chapter vi problems of smes in chennai and tiruvallur...

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CHAPTER – VI

PROBLEMS OF SMEs IN CHENNAI AND

TIRUVALLUR DISTRICT

An earnest attempt has been made in this chapter to present a vide picture

of the multifarious problems faced by the small-scale industrial units in Chennai

and Trivallur district. These problems are of varied nature, some of them are

purely in an internal nature and some others are caused by some extraneous

forces, conditions and circumstances. In order to have a first hand information

and practical insight into these problems faced by the SME units in Chennai and

Tiruvallur district a questionnaire has been constructed and administered to the

managements of the SME units, inviting their responses to the various questions

posed therein. Addition to this, several unstructured interviews has also been

conducted with various officials and non-officials dealing with the problems of

small-scale industrialists in Chennai and Tiruvallur district. Resources have also

been taken by referring a number of documents and papers both published and

unpublished and available in the offices of the Central and State Governments as

well as the small-scale units chosen for a detailed study of this research work, in

order to have insight into the problems encountered by the small-scale units in

Chennai and Tiruvallur district. The logical outcomes of all these exercises at

analyzing the various problems faced by the small-scale industries in the districts

are presented in this chapter. The sophisticated statistical tools are employed to

produce torrent of results.

This chapter is broadly divided into two sections, section one dealing with

the profile of the sample units chosen for a detailed study of this research work

and section two with the analysis of various problems faced by the small-scale

industries of Chennai and Tiruvallur district. In part one, profile of the sample

units, detailed analysis of the age, pattern of ownership, education qualifications

199

of the SMEs, product lines manufactured, relationship with ancillaries, capacity

utilization, size of investment and extent of borrowing etc., of the sample small-

scale units in the districts are presented. In section two, detailed discussions on

the problems faced by the small-scale industries in the district, for example,

problems of setting up of the units, technological problems, problems arising

accomplying with the various government rules and regulations and procedures,

bureaucratic delays. Problems of choice of the line of business, problems arising

in the course of availing the various incentives offered by the Government to

small-scale units, production problems, problems of procedural usage of raw

materials, problems of supply of power, high cost of production, utilization of

waste materials and manufacturing of by-products, problems arising out of the

utilization of the installed capacity, financial problems, marketing problems and

a number of other miscellaneous general problems of the small-scale units.

Small-scale industries have been playing an important role in the

development of Indian economy. These small-scale industries not only help to

create employment opportunities, but also generate income, investment and

savings in the economy. Further, these industries may also help in developing

regional economy, promotion of export potential, promotion of market facilities,

development of infra-structural facilities etc. Small-scale industries may also

help in the eradicating poverty, unemployment, social-economic inequality etc.

in the economy.

Factors of financial problems

Majority of the sample SMEs of small-scale have raised initial capital

form self source, relatives and friends whereas only 21% of the new and 27% of

the established SMEs have availed financial help from Institutions. The small-

scale industry owners felt that the financial institutions and commercial banks

hesitate to provide initial capital and as they go to private sources, they incur

heavy interest burden. The SMEs also expressed that the quantum of assistance

by the Government institutions is also inadequate and delayed.

200

Procuring Term Loans

It is observed that nearly 95% of the new and 97% of the established

small-scale industries have received Term loans from the Tamilnadu Industrial

Investment Corporation and Commercial banks. While availing the term loans

from these institutions, the sample SMEs has experienced inordinate delay,

which ranges from 30 to 120. They also expressed that the complicated

procedures of institutions cause delay in the disbursement of loan.

Problems relating to working capital

It is evident from the study that nearly 74% of the new and 79% of the

established small-scale industries have raised working capital from the

Government Institutions and Banks. Only 26% of the new and 21% of the

established small-scale industries have raised working capital from private

sources. The small-scale industries complaint hat apart from the complicated

procedures, the banks are now insisting on collateral security for giving working

capital assistance. Their complaint is against insufficient working capital and

the moneylender attitude of the financial institutions. Heavy delay is caused

before effecting disbursement.

201

Factor analysis by principle component method is applied on 9 variables of

financial problems.

Table 6.1

Total Variance Explained for analysis by principal component method is

applied on 9 variables of financial problems.

Component Initial Eigenvalues

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 3.391 37.675 37.675 3.319 36.879 36.879

2 1.232 13.688 51.363 1.304 14.484 51.363

3 .994 11.045 62.408

4 .814 9.046 71.455

5 .670 7.443 78.897

6 .630 7.004 85.901

7 .494 5.487 91.388

8 .428 4.756 96.144

9 .347 3.856 100.000

Extraction Method: Principal Component Analysis.

202

Table 6.2

Rotated Component Matrix for analysis by principal component method is

applied on 9 variables of financial problems

Component

1 2

SMEProblem38 .825

SMEProblem34 .789

SMEProblem36 .751

SMEProblem35 .726

SMEProblem37 .660

SMEProblem33 .504

SMEProblem31 .739

SMEProblem30 .554

SMEProblem32 -.437


Rotation Method: Varimax with Kaiser Normalization.

a Rotation converged in 3 iterations.

From the tables 6.1 and 6.2, it is ascertained that the variables explain

51.363% of the total variance and two factors are extracted. The first factor is

called “Interest rate and Payment delay” (IRDP and the second factor is named

as “Loan difficulties” (LD) due to following factor loadings.

Factor 1:

33 - Private moneylenders demand high rate of interest

34 - Delay in payment on Government supply 45 to 90 days

35 - Loan sanction depends upon the ability of SMEs

36 - Discretion of authorities of financial institutions also creates financial

problems

37 - Enthusiasm and energy of SMEs are wasted on proving the

eligibility and quantum of assistance sought.

203

38 - SIDCO provide 80% on the bills supplied to SME and remaining 20% at

the time of return

Factor 2:

30 - It is difficult to get loans from authorized financial institutions

31 - Tiresome procedures are followed in all nationalized banks

32 - The credit worthiness of SMEs is weak

The one sample t-test and paired sample test are applied on the factors of

financial problems. The factors IRDP (mean = 4.37) is prevailing more in SMEs

in Chennai and Tiruvallur district followed by LD (mean = 3.78). Between these

two financial problems IRDP has more vigour in affecting the progress of SMEs.

Table 6.3

One-Sample Statistics for principal component method is applied on 9

variables of financial problems.

N Mean Std. Deviation Std. Error Mean

IRDP 402 4.3673 .69770 .03480

LD 402 3.7819 .83012 .04140

Table 6.3 clearly revealed that IRDP (mean=4.37) is existing more than LD

(mean=3.78). The significance of the mean is checked by the following one

sample t-test

204

Table 6.4

One-Sample Test for principal component method is applied on 9 variables

of financial problems.

Test Value = 3

t df

Sig. (2-

tailed)

Mean

Difference

95% Confidence Interval of

the Difference

Lower Upper

IRDP 39.293 401 .000 1.36733 1.2989 1.4357

LD 18.886 401 .000 .78192 .7005 .8633

Table 6.4 revealed that both the IRDP (t=39.293) and LD (t=18.886) are

significant among the SME units.

Table 6.5

Paired Samples Test

t df Sig. (2-tailed)

Pair 1 IRDP - LD 12.991 401 .000

Table 6.5 indicates that there is a significant difference between the two

financial problems of SME. Between these two factors, the IRDP is dominant

factor affecting the SME than LD. It is ascertained that the proprietors of small-

scale industries in Chennai and Tiruvallur districts are continuously affected by

the heavy interest rates for the loan amounts. They are supplying to their

purchasers in time, but the purchasers procrastinate their payments. This leads to

serious financial crisis for the small-scale industries. The entrepreneurs are not

able to get the loans in time from the government sources and private sources.

There is a popular feeling prevails among the sources of loans of SMEs that their

repaying capacity is very low.

205

Problems relating to raw material

The raw material problems are also emerging in SME units in Chennai

and Tiruvallur district. Nearly 85% of the new and 69% of the established small-

scale industries have purchased raw materials from the private agencies and only

15% of the new and 31% of the established SMEs have purchased SIDCO

depots. The SMEs faced ever-present price fluctuation with the private

agencies, and limited variety and supply of raw materials by SIDCO depots.

Factors of raw material

To identify the major factors are raw material problems, factors analysis

is applied on 9 variables and the following results are obtained.

Table 6.6

Total Variance Explained for Raw material

Component

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 4.931 54.783 54.783 4.931 54.783 54.783

2 .926 10.286 65.070

3 .762 8.470 73.540

4 .545 6.054 79.594

5 .460 5.111 84.705

6 .390 4.334 89.039

7 .365 4.054 93.093

8 .352 3.911 97.004

9 .270 2.996 100.000


206

The table 6.6 clearly revealed that a single factor is extracted with

54.783% of the total variance. So the factor is called raw material problem

(RM). To check its severity among SME units a one-sample t-test is used with

test value 4.

Table 6.7

One-Sample Statistics for raw material

N Mean

Std.

Deviation

Std.

Error

Mean

RM 402 4.3021 .74416 .03712

Table 6.8

One-Sample Test for raw material

Test Value = 4

t df

Sig. (2-

tailed)

Mean

Differen

ce

95% Confidence

Interval of the

Difference

Lower Upper

RM 8.139 401 .000 .30210 .2291 .3751

The table 6.7 and 6.8 indicate that the SME units in Chennai and

Tiruvallur district are very much affected by the problems of raw material (mean

= 4.30, t = 8.139, p = 0.000).

It is found that the SMEs in Chennai and Tiruvallur districts are facing

enormous amount of Raw material problem. They are not able to get the raw

materials in time, both government and private suppliers are delaying in their

supply. This attitude of the suppliers affect the continuous flow of production of

small-scale industries in the two districts.

207

Power problem

The study reveals that the sample small-scale industries have suffered doe

to procedural difficulties and delay caused by the TNEB in giving initial power

connection. Majority of the SMEs have waited for more than 6 months to get

power connection.

The SME units are facing the notorious power problems and stumbled by

its regulations and tariffs. In order to make a microscopic examination over

power problems, the factor analysis has become indispensable in this context.

It is observed from the study that the sample SMEs face the following

problems regarding power supply:

01.High power tariff

02.Power-shedding

03.Fluctuation in voltage etc.

Factor analysis is applied on the seven variables of power problems and

the following results are obtained.

208

Table 6.9

Total Variance Explained for power problem



Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 2.649 37.838 37.838 2.407 34.389 34.389

2 1.217 17.386 55.224 1.458 20.835 55.224

3 .808 11.547 66.771

4 .706 10.087 76.858

5 .606 8.652 85.510

6 .553 7.900 93.410

7 .461 6.590 100.000


Table 6.10

Rotated Component Matrix for power problem

Component

1 2

PWProblem50 .769

PWProblem52 .739

PWProblem53 .685

PWProblem51 .676

PWProblem48 .547

PWProblem55 .836

PWProblem54 .769



Rotation converged in 3 iterations

209

The table 6.9 and 6.10 clearly explains that the two factors emerged with

55.224% of the total variance.

The first factor is called “Limited hours supply” (LPS) with the

variable loadings in it.

48 - Power supply is not properly regulated

50 - Within the limited hours of power supply, it is difficult to complete the

production

51 - Many labour hours are wasted and unutilized during power – cut

52 - SMEs cannot go for installing alternatives like generators and thermal

power units

53 - Time management to maximize the production within the specific hours

is highly difficult

The second factor is known as “High tariff and Power fluctuation”

(HTPF) with the variable loadings

54 - Tariff of power is high for small-scale in SMEs

55 - Captive power in Tamil nadu plant leads to fluctuation of power affects the

small-scale industries.

The one sample t-test and paired sample t-test clearly revealed these two

factors LPS and HTPF are equally prominent. So it is ascertained that the SMEs

in Chennai and Tiruvallur districts are facing constraints of limited hours of

power supply and High Tariff for the current usage. These are affecting their

continuous production and severe financial crisis. It is identified that around

20% of SMEs in these two districts have been closed by the action Tamil nadu

electricity board for non-payment of electricity bills.

210

In marketing the products, the SMEs are facing the formidable

hindrances. By means of factor analysis, the marketing problems are classified

in the following ways.

Table 6.11

Total Variance Explained for marketing problem



Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 4.244 53.055 53.055 3.011 37.639 37.639

2 1.146 14.323 67.378 2.379 29.739 67.378

3 .591 7.391 74.769

4 .535 6.686 81.456

5 .507 6.338 87.794

6 .386 4.820 92.614

7 .362 4.530 97.143

8 .229 2.857 100.000


Table 6.12

Rotated Component Matrix for marketing problem

Component

1 2

MktProblem58 .782

MktProblem60 .754

MktProblem59 .753

MktProblem56 .751

MktProblem57 .713

MktProblem63 .867

MktProblem61 .811

MktProblem62 .794



Rotation converged in 3 iterations

211

The eight variables of marketing problems are converted into 2 major

factors namely “Advertisement and local market “ (ALM) and competition

(COM) with following factors loading and 67.38% of total variance.

ALM

56- Ancillary SME units are forced to sell their products in a local market

57- The productions of SME have to travel long distance for marketing

58- Lack of procuring the distant markets minimize the operations

59- SME units are not able to advertise in mega manner.

60- Absence of well-defined system creates big problems of marketing

COM

61- Buyer – seller meet is arranged by SIDCO

62- Competition in marketing the productions

63- Difficulty in availing the help from SIDCO in trade fair participation

The one sample t-test and paired sample t-test are applied on these two

factors of marketing problems. It is found that the ALM (mean=4.51) is more

affecting the SMEs in Chennai and Tiruvallur districts than COM (mean=4.23).

This result clearly revealed that the SMEs products have not got proper

advertisement and popularity of the products is also less. In fact they are not able

compete with the productions of large industry in price as well as the popularity.

They are forced to sell their products to a specified buyers with fixed profit.

Their scope for different marketing avenues are totally obscured by the these sort

of buyers. So far government has not taken strenuous efforts to curb the

marketing problems of SMEs.

212

General problems

Besides the above-mentioned prominent problems, the SMEs in Chennai

and Tiruvallur districts are facing some general problems also.

Problems relating to labour

It is found from the study that nearly 35% of the SMEs had complained

about the high labour turnover and nearly 27% of the SMEs complain about

absenteeism. Nearly 16% of the SMEs had encountered strikes. The SMEs

expressed concern over shortage of skilled labour and interference by the Labour

Union.

Problem relating to subsidies and incentives:

Only 31% of the SMEs are eligible to get backward area concessions.

They complain that the officials do not implement the schemes immediately and

they take enormous time to sanction the subsidy. The officials insist on a lot of

documents and certificates for availing the benefit, with the result, for getting a

small amount of incentive, they have to spend large amount of time and energy.

The SMEs complain that every time they had to travel to the city to answer the

queries of the files. They felt that the subsidies and incentives are not easily

available and hence do not help the SMEs in time.

Problems relating to infrastructure facilities:

Most of the sample SMEs face variety of problems relating to sheds,

transports, water, power and other civic amenities, which form the basic

infrastructure for a unit to function smoothly. Due to lack of all these facilities,

the SMEs meet losses and many workers hesitate to take up jobs in the industrial

estates situated in backward areas.

Problems relating to payment from big companies:

It is clear form the study that majority of the SMEs are living at the mercy

of big companies, in the sense, they have to wait for payment from big

213

companies. Unfortunately only 15% of the SMEs get their bills collected within

45 days. Most of the SMEs felt this inordinate delay creates further problems in

the unit, like shortage of working capita, non-payment of wages lack of funds to

meet contingency expenses and to make payment to creditors, particularly the

suppliers of raw materials.

Problems relating to diversification, expansion and modernization:

Most of the established SMEs could not make much headway in the

expansion of the unit due to (a) Lack of finance (b) non-availability of improved

technology (c) non-availability of spare parts and skilled workers and (d)

absence of testing facilities in respect of new products. The SMEs further

expressed the view that the banks and financial institutions insist for more

collateral security for entertaining their applications for developmental activities.

So the factor analysis is applied on these 13 variables of general problems

214

Table 6.13

Total Variance Explained for general problems



Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 2.833 21.790 21.790 2.041 15.697 15.697

2 1.591 12.235 34.025 1.906 14.665 30.362

3 1.534 11.804 45.829 1.769 13.609 43.971

4 1.202 9.243 55.072 1.355 10.426 54.396

5 1.135 8.734 63.806 1.223 9.410 63.806

6 .883 6.791 70.597

7 .728 5.600 76.197

8 .693 5.330 81.527

9 .599 4.605 86.132

10 .541 4.165 90.297

11 .480 3.693 93.990

12 .413 3.174 97.164

13 .369 2.836 100.000


215

Table 6.14 - Rotated Component Matrix for general problems

Component

1 2 3 4 5

GenProblem67 .702

GenProblem71 .690

GenProblem66 .642

GenProblem72 .820

GenProblem70 .749

GenProblem73 .536

GenProblem69 .502

GenProblem64 .851

GenProblem65 .833

GenProblem74 .850

GenProblem68 .634

GenProblem76 .808

GenProblem75 .604

Extraction Method: Principal Component Analysis. Rotation

Method: Varimax with Kaiser Normalization.

Rotation converged in 9 iterations.

From the above tables 6.13 and 6.14, it is found that 5 problems arise in SME

units of these two districts namely “Rental and mortality” (REMO), “Poor

planning and workers” (PPW), “Competition with large industries” (CLI),

“Modern technology” (MOT) and “Quality development “(QD)

The above-mentioned factors have the following loadings with 63.81% of total

variance.

216

REMO:

66- The financial institutions are not sufficient

67- Rental problems of business establishment.

71- High rate of mortality

PPW:

69 - There are no established channels of negotiation between employers and

employee

70 - The sudden non-cooperation of workers leads to closure of SMEs

72 - Feasibility studies are not followed by SMEs

73 - Lack of technology

CLI:

64 - When SME elevates to higher Order industry, they are not able to get

proper encouragement

65 - Facing open competition with large-scale industries

MOT:

68 - Deteriorating industrial relations

74 - There is no special help from Government and other organization for

modernization

QD:

75- Difficulty in improving quality standards and productivity

76- SME are not considered as skill development centers

The extracted factors are subjected to one-sample t-test to identify and order the

predominant factors of general problems faced by SMEs in Chennai and

Tiruvallur districts.

217

Table 6.15

One-Sample Statistics for general problems


REMO 402 3.5398 .99357 .04955

PPW 402 3.5591 .82591 .04119

CLI 402 4.3731 .77609 .03871

MOD 402 3.1455 1.15963 .05784

QD 402 3.0833 1.15285 .05750

Table 6.16

One-Sample Test for general problems

Test Value = 3

t df

Sig. (2-

tailed)

Mean

Difference


the Difference

Lower Upper

REMO 10.893 401 .000 .53980 .4424 .6372

PPW 13.572 401 .000 .55908 .4781 .6401

CLI 35.474 401 .000 1.37313 1.2970 1.4492

MOD 2.516 401 .012 .14552 .0318 .2592

QD 1.449 401 .148 .08333 -.0297 .1964

From the above tables 6.15 and 6.16 it is found that the SMEs in Chennai

and Tiruvallur districts are facing severe competition from large industries

followed by poor planning and workers, Rental and mortality, modern

technology. But they are profoundly believed that the products they produce are

known for their good quality.

It is inferred that the SMEs in Chennai and Tiruvallur districts are

helpless in selling the products in the midst of heavy competition from the large

industries. The entrepreneurs of SMEs possess poor planning and insincere

218

workers due to financial and other prominent constraints. The financial

constraints cease their development in the form of rent and other important

expenses. The modern technology is identified as a one of the serious problems

faced by the SMEs in present situation. They are not in the position to modernize

their industry with small capital.

Classification of SME units in Chennai and Tiruvallur district:

The problems of finance, raw material, marketing power and other are

playing crucial role in affecting the progress of SME units. Based on the factors

of problems, the SME units in Chennai and Tiruvallur districts are classified into

3 groups using K-means cluster analysis.

Table 6.17

Final Cluster Centers for SME units in Chennai and Tiruvallur district

Cluster

1 2 3

IRDP 3.60 4.59 4.65

LD 3.46 4.06 3.76

RM 3.37 4.58 4.64

LPS 3.40 4.37 4.51

HTPF 4.08 4.58 4.84

ALM 3.47 4.54 4.58

COM 3.35 4.65 4.84

REMO 3.18 4.02 3.38

PPW 3.12 3.94 3.53

CLI 3.37 4.64 4.76

MOD 2.91 4.16 2.49

QD 2.88 3.89 2.57

219

Table 6.18

Number of Cases in each Cluster for SME units in Chennai and Tiruvallur

district

Cluster 1 100.000

2 133.000

3 169.000

Valid 402.000

The table 6.18 indicates that the three clusters are formed with respect to

the different problems faced by SME. The first cluster consists of 100 SME

units (24.87%), second cluster comprises 122 SME units (33.09%) and the third

cluster has the frequency of 160 SME units (42.04%). The following table

explains the measure of problems faced by SME units in Chennai and Tiruvallur

district based on clusters of SMEs. The mean values of each problem of SMEs in

the respective clusters are mentioned below:

Table 6.19 indicates the mean scores and standard deviations of

multifarious problems of SMEs in cluster one

Table 6.19

One-Sample Statistics for multifarious problems of SMEs


IRDP 100 3.5967 .69839 .06984

LD 100 3.4567 .74559 .07456

RM 100 3.3667 .67179 .06718

LPS 100 3.4020 .69077 .06908

HTPF 100 4.0750 .83900 .08390

ALM 100 3.4680 .73538 .07354

COM 100 3.3500 .88335 .08833

REMO 100 3.1750 .93034 .09303

PPW 100 3.1150 .64297 .06430

CLI 100 3.3650 .73805 .07381

MOD 100 2.9100 .83901 .08390

QD 100 2.8800 1.05916 .10592

a Cluster Number of Case = 1

220

From the above table it is found that the mean values of problems of

SMEs are ranging from 2.88 (QD) to 4.07(HTPF). Table 6.22 depicts t-test

values of the mean scores with the test value 3.

Table 6.20

One-Sample Test for multifarious problems of SMEs

Test Value = 3

T df

Sig. (2-

tailed)

Mean

Difference


the Difference

Lower Upper

IRDP 8.543 99 .000 .59667 .4581 .7352

LD 6.125 99 .000 .45667 .3087 .6046

RM 5.458 99 .000 .36667 .2334 .5000

LPS 5.820 99 .000 .40200 .2649 .5391

HTPF 12.813 99 .000 1.07500 .9085 1.2415

ALM 6.364 99 .000 .46800 .3221 .6139

COM 3.962 99 .000 .35000 .1747 .5253

REMO 1.881 99 .063 .17500 -.0096 .3596

PPW 1.789 99 .077 .11500 -.0126 .2426

CLI 4.945 99 .000 .36500 .2186 .5114

MOD -1.073 99 .286 -.09000 -.2565 .0765

QD -1.133 99 .260 -.12000 -.3302 .0902


From the t-values in the above table it is inferred that in first cluster

SMEs in Chennai and Tiruvallur district are not facing the REMO, PPW, MOD

and QD significantly, 24.87% of SMEs are effectively generating the income for

rent and other expenses They have modern technology in their premises to

produce the high quality products. But they face so many other problems like

loans, raw material and power problems. Table 6.21 presents the means and

standard deviations of problems prevailing in second cluster

221

Table 6.21

One-Sample Statistics for problems prevailing in second cluster


IRDP 133 4.5915 .43105 .03738

LD 133 4.0551 .82794 .07179

RM 133 4.5823 .39287 .03407

LPS 133 4.3669 .50223 .04355

HTPF 133 4.5827 .74666 .06474

ALM 133 4.5429 .53005 .04596

COM 133 4.6504 .46866 .04064

REMO 133 4.0226 .86791 .07526

PPW 133 3.9361 .66902 .05801

CLI 133 4.6353 .45697 .03962

MOD 133 4.1579 .66677 .05782

QD 133 3.8910 .76212 .06608


From the mean values in the above table it is ascertained that the second

cluster SMEs face more problems of CLI and less problems of QD. Table 6.22

indicates t-test values of means of cluster two

222

Table 6.22

One-Sample Test for problems prevailing in second cluster

Test Value = 4

T df

Sig. (2-

tailed)

Mean

Difference


the Difference

Lower Upper

IRDP 15.825 132 .000 .59148 .5175 .6654

LD .768 132 .444 .05514 -.0869 .1971

RM 17.093 132 .000 .58229 .5149 .6497

LPS 8.425 132 .000 .36692 .2808 .4531

HTPF 9.000 132 .000 .58271 .4546 .7108

ALM 11.811 132 .000 .54286 .4519 .6338

COM 16.004 132 .000 .65038 .5700 .7308

REMO .300 132 .765 .02256 -.1263 .1714

PPW -1.102 132 .273 -.06391 -.1787 .0508

CLI 16.034 132 .000 .63534 .5570 .7137

MOD 2.731 132 .007 .15789 .0435 .2723

QD -1.650 132 .101 -.10902 -.2397 .0217


The above table clearly presents that the SMEs in second cluster face all

the problems severely. It is found that 33.09% of SMEs in Chennai and

Tiruvallur districts face severe financial problems, raw material problems, power

problems and marketing problems. The SMEs in second cluster are very much

confident about their production.

Table 6.23 presents the mean scores and standard deviations of problems

of third cluster

223

Table 6.23

One-Sample Statistics for problems prevailing in third cluster


IRDP 169 4.6469 .50651 .03896

LD 169 3.7594 .80996 .06230

RM 169 4.6351 .48993 .03769

LPS 169 4.5065 .47910 .03685

HTPF 169 4.8432 .39787 .03061

ALM 169 4.5775 .61622 .04740

COM 169 4.8373 .35586 .02737

REMO 169 3.3757 .98165 .07551

PPW 169 3.5251 .89544 .06888

CLI 169 4.7633 .37427 .02879

MOD 169 2.4882 1.08006 .08308

QD 169 2.5680 1.11662 .08589


The above table clearly revealed that the mean scores are ranging from

2.48(MOD) to 4.84(HTPF) Table 6.24 shows the significance of mean scores

Table 6.24

One-Sample Test for problems prevailing in third cluster

Test Value = 4

T Df

Sig. (2-

tailed)

Mean

Difference


the Difference

Lower Upper

IRDP 16.604 168 .000 .64694 .5700 .7239

LD -3.862 168 .000 -.24063 -.3636 -.1176

RM 16.852 168 .000 .63511 .5607 .7095

LPS 13.744 168 .000 .50651 .4338 .5793

HTPF 27.550 168 .000 .84320 .7828 .9036

ALM 12.183 168 .000 .57751 .4839 .6711

COM 30.587 168 .000 .83728 .7832 .8913

REMO -8.267 168 .000 -.62426 -.7733 -.4752

PPW -6.894 168 .000 -.47485 -.6108 -.3389

CLI 26.513 168 .000 .76331 .7065 .8201

MOD -

18.197 168 .000 -1.51183 -1.6759 -1.3478

QD -

16.671 168 .000 -1.43195 -1.6015 -1.2624


224

The SMEs in third cluster are not facing MOD and QD where as they are

facing large dimensions of HTPF and COM. It is ascertained that the third

cluster differ from first cluster in financial problems and resemble in technology

and quality. It is identified that 42.04% face serious financial problems and they

are not able to meet all the expenses of their industries. Besides that all the three

clusters face power problems with different dimensions of severity. The third

cluster SMEs also face raw material and marketing problems and hampered by

their severity. Table 6.25 summarizes the above discussions regarding problems

faced by SMEs in Chennai and Tiruvallur district

Table- 6.25

Problems faced by SMEs in Chennai and Tiruvallur district

Cluster1 Cluster 2 Cluster 3

IRDP Moderately affected Affected Fully affected

LD Moderately affected Fully affected Affected

RM Moderately affected Affected Fully affected

LPS Moderately affected Affected Fully affected

HTPF Moderately affected Affected Fully affected

ALM Moderately affected Affected Fully affected

COM Moderately affected Affected Fully affected

REMO No comments Fully affected Affected

PPW No comments Fully affected Affected

CLI Moderately affected Affected Fully affected

MOD No comments Fully affected Not Affected

QD No comments Affected Not Affected

The above table clearly revealed that 24.87% SME units in first cluster

are moderately affected by IRDP, LD, RM, CPS, HTPF, ALM, COM, CLI

where they do not worry about REMO, PPW, MOD and QD. It is also extracted

that 33.09% of SME units in cluster II are fully affected by LD, REMO, PPW

225

and MOD whereas 42.04% in cluster III are fully affected by IRDP, RM, LPS,

HTPF, ALM, COM and CLI and not at all affected by MOD and QD.

So on the whole it is summarized that the SMEs in second and third

clusters(33.09%+44.04%=77.13%) are facing raw material, power, marketing,

and other general problems with severity ranging from moderate to high. The

first cluster SMEs with 23.87% of frequency are not accessible to rent, planning,

workers, modern technology and quality. It is also found that the quality of

products of SMEs in Chennai and Tiruvallur districts are good and welcome by

the purchasers.

The cluster classification of SME units in Chennai and Tiruvallur district

is justified using discriminate analysis. In this analysis the factors of problems

are considered as independent variables and cluster as grouping variables.

Table 6.26

Tests of Equality of Group Means

Wilks' Lambda F Df1 df2 Sig.

IRDP .594 136.461 2 399 .000

LD .925 16.065 2 399 .000

RM .475 220.907 2 399 .000

LPS .589 139.334 2 399 .000

HTPF .821 43.607 2 399 .000

ALM .632 116.330 2 399 .000

COM .458 236.066 2 399 .000

REMO .877 28.086 2 399 .000

PPW .858 32.995 2 399 .000

CLI .435 259.302 2 399 .000

MOD .601 132.171 2 399 .000

QD .745 68.189 2 399 .000

226

Table 6.27

Eigenvalues

Function Eigenvalue % of Variance Cumulative % Canonical Correlation

1 3.417(a) 67.0 67.0 .880

2 1.680(a) 33.0 100.0 .792

a First 2 canonical discriminant functions were used in the analysis.

Table 6.28

Wilks' Lambda

Test of

Function(s)

Wilks'

Lambda

Chi-

square df Sig.

1 through 2 .084 972.478 24 .000

2 .373 387.950 11 .000

The table 6.27 revealed the significant contribution independent variables

in the analysis. Table 6.27 and 6.28 states the justification of cluster

classification by the significant canonical correlation values, Wilk’s lambda

values and chi square values. It is ascertained that the clusters of SMEs

prevailing in Chennai and Tiruvallur districts are justified accurately.

Government encouragements

SMEs where recognized by the government of India after independence

of the country in the Industrial Policy Resolution of 1948 that small industries

particularly suited for better utilizations of local resources to achieve self

sufficiency in respect of certain types of essential consumer goods. With the

inception of the five-year plans a more comprehensive programme of assistance

to small-scale industries was initiated. In the first Five Year Plan it was

emphasized that “small industries derive part of their significance from their

potential value for the employment of trained and educated persons”2 In order to

help these industries, some protection need to be provided by reserving certain

spheres of productive activities only four small-scale industries.

227

The aim of the state policy, as enunciated in the Industrial Policy

Resolutions of 1948 and 1956, was to ensure that the small industries sector

would require sufficient vitality to be self-supporting and that its development

was integrated with that of the large scale. It was, therefore, felt that the

government should concentrate on measures design to remains the basic

handicaps of small-scale industries such as lack of technical and financial

assistance, suitable working accommodation inadequacy of tooling, repairs and

maintenance facilities etc. It was also laid down that the technique of production

of the small-scale industries should be constantly improved and modernized, the

pace of transformation being regulated so as to avoid, as far as possible,

technological unemployment.

During the First Five Year Plan, the following two important steps were

taken by the central government of substantial finance for the development of

village and small-scale industries the building up of a network of all India

boards to deal with the problems of the handloom industry, Khadi, and village

industries, handicrafts, small-scale industries, sericulture and coir industry.

Greater attention on the part of the central and state government and the

expending activities of the all India Board have increased production and

employment in a number of industries. The setting up of four regional small

industries services institutes with a number of branch units to provide technical

services, advices and assistance was a step from which may be expected in the

future.

The Government is playing the crucial role for the development of SME

units. The close association of encouragement of Government can be identified

by the important factors using factors analysis, which is applied on 24 variables

of Government encouragement

228

Table 6.29

Total Variance Explained for Government encouragements



Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 5.425 22.602 22.602 3.544 14.765 14.765

2 2.192 9.131 31.734 2.973 12.388 27.154

3 1.995 8.313 40.046 1.993 8.303 35.457

4 1.404 5.852 45.898 1.572 6.549 42.006

5 1.341 5.590 51.488 1.526 6.359 48.366

6 1.178 4.908 56.395 1.387 5.781 54.147

7 1.084 4.517 60.913 1.360 5.668 59.814

8 1.042 4.340 65.253 1.305 5.439 65.253

9 .966 4.024 69.277

10 .888 3.699 72.977

11 .677 2.821 75.797

12 .657 2.736 78.533

13 .622 2.593 81.126

14 .604 2.516 83.643

15 .545 2.272 85.914

16 .517 2.155 88.069

17 .487 2.029 90.098

18 .433 1.803 91.901

19 .422 1.759 93.660

20 .357 1.489 95.149

21 .347 1.447 96.596

22 .309 1.287 97.883

23 .269 1.119 99.002

24 .240 .998 100.000


229

Table 6.30

Rotated Component Matrix for Government encouragements

Component

1 2 3 4 5 6 7 8

GovProblem85 .777

GovProblem84 .772

GovProblem86 .750

GovProblem87 .672

GovProblem82 .618

GovProblem77 .597

GovProblem96 .774

GovProblem99 .685

GovProblem98 .680

GovProblem100 .661

GovProblem97 .639

GovProblem95 .540 .408

GovProblem89 .831

GovProblem90 .722

GovProblem92 .567

GovProblem79 .763

GovProblem78 .642

GovProblem80 .543

GovProblem94 .786

GovProblem93 .707

GovProblem81 .802

GovProblem91 .769

GovProblem83 .576

GovProblem88 .711



Rotation converged in 15 iterations.

230

The table 6.30 indicates that the 24 variables explain 65.253% of total

variance and gives out 8 factors of encouragement of Government. Table 6.30

revealed that

Factor 1 has the following variables in its loading.

77 - The installation of SIDO, SISI, Kadhi developments are helping SMEs to

revive.

82 - Technical assistance is given to SMEs by the Government to increase

production.

84 - Industrial estate programme helps to build good organizational setup

and infrastructure

85- SMEs are treated as priority sector by the government to help them

financially

86- The provision of long term and medium term loans are useful for their

development

87- RBI provides finance for traditional industries through co-operative

banking system.

and it is called as “Loan and development programs’ (LDP).

Factor 2 is called “Subsidy and Tax exemption” (STE) because of its

factor loadings

95- Tax holiday for new industrial undertakings encourages SMEs

96- Investment allowances are given to encourage SME units

97- Capital subsidies to industries in backward areas encourage rural

SMEs

98- Total exemption from excise duty

99- Area development schemes directly promotes SMEs

100- Government encourages developing ancillary units connected to public

Sector enterprises.

231

The third factor is due to the variables

89- State SME corporations rationally distribute the raw materials during

scarcity.

90- Government’s decision to build up a buffer stock prevents raw material

scarcity.

92- Price preference is given by public sector purchase.

Hence it is known as “Smooth raw material supply” (SRM).

“Special policies”(SP) is the fourth factor with factor loadings of the

variables

78- The region and district officers of SME directorate often interacting with

SMEs

79- Policy formation, coordination and continuous monitoring are taken by

the Government.

80- Reservation of certain products for SMEs avoids competition from large-

scale industries.

The fifth factor is named as “Quality and sales outlets”(QSO). It

consists of variables

93- Provision of quality control and testing facility increase the

competitiveness of the product.

94- Arranging market outlets like sales emporium, state cooperative

societies, and trade fairs

The sixth factor is known as “Government Purchase”(POG) obtained

due to the variables: - 81- Government’s decision to purchase reserved products

from SME reduces the marketing burden.

232

The seventh is derived from the two variables

83- The Government special directions for profit operations

91- Government makes direct purchase from SMEs reduces the marketing

burden hence, it is called as “Profit operations”.

The eighth factor is a “less interest rate” (LIR) due to the variables

88- Nationalized banks are given directions to disburse loans for SME units

for less interest.

The eight factors derived through factor analysis are considered to identify and

order based on their predominance. The one sample t-test is applied and the

following results are obtained

Table 6.31

One-Sample Statistics for Government encouragements


LDP 402 4.2380 .77785 .03880

STE 402 3.8794 .74099 .03696

SRM 402 3.8972 1.02230 .05099

SP 402 3.9071 .93851 .04681

QSO 402 3.8296 1.10042 .05488

POG 402 3.2761 1.60761 .08018

POP 402 3.2251 1.06682 .05321

LIR 402 3.9080 1.36175 .06792

233

Table 6.32

One-Sample Test for Government encouragements

Test Value = 3

T df

Sig. (2-

tailed)

Mean

Difference


the Difference

Lower Upper

LDP 31.910 401 .000 1.23798 1.1617 1.3142

STE 23.794 401 .000 .87935 .8067 .9520

SRM 17.596 401 .000 .89718 .7969 .9974

SP 19.380 401 .000 .90713 .8151 .9992

QSO 15.116 401 .000 .82960 .7217 .9375

POG 3.444 401 .001 .27612 .1185 .4337

POP 4.231 401 .000 .22512 .1205 .3297

LIR 13.369 401 .000 .90796 .7744 1.0415

From the above tables it inferred that the SMEs are accepting that the

government has taken only the moderate efforts for issuing the quick loans for

less interest, subsidies, raw materials, sales out lets and policies for over all

developments. In the domain of priority sector SMEs are considered as most

important for development. In fact the central government has directed the

public sector banks to issue loans for SMEs for entrepreneurial development and

to solve unemployment problems.

Products and clusters of SME

In this analysis, how the SME units are facing the problems based on the

products produced by them.

234

Table 6.33

Products and clusters of SME

Cluster1

N=100

Cluster2

N=133

Cluster3

N=169

Food products = 30

(7.46%)

Cotton textiles = 26

(6.47%)

Jute products = 17(4.23%)

Beverages = 20

(4.98%)

Silk Products = 28

(6.97%)

Paper & paper

Products = 29 7.21%)

Wooden products =

38 (9.45%)

Textile Products = 22

(5.47%)

Rubber,

plastic =32 (7.96%)

Leather products = 10

(2.49%)

Chemicals = 20 (4.98%) Non-metalic and

Mineral products=49

(12.19%)

Others = 2 (0.05%) Metal products = 14

(3.48%)

Basic metal

products=18(4.48%)

Machinery parts =10

(2.49%)

Transport equipment and

Products = 24 (5.97%)

Electrical

machines=13(3.23%)

And appliances

The above table clearly revealed that 23.87% of SMEs in Chennai and

Tiruvallur districts are producing food products, beverages, wooden products,

leather products and others and they do not face the financial, technology and

quality problems. It is also found that 33.09% of SMEs are producing Cotton

textiles, silk products, textile products, chemicals, metal products, machinery

parts, and electrical machines and facing the problems of finance, raw material,

power and marketing problems. It is revealed that 44.04% of SMEs are

producing jute products, paper products, rubber and plastic products, non-

235

metallic and mineral products, base metal products and transport equipments and

they do not face the problems of technology and quality.,

Association between cluster of SME units and their profile

The cluster classification of SME units based on the problems faced by

them and their profile like registration, ownership, investment proportion, loan

source, business establishment, annual turnover and nature of competition. To

find the association a non-parametric chi-square test is applied.

Association between cluster and registration of SME units

In Chennai and Tiruvallur districts both registered and unregistered SME

units are emerging. These two types of SME units are facing many problems.

The association between the registered and unregistered SMEs with different

clusters are established in table 6.34.

Table 6.34

Association between cluster and registration of SME units

Cluster Number of Case

Total 1 2 3

registe

red

1.00 65 94 112 271

2.00 35 39 57 131

Total 100 133 169 402

236

Table 6.35

Chi-Square Tests for Association between cluster and registration of SME

units

Value df

Asymp.

Sig. (2-

sided)

Pearson Chi-

Square 1.010(a) 2 .603

Likelihood Ratio 1.018 2 .601

Linear-by-Linear

Association .004 1 .949

N of Valid Cases 402

a 0 cells (.0%) have expected count less than 5. The minimum

expected count is 32.59.

From the table 6.35, it is found that chi-square = 1.010, p = 0.603 for 3

degrees of freedom and there is no association between clusters and registration

of SME units. So it is inferred that both the registered and un registered of SMEs

in Chennai and Tiruvallur districts are distributed over all the three types of

clusters and facing problems of finance, raw material, marketing and power

problems. The registered SMEs are able to get the aid from the government

easily than the unregistered units.

(B) Association between cluster and ownership of SME units:

In Chennai and Tiruvallur districts the ownership is categorized as sole,

partnership and private limited and these three types of SME ownership are

facing so many problems. The association is established in Table 6.36

237

Table 6.36

Association between cluster and ownership of SME units


Total 1 2 3

Owner

ship

1.00 33 40 50 123

2.00 29 41 41 111

3.00 38 52 78 168

Total 100 133 169 402

Table 6.37

Chi-Square Tests for Association between cluster and ownership of SME

units

Value df

Asymp.

Sig. (2-

sided)

Pearson Chi-

Square 2.857(a) 4 .582


Linear-by-Linear

Association 1.273 1 .259


0 cells (.0%) have expected count less than 5. The minimum

expected count is 27.61


degrees of freedom and there is no association between clusters and ownership

of SME units. So it is revealed that the owner ship is independent of problems

faced by the SMEs. The SMEs under sole proprietorship, partnership, private

limited are distributed over all the three clusters of SMEs and facing numerous

problems.

238

(C) Association between cluster and loan source of SME units:

In Chennai and Tiruvallur districts the SMEs obtain loan from public

sector banks, private sector, private sources and foreign banks respectively. It is

found that they get maximum help from public sector banks and private

moneylenders.

Table 6.38

Association between cluster and loan source of SME units


Total 1 2 3

Loan

obtained

1.00 46 67 88 201

2.00 15 14 14 43

4.00 39 52 67 158

Total 100 133 169 402

Table 6.39

Chi-Square Tests for Association between cluster and loan

source of SME units

Value df

Asymp.

Sig. (2-

sided)

Pearson Chi-

Square 3.132(a) 4 .536


Linear-by-Linear

Association .064 1 .800





degrees of freedom and there is no association between clusters and loan source

of SME units. It is inferred that the SMEs in Chennai and Tiruvallur districts are

obtaining loans from all the sources. They get their loans from public sector

239

banks, private sector banks, private finance and foreign banks according their

conveniences like quick loan system and less interest loans with subsidies.

(D) Association between cluster and Business establishment of SME units:

In Chennai and Tiruvallur districts the SMEs are running in proprietors

own places, leased lands and rental premises. The table revealed that the

maximum number of industries are producing the product from rental premises.

Table 6.40

Association between cluster and Business establishment of SME units


Total 1 2 3

Buss

establishm

ent

1.00 37 55 81 173

2.00 6 6 11 23

3.00 57 72 77 206

Total 100 133 169 402

Table 6.41

Chi-Square Tests for Association between cluster and Business

establishment of SME units

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.340(a) 4 .362


Linear-by-Linear Association 3.647 1 .056





degrees of freedom and there is no association between clusters and business

establishment of SME units. It is inferred that the Business establishment place

is independent of problems faced by the SMEs in Chennai and Tiruvallur

districts. They have own, leased, and rent establishments and present rationally

in all the three clusters.

240

(E) Association between cluster and Annual turnover of SME units:

In Chennai and Tiruvallur districts the annual turn over ranges from 1

lakh to 5 lakhs respectively. It is clear from the table that the maximum number

of SMEs in these two districts are creating a turn over above 5 Lakhs

Table 6.42

Association between cluster and Annual turnover of SME units


Total 1 2 3

Turno

ver

1.00 37 55 81 173

2.00 6 6 11 23

3.00 57 72 77 206

Total 100 133 169 402

Table 6.43

Chi-Square Tests for Association between cluster and Annual turnover of

SME units







expected count is 5.72


degrees of freedom and there is no association between clusters and annual

turnover of SME units. It is found that annual turn over does not distinguish the

SMEs in Chennai and Tiruvallur districts based on their problems. There is

general opinion of SMEs with small and high turn over that they face financial,

raw material, power and marketing problems regularly.

241

(F) Association between cluster and competition of SME units:

In Chennai and Tiruvallur districts the SMEs are facing formidable

competition from large industries. The dimensions of competition are

categorized as small, medium and large. The SMEs agree that they face

maximum number of small competition from various industries.

Table 6.44

Association between cluster and competition of SME units


Total 1 2 3

competition 1.00 67 102 106 275

2.00 24 27 38 89

3.00 9 4 25 38

Total 100 133 169 402

Table 6.45

Chi-Square Tests for Association between cluster and competition of SME

units








From the table 6.45 it is found that chi-square = 13.495, p = 0.009 for 4

degrees of freedom and there is a association between clusters and competition

of SME units. It is inferred that the first and third clusters of SMEs do not face

severe competition problems from the various industries. The clusters are mainly

classified under the problems of competition.

242

Analysis of variance for characteristics of SME and encouragement of

Government

Factor analysis clearly brought out five factors of characteristics of SME

and eight factors of encouragement of government as stated previously. The

analysis of variance (ANOVA) is useful to identify the significant difference

among the means of the variables of different clusters of the study.

Table 6.46

ANOVA for characteristics of SME and encouragement of Government

Sum of

Squares Df

Mean

Square F Sig.

BAP Between

Groups 74.331 2 37.165 96.657 .000

Within

Groups 153.418 399 .385

Total 227.749 401

LC Between

Groups 32.571 2 16.285 15.631 .000

Within

Groups 415.713 399 1.042

Total 448.283 401

DI Between

Groups 39.994 2 19.997 19.654 .000

Within

Groups 405.959 399 1.017

Total 445.953 401

ED Between

Groups 7.858 2 3.929 1.790 .168

Within

Groups 875.933 399 2.195

Total 883.791 401

EMC Between

Groups 4.060 2 2.030 1.614 .200

Within

Groups 502.042 399 1.258

Total 506.102 401

The above table clearly indicates that the acquaintance of characteristics

of SME differ significantly in all the three clusters of problems of SME units the

243

factors BAP (F=96.657, p = 0.000), LC (F = 15.631, p = 0.000), DICF = 19.654,

p = 0.000 differ in their means, whereas ED and EMC are viewed by all the three

clusters equally. So it is concluded that SMEs expect good employment

opportunities with minimum capital through SMEs. The SMEs in the three

clusters expect various out puts in their SME units.

Analysis of variance of government’s encouragement with respect to

clusters.

The eight factors of encouragement of government in increasing loan

distribution, subsidies, sales out lets and responsible for marketing the products

are tested for group means with respect to three clusters.

Table 6.47

ANOVA for of government’s encouragement with respect to clusters

Sum of

Squares Df

Mean

Square F Sig.

LDP Between

Groups 73.153 2 36.576 86.115 .000

Within

Groups 169.470 399 .425

Total 242.622 401

STE Between

Groups 45.215 2 22.607 51.556 .000

Within

Groups 174.962 399 .439

Total 220.176 401

SRM Between

Groups 29.741 2 14.870 15.239 .000

Within

Groups 389.343 399 .976

Total 419.083 401

SP Between

Groups 20.377 2 10.189 12.214 .000

Within

Groups 332.822 399 .834

Total 353.200 401

244

QSO Between

Groups 52.988 2 26.494 24.437 .000

Within

Groups 432.590 399 1.084

Total 485.578 401

POG Between

Groups 7.794 2 3.897 1.512 .222

Within

Groups

1028.55

7 399 2.578

Total 1036.35

1 401

POP Between

Groups 13.811 2 6.906 6.226 .002

Within

Groups 442.565 399 1.109

Total 456.376 401

LIR Between

Groups 73.643 2 36.821 21.930 .000

Within

Groups 669.952 399 1.679

Total 743.595 401

Table 6.47 clearly indicates that the variables LDP (F=86.115, p = 0.000),

STE (F=51.556, p = 0.000), SRM (F=15.239, p = 0.000) SP (F = 12.214, p =

0.000), QSO (F = 24.437, p = 0.000), POP (F=6.226, p = 0.002) and LIR (F =

21.930, p = 0.000) differ significantly in their means with respect to cluster of

SME. The factor POG is viewed by all the clusters equally. It is concluded that

SMEs expect Government to purchase all their products and they face different

dimensions of problems in acquiring the loans and subsidies based on the

products they produce.

chapter vi problems of smes in chennai and tiruvallur...

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