maruti and hyundai

8/14/2019 Maruti and Hyundai

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IPER - PGDM

REPORT ON

Analysis of sales and Pat of Maruti

suzuki and Hyundai

SUBMITTED BY- KUNAL MATANI

Date- 4 November, 2009


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CONTEXT

S.no PARTICULARS PAGE NO.

1 Introduction of MARUTI 4

2 Material 4

3 Method 5

4 Appendices 6-8

5 Result 9

6 Recommendation 107 Refrence 11

8 Introduction of HYUNADAI 12

9 Material 12

10 Method 13

11 Appendices 14-15

12 Result 16

13 Recommendation 17

14 Refrence 18


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SYNOPSIS

Title: Analysis of sales and Pat of Maruti

suzuki and Hyundai

Objectives:

Development of a predictive model using PAT and sales

of Maruti Suzuki and Hyundai.

Technique:

Simple Linear Regression

Research methodology:

a) Data requirement: Annual reports, quarterly reports and Profit &loss A/C of both the companies of last five year.

b) Data collection: Browsing websites related with Maruti Suzukiand HyundaiAND Moneycontrol
http://www.marutisuzuki.com/cars-/pdf/PROFIT-LOSS-ACCOUNT-08-09.pdfhttp://www.hyundai.com/http://www.hyundai.com/http://www.moneycontrol.com/http://www.hyundai.com/http://www.moneycontrol.com/http://www.marutisuzuki.com/cars-/pdf/PROFIT-LOSS-ACCOUNT-08-09.pdf


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REPORT

MARUTI SUZUKI

INTRODUCTION

Maruti Suzuki is automobile company with high turnover of sales

but with low profit. Success of business is not totally dependent

on the profit. For successful manager or decision maker one

should know factors effecting the business. In this we are

predicting how sales help in the profit and also other independent

variable affecting the profit.

MATERIAL

The data related to Maruti Suzuki sales and profit after tax(pat)

from year 2005 to 2009 on the quater to quater basis using 2007

excel software to regression analysis.

METHOD


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Simple linear regression

The data obtained is used to predict relationship between sales

(independent) with profit (dependent).

Y=a+bx

Y=profit

X=sales

A =intercept coefficant

B =x coefficant

Procedure to be followed

The relation between dependent variable (y) and independent

variable (x) inspected in excel. It was observed that the relation

between (Y) and (X) is not linear. There some other independent

variable which affect the profit of the company.

Independent factors affecting the profit of the company like inexport of cars change in currency,new cars are coming changesin royalty. The regression equation was estimated by applying astepwise regression procedure in the excel 2007 software. In thestepwise regression procedure, the R square is 0.11 so wecannot move further for the prediction.

Note- If Rsquare is not near about 1 then there are some

independent factors which affect the dependent.

APPENDICES


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2005 Y(PROFIT) X(SALES) YHAT

Q1 259 3045 335.09

Q2 259 2627 322.55

Q3 262 3149 338.21

Q4 339 3114 337.16

2006

Q1 360 3277 342.05

Q2 369 3125 337.49

Q3 367 3419 346.31

Q4 376 3679 354.11

2007

Q1 448 4429 376.61

Q2 499 3931 361.67

Q3 466 4547 380.15

Q4 467 4674 383.96

2008

Q1 306 4762 386.6

Q2 465 4753 386.33

Q3 296 4993 393.53


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Q4 213 4625 382.49

2009

Q1 243 6432 436.7Q2 583 6493 438.53

SUMMARY OUTPUT:- MARUTI SUZUKI

TABLE NO1

REGRESSION STATISTICS

Multiple R 0.331839

R square 0.110117Adjusted R square 0.054499

Standard Error 103.1149

Observations 18

ANOVA


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df ss ms F Significa

nce f

Regressi

on

1 21051.

58

21051.

58

1.9798

95

0.17853

Residual 16 17012

2.9

10632.

68

Total 17 19117

4.4

Coeff

icients

Sta

ndard

Erro

r

t-

stat

P-

value

Low

er

95

%

Upp

er

95

%

Low

er

95.

0%

Uppe

r95.0%

Inte

rce

pt

231.

8361

96.

715

1

2.3

971

03

0.0

290

86

26.

809

21

436

.86

29

26.

809

21

436.8

629

X 0.03

1582

0.0

224

45

1.4

070

87

0.1

785

3

-

0.0

16

0.0

791

62

-

0.0

16

0.079

162


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RESIDUAL OUTPUTPROBABILITY OUTPUT

OBSERVATI

ON

PREDICTE

D Y

RESIDUA

LS

PERCENT

ILE

Y

1 328.0018 -

69.0018

2.777778 21

3

2 314.8007 -

88.8007

8.333333 24

3

3 331.2863 -

69.2863

13.88889 25

9

4 330.1809 8.81909

2

19.44444 25

95 335.3287 24.6713 25 26

2

6 330.5283 34.4717 30.55556 29

6

7 339.8133 27.1867

3

36.11111 30

6

8 348.0245 27.9755

3

41.66667 33

9


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9 371.7106 75.2893

9

47.22222 36

0

10 355.983 143.017 52.77778 36

7

11 375.4372 90.5627

8

58.33333 36

9

12 379.4481 87.5519

2

63.88889 37

6

13 382.2273 -76.2273

69.44444 448

14 381.943 83.0569

8

75 46

5

15 389.5226 -

93.5226

80.55556 46

6

16 377.9006 -

164.901

86.11111 46

7

17 434.9684 -

191.964

91.66667 49

9

18 436.8949 146.105

1

97.22222 58

3


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Result

The result of regession is presennted in the table 1. As we seenfrom table that Rsquare is 0.11 and we can the form equation thatY=231.83+0.03X . This equation is not linear because we cannot

predict the value of Y by putting X in this equation. As there aresome independent variable which also needed to predict thevalue of dependent variable(X). Each coefficient does notdemonstrate relation between variable. However from sales wecannot predict profit exactly. There some external factors andinternal factors which affect the profit of the company .Sales isalso the part of the independent factor.

Recommendation

This model is not appropriate model for the business use . As we

recommend that profit is not dependent on sales . There aresome other independent factors which predict profit of thecompany, so if individual want to predict the profit of the companythen he must take independent variable like cost of good, thechanges in the marketing policy,Other costs including buildingand machinery depreciation, repairs and maintenance and


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miscellaneous .External factors or independent related to exportlike adverse currency movement (depreciation of INR Vs USDand appreciation of yen Vs USD) could lead to higher input costsand higher other expenses (on account of forex loss) and affect

profit . However, appreciation of Euro vs USD could bring inhigher export revenues and offset part of the loss incurred.Royalty costs could continue to rise with new launches.

REFRENCES

Book Reference Statistical Techniques in business and

economics

By Douglas A Lind

William G Marchal Samuel A Wathen

Web refrences www.marutisuzuki.com

www.moneycontrol.com

http://www.articlesnatch.com/topic/SAP+

experts www.economictimes.com

www.hdfcsecurities.com
http://www.marutisuzuki.com/http://www.moneycontrol.com/http://www.articlesnatch.com/topic/SAP+expertshttp://www.articlesnatch.com/topic/SAP+expertshttp://www.economictimes.com/http://www.hdfcsecurities.com/http://www.marutisuzuki.com/http://www.moneycontrol.com/http://www.articlesnatch.com/topic/SAP+expertshttp://www.articlesnatch.com/topic/SAP+expertshttp://www.economictimes.com/http://www.hdfcsecurities.com/


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HYUNDAI MOTOR

INTRODUCTION

Hyundai Motor is automobile company with high turnover of sales

but with low profit. Success of business is not totally dependent

on the profit. For successful manager or decision maker one

should know factors effecting the business. In this we are

predicting how sales help in the profit and also other independent

variable affecting the profit.

MATRIAL

The data related to Hyundai Motor sales and profit after tax(pat)

from year 2005 to 2009 on the quater to quater basis using 2007

excel software to regression analysis.


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METHOD

Simple linear regression

The data obtained is used to predict relationship between sales

(independent) with profit (dependent).

Y=a+bx

Y=profit

X=sales

A =intercept coefficant

B =x coefficant

Procedure to be followed

The relation between dependent variable (y) and independent

variable (x) inspected in excel. It was observed that the relation

between (Y) and (X) is not linear. There some other independent

variable which affect the profit of the company.

Independent factors affecting the profit of the company like in

export of cars change in currency,new cars are coming changesin royalty. The regression equation was estimated by applying astepwise regression procedure in the excel 2007 software. In thestepwise regression procedure, the R square is 0.09 so wecannot move further for the prediction.


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Note- If Rsquare is not near about 1 then there are someindependent factors which affect the dependent.

APPENDICES

2005 Y(profit) X(sales) YHAT

Q1 509777 6170228 335020.12

Q2 613195 6946477 366070.08

Q3 534888 6149747 334200.88

Q4 690861 8117285 412902.4

2006

Q1 342387 6861517 362671.68

Q2 403147 7002803 368323.12

Q3 282794 5886935 323688.4

Q4 497735 7584113 391575.52

2007

Q1 307386 6684072 355573.88

Q2 611537 8026939 409288.56

Q3 425478 7083878 371566.12

Q4 338018 8774795 439202.8


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2008

Q1 392652 8197811 416123.44

Q2 546931 9106761 452481.44Q3 264772 6054569 330393.76

Q4 243549 8836645 441676.8

2009

Q1 224980 6031953 329489.12

Q2 811851 8079940 411408.6

SUMMARY OUTPUT HYUNDAI MOTOR

REGRESSION STATISTICS

Multiple R 0.31246

R square 0.097631

Adjusted R square 0.041233

Standard Error 162811.9

Observations 18


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ANOVA

df ss ms F Significa

nce f

Regressi

on

1 4.59E+

10

4.59E+

10

1.7311

09

0.206811

Residual 16 4.24E+

11

2.65E+

10

Total 17 4.7E+1

1

Coeff

icient

s

Stan

dar

d

Erro

r

t-

stat

P-

valu

e

Lo

wer

95

%

Upp

er

95%

Lo

wer

95.

0%

Uppe

r95.0

%

Inte 8821 275 0.32 0.75 - 671 - 6716


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rcep

t

1.09 211.

9

052

1

272

2

495

212

634.

1

495

212

34.1

X 0.049

045

0.03

727

6

1.31

571

6

0.20

681

1

-

0.0

299

8

0.12

806

7

-

0.0

299

8

0.128

067

RESIDUAL OUTPUTPROBABILITY OUTPUT

OBSERVATI

ON

PREDICTE

D Y

RESIDUA

LS

PERCENT

ILE

Y

1 390829.4 118947.

6

2.777778 21

3

2 428900.5 184294.

5

8.333333 24

3

3 389824.9 145063.

1

13.88889 25

9

4 486322.7 204538.

3

19.44444 25

95 424733.6 -

82346.6

25 26

2

6 431663 -28156 30.55556 29


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6

7 376935.4 -

94141.4

36.11111 30

6

8 460173.3 37561.6

9

41.66667 33

9

9 416030.9 -108645 47.22222 36

0

10 481891.7 129645.

3

52.77778 36

711 435639.3 -

10161.3

58.33333 36

9

12 518570.2 -180552 63.88889 37

6

13 490272.1 -

97620.1

69.44444 44

8

14 534851.5 12079.5

4

75 46

5

15 385156.9 -120385 80.55556 46

6

16 521603.6 -278055 86.11111 467

17 384047.7 -159068 91.66667 49

9


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18 484491.1 327359.

9

97.22222 58

3

Result

The result of regession is presennted in the table 1. As we seenfrom table that Rsquare is 0.090 and we can the form equation

that Y=88211.09+0.049X . This equation is not linearbecause we cannot predict the value of Y by putting X in thisequation. As there are some independent variable which alsoneeded to predict the value of dependent variable(X). Eachcoefficient does not demonstrate relation between variable.

However from sales we cannot predict profit exactly. There someexternal factors and internal factors which affect the profit of thecompany .Sales is also the part of the independent factor.

Recommendation

This model is not appropriate model for the business use . As werecommend that profit is not dependent on sales . There are

some other independent factors which predict profit of thecompany, so if individual want to predict the profit of the companythen he must take independent variable like cost of good, thechanges in the marketing policy,Other costs including buildingand machinery depreciation, repairs and maintenance andmiscellaneous .Hyundai old contracts with supplier is ending so


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new contracts charge more money this can directly affect profitRoyalty costs could continue to rise with new launches.

REFRENCES

Book Reference Statistical Techniques in business and

economics

By Douglas A Lind

William G Marchal

Samuel A WatheR

Web refrences www.hyundaiworld.com

www.moneycontrol.com

http://www.articlesnatch.com/topic/SAP+

experts www.economictimes.com

www.capitalmarket.com
http://www.hyundaiworld.com/http://www.moneycontrol.com/http://www.articlesnatch.com/topic/SAP+expertshttp://www.articlesnatch.com/topic/SAP+expertshttp://www.economictimes.com/http://www.capitalmarket.com/http://www.hyundaiworld.com/http://www.moneycontrol.com/http://www.articlesnatch.com/topic/SAP+expertshttp://www.articlesnatch.com/topic/SAP+expertshttp://www.economictimes.com/http://www.capitalmarket.com/


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