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QUANTITATIVE METHOD S PROJECT

Group Name:- CHANAKYAS

Group Members

1) Anish Buche

2) Gaurav Madan3) Jayant Agrawal

4) Kapil Gupte

5) Mitesh Jain

6) Shouvik Banerjee

7) Viral Shah

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AMBUJA CEMENT

Ambuja cement was set up in 1986. The cement industry presented an opportunity of steady

growth and ethical competition to the promoters. In the last decade the company has grown

tenfold .The total cement capacity of the company is 18.5 million tones. Its plants are some of

the most efficient in the world with the environment protection measures that are on par with the

finest in the developed world.

However, a decade later, it became one of worlds most efficient cement companies producing

the finest cement in the world at the lowest cost, while adhering to the most stringent

international pollution-control norms. The companys distinctive attribute however, is its

approach to business Ambuja follows the philosophy of giving its people the authority to set

their own targets and the freedom to achieve their goals. This simple vision has created an

environment where, there are no limits to excellence, no limits to efficiency and has proved to be

a powerful engine of the growth for the company.

Today, Ambuja is the 3 rd largest cement company in India, with an annual plant capacity of 16

million tones including Ambuja Cement Eastern Ltd. and revenue in excess of Rs.3298 crores.

As a result Ambuja is the most profitable cement company in India, and one of the lowest cost

cement producer in the world.

ACHIEVEMENTS

Benchmarking the quality standards for the industry, its also the first to receive ISO 9002

quality certification.

Reinventing transportation ambuja became the first company to introduce the concept of

bulk cement movement by sea in India, which made its transportation speedy and cheaper

as compare to other transport like rail and road.

National Award for commitment to quality by the Prime Minister of India.

National Award for outstanding pollution control by the Prime Minister of India.

Eco-Gold Star by TERI(Tata Energy Research Institute)

Best Export Award by CAPEXIL.

Award for Corporate Social Responsibility by Business World- FICCI

International Award for Rural Development by Asian Management Institute.

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ISO 14000 Certification for Environmental Systems.

STRENGTHS-

Infrastructure- over 40% of the cement cost is comprised of power the company set up its own

power plant at a substantially lower cost than National grid , in order to control its cost of power.

The result is that today they are in a position to sell their excess power to the local state

government.

The sea borne bulk transportation facilities meanwhile have brought many coastal markets within

reach. It has also made ambuja Indias largest exporter of cement consistently for the last five

years

1)Analysis Performed:

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1.1) Estimating the share price:

Time Period

(2006 (2008)

Market Price of

Ambuja

Sensex

(BSE)

Market

Return Jan 88.5 9919.89

Feb 88.2 10370.244.5398688

9

Mar 103.25 11279.968.7724102

82

Apr 124.05 12042.566.7606622

72

May 92.9 10398.61

-13.651167

19

Jun 99.5 10609.25

2.0256553

52

Jul 104.7 10743.881.2689869

69

Aug 112.35 11699.058.8903636

3

Sep 116.85 12454.426.4566781

06

Oct 117.25 12961.94.0746979

79

Nov 144.15 13696.315.6659131

76

Dec 141.3 13786.91

0.6614920

37

Jan 137.15 14090.922.2050626

28

Feb 115.95 12938.09

-8.1813678

6

Mar 106.7 13072.11.0357788

51

Apr 117.95 13872.376.1219696

91

May 113.15 14544.464.8448102

23

Jun 124.55 14650.510.7291436

05

Jul 131.5 15550.996.1464071

9

Aug 133.45 15318.6

-1.4943743

13Sep 143.8 17291.1 12.876503

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07

Oct 144.65 19837.9914.729485

11

Nov 150 19363.19

-2.3933876

37

Dec 146.9 20286.994.7709080

99

Jan 119.6 17648.71

-13.004787

8

Feb 120.95 17578.22

-0.3994059

62

Mar 121.05 15644.44

-11.001000

1

Apr 113.9 17287.31 10.5013027

May 95.2 16415.57

-5.0426584

59

Jun 75.7 13461.6

-17.994927

99

Jul 81.2 14355.756.6422267

78

Aug 80.2 14564.531.4543301

46

Sep 78.4 12860.43

-11.700343

23

Oct 60.95 9788.06

-23.890103

21

Nov 52.3 9092.72

-7.1039613

57

Dec 69.7 9647.316.0992750

240.3261841

92

We are using the tools of statistics to perform some financial functions. We have calculated the

market risk of Ambuja Cement Beta () through the use of statistical functions of covariance and

variance. Covariance suggests us the fluctuations in the price of Ambuja Cement with respect to

the changes in BSE Sensex. With the help of it we can calculate the expected return from the

shares of Ambuja cement to the investors point of view. The share price in this regards comes to

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be around Rs. 74.17. The current price is Rs. 100.55. Thus we can see that the price of Ambuja

Cement is overpriced.

1.2) Analyzing the Different prices in different cities:

Cityprices ofcement(fo) fe

(fo-fe)^2

(fo-fe)^2/fe

Mumbai 172.00150.4

3465.2

6 3.09

Delhi 158.00150.4

3 57.30 0.38

Kolkatta 174.00150.4

3555.5

4 3.69

Chennai 145.00

150.4

3 29.48 0.20

Hyderabad 114.00150.4

31327.

14 8.82

Ahmedabad 151.00150.4

3 0.32 0.00

Jaipur 139.00150.4

3130.6

4 0.87

150.432565.

71 17.06

Looking at the data we found out that the price of cement in 7 different cities that includesMumbai, Delhi, Chennai, Kolkata, Jaipur, Ahmedabad and Hyderabad were different. We tried

to find out whether there is a dependence of price on the region or not. Performing a chi square

test we conclude that the price of the cement is dependent on the region at 90% level of

confidence. We infer that the price of the cement depends on the demand of the particular region.

For example in Mumbai which is the financial capital of the country we found out cement prices

are maximum as the price of real state is also the maximum in Mumbai which shows that region

determines price.

1.3) Impact of recession on Ambuja:

year sales(yearly)n1 % growth

2003 1735.00 25.36%

2004 1968.00 13.43%

2005 2606.00 32.42%

23.74% u1

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n2

2006 4178.67 60.35%

2007 4370.67 4.59%

2008 4186.00 -4.23%

20.24% u2

Ho:u1=u2

No impact ofrecession

H1:u1>u2

Recession hasimpacted sales

STD.DEV. 1 9.60%STD.

DEV. 2 35.01%STD.ERR. 0.39

Dof 4.00

Ho:u1=u2

H1:u1>u2

t=u1-u2/std errrTactual= 0.09

Tcritical

= 2.13

Tcritical > Tactual

Thus Ho is rejected

We have taken two samples of percentage growth in sales of three years each. The first sample is

from 2003-2005 and the second is from 2006-2008. By using the Hypothesis testing of means for

small samples using the t-test, we arrive at the conclusion whether there is any significant

difference between the means. As per the calculations done by us , we infer that there is certain

difference in the means which we believe is due to the economic recession and subsequent

decrease in the infrastructure projects and purchasing power thereby reducing the demand of

cells.

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ANALYSIS OF SHARE PRICE BEFORE AND AFTER DIVIDEND

ACCOUNCEMENT

8

Price Before Dividend announcement Price after announcement

1 98 95

2 97.5 100

3 97 99.5

4 96 108.5

5 96.5 108

Average 97 102.2

Variance 0.625 34.325

H0: 1 =2

H1: 1 2

= .05

Pooled Estimate 17.475

Standard error 11.05216042

t-actual -0.470496247t-critical 2.306

Thus we accept the hypothesis

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IMPACT OF ANNOUNCEMENT OF DIVIDEND ON SHARE PRICE

Ambuja cement on the 23rd of July 2009 announced a dividend to its shareholders. It also

announced about the operational performance of the quarter which was considerably low in

comparison to the performance of the same quarter last year. We made an analysis on the share

prices that were prevalent in the BSE before 5 days of the announcement and after 5 days of the

announcement to see if their was any effect due to it.

We made a test of hypothesis of difference in mean of small sample size to further to test our

analysis and tried to test our analysis at alpha = .05, whereby the confidence interval is .95. As

per our analysis, we accepted our null hypothesis which suggested that there was no considerable

change in the share price due to the announcement of dividend. The value of t-actual lies within

the relevant range given my t-critical.

From the above analysis we can conclude that the market price of Ambuja is not being affected

much by the internal operations of the company or the declaration of any sorts of benefit. It is

more dependent on the market factors or the forces that drive the sensex of BSE. Generally we

believe that the impact of various company operational aspects such as increase or decrease in

the production, sales, profit etc should have a direct impact on the share prices of the company.

However, in this case the hypothesis testing shows completely opposite results.

Thus we can conclude that Share price of Ambuja Cement is not that much detrimental to the

operational performance of the company rather it is more determined by the market factors. The

fluctuation in sensex and positive or negative externalities in the market seems to be most

important factor impacting the share price.

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CHI-SQUARE ANALYSIS

SALES ARE DEPENDENT ON REGION OR NOT

Region Sales(in million tones) fo fe (fo-fe) (fo-fe)2 (fo-fe)2/feWestern 27.5 28.2 -0.7 0.49 0.02

Northern 33.5 28.2 5.3 28.09 1.00

Eastern 23.6 28.2 -4.6 21.16 0.75

Sum = 84.6

Ho:The sales are not dependent on the region

H1:The sales are dependent on theregion

Xcritical = (fo-fe)2/fe 1.76

Xactualdof=2LOS=10%

0.21

Thus Ho is rejected.

Thus sales are dependent on the region

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EFFECT OF PRODUCTION UNIT ON CAPACITY

LocationCapacity (tonnes)fo

Capacity(fe) fo-fe (fo-fe)^2

[(fo-fe)^2]/fe

Ambujanagar (Amreli,GUJ) 5180 1887 3293 10843849 5746.6

Bhatapara (Raipur, CTG) 1110 1887 -777 603729 319.9

Bhatinda (Bathinda, PUN) 740 1887 -1147 1315609 697.2

Darlaghat (Solan, HP) 1480 1887 -407 165649 87.7

Farakka (Murshidabad,WB) 185 1887 -1702 2896804 1535.

Maratha (Chandrapur,MAH) 3515 1887 1628 2650384 1404.5

Rabriyawas (Pali, RAJ) 2035 1887 148 21904 11.6

Roorkee (Hardwar, UTR) 185 1887 -1702 2896804 1535.Ropar (Rupnagar, PUN) 3145 1887 1258 1582564 838.6

Sankrail (Haora, WB) 1295 1887 -592 350464 185.7

Sum = 18870

Ho: No effect of production unit on capacity

H1: Effect of production unit on capacity

X= [(fo-fe)2/fe]

Xactual = 12362.35

Xcriticaldof=9L.O.S = 10% 14.68

Thus Ho is rejected

Thus the the capacity of production in affected by the location of unit.

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PRODUCTION AFFECTED BY LOCATION OR NOT

LocationProduction(fo) fe (fo-fe) (fo-fe)2

(fo-fe)2/fe

Ambujanagar (Amreli,GUJ) 4721.1

1719.83

3001.27

9007621.61 5237.51

Bhatapara (Raipur, CTG) 1011.661719.8

3 -708.17 501504.75 291.60

Bhatinda (Bathinda, PUN) 674.441719.8

3

-1045.3

91092840.2

5 635.44

Darlaghat (Solan, HP) 1348.891719.8

3 -370.94 137596.48 80.01

Farakka (Murshidabad,

WB) 168.61

1719.8

3

-1551.2

2

2406283.4

9 1399.14Maratha (Chandrapur,MAH) 3203.61

1719.83

1483.78

2201603.09 1280.13

Rabriyawas (Pali, RAJ) 1854.721719.8

3 134.89 18195.31 10.58

Roorkee (Hardwar, UTR) 168.611719.8

3

-1551.2

22406283.4

9 1399.14

Ropar (Rupnagar, PUN) 2866.381719.8

31146.5

51314576.9

0 764.36

Sankrail (Haora, WB) 1180.281719.8

3 -539.55 291114.20 169.27

Sum =17198.3

Ho:The production of plant is not affected by thelocation.

H1:The production of plant is affected by the location.

(fo-fe)2/fe =11267.17

Xcritical = 11267.17

Xactualdof=9LOS=10%

4.168

Thus Ho is rejected

Thus the production of plant is affected by the location.

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Chi-Square analysis of the production and capacity of the plants, whether

they are dependent on the plant location and analysis of whether the sales the

company are dependent on the region of sales.

Chi-square analysis of the effect of capacity of plant on production unit.

Ambuja cement has around 10 production units located at different parts of the country. The

proximity to raw materials as well as to enhance the level of distribution and sales, Ambuja went

with its expansion plan in various parts of the country. Different units are of different capacity.

Each capacity is subjected to cater the needs of the particular area or sector. Some plants are

closely located to major metropolitan or the areas where the infrastructural development are at a

high speed, thus those plants are operating at a higher capacity whereas others are not. However

the idea of having multiple plants is to get the economies of scale both in production as well as

sales.

The analysis considers the capacity of different units and also the location of all the plants. Using

this data chi-square analysis is carried out, whether capacity are dependent on the location of

unit. The null hypothesis here is that the capacity of production unit is not affected by the

location of unit & the alternate hypothesis is the capacity of production unit is affected by the

location of unit. After analysis it is found that the critical value is much greater than the actual

value and thus the null hypothesis is rejected. Thus it is concluded that the capacity of the

production unit is affected by the location of the unit.

Chi-square analysis of the effect of total production on the location

The production output of plants is dependent on a number of factors such as operational

efficiency, availability of cheap labor, government policies for the particular area etc. Also

nearness to highly demanded area of cement is also an important factor of consideration. The

plants which are near to developing areas (for e.g. Mumbai and other metros) where there are lot

of construction projects under operation, there will be high demand for cement. Other factors

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which can affect the production of the plant can be management problems in the plant, improper

coordination among the workers in plant, whether lower wages are provided to labors. Thus

considering all these factors, there are a lot of other factors also which directly or indirectly

affect the production of plants. For e.g. improper utilization of available resources, other social

problems etc.

The data used for analysis here is the production of all the plants. Chi-square analysis is carried

out using this data to see whether the production of the plant is dependent on the location. Null

hypothesis here is that the production is not dependent on the location and the alternate

hypothesis is production is dependent on the location. On conducting the analysis, it found that

the null hypothesis is rejected. Thus it is concluded that the production of plant is dependent on

the respective location of plant.

Chi-square analysis of effect of the sales of company on respective region.

The sales of a particular region differ a lot depending on variable factors such as the economic

conditions of the region, current growth rate of the region, demand etc. Also the sales are

dependent on the number of people migrating to a specific region. For e.g. consider the city of

Mumbai, the financial capital of India. Thousands of people migrate to Mumbai every year for

jobs. Thus the rate of construction of homes in Mumbai is very fast, resulting in an high demand

of cement. The same factor is also responsible for other developing regions in the country

especially other metros and cosmopolitan cities. Thus there is lot of fluctuations in the demand

for cement in different areas which in turn affects the sales in that particular area.

Here the data used is the region wise (North, East, West) sales in the respective regions. The

analysis is then carried out to check whether the sales of company are affected by the respective

region. The null hypothesis considered here that the sales are not affected by the respective

region and the alternate hypothesis is that the sales are affected by the respective region. After

conducting the analysis it is found that the null hypothesis is rejected. Thus it is concluded that

the sales of company are dependent on the respective region of sales.

REGRESSION ANALYSIS

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Production v/s Raw Materials

S UM MA R Y O U T P UT

Re gre ssion Statistics

M ultiple R 0 .9481135

R Square 0 .898919209

Adjusted R Squ are 0.865 22 56 13

S ta nda rd E rror 1 7 38 .7 0 57 4 6O bservations 5

Multiple Regressions

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.971276132

R Square 0.943377325

Adjusted R Square 0.88675465

Standard Error 1593.795497Observations 5

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Production v/s Energy

SUMM ARY OUTPUT

Regression Statistics

M ultiple R 0 .336975665

R Square 0 .113552599

Adjusted R Square -0.18192 987

Sta nda rd E rror 5 14 8.9 45 08 5

O bservations 5

Year Production (thousand tonnes)(y)

2004 1036

2005 1280

Regression analysis:

Here we have done a regression analysis to determine the effects of various factors like sales,

energy consumed, raw materials on dependent variables like dividends, production. The purpose

of such an analysis is to determine the degree to which each of these independent factors affect

the dependent factors mentioned above, and to determine that how will a change in a particular

factor will increase or decrease the dependent factors such as dividends or production. Such an

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analysis gives the company an insight into the decisions to be taken or changes to be made

urgently for maximizing production and profit. Let us see the various regression analysis and try

to understand how they play a major role in the decision making of the company.

Dividend and Sales:-

Here we have used the regression analysis to study whether there is any relation between the

sales per year and the dividends offered. We take the dividends offered as the dependent

variables and find its dependence on the independent variable sales.

We come to know that the value of coefficient of determination i.e. R square is 0.6919. Also the

standard error between the y estimate and y is very high i.e. 6.50. This indicates that the

dependence of dividends offered by the company on sales made is not very high. While offering

dividends, sales would not be the most determining factor on the basis of which the company

would offer dividends to its shareholders. On the other hand if the sales of the company are not

high in a particular year, the profits gained by the company in that year are also reduced,

reducing further the capacity of the company to offer dividends to its shareholders. Now the

main motive of any company is to increase the shareholders wealth. If the company is not in a

position to offer dividends it might loose the confidence of the investors in the company , putting

the company in a very undesirable condition. So the company must make efforts to continuously

increase the sales of its products by getting hold of the new opportunities offered by the ever

increasing realty and infrastructure market in India.

Since effect of sales is low in this case, it might not be a crucial factor while making a decision

on dividends to be offered . So a more detailed research is required to ascertain the factors which

actually affect the variable of dividends more than the sales made

Energy consumption and Raw materials on production: -

We have made an effort to determine the effect of energy consumed and Raw materials on the

production levels per year. Energy consumed and Raw materials are very necessary factors

affecting the production of the company, but we need to study their combined as well as

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individual effect on production levels to better understand how the costs can be minimized

without reducing the level of production.

Now if we take an assumption that one of the factors is kept constant and then it gives us the

ability to study the effect of other factor in ideal conditions.

So we calculate the regression separately between production and energy consumed and

production and raw materials required. Also the multiple regression is calculated for the effect of

energy consumed and raw materials used on the production level. So we have three different

cases here:

Case 1: Now if we keep raw materials constant to study the effect of energy consumed on

production we can see that the valur of coefficient of determination (R square) is o.113 which is

very low. The standard error is 5148.94.

Case 2: If we keep energy consumed constant to study the effect of raw materials on production

we see that the R square is 0.8989, and the standard error is 1738.706.

We can see from the above calculations the effect of raw materials is much more on the

production per year than the effect of energy consumed, as the value of R square in the case of

raw materials is much more than in case of energy consumed. Also the standard error is very less

in raw materials as compared in the case of energy consumed. This indicates that if the cost of

raw materials can be reduced, the production costs of the company can be significantly reduced.

This will help the company in putting a more competitive product in the market and also to fare

better than other companies in times of recession. But no company can produce goods on the

basis of raw materials alone, hence it is essential that we make a study of effect of both the

energy consumed and raw materials on production.

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Case 3: We can see that there is a tremendous increase in the value of R square(0.9433), which

is much more than the value in case 1 and case 2. Also the standard error is 1593.79, less than

the value of standard error in both the above cases. If we go for individual standard errors in the

case of multiple regression, the S.E(0.0002) for raw materials is very low as compared to

S.E(3.961) for energy consumed.

All the cases 1, 2 and 3 tend to give us the same indications. The cost of raw materials affects the

cost of production more than the energy consumed. So to be economical, the company should

put more efforts in finding cheaper raw materials which give equal or high production levels.

Regression analysis of purchase of raw material with the sales.

We have taken data from last 5 years i.e. from 2004 to 2008 for use of raw material & sales.

We have analyzed if purchase in raw material is affecting the sales.

Results of the analysis are as:

R Square

0.5195

85934

Standard Error 1855.772185

We can see that coefficient of determination (R square) is very weak i.e. only 51 % of the

variation in sales is determined by the purchase of raw materials for that particular year.

Also we can see that the standard error is very high, which tells about the error in judgment, the

larger the standard error of estimate, the greater the scattering of point along regression line &

thus weak is the relation.

Thus we can say that purchase of raw materials (i.e. gypsum, iron ore, limestone, clay & silica)

doesnt have much effect on sales of the cement. We see that there is a relationship of association

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between purchase of raw materials & sales but there seems to be no effective relation of cause &

effect.

Reasons for the behavior:

Sales doesnt only relate with the purchase of raw material but also how much is been used. It

may happen that because of some of the seasonal factor there may be temporary halt in

production, so sales will not vary in proportion with the purchase in raw materials.

There may be temporary shut down of the plant or there may be delay in production cycle. Some

times due to unfavorable political situations & adverse government regulations company may

want to delay the production cycle.

Also we have taken into account not all the raw materials that is been use for production we have

done analysis mainly for gypsum, iron ore, limestone, clay & silica. Relationship may be a bit

stronger if we had taken all the raw material into account.

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COMPETITOR ANALYSIS

TREND OF ACC PRODUCTION

YearProduction(Y) Tonnes in1000 Coded (X) XY X2

2005 12931 -3

-38793 9

2006 18733 -1

-1873

3 1

2007 19921 119921 1

2008 20836 362508 9

Sum24903 20

Average 18105.25

a 18105.25

B 1245.15

y 18105.25 + 1245.15X

Production foryear Growth

2009 24331

16.7738529

5

2010 26821.310.23509104

2011 29311.69.284784854

2012 31801.9 8.49595382

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TREND OF ACC SALES

Year Sales (Y) RsCodedTime (X) XY X2

2005 2765.94 -3 -8297.82 9

2006 5310.69 -1 -5310.69 1

2007 6400.08 1 6400.08 1

2008 7048.85 321146.55 9

Sum13938.12 20

Average 5381.39

a 5381.39

b 696.906

y =5381.39 +696.906X

Forecasted Sales Growth %

2009 8865.9225.7782475

2010 10259.73215.7210081

2011 11653.54413.5852671

2012 13047.35611.9604131

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TREND OF AMBUJA PRODUCTION

YearProduction (thousandtonnes) (Y)

CodedTime(X) XY X2

2005 12804.03 -3 -38412 9

2006 22632.98 -1 -22633 1

2007 16861.08 116861.1 1

2008 17757.71 353273.1 9

Sum9089.14 20

Average

: 17513.95

a = 17513.95

b = 454.457

y = 17513.95 + 454.457X

Year Forecast production Growth %

2009 19786.23511.4233479

2010 20695.1494.59366827

2011 21604.0634.39191813

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2012 22512.9774.20714381

TREND OF AMBUJA SALES

Year Sales (Y) Rs. Coded (X) XY X2

2005 3023.52 -3 -9070.56 9

2006 7010.47 -1 -7010.47 1

2007 6454.75 1 6454.75 1

2008 7075.51 3

21226.5

3 9

Sum11600.25 20

Average 5891.0625

a 5891.0625

b 580.0125

y =5891.063+580.0125X

ForecastSales Growth %

2009 8791.125 24.2472274

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1

2010 9951.1513.19541014

2011 11111.175 11.6571954

2012 12271.210.44016497

PRODUCTION AND SALES ANALYSIS WITH ACC:

Here with the use of trend analysis we are able to forecast the production and sales of both ACC

and Ambuja from 2009 2012. On the basis of annual data of last four years we are able to

identify a trend that is in the industry. There has been a forecast of increment of sales in the

cement industry of 10.43%. However if we see here, the sales of Ambuja is expected to increase

by 24% and of ACC by 25%. This shows that the cement industry has a good prospect in sales

for the year 2009. This might be the result of the improvement in the economic condition.

Countries are believed to overcome the recessionary pressure they are facing. Even the Indian

Government in the current year budget has placed a special importance on the infrastructure

development which are positive vibes for cement industry. However after the year 2009 the

increment in sales as per the previous years seems to be increasing at a rate of average of 12%

for Ambuja and average rate of 13% for ACC. Thus we can derive that year 2007-2008 does not

seem to be a very happy time for the cement industry.

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We can also see that the forecasted production seems to increase considerably for the year 2009

whereas the increment in the production after that shows a declining trend. The production of

ACC is to be increased by 16% and that of Ambuja by 11% for the year 2009. This might be the

result of increased capacity utilization by ACC. We see the impact of recession on production

also. The sales is to be increased more than 20%, however production only by around 10% -

15%, thus showing that the facilities right now are probably having lots of inventory level with

them in the preceding two years of market downturn. The production after 2009 is expected to

increase around 4.5% for Ambuja and around 9.5% for ACC.

On the basis of trend analysis conducted for production and sales and forecasting both the

variables for next four years from the year 2009 we conclude that ACC is predicted to do better

than Ambuja. This also shows an increment in the market share for ACC as well as the

operational efficiency it will achieve

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AMBUJA AND ACC IN TIMES OF RECESSION:

ACC n1YEAR SALES

2765.94

2006 4,549.80 39.21

20076400.0

8 28.91

20087048.8

5 9.20

x1 25.77stddev 1 15.25

AMBUJA n2

YEAR SALES

20064178.6

7 27.64

20076454.7

5 35.26

20087075.5

1 8.77

x2 23.89stddev 2 13.64

Ho:u1=u2

Peformance of ACC and Ambuja is simialr during downturn ineconomy

sp 7.23stderror 5.91Ho:u1>

u2

Performance of ACC is better than Ambuja during

downturn

Ho is accepted .Not much of a difference between salesof Ambuja and ACC

alpha 0.95

dof 2.00 Tactual 0.32

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=Tcritical= 2.92

ANALSYSIS

The recent downturn in the economy has had a huge impact on industries in terms of the total

sales and the cement industry is no exception. The cement industry thrives on its demand from

the infrastructure and the real estate sector both of which have taken a huge beating in terms of

their sales. Although government funding has enabled the infrastructure projects to redeem

themselves but the overall picture of the cement industry still seems to be pretty gloomy due to

the dampened demand from the private players. Owing to such situations of unpredictability, it is

of utmost importance to analyze the sales performance of companies with respect to their

competitors.

Thus we focus on the sales of ACC for the years 2006-2008 and compare it with that of Ambuja

and thereby conclude whether there exists any significant difference between both the sales. The

statistical tool that we use in this case is hypothesis. Following is a description of the test. The

null hypothesis for this test is that there is no significant difference between the sales of ACC

and Ambuja and the alternate hypothesis says that the sales of ACC are better than that of

Ambuja. We follow a one-tailed T-test with level of significance of 95%.

Based on our calculations for the value Tcritical and Tactual, our Null calculation is accepted

and thus the sales of ACC are similar to that of Ambuja during the recessionary period. The

recessionary period has had an equally impelling impact on all the firms and nota an industry in

particular. Private real esatate players have slowed down work on many of the projects in bigger

cities such as Mumbai, Delhi thereby affecting the bigger players such as Ambuja and ACC

operating in these regions. As we can see in the data sheet as shown, growth rate of both Ambuja

and its major competitor ACC have had an average growth rate of 8-9% during the year 2007-

2008.

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Thus if a comparative study is conducted between ACC & Ambuja, it can be safely concluded

that Ambuja is at par in terms of sensitivity to market returns and also the market trends. It also

provides evidence of the fact that the point of differentiation is very low in the products of

cement industry. Products in the cement industry have undergone a phase of standardization and

it has in turn led to commoditization of the cement.

This also provides food for thought for the cement manufacturing firms in terms of the difference

that can be brought about in their products to better sales than their competitors in times of

economic downturn. Difference can also be brought about in terms of the service that is provided

to the customer. Better delivery norms can be established by the organization, which can pioneer

the firm as a service differentiator. Supply and inventory storage norms can also be worked upon

for improvement and thereby bring variation in terms of the end product. Moreover innovation in

packaging can also be major factor in establishing the essence of your products in terms of its

value to the end user.