A statistical study has been conducted on few companies in FMCG sector to understand the relationship between working capital management and profitability

1. 1 | W C M I N F M C G S E C T O R
ADVANCED CORPORATE FINANCE
A PROJECT ON
RELATIONSHIP BETWEEN WORKING CAPITAL
MANAGEMENT AND PROFITABILITY
SECTOR: FMCG
Submitted to
Prof. Vipul Kumar Singh
Submitted by
Sirsa Majumder (2013282)
Sneha Malhotra (2013284)
Soumitra Kansabanik (2013287)
Souvik Chakraborty (2013289)
Vijaya Srivastava (2013322)

2. 2 | W C M I N F M C G S E C T O R
CONTENTS
ACKNOWLEDGEMENT---------------------------------------------------------------- 3
1. INTRODUCTION--------------------------------------------------------------------------4
2. USES & APPLICATION OF WCM IN FMCG SECTORS--------------------------5
3. METHODS----------------------------------------------------------------------------------6
3.1.DATA SOURCE & SAMPLE DESIGN------------------------------------------- 7
3.2.VARIABLES SELECTED------------------------------------------------------------7
3.3.DATA COLLECTION---------------------------------------------------------------- 8
4. MODEL SPECIFICATION--------------------------------------------------------------- 9
5. RESULTS & DISCUSSIONS----------------------------------------------------------- 10
5.1. TEST OF NORMALITY-------------------------------------------------------------10
5.2. DESCRIPTIVE STATISTICS------------------------------------------------------ 10
5.3.CORRELATION STATISTICS----------------------------------------------------- 11
5.4.REGRESSION STATISTICS-------------------------------------------------------- 13
5.5.HYPOTHESIS TESTING------------------------------------------------------------ 16
6. SCORING & RANKING------------------------------------------------------------------17
7. ACCOUNTING RATIO ANALYSIS (NON STATISTICAL)-----------------------19
8. CONCLUSION----------------------------------------------------------------------------- 20
9. BIBLIOGRAPHY---------------------------------------------------------------------------21

3. 3 | W C M I N F M C G S E C T O R
ACKNOWLEDGEMENT
I express my deepest and most sincere thanks to my project guide Mr. Vipul Kumar Singh,
Professor, IMT Nagpur. The project could not be completed without his able support, knowledge
sharing and guidance, for helping me and providing me with useful information.
I take this opportunity to thank all of my colleagues, without their cooperation it was not possible
to complete this project.

4. 4 | W C M I N F M C G S E C T O R
1. Introduction:
The FMCG companies or Fast Moving Consumer Goods companies are typically those
companies that sell non durable goods at competitive prices. These goods have a low shelf life
and are usually sold in large volumes at low margins. This sector benefits from the economies of
scale and optimum utilization of capacity. The manufacturers need to have a wide distribution
network to increase turnover. Given such characteristics, we can easily understand the scope of
Working Capital Management in such a sector.
Company Name High Low Last Price Prv Close Change % Gain
Godrej
Consumer
745.80 719.00 739.30 713.85 25.45 3.57
Tata Global Bev 144.20 139.70 142.10 138.90 3.20 2.30
United Brewerie 830.85 805.00 818.20 802.15 16.05 2.00
Dabur India 171.40 167.15 171.00 168.30 2.70 1.60
Nestle 5,039.90 4,973.50 5,028.60 4,970.30 58.30 1.17
Colgate 1,315.50 1,302.65 1,313.95 1,305.55 8.40 0.64
United Spirits 2,663.00 2,605.00 2,619.45 2,606.05 13.40 0.51
Marico 213.50 210.50 210.85 210.35 0.50 0.24
Table 1
The FMCG sector is the fastest growing sector in the manufacturing sector. It is growing in
double digits due to high inflationary pressures on prices of food and other input prices and
because of the high population that caters to frequent buying of such products (See Table 1). It is
therefore of particular interest to students of corporate finance to analyze such companies in
context of their working capital and profitability and derive a link between both of them.

5. 5 | W C M I N F M C G S E C T O R
Some of the companies that are to be studied in this report are among the top 15 companies of
India. Asian Paints, Marico, Britannia, ITC and HUL are the companies of research in this
report.
The Primary motive of choosing such companies-
All of them started as small units and gradually made big in the market
Inventory levels are almost similar in the same industry
Availability of substitutes makes it difficult for companies to build customer brand
loyalty, thus consistent good performance is essential.
2. USES AND APPLICATIONS OF WCM IN THE FMCG SECTOR:
Working Capital can be basically described as the capital which is required for the businesses in
their day to day operations such as purchasing of raw materials, payments for direct and indirect
expenses to carry on production, investments in stock and store, etc. Decisions that are related to
working capital and short- term financing are referred to as Working Capital Management which
means the administration of all the aspects of current assets as well as current liabilities.
The important and essential part of managing working capital in the FMCG sector is maintaining
the firms liquidity in day-to-day operations in order to ensure smooth running of the businesses
and that obligations are met. This is not an easy task because managers must ensure that the firm
is running in efficient and profitable manner. But also there are high possibilities of mismatch of
current asset and current liability during this process. If this happens and company manager fails
to manage it properly then it will affect growth and profitability which will further escalate to
financial distress and bankruptcy. Managers of FMCG companies use up considerable time on
day-to-day problems that involving working capital decisions the major reason is that current
assets are short-term investments and are continually converted into other assets. As far as
current liabilities, the firm is responsible for paying these obligations as and when they arise.
Liquidity for the FMCG Company is not reliant on the value of liquidation of its assets, but on
the operating cash flows generated by those assets. Thus, when taken together, decisions on the
level of different working capital components become frequent, repetitive, and time consuming
thus we can conclude that working capital management is a very sensitive area in the field of
financial management and it involves the high level decision of the amount and composition of
current assets and the financing of these assets as well as sources of financing.
The working capital management of a FMCG company partly affects its profitability. Firms have
to maintain an optimal level of working capital that maximizes its value. Huge inventory base
and a liberal trade credit policy may lead to lofty sales, whereas larger inventory reduces the risk
of a stock-out. On the other hand, trade credit may stimulate sales because it allows customers to
assess product quality before

6. 6 | W C M I N F M C G S E C T O R
paying One of the well-accepted measures of working capital is the Cash Conversion Cycle
(CCC), i.e., the time lag between the expenditure for the purchases of raw materials and the
collection of sales of finished goods. The longer this time lag, the larger the investment in
working capital . A longer CCC might increase profitability because it leads to higher sales.
However, corporate profitability might also decrease with the CCC, if the costs of higher
investment in working capital rise faster than the benefits of holding more inventories and/or
granting more trade credit to customers.
3. METHODS:
The sample companies and variables included in the study, the distribution patterns of data and
applied statistical techniques in exploring the relationship between working capital management
and profitability of FMCG companies in India.
3.1 Data Source and Sample Design:
The data used in the study was acquired from Bloomberg. Preferred samples of 5 FMCG
companies from the year 2008-09 to 2012-13 were utilized in this analysis. The sample size has
been restricted to only 5 FMCG companies as complete financial data for these companies were
available.
Table 2: Selected FMCG companies
S.
No.
FMCG Companies
1 Hindustan Unilever Limited (HUL)
2 ITC Limited
3 Asian Paints (India )
4 Britannia Industries Limited
5 Marico Industries Limited
3.2 Variables Selected:
Different Variables have been selected in order to perform different statistical studies performed.
There are two kinds of variables used. They are:
(1) Independent Variables – Commonly Used Financial ratios
(2) Dependent Variables - Used as profitability of sample companies

7. 7 | W C M I N F M C G S E C T O R
Table 3: Selected Variables
S.No. Independent Variables Dependent Variables
1 Cash Conversion Cycle Working Capital
2 Age of Inventory Return on Assets
3 Age of Creditors
4 Age of Debtors
5 Acid Test Ratio
6 Inventory Turn Over Ratio
7 Current Ratio
CCC is used as a comprehensive measure of working capital as it shows the time lag between
expenditure for the purchases of raw materials and the collection of sales of finished goods. The
longer the cycle, the larger the funds blocked in working capital.
CCC = (No. of Days Accounts Receivables + No. of Days Inventory) – No. of Days
Accounts Payable
Age of Inventory or DSI is the Inventory Conversion period which is the average time required
to convert materials into finished goods and then to sell those goods.
(Average Inventory/Average Cost of Sales) X 365days.
Age of Debtors or DSO is Receivables Collection Period which is the average length of time
required to convert the firm’s receivables into cash, that is, to collect cash following a sale.
(Average Debtors/Average Annual Credit Sales) X 365 days.
Age of Creditors or DSP is Payables deferral period which is the average length of time between
the purchases of materials and labor and the payment of cash for them.
(Average Creditors/Average Cost of Sales) X 365 days.
Acid Test Ratio is the conservative approach as it deducts the inventories which we cant liquefy
easily into cash
(Current Asset-Inventories)/ Current Liabilities
Inventory Turn Over ratio is calculated to consider the adequacy of the quantum of capital and its
justification for investing in inventory. A firm must have reasonable stock in comparison to sale.
It is the ratio of cost of sale and average inventory. This helps in evaluating inventory policy.
This ratio analyzes the number of times finished stock is turned over during a given accounting
period. This ratio is used for measuring the profitability.
(Cost Of Goods Sold/Average Inventories)
Current Ratio of a firm measures its short-term solvency, its ability to meet short-term
obligations. As a measure of short-term current financial liquidity, it includes the rupees of
current assets available for each rupee of current liabilities / obligation. The higher the current
ratio the larger the amount of rupees available per rupee of current liability.
Return on Assets is better one since it relates the profitability of the business to the asset base.
ROA=PBIT/Total Assets

8. 8 | W C M I N F M C G S E C T O R
3.3 DATA collection:
Data collection is the most important part in doing in any analysis. We have taken secondary
data into account. All the datas have been taken from Bloomberg. Some of the datas are as
follows:
Table 4: Company wise Current Asset, Current Liabilities and net profit
Sl No. Company
2009 2010
Current
Asset
Current
Liabilities
Net
Profit
Current
Asset
Current
Liabilities
Net
Profit
1 HUL 56009 57838.03 24964.49 61396.7 67332 22020
2 ITC 101051.7 47748 32635 122079.4 80609 40610
5 Asian Paints (India) 17987.1 14170 3978.4 23753.3 18545.7 8356.4
6 Britannia Industries Ltd. 9080.57 5036.54 1514.85 6312.85 6430.04 1031.8
8 Marico Industries Ltd. 5760.5 4139.5 1421 8014.6 5963.7 2350
Sl No. Company
2011 2012
Current
Asset
Current
Liabilities
Net Profit
Current
Asset
Current
Liabilities
Net
Profit
1 HUL 69742 66200 23059 77986 64487 26914
2 ITC 130455.4 84774.8 49876 142630.5 91262.9 61623
5 Asian Paints (India) 30389 20806.6 8432.4 35066.7 25870.3 9887.3
6 Britannia Industries Ltd. 8236.8 6661.9 1343.5 9798.9 12205.4 1995.5
8 Marico Industries Ltd. 7157.9 5051.1 3153.2 10040.1 6064.9 3365.9
Sl No. Company
2013
Current
Asset
Current
Liabilities
Net
Profit
1 HUL 75699 76558.6 37966.7
2 ITC 175404.7 103307.3 74183.9
5 Asian Paints (India) 40065 29314.1 11138.8
6 Britannia Industries Ltd. 8620.9 11208.1 2595
8 Marico Industries Ltd. 13260.3 7960 4290.9
Table 5: Return On Asset
S.No. Company 2008-09 2009-10 2010-11 2011-12 2012-13
1 HUL 16.28 20.25 23.43 25.49 33.79
2 ITC 17.77 19.12 20.59 22.65 23.55
3 Asian Paints 17.07 28.98 22.18 22.16 20.11
4 Britannia Industries Ltd. 14.53 8.96 10.34 11.84 13.94
5 Marico Industries Ltd. 16.39 21.98 21.57 17.97 16.41

9. 9 | W C M I N F M C G S E C T O R
4. MODEL SPECIFICATIONS:
1. Test of Normality: Before doing any kind of regression analysis normality test should be
carried out in order to show that the data is normally distributed. Two generally used
methods for Normality test are Shapiro-wilks’ test and Kolmogorov-smirnov Test. Over
here we have used Kolmogorv-smirnov’s test to reach to the conclusion.
Apart from this Skewness test and Kurtosis test has also been performed in order to see
whether the datas are perfectly symmetricl or is skewed towards certain direction.
2. Descriptive Statistics : This has been incorporated in order to see what is the mean value
of data and the standard deviation. The maximum and the minimum values both in
respect of companies and in respect of years.
3. Correlation Statistics: Correlation coefficient or Pearson’s Coefficient has been
calculated in order to generate relation between two variables. The coefficient gives an
insight into the nature and extent of relationship i.e., it is strongly correlated, moderately,
poorly or negatively correlated. The Pearson’s coefficient is defined only if both of the
standard deviations are finite and both of them are nonzero.
4. Regression Statistics: Over here an attempt has been made to examine the composite
effect of working capital management on profitability through multivariate regression
analysis and with the help of ‘t’ test regression coefficients are tested. The regression
model have been used in this analysis:
ROAit = £+β1CCCit+ADit+AIit+ACit
Where ROAit = Return on Asset counted yearly of each firm
CCCit = Cash Conversion Cycle counted yearly of each firm
ADit = Age of Debtors counted yearly of each firm
AIit = Age of Inventory counted yearly of each firm
ACit = Age of Creditors counted yearly of each firm
The strength of the relationship between the dependent variable, ROA and all the
independent variables taken together of all firms and the impact of these independent
variables on the profitability.
5. Hypothesis testing: A hypothesis is an assumption to be tested. The statistical testing of
hypothesis is the important technique in statistical inference. Hypothesis testes are widely
used in business and industry for making decisions.
6. Ranking system: A ranking system has been developed using variables like Cash
Conversion Cycle, Quick Ratio/Acid test ratio, Inventory turn over ratio, Current ratio.
Considering these variables a net score has been developed and lower the score better is
their working capital management.

10. 10 | W C M I N F M C G S E C T O R
7. Accounting ratios has been holistically analyzed and an inference has also been brought.
5. RESULTS & DISCUSSION:
Below all the results and discussion have been done and the calculations are attached at last
5.1.Test of Normality: The Kolmogorov-Smirnov tests determines whether the data sets are
normally distributed or not. Table 6 below shows the following result.
Table 6: Raw data of Normality Tests for all variables for all firm over the 5-year period
Variables Details
Shape Kolmogorov-Smirnov
Skewness Kurtosis Stat. Sig.
1 Cash Conversion Cycle 1.232 1.060 0.176 0.05
2 Age of Inventory 0.868 -0.244 0.181 0.05
3 Age of Debtors -0.178 -0.804 0.1 0.01
4 Age of Creditors 0.681 -0.650 0.119 0.2
5 Return on Assets 0.369 0.797 0.099 0.1
6 Operating Cycle 0.533 -0.534 0.735 0.05
It shows that all the datas are normally distributed and the skewness of the data sets are generally
bit right shifted and for Age of debtors its left shifted. The Dn chart used for Kolmogorov
Statistics calculation is attached.
5.2.Descriptive Statistics: Table 7 provides descriptive statistics of the collected dependent
and independent variables of all 5 FMCG companies over 5-year period. All variables are
calculated using accounting ratios
Table 7: Descriptive statistics results for all variables tested for all firms over the 5-year
period
Variables Details N Maximum Minimum Mean Std. Dev
1 Cash Conversion Cycle 25 117.19 5.30 43.90 32.16757
2 DSI/ Age of Inventory 25 145.57 23.43 70.90 36.86839
3 DSO / Age of Creditors 25 26.59 6.44 16.19 5.965162
4 DSP / Age of Debtors 25 105.89 14.34 52.01 28.002
5 Return on Assets 25 33.79 8.96 19.49 5.585691
6 Operating Cycle 25 163.28 30.73 87.09 39.97367

11. 11 | W C M I N F M C G S E C T O R
The average return on asset is 19.49% with a standard deviation of 39.97%. The
maximum Return the companies got is 337.9% and a minimum of 89.6% which is fairly a
good return.
CCC is used to check the efficacy in working capital management was on average 44days
and standard deviation was about 32 days. The selected companies take an average of
70days to sell inventory with standard deviation of 37 days. Here, maximum time taken
by a company is 145-146days whereas the minimum time taken is 23-24days. This can be
considered to be an average time period in terms of converting inventory into sales.
The companies receive payment from debtors after an average of 52 days whose standard
deviation is 28 days. Minimum time taken is 14 days and maximum time taken is 106
days. So the average value is fairly good. Companies usually take an average of 16 days
to pay their creditors with standard deviation of only 6 days. Here, minimum time taken
by a company was 6-7 days and maximum was 27 days. The net operating cycle of
companies has been 87 days with a standard deviation of 40 days with a minimum
operating day of 31 and maximum 163 days.
5.3.Correlation Statistics:
Table 8: Pearson Correlations for all variables tested for all firms over the 5-year period
Variables CCC AI/DSI AD/DSO AC/DSP
ROA/Profitabilit
y
Operatin
g Cycle
Cash Conversion Cycle 1 _ _ _ _ _
DSI/ Age of Inventory 0.86 1 _ _ _ _
DSO / Age of Creditors 0.4 0.88 1 _ _ _
DSP / Age of Debtors -0.09 0.26 0.27 1 _ _
Return on Assets -0.28 0.29 -0.51 0.48 1 _
Operating Cycle 0.85 0.99 0.57 0.28 0.34 1
The table reveals Pearson’s correlation analysis among all the variables under
investigation. Pearson’s Correlation analysis has been used to see the relationship
between working capital management and profitability. If efficient working capital
management increases profitability, one should expect a negative relationship between
the measures of working capital management and profitability and vice versa.
As hypothesized, profitability has an inverse relationship with the CCC and the
components of this cycle; namely, DSI, DSO and DSP the results imply that the firm’s
financial health is inversely related to the CCC and few of its components.

12. 12 | W C M I N F M C G S E C T O R
A sample calculation is herewith attached
Table 9: Calculation of Pearson’s Coefficient
OC-ROA
X y xy x^2 y^2
62.48 16.28 1017.16 3903.66 265.04
66.93 20.25 1355.38 4479.94 410.06
77.51 23.43 1816.13 6008.30 548.96
61.62 25.49 1570.77 3797.41 649.74
56.36 33.79 1904.26 3175.98 1141.76
163.28 17.77 2901.51 26660.74 315.77
157.94 19.12 3019.75 24944.05 365.57
152.34 20.59 3136.77 23208.81 423.95
147.06 22.65 3330.92 21626.83 513.02
145.04 23.55 3415.70 21036.66 554.60
79.77 17.07 1361.67 6363.26 291.38
90.34 28.98 2618.04 8161.24 839.84
94.71 22.18 2100.59 8969.31 491.95
92.08 22.16 2040.53 8479.04 491.07
97.57 20.11 1962.15 9520.06 404.41
40.33 14.53 586.00 1626.54 211.12
37.38 8.96 334.94 1397.38 80.28
34.98 10.34 361.74 1223.89 106.92
37.68 11.84 446.17 1420.02 140.19
30.73 13.94 428.39 944.37 194.32
75.49 16.39 1237.28 5698.71 268.63
89.99 21.98 1978.01 8098.46 483.12
99.44 21.57 2144.90 9888.17 465.26
92.83 17.97 1668.07 8616.54 322.92
93.43 16.41 1533.26 8729.99 269.29
2177.33 487.35 44270.10 227979.36 10249.20
N 25
Numerator 45632.83989
Denominator 133968.3267
R 0.34

13. 13 | W C M I N F M C G S E C T O R
Similarly the rest of the coefficients has also been determined.
5.4.Regression Statistics
Table 10: Regression Analysis model for DSO and Return On Asset
DSO-ROA
X y xy x^2 y^2
9.56 16.28 155.62 91.37 265.04
14.07 20.25 284.93 197.98 410.06
17.56 23.43 411.44 308.36 548.96
13.34 25.49 340.11 178.03 649.74
13.47 33.79 455.14 181.43 1141.76
17.72 17.77 314.81 313.85 315.77
19.25 19.12 368.09 370.62 365.57
17.80 20.59 366.57 316.95 423.95
16.73 22.65 379.01 280.01 513.02
16.26 23.55 383.02 264.53 554.60
26.59 17.07 453.82 706.80 291.38
23.60 28.98 684.04 557.14 839.84
20.48 22.18 454.30 419.54 491.95
23.04 22.16 510.57 530.86 491.07
25.93 20.11 521.55 672.60 404.41
7.89 14.53 114.71 62.33 211.12
7.09 8.96 63.51 50.24 80.28
6.44 10.34 66.57 41.45 106.92
7.55 11.84 89.44 57.06 140.19
7.31 13.94 101.84 53.37 194.32
16.93 16.39 277.52 286.71 268.63
20.67 21.98 454.36 427.31 483.12
20.71 21.57 446.79 429.06 465.26
19.10 17.97 343.27 364.91 322.92
15.61 16.41 256.14 243.63 269.29
404.73 487.35 8297.18 7406.15 10249.20
N 25
Covariance S(x,y) 16.97624762
Var S(Y)^2 31.19994167
Var S(X)^2 35.58315534
Mean Y y(m) 19.494
Mean X x(m) 16.18907639

14. 14 | W C M I N F M C G S E C T O R
B 0.477086629
A 11.77040813
SSE 737.3485209
SEE 5.662034205
SE (b) 0.193751306
SE (a) 0.206914048
R^2 0.15291267
Model Coefficients Std Error t-stat P-value
Constant 11.77040813
0.2069140
48 56.88549545 0.00
DSO 0.477086629
0.1937513
06 2.462365996 0.00
Dependent Variable: ROA
Predictors(Constant): DSO
Model R R^2 SEE
1
0.3910404
96 0.15291267 5.662034205
The value of R denotes the response of working capital management indicator (DSO)
with change in profitability.
As per the results shown above the multiple correlation coefficient between the
dependent variable ROA and the independent variable DSO was 0.391. It indicates that
that profitability is responsive by its working capital management indicators. It is also
evident from the value of R2
that 15.3% of variation in ROA was accounted by DSO.
Table 11: Regression analysis between Working Capital and Net Profit obtained from Balance
sheet
WC-Profit
X y xy x^2 y^2
-1829.03 24964.49 -45660801.1 3345350.74 623225760.96
53303.70 32635.00 1739566249.50 2841284433.69 1065043225.00
3817.10 3978.40 15185950.64 14570252.41 15827666.56
4044.03 1514.85 6126098.85 16354178.64 2294770.52
1621.00 1421.00 2303441.00 2627641.00 2019241.00
-5935.30 22020.00 -130695306.00 35227786.09 484880400.00
41470.40 40610.00 1684112944.00 1719794076.16 1649172100.00

15. 15 | W C M I N F M C G S E C T O R
5207.60 83564.40 435169969.44 27119097.76 6983008947.36
-117.19 1031.80 -120916.64 13733.50 1064611.24
2050.90 2350.00 4819615.00 4206190.81 5522500.00
3542.00 23059.00 81674978.00 12545764.00 531717481.00
45680.60 49876.00 2278365605.60 2086717216.36 2487615376.00
9582.40 8432.40 80802629.76 91822389.76 71105369.76
1574.90 1343.50 2115878.15 2480310.01 1804992.25
2106.80 3153.20 6643161.76 4438606.24 9942670.24
13499.00 26914.00 363312086.00 182223001.00 724363396.00
51367.60 61623.00 3165425614.80 2638630329.76 3797394129.00
9196.40 9887.30 90927565.72 84573772.96 97758701.29
-2406.50 1995.50 -4802170.75 5791242.25 3982020.25
3975.20 3365.90 13380125.68 15802215.04 11329282.81
-859.60 37966.70 -32636175.32 738912.16 1441470308.89
72097.40 74183.90 5348466311.86 5198035086.76 5503251019.21
10750.90 11138.80 119752124.92 115581850.81 124072865.44
-2587.20 2595.00 -6713784.00 6693603.84 6734025.00
5300.30 4290.90 22743057.27 28093180.09 18411822.81
326453.41 533915.04 15240264254.09 15138710221.84 25663012682.59
N 25 b 0.80955795
Covariance S(x,y) 909722.2908 a 0
Var S(Y)^2 736472.9124 SSE 17675330.47
Var S(X)^2 1123727.204 SEE 876.6371137
Mean Y y(m) 171.636 SE (b) 0.168804409
Mean X x(m) 212.012 SE (a) 0.208514299
R^2 0.109923656
Model Coefficients Std Error t-stat P-value
Constant 0 0.208514299 0 0.00
WC 0.80955795 0.168804409 4.795834159 0.00
Dependent Variable: Profit
Predictors(Constant): WC
Model R R^2 SEE
1 0.331547 0.109923656 876.6371137

16. 16 | W C M I N F M C G S E C T O R
The value of R denotes the response of Net Profit with change in Working Capital
Management.
As per the results shown above the multiple correlation coefficient between the
dependent variable Net Profit and the independent variable Working Capital Management
was 0.331. It indicates that that profitability is responsive by its working capital
management. It is also evident from the value of R2
that 11% of variation in Net profit
was accounted by Working Capital.
5.5. Hypothesis Testing:
Hypothesis 1
H0: The DSO & ROA of the companies have no linear relationship.
H1: The DSO & ROA of the companies have a linear relationship.
The t-statistic value obtained from the regression test = 2.462366
t(α,v) = t(0.025,23) lies between -2.064 & 2.064.
Since t-statistic value > 2.064, H0 is rejected.
Hence, it is proved that DSO & ROA of the companies have a linear relationship.
Hypothesis 2
H0: The Working Capital & Profit of the companies have no linear relationship.
H1: The Working Capital & Profit of the companies have a linear relationship.
The t-statistic value obtained from the regression test = 4.795834
t(α,v) = t(0.025,23) lies between -2.064 &
2.064.
Since t-statistic value > 2.064, H0 is rejected.
Hence, it is proved that Working Capital & Profit of the companies have a linear relationship.

17. 17 | W C M I N F M C G S E C T O R
6. Ranking and Scoring:
Variables which probably affect the Working Capital Management are taken into
consideration in order to develop a ranking system. The variables taken are as follows:
a. Cash Conversion Cycle
b. Acid Test Ratio
c. Inventory turnover ratio
d. Current ratio
The value of the ratios are calculated and mentioned below:
Cash Conversion Cycle
Table 12: Company wise CCC values
2009 2010 2011 2012 2013
HUL 5.299 37.441 28.375 10.842 28.304
ITC 68.189 59.092 117.187 115.865 114.712
Asian Paints
(India)
44.894 40.344 30.169 35.585 38.835
Britannia
Industries Ltd.
25.992 21.305 12.932 10.674 6.128
Marico
Industries Ltd.
33.106 41.746 64.164 56.142 50.270
Acid Test Ratio
Table 13: Company wise Acid Test Ratio Values
2009 2010 2011 2012 2013
HUL 0.522 0.581 0.908 0.796 0.635
ITC 1.112 0.883 0.862 0.859 0.970
Asian Paints
(India) 0.884 0.869 0.945 0.867 0.862
Britannia
Industries Ltd. 1.230 0.509 0.716 0.449 0.435
Marico
Industries Ltd. 0.573 0.598 0.227 0.468 0.582

18. 18 | W C M I N F M C G S E C T O R
Inventory turnover ratio
Table 14: Company wise Inventory turnover ratio
2009 2010 2011 2012 2013
HUL 6.897 6.905 6.088 7.560 8.511
ITC 2.507 2.632 2.713 2.801 2.834
Asian Paints
(India) 6.863 5.469 4.918 5.287 5.095
Britannia
Industries Ltd. 11.253 12.049 12.786 12.114 15.581
Marico
Industries Ltd. 6.233 5.265 4.636 4.951 4.690
Current Ratio
Table 15: Company wise Current Ratio
2009 2010 2011 2012 2013
HUL 0.968 0.912 1.054 1.209 0.989
ITC 2.116 1.514 1.539 1.563 1.698
Asian Paints
(India) 1.269 1.281 1.461 1.355 1.367
Britannia
Industries Ltd. 1.803 0.982 1.236 0.803 0.769
Marico
Industries Ltd. 1.392 1.344 1.417 1.655 1.666
Considering these values each of the variables has been converted into one meaningful
additive score, each is normalized ( it measures the relative performance of all the firms
under study) as follows :
Normalization has been done only for the last years ratio to find out the current score that
will measure the performance of the company
Normalization Cash Conversion Cycle:
= (Highest Overall CCC-Company CCC)/(Highest Overall CCC-Lowest Overall CCC)
Normalization Acid Test Ratio:
= (Highest Acid test ratio-Company Acid Test Ratio) / (Highest Overall Acid Test Ratio-
Lowest Overall Acid test ratio)
Normalization Inventory turnover ratio:
= (Highest Inventory turnover ratio-Company Inventory turnover ratio) / ( Highest
Overall Inventory turnover ratio- Lowest Overall Inventory turnover ratio)

19. 19 | W C M I N F M C G S E C T O R
Normalized Quick Ratio:
= (Highest Quick Ratio-Company Quick Ratio) / (Highest Quick Ratio-Company Qucik
Ratio)
After finding out the ratio a priority has been assigned to these ratios on the basis which
ratio has more impact on Working Capital management and the priority assigned is as
follows CCC – 0.5; Quick Ratio – 0.2; Current Ratio-0.2; Inventory turnover ratio-0.1.
And the company with lowest normalized score has been rated as better company than
other in case of Working Capital Management and the result is as follow.
Table 16: Scoring & Ranking Table
Sl. No. Company Normalised Score Rank
1 ITC 0.1 1
5 Britannia Industries Ltd. 0.5341 2
2 Asian Paints Ltd. 0.5433 3
3 HUL 0.73124 4
4 Marico Industries Ltd. 0.9 5
7. Accounting Ratio Analysis (Non-Statistical):
The Current ratio and Quick ratio gives a measurement of the solvency of the company. The
companies that we have taken for analysis have maintained a current ratio above 0.5 in most
cases. The ideal current ratio, as is said by experts, is supposedly 2. This means that the company
must have twice the amount of current assets at any point in time to pay off its current liabilities.
Contrary to such a belief some companies like HUL and Britannia consistently maintain a
Current Ratio between 0.5-1.5. This is because, a lot of money is believed to be tied up in
inventory and accounts receivable and this hampers the smooth utilization of cash. On the other
hand similar companies like ITC, Marico and Asian paints maintain a current ratio above 1.5.
Such a difference can be attributed to its lending and payment policies to suppliers, Inventory
management principles and short term investment plans.
The Inventory Turnover ratio measures the efficiency of the business in changing its inventory to
sales. It measures the briskness of the inventory management chain. In FMCG companies, a
high inventory turnover ratio has been observed. Companies that have high turnover ratio
suggests that they keep low inventory through-out the year to account for obsolesce and
perishability or that it buys too often in small quantities and its cost of sales is therefore very
high. Again, companies that have a low turnover ratio means either they are overstocking or

20. 20 | W C M I N F M C G S E C T O R
planning systematically for the rise in prices in future. While some companies keep high
inventory levels and owing to its high cost of sales, the turnover ratio is near about 5-8. The latter
is applicable for Asian Paints. Both over stocking and under stocking is not healthy for a
business. Yet Companies like ITC have maintained low turnover ratio and high profits, whereas
juxtaposed to this Britannia has maintained high turnover ratio and low operating profits.
8. Conclusion-
The analysis done so far suggests that CCC, working capital, turnover ratios, solvency ratios and
profitability are all interconnected in a fine mesh. Lower the time taken to turn the inventory into
cash, lower the time taken to get payment from customers and higher the time taken to pay to the
suppliers- everything has a variation on the bottom line of the company’s Balance Sheet. We
have analyzed companies in the same segment and their Balance Sheets have revealed how the
working capital management of each company has a bearing to its profitability. ITC, Marico,
Asian Paints have consistently maintained a double digit profit whereas Britannia has been on
the lower rung when it comes to earning profits. The methods and statistical tools used give a
wholesome picture of the industry so far.

21. 21 | W C M I N F M C G S E C T O R
9. BIBLIOGRAPHY:
[1] Bringham Eugene F. , Ehrhart Michael C. 2013. Financial Management Theory &
Practice
[2] Levin Richard I. , Rubin David S. 2007. Statistics For Management
[3] Hair Joseph F. Hair Jr. , Black William C. , Babin Barry J. , Anderson Rolph E. ,
Tatham Ronald L. 2006 Multivariate Data Analysis
[4]Soenen LA, 1993. Cash conversion cycle and corporate profitability. Journal of Cash
Management
[5] Bagchi B. , Khamui B. 2012. Relationship between Working Capital Management
and Profitability: A Study of Selected FMCG Companies in India. Business and
Economics Journals.
[6] Kaur Harsh V. , Singh Sukhdev. 2012. Managing Efficiency and Profitability
Through Working Capital: an Empirical Analysis of BSE200 Companies. Asian Journal
of Business Management.