a financially underdeveloped economy characterized by imperfect capital
markets
Specific research studies exclusively on the impact of WCM on profitability of
manufacturing firms are scanty especially for the case of India. India is attracting
significant attention as an attractive location for manufacturing industries in recent times.
As an important sector in the overall economic growth, manufacturing sector requires in
depth analysis at industry as well as firm level. Keeping this in view and the wider
recognition of the potential contribution of the manufacturing sector to the economy of
developing countries, the following objectives have been made for the study

• To make a panel data analysis of the manufacturing firms listed in Centre for
Monitoring Indian Economy (CMIE) for a period of 5 years
• To study the variables affecting the profitability of the Indian firms
• To establish a relationship between the profitability and the variables affecting the
manufacturing firms
• To find out the relationship between profitability and size of the firm
• To find out the contribution of debtors days to the profitability of the firm

The inferences of the study are expected to be beneficial to the Indian
manufacturing firms in understanding the variables that influence their profitability. The
panel data analysis and group-wise weighted least squares analysis made in Indian
context are the contributions to the literature by the authors. The regression results are
able to predict the percentage improvement in profit that can be obtained by controlling
the number of days of debtors, number of days of inventory and creditors’ days and cash
conversion cycle.
The rest of the paper is organized as follows: Section 2 looks at the relevant literature.
The data and the variables are explained in section 3. Section 4 explains the empirical
analysis and its interpretation, by providing the results of descriptive statistics, correlation
and regression analysis. Conclusions are given in section5.

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2. LITERATURE REVIEW
Smith (1973) identified eight major approaches taken towards the management of the
working capital. Efficiency of working capital management is based on the principle of
speeding up collections as much as possible and slowing down disbursements as much as
possible. This working management principle, based on the traditional concepts of the cash
conversion cycle introduced by Richards and Laughlin (1980), is a powerful performance
measure for assisting how well a company is managing its working capital.
Gentry, Vaidyanthan and Lee(1990) develop a weighted cash conversion cycle, which
scales the timing by the amount of funds in each step of the cycle. Larger inventory reduces
the risk of a stock out. Trade credit may stimulate sales because it allows customers to assess
product quality before paying (Deloof & Jegers, 1996; Long, Malitz, & Ravid, 1993).
As a part of a study of the fortune 500s financial management practices, Gilbert and
Reichert (1995) found that account receivable management models are used in 59% of these
firms to improve WCM projects, while, inventory management models were used in 60% of
the companies. Shamsud and James (1996) analyze the content and process of turnaround
strategies in smaller manufacturing firms. Weinraub and Visscher (1998) observe a tendency
of firms with low levels of current ratios to have low levels of current liabilities. Shin and
Sonen (1998) found a strong negative relation between the cash conversion cycle and
corporate profitability for a large sample of listed American firms for the 1975-1994 periods.
Howorth and Westhead (2003) examined working capital management routines of a large
random sample of small companies in the UK.
Deloof (2003) investigates the relation between WCM and corporate profitability of
1,009 large Belgian non financial firms. From the studies conducted to identify the trends in
WCM and its impact on Mauritian small manufacturing firms, Pandachi, (2006) identify that
the working capital needs of an organization changes over time as does its internal cash
generation rate. Raheman and Nasr (2007) conducted a study to analyze the relationship
between WCM and profitability in case of Pakistani firms. The result shows that, there is a
strong negative relationship between variables of WCM and profitability of the firm. The
firms can increase their profitability by reducing investment on accounts receivable and
inventories to a reasonable minimum, indicated by the benchmarks for their industry (Teruel
& Solano, 2007).
The above discussion clearly implies the importance of working capital management
(WCM) in determining the firm’s success. The present work tries to identify the various
factors of working capital management influencing profitability of manufacturing firms in
India.
3. DATA AND VARIABLES
This study uses financial statements of executive summary, assets and liability
statements of manufacturing firms listed in Centre for Monitoring Indian Economy (CMIE)
for a period of 5 years (i.e. 2005-06 to 2009-10). The data was collected for 1211 firms and
the firms with the 1% outlying values for Debtors Days (DTRDAYS), Inventory Days
(INVDAYS), and Creditors Days (CTRDAYS) were left out. Thus the samples size consists
of a balanced panel set of 5990 firm year observations of 1198 firms.Profit before
depreciation tax accounts (PBDTA), the dependent variable, is taken as a proxy for
profitability. Table 1 presents the independent and control variables, notations and its
calculation methods used in the analysis.

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Table 1. Variables Used in the Analysis
No Variable Notations Calculation Method

1 Debtors Days DTRDAYS [accounts receivable * 365] / Sales.(It is
taken from executive summary of the firms)
2 Inventory Days INVDAYS [Inventories*365] / Cost of goods sold

3 Creditors Days CTRDAYS [accounts payable*365]/ Cost of goods sold.
(It is taken from executive summary of the
firms)
4 Cash Conversion Cycle CCC DTRDAYS + INVDAYS – CTRDAYS

5 Current Ratio CR Current Assets / Current Liability

6 Ratio of Current Liability CLTOTA Current Liability/ Total Assets
to Total Assets.
7 Financial Assets to Total FATOTA Financial Assets / Total Assets
Assets
8 Size SIZE Natural Logarithm of Total Assets

9 Assets Turnover Ratio ATR Sales/total asset

Debtors Days, Inventory Days, Creditors Days and Cash Conversion Cycle are used
as independent variables. CCC is used as the comprehensive measure of working capital as it
shows the time lag between expenditure for the purchase of raw materials and the collection
of sales of finished goods. CR, CLTOTA, FATOTA, SIZE and ATR are used as the control
variables.

4. DATA ANALYSIS AND INTERPRETATION

4.1 Descriptive Statistics

Table 2 shows the descriptive statistics. The manufacturing industry is having on an
average 22 % of profit for its sales and most of the firms included in the analysis show profit
of a 9%. The average CCC is 57.63 days (median is 49.45days), it shows two month’s time
for the cash conversion cycle. The firm receives the payment on sales after an average of
65.49days with median 51.26 days. It takes on average 81.83days (median is 58.27) to
convert the raw materials and sell the finished goods inventory and firms take on an average
105.21days (with median 64.20days) to pay purchases. The analysis of Indian manufacturing
firms shows that the firms less credit period to the customers in comparison with what they
are enjoying.

124


Table2. Descriptive Statistics

Variables Mean Minimum Median Maximum Std. Dev
DTRDAYS 65.49 0.00 51.26 3174.30 90.00
INVDAYS 73.97 0.00 58.27 4584.80 100.26
CTRDAYS 81.83 0.00 64.20 4610.60 122.44
CCC 57.63 -2567.40 49.45 4498.50 130.85
CR 3.76 -1.28 2.26 1080.00 17.24
CLTOTA 0.31 -0.20 0.21 144.22 1.98
FATOTA .03513 -.00044 0.00053 56.27060 0.76002
SIZE 5.13 -3.22 5.07 12.43 1.71
ATR 1.30 0.00 1.02 384.74 5.29
PBDTA 131.59 -252.19 13.600 31057. 927.93

CR is the traditional measure of liquidity. It indicates the availability of current assets in
rupees for every one rupee of current liability. The industry has a high liquidity with the average
current ratio being 3.76. For manufacturing firms, the current liabilities are 31% of total assets. Sales
are 1.3 times the total assets employed. Financial assets employed in the firm are only an average of
3.5% of the total assets. The size of the company is calculated as the logarithm of total assets. The
mean value of ATR shows that sales of the firms are on an average of 1.3 times of the total assets. For
most of the manufacturing firms in India, the sales are equal to the total assets employed by the firm.

4.2 Correlation Analysis

Table3 presents correlation coefficients at 5% critical value (two tailed) (=0.0253) for all
variables considered. There is a negative relation between PBDTA and measures of working capital
management such as DTRDAYS, INVDAYS, CTRDAYS and CCC. This is consistent with the view
that, when we consider the variables independently, the time lag between the expenditure for the
purchase of raw materials and the collection of sales of finished goods can be too long, and that
decreasing this time lag increases profitability.
PBDTA shows a negative correlation with current ratio implies that profitability and liquidity
are inversely related. PBDTA shows a positive correlation with CLTOTA, FATOTA SIZE and ATR.
So, these variables have high influence on the return on assets. Size of the firm shows a negative
correlation with PBDTA.

Table3. Correlation Matrix
DTRDAYS INVDAYS CTRDAYS CCC CR CLTOTA FATOTA SIZETA ATR PBDTA
DTRDAYS 1.0000 0.0305 0.5610 0.1863 0.0133 0.0041 -0.0022 -0.0997 -0.0429 -0.0582
INVDAYS 1.0000 0.1715 0.6268 0.0347 -0.0148 -0.0066 0.0072 -0.0355 -0.0249

CTRDAYS 1.0000 -0.4185 -0.0532 0.0362 0.0065 0.0219 -0.0289 -0.0077

CCC 1.0000 0.0855 -0.0424 -0.0126 -0.0836 -0.0297 -0.0519
CR 1.0000 -0.0176 -0.0024 -0.0810 -0.0082 -0.0153

CLTOTA 1.0000 0.8990 -0.0372 0.9156 0.1056

FATOTA 1.0000 -0.0033 0.8954 0.1080
SIZE 1.0000 -0.0464 0.3355

ATR 1.0000 0.1058
PBDTA 1.0000

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4.3 Regression Analysis

Regression analysis is used to estimate the causal relationship between profitability
and the other chosen variables. The determinants of corporate profitability are estimated by
using group wise weighted least squares. This study uses panel data regression analysis of
cross-sectional and time series data. The specific forms of the models used for the linear
regression analysis are as follows:

ܲ‫ܣܶܦܤ‬௜௧ ൌ ߚ଴ ൅ ߚଵ ‫ܻܵܣܦܴܶܦ‬௜௧ ൅ ߚଶ ‫ܴܥ‬௜௧ ൅ ߚଷ ‫ܣܱܶܶܮܥ‬௜௧ ൅ ߚସ ‫ܣܱܶܶܣܨ‬௜௧ ൅ ߚହ ܵ‫ܧܼܫ‬௜௧
൅ ߚ଺ ‫ܴܶܣ‬௜௧ ൅ ߝ௜௧ … … … … … … … … … … … … … … … ሺ1ሻ

ܲ‫ܣܶܦܤ‬௜௧ ൌ ߚ଴ ൅ ߚଵ ‫ܻܵܣܦܸܰܫ‬௜௧ ൅ ߚଶ ‫ܴܥ‬௜௧ ൅ ߚଷ ‫ܣܱܶܶܮܥ‬௜௧ ൅ ߚସ ‫ܣܱܶܶܣܨ‬௜௧ ൅ ߚହ ܵ‫ܧܼܫ‬௜௧
൅ ߚ଺ ‫ܴܶܣ‬௜௧ ൅ ߝ௜௧ … … … … … … … … … … … … … … … ሺ2ሻ

ܲ‫ܣܶܦܤ‬௜௧ ൌ ߚ଴ ൅ ߚଵ ‫ܻܵܣܦܴܶܥ‬௜௧ ൅ ߚଶ ‫ܴܥ‬௜௧ ൅ ߚଷ ‫ܣܱܶܶܮܥ‬௜௧ ൅ ߚସ ‫ܣܱܶܶܣܨ‬௜௧ ൅ ߚହ ܵ‫ܧܼܫ‬௜௧
൅ ߚ଺ ‫ܴܶܣ‬௜௧ ൅ ߝ௜௧ … … … … … … … … … … … … … … … … ሺ3ሻ

ܲ‫ܣܶܦܤ‬௜௧ ൌ ߚ଴ ൅ ߚଵ ‫ܥܥܥ‬௜௧ ൅ ߚଶ ‫ܴܥ‬௜௧ ൅ ߚଷ ‫ܣܱܶܶܮܥ‬௜௧ ൅ ߚସ ‫ܣܱܶܶܣܨ‬௜௧ ൅ ߚହ ܵ‫ܧܼܫ‬௜௧ ൅
ߚ଺ ‫ܴܶܣ‬௜௧ ൅ ߝ௜௧ … … … … … … … … … … … … … … … … … … … … … . … ሺ4ሻ

Where

ߚ଴ = Intercept of the equation
ߚଵ , ߚଶ , … . . ߚ଺ = Coefficient of the variables
ε = Error tem
i = Number of firms, 1 to 1198.
t = Time period, 1 to 5.
Four regression models are modeled to study the impact of the independent variables
individually.

The pooled ordinary least squares (OLS) regression model shows heteroskedasticity.
Because of heteroskedasticity, t–test and F – test fail. To counter this problem, it is
recommended, the analysis is conducted by using Weighted Least Squares (Wooldridge,
2004). It is a generalized least squares technique using weight. We conduct group wise
weighted least squares. Weights are based on per unit error variance. The analyses are
conducted using Gretl software. Table 4 shows the results of regression analysis.
Regression model (1) is estimated with DTRDAYS being considered as independent
variable. The coefficient of DTRDAYS is negative and implies that a decrease in the number
of days of accounts receivable by one day is associated with an increase of profitability by
3.195% and it is significant with 99 percentage level of significance.

126


Table 4. Group wise Weighted Least Squares

Variable Regression model Regression Regression Regression
(1) model(2) model(3) model(4)
Constant -227.959 -224.610 -229.190 -227.531
(0.0000***) (0.0000***) (0.0000***) (0.0000***)
DTRDAYS -0.0319540 ------ ------- -------
(0.0015***)
INVDAYS ------- -0.0800197 ------- -------
(1.73e-014***)
CTRDAYS ------- --------- -0.0284547 --------
(0.0002***)
CCC ------- ------- -------- -0.0279363
(0.0004***)
CR 0.128708 0.243744 0.0952759 0.186840
(0.0003***) (0.0002***) (0.0775*) (0.0019***)
CLTOTA 17.2429 16.7878 19.2520 15.7647
(8.79e-013***) (3.35e-013 ***) (6.58e-014***) (1.57e-011***)
FATOTA -4.25933 -3.13904 -4.04744 -3.75683
(0.0282**) (0.1400) (0.0537*) (0.0604*)
SIZE 51.2588 51.3723 51.5616 51.0355
(0.0000***) (0.0000***) (0.0000***) (0.0000***)
ATR 5.59321 4.78390 5.10953 5.60711
(1.72e-024***) (1.18e-019***) (1.51e-023***) (1.36e-029***)
R2 Value 0.349020 0.349369 0.349801 0.347296
Adj. R2 0.348367 0.348717 0.349149 0.346641
F test 534.6262 535.4481 536.4675 530.5806
(Sig.) (0.000000) (0.000000) (0.000000) (0.000000)

***
The variable with 99 percentage level of significance.
**
*
In regression model (2), profitability and number of days of inventories (INVDAYS)
has negative relationship with 99 percentage level of significance. This means that the
increase in number of days of inventory will lead to increase in profitability and vice versa.
The regression model (3) shows that CTRDAYS is inversely related to profitability.
The relationship is also found significant at 99 percentage. Long number of days of accounts
payable led the firm to a low level of profitability and vice versa. An alternate explanation is
less profitable firms wait longer to pay their bills. The cash conversion cycle (CCC) is taken
as an independent variable in regression model (4). The coefficient of CCC is negatively
related to profitability with 99 percentage level of significance. A decrease in the cash
conversion cycle by one day is associated with an increase of profit by 2.79%.
In all regression models PBDTA is positively related with CR, CLTOTA, SIZE and
ATR with 99 percentage significance. FATOTA is negatively related to PBDTA at 95%
significance level. The results of regression models (1) to (4) suggests that the managers can
increase the corporate profitability by decreasing Debtors Days (DTRDAYS), Creditors Days
(CTRDAYS), Inventory Days (INVDAYS), and Cash Conversion Cycle (CCC).

127


The natural logarithm of the total assets is taken as a proxy for the size of the firm. The size
of the firm is found to be significantly positively related to profitability of the firm. This
indicates that higher the size of the firm the profitability increases. A 1% increase in SIZE is
associated with an average 0.51% increase in profitability.
The F test proves that there is no possibility of getting zero values for all regression
coefficients of variables or there is a possibility that at least one regression coefficient will
get more than a zero value. The F test shows that the model has the possibility of predicting
PBDTA with a high significant level since the p value is (0.00). The adjusted R2 of the
regressions are 35%, means that 35% of variability in variances are explained by the model.

5. CONCLUSION

Most firms have a large amount of cash invested in working capital. It can therefore
be expected that the way in which working capital is managed will have significant impact on
the profitability of firms. The descriptive and regression analyses have identified critical
management practices and are expected to assist mangers in identifying areas where they
might improve the financial performance of their operation.
The study has been conducted on manufacturing industries, irrespective of the
business differences. The findings of the analysis show a significant negative relationship
between profitability and debtors’ days, inventory days, creditors’ days and cash conversion
cycle. The results suggest that the managers can create value for their share holders by
reducing cash conversion cycle. The negative relationship between creditors days and
profitability suggest that long number of days of accounts payable leads the firm to a low
level of profitability and vice versa.

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Working capital management profitability an empirical analysis

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