CHAPTER 4 RESULTS AND DISCUSSIONS -...

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CHAPTER 4

RESULTS AND DISCUSSIONS

4.1 INTRODUCTION

This chapter consists of seven sections; first section presents descriptive

analysis of characteristics of the manufacturing Industries in UT of Puducherry.

Followed by dimensions wise analysis section two contains supply chain concerns ,

section three is supply chain competence, section four is supply chain practices,

section five is supply chain performance and section six is organizational performance

were analyzed using statistical tools namely simple mean analysis, simple mean, chi-

square test, independent sample t-test, Analysis of Variance (ANOVA), factor

analysis, cluster analysis, discriminant analysis, correspondence analysis and

canonical correlation. Finally section seven is causal model and hypotheses testing

has been tested by Structural Equation Modeling (SEM) contains measurement model

and structural model was analyzed using LISREL 8.72 software. Results are

represented in tabular and figurative forms

4.2 NOTATIONS

B Beta Coefficient

df Degree of Freedom

F F- Statistics

N Number of observation

p-value Significance level

R 2 Coefficient of determination

t t- Statistics

Z1 Domain functions of discriminate analysis

θδ Sum of the error variance terms for a construct

ϰ2 Chi-Square value

λ Wilk’s lambda value

ρc Construct reliability

% Percentage

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4.3 DATA ANALYSIS PROCESS

The following Figure 4.1 illustrates various stages of data analysis used in the

present research work which is given below:

Figure: 4.1 Data Analysis Process

* SC_CON-Supply Chain Concerns, SC_COM-Supply Chain Competence,

SC_PRA-Supply Chain Practices, SC_PER-Supply Chain Performance

OP-Organizational Performance.

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4.4 PROFILE OF MANUFACTURING INDUSTRIES ANALYSIS

The profile of the sample manufacturing firms studied is portrayed in this

chapter. The profile of manufacturing industries include nature and type of industry to

which the units belong, number of employees in the units, total capital invested by the

units, type of goods produced by the units, form of business organization used by the

units, annual turnover managed by the units, type of ownership and listing of shares of

the units, the kind, process and pattern of manufacturing adopted by the units, market

coverage and scope of market of the units, length of life of the units, software used by

the units, and the Supply chain position and the of supply chain of units.

4.4.1 Type of Industry

Based on the nature of business carried on by the units studied, the sample

units may be categorized as those engaged in agro-based business, plastics, chemicals,

metal manufacturing, food, furniture, construction materials, Automobiles,

Electronics, and Textiles has been portrayed in Table 4.1.

Table 4.1 Type of Industry

Industry Frequency Percentage

Agro-based 12 4.7

Chemical 37 14.5

Food 25 9.8

Furniture 7 2.7

Electronics 36 14.1

Plastic 24 9.4

Automobile 20 7.8

Textile 15 5.9

Building Materials 22 8.6

Metal 27 10.6

Pharmaceutical 8 3.1

Others 22 8.6

Total 255 100.00

It can be inferred from Table 4.1 that majority of the manufacturing units in

The Union Territory of Puducherry are engaged in the manufacture of Chemicals,

followed by Electronics, and Metal manufacturing. Units engaged in the manufacture

of building materials and plastics also occupy a sizeable portion of the respondents.

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4.4.2 Number of Employees

The number of employees employed by a business unit signifies the size of

the unit, as a large unit shall be invariably employing large number of employees. The

units studied have been categorized based on the number of employees as those

employing Less than 100, those employing 100-300, 300-600, 600-1200, and those

employing more than 1200 has been shown in Table 4.2.

Table 4.2 Number of Employees

Number of Employees Frequency Percentage

Less Than 100 128 50.2

100-300 61 23.9

300-600 24 9.4

600-900 17 6.7

900-1200 18 7.1

More than 1200 7 2.7

Total 255 100

It can be inferred from Table 4.2 that a simple majority of the business units

studied (50.2%) are employing less than 100 employees, while a shade under quarter

of them (23.9%) of them are employing 100 to 300 employees. A shade under one-

tenth of the units (9.4%) are employing 300 to 600 employees, while those employing

600 to 900 and 900 to 1200 employees are almost identical in number with very

marginal difference (6.7 and 7.1% respectively). A very few units are engaging more

than 1200 employees (a mere 2.7%).

4.4.3 Quantum of Capital Invested

The volume of capital employed by a business unit is another indicator of the

size of a business unit. A large business unit shall be invariably employing quite a

huge amount of capital. Manufacturing business units are invariably required to invest

sizeable amount of capital as huge investment is required to be made in plant and

machinery and land and buildings. The units studied have been grouped into

categories based on the quantum of capital invested as those which have invested less

than Rs. 50 Lakhs, those which have invested Rs. 50 Lakhs to One crore, Rs. 1-50

crores, and those which have invested more than Rs. 50 crores has been portrayed in

Table 4.3.

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Table 4.3 Quantum of Capital Invested

Capital Invested (in Rs.) Frequency Percentage

Less than 50 Lakhs 87 34.1

50 Lakhs to 1 Crore 73 28.6

1 Crore to 50 Crores 60 23.5

More than 50 Crores 35 13.7

Total 255 100.00

It can be observed from Table 4.3 that a shade above one-third of the units

studied (34.1%) have invested less than Rs. 50 Lakhs, while a shade above one

quarter of them (28.6%) have invested Rs. 50 Lakhs to One crore. Table 4.3 further

reveals that a shade under quarter of the units studied (23.5%) have invested Rs. 1-50

crores, while the least number of units (13.7%) have invested more than Rs. 50 crores.

It can be hence concluded that majority of the manufacturing units in UT of

Puducherry are employing capital in a small volume.

4.4.4 Position in Supply Chain

A manufacturing unit may be engaged in the process of assembling and

distribution, or in the manufacture of raw materials for other goods, sub-products to

be delivered to manufacturers of end products, or in the manufacture of final products.

Based on these four categories, the sample business units studied in UT of Puducherry

have been categorized into four types and the frequency of each of such four

categories has been portrayed in Table 4.4.

Table 4.4 Position in Supply Chain

Position Frequency Percentage

Raw Material Manufacturer 45 17.6

Sub product/Assemble Manufacturer 90 35.3

Final Product Manufacturer 76 29.8

Assemble and Distribution 44 17.3

Total 255 100.00

Table 4.4 reveals that a shade above one-third of the business units studied

(35.3%) are engaged in the manufacture of sub products assembling, while almost

three-tenth of them (29.8%) are engaged in the manufacture of final products. Table

4.4 further indicates that a shade above one-sixth of the units studied (17.6%) of the

companies are engaged in the manufacture of raw materials, while almost an identical

number of them (17.3%) of them are engaged in Assembling and Distribution.

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4.4.5 Scale of Operation

A manufacturing business unit may be categorized as small scale, medium

scale and large scale industry based on the size of capital invested in its plant and

machinery (excluding the amount invested on land and buildings). Those business

units which have invested less than one crore on plant and machinery are categorized

as small scale industry, while those which have invested Rs. 1-5 crores have been

categorized as medium scale industry, and those which have invested more than Rs. 5

crores are categorized as large scale industries. The scale of operation of the business

units studied has been portrayed in Table 4.5.

Table 4.5 Scale of Operation

Scale Frequency Percentage

Small Scale 115 45.1

Medium Scale 94 36.9

Large Scale 46 18.0

Total 255 100.00

Table 4.5 suggests that a clear majority of the business units studied (45.1%)

are operating in small scale while a shade above one-third of the units (36.9%) are

operating in medium scale and a shade under one-fifth of the units (18.0%) are

operating in large scale. Hence, it can be concluded that majority of the

manufacturing business units in UT of Puducherry are operating in small scale.

4.4.6 Type of Goods Produced

A manufacturing business unit may be engaged in the manufacture of

consumer goods, which are meant for ultimate consumption, or in the manufacture of

capital goods, which are meant to be utilized as inputs in the manufacture of other

goods. The frequency of business units engaged in the manufacture of industrial and

consumer goods are portrayed in Table 4.6.

Table 4.6 Type of Goods Produced

Goods Produced Frequency Percentage

Industrial Goods 134 52.5

Consumer Goods 121 47.5

Total 255 100.00

It can be observed from Table 4.6 that a simple majority of the business units

studied (52.5%) are engaged in the manufacture of Industrial goods, while 47.5% of

the units are engaged in the manufacture of capital goods. Hence, it can be concluded

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that majority of the business units in UT of Puducherry are engaged in the

manufacture of consumer goods.

4.4.7 Form of Business Organization

There are five forms of business organizations. They are Sole Proprietorship,

Partnership, Joint Stock Company, Public Sector Undertaking, and Cooperative form.

The form of organization used by the sample units studied has been indicated in

Table 4.7.

Table 4.7 Form of Business Organization

Type Of Business Organization Frequency Percent

Sole proprietor 41 16.1

Partnership 70 27.5

Private Limited company 115 45.1

Public Limited Company 29 11.4

Total 255 100.00

Table 4.7 portrays that majority of the business units studied (45.1%) are

Private Limited Companies, while a shade above quarter of them (27.5%) are using

the Partnership form of business organization, while a shade under one-sixth of them

(16.1%) are using the Sole Proprietorship form and a shade above one-tenth of them

(11.4%) are Public Limited Companies. Hence, it may be concluded that majority of

the business units engaged in manufacturing in Puducherry are Private Limited

Companies.

4.4.8 Type of Ownership

Businesses operate either in the Public sector or private sector. In a mixed

economy like India, majority of the businesses operate with public-private

partnership. The sample units studied have been categorized into four groups

depending on the sector to which they belong, as Public, Private , Joint sectors and

foreign company has been portrayed in Table 4.8.

Table 4.8 Type of Ownership

Sector Frequency Percentage

Public sector 24 9.4

Private sector 204 80.0

Joint sector 15 5.9

Foreign Company 12 4.7

Total 255 100.00

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It can be inferred from Table 4.8 that more than three-fourth of the sample

units studied are operating their business in the private sector, while a shade below

one-tenth of the sample units (9.4%) are operating their business in the public sector.

A very few of the sample units studied (5.9% and 4.7%) are operating their business

in the Joint sector and as Foreign company.

4.4.9 Type of Listing

Public Limited Companies gets their shares listed in recognized stock

exchanges to facilitate trading of their shares. Listing is indispensable for Public

Limited Companies, while the business units adopting other forms of business

organizations cannot get their shares listed has been depicts in Table 4.9.

Table 4.9 Type of Listing

Type of Listing Frequency Percent

Listed in India 64 25.1

Listed only abroad 12 4.7

Not listed 146 57.3

Listed in India and Abroad 33 12.9

Total 255 100

It can be observed from Table 4.9 that well above half of the sample units

studied (57.3%) have not got their shares listed in any stock exchanges. This is

probably due to the fact that the sample units studied have adopted forms of

organization other than Public Limited Company. It can further be inferred from the

Table 4.9 that one quarter of the sample units surveyed (25.1%) have got their shares

listed in Indian stock exchanges, while 12.9% of them have got their shares listed in

India and abroad. It can be noted that a mere 4.7% of the sample units have got their

shares listed purely in foreign stock exchanges. Hence, it can be concluded that

majority of the sample units in UT of Puducherry which have got their shares listed

have managed to get their shares listed in Indian stock exchanges.

4.4.10 Kind of Manufacturing

Business units may be engaged in the manufacture of products or processes or

both products and processes. Depending on the nature of manufacturing activities of

the sample units surveyed, they have been grouped into three categories and the

frequency of units falling under each group is displayed in Table 4.10.

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Table 4.10 Kind of Manufacturing

Kind of Manufacturing Frequency Percentage

Product 149 58.4

Process 51 20.0

Both 55 21.6

Total 255 100.00

Table 4.10 portrays that more than half of the sample units surveyed (58.4%)

are engaged in the manufacture of products, while exactly one-fifth of them (20%) are

engaged in the manufacture of processes. The balance 21.6% of the business units are

engaged in the manufacture of both products and processes. Hence, it may be said that

majority of the business units in UT of Puducherry are engaged in the manufacture of

products.

4.4.11 Manufacturing Pattern

Business units engaged in manufacture follow different patterns of

manufacturing. Some of them don’t follow any schedule of manufacture. They

manufacture just to utilize the resources available with them without following any

scientific pattern or understanding the actual requirements. Such units will end up

with serious trouble. However, most of the business units follow a strict schedule of

manufacture and draft their manufacture plan by framing suitable Production and

Materials budgets and following strict Inventory control. Some business units may

manufacture based on orders received from customers, while some units may

manufacture in advance and wait for orders. Some units engaged in assembling of

electronic items such as computers, transformers, etc. have inventory of spares for

their products and assemble them according to the configuration required by the

customers. Manufacturers of some metallic items have their products ready but

change the design of their products according to the tastes of the customers.

According to the manufacturing pattern followed by the business units surveyed, the

sample units have been grouped into four categories and the frequency distribution of

the units under each of the group is displayed by Table 4.11.

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Table 4.11 Manufacturing Pattern

Manufacturing Pattern Frequency Percentage

Make-to-order 104 40.8

Make-to-stock 80 31.4

Assemble-to-order 40 15.7

Engineer-to-order 31 12.2

Total 255 100.00

Table 4.11 depicts a picture wherein a shade over four-tenth of the sample

units surveyed (40.8%) follow the manufacture pattern of made – to – order, which

implies that majority of the sample units surveyed are engaged in the manufacture of

products or processes based on orders received by them. It can also be inferred that a

shade below one-third of the sample units surveyed (31.4%) are following the

manufacture pattern of made to stock, while a shade under one-sixth of the sample

units surveyed (15.7%) follow the manufacture pattern of Assemble to order, while a

shade under one-eighth of the sample units (12.2%) follow the manufacture pattern of

Engineer to order.

4.4.12 Type of Process

The type of process employed by a business unit signifies the nature of the

unit. The units studied have been categorized based on the type of process as job

order, continuous, batch and repetitive assemble has been portrayed in Table 4.12.

Table 4.12 Type of Process

Type of Process Frequency Percent

Job order 77 30.2

Continuous 152 59.6

Batch 4 1.6

Repetitive assemble 22 8.6

Total 255 100.00

It can be inferred from Table 4.12 that more than half of the sample units

studied are operating their business in the continuous process, next is job order (30%)

, batch process is 1.6% and Repetitive assemble process type is 8.6 %.

4.4.13 Annual Turnover

The magnitude of turnover of a business unit indicates the size of any business

unit. A large unit naturally can manage a significant volume of annual turnover,

whereas those units managing lesser turnover are invariably small in size. The

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business units surveyed have been categorized into six groups according to the

volume of annual turnover managed by them. The frequency distribution of units

falling under these six categories has been displayed in Table 4.13.

Table 4.13 Annual Turnover

Annual Turnover Frequency Percentage

50 Lakhs to 1 Crore 55 21.6

1 Crore to 3 Crores 74 29.0

3 Crores to 6 Crores 41 16.1




Total 255 100.00

Table 4.13 portrays that just below one-third of the sample units surveyed

(29.0%) are able to achieve an annual turnover of Rs. 1-3 Crores, while a shade above

one-fifth of them (21.6%) could manage an annual turnover of 50 Lakhs to One

Crore. Table 4.13 further reveals that 13.3 6% of the units could manage an annual

turnover of Rs. 6-10 Crores, while those units managing an annual turnover of Rs. 3-6

Crores constitute only 16.1%, while those units managing an annual turnover of Rs.

10-50 Crores and those managing Above Rs. 50 Crores constitute 12.5% and 7.5%

respectively. Hence, it can be said that majority of the business units in UT of

Puducherry are able to achieve an annual turnover of Rs. 1-3 Crores.

4.4.14 Market Coverage

The business units may operate at national level or international level. Those

units which have market beyond the frontiers of our country are categorized as those

operating in International Markets, while some units may cover both local and

international markets, while some others may cover domestic markets alone has been

portrayed in Table 4.14. It can be said that those units which have expanded beyond

the frontiers of our country have expanded quite well while those operating at

domestic level may not have expanded as much as the domestic units.

Table 4.14 Market Coverage

Market Coverage Frequency Percentage

Domestic Market 171 67.1

International Market 18 7.1

Both 66 25.9

Total 255 100.00

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It can be inferred from Table 4.14 that a shade above two -thirds of the sample

units surveyed are concentrating on domestic market only. It can further be noted that

a shade above one-quarter of the sample units (25.8%) are catering to the needs of

both the local and foreign markets, while 7.1% are able to cover international market

only. Hence, it may be said that majority of the manufacturing business units in UT of

Puducherry are able to concentrate merely on domestic market.

4.4.15 Area of Market

Market area of Business firms grows and expands with growth in size of the

firms. The firms expand their market beyond the regional level, and then try to

concentrate on capturing the zonal market, followed by the national market, upon

which they will try to expand to the foreign markets, thus trying to become a

transnational and multi-national company. The area of market of the sample units

surveyed has been grouped into five categories and the frequency of each category

has been displayed by Table 4.15.

Table 4.15 Area of Market

Area of Market Frequency Percentage

Within Pondicherry & Tamil Nadu 89 34.9

Southern Region 62 24.3

Entire India 36 14.1

India and abroad 64 25.1

Only export 4 1.6

Total 255 100.00

Table 4.15 clearly suggest that just over a third of the business units surveyed

(34.9%) have only local market of Tamilnadu and Puducherry, while a shade under a

quarter of them (24.3%) have succeeded in penetrating into the market of Southern

India. A shade over quarter of the business units (25.1%) have succeeded in capturing

the national market of India and venturing into the global market, while a shade over

one-seventh of the sample units surveyed (14.1%) are able to capture the national

market alone. Table 4.15 also depicts that a mere 1.6% of the sample units surveyed

are concentrating only on foreign markets. Hence, it may be said that many of the

manufacturing units in Puducherry are concentrating on merely on local markets, and

most of them restrict themselves to the zonal level at the most.

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4.4.16 Length of Business Experience

The period of existence of any business units play a vital role in deciding

various issues related to a business unit. For instance, a unit which is in existence for a

long period may be enjoying good element of goodwill and its borrowing capacity

will also be large, due to which it can maintain minimum cash reserves and utilize its

funds on long term capital assets. To the contrary, if the unit has limited goodwill, its

borrowing capacity will be restricted, as a result of which it will have to maintain

higher cash reserves, which may adversely affect the long term profitability of the

unit, though the liquidity position of the firm may be very good.

Table 4.16 Number of Years in Business

No. of years Frequency Percentage

Less than 5 years 35 13.7

5-10 years 66 25.9

10-15 years 67 26.3

More than 15 years 87 34.1

Total 255 100.00

Table 4.16 portrays that more than one-third of the sample units surveyed

(34.1%) are engaged in business for a period of more than 15 years, while a little over

one-fourth of them (25.9%) are in existence for a period of 5-10 years, while almost

an identical margin of 26.3% are in existence for a period of 10-15 years. A minimum

of 13.7% of the business units are in existence for a period of less than 5 years.

Hence, it can be said that quite a large proportion of the manufacturing business units

in UT of Puducherry are engaged in business for long period of time.

4.4.17 Usage of SCM Software

Automation has become the order of the day. Computerization has crept into

all forms of business, irrespective of their size and nature. The business units

surveyed were asked whether they are utilizing the ERP/SCM/CRM Software System

Application, and the response of the business units is represented in Table 4.17.

Table 4.17 ERP/SCM/CRM Software System Application

ERP/SCM/CRM software Frequency Percentage

Yes 77 30.2

No 178 69.8

Total 255 100.00

It can be observed from Table 4.17 that bulk of the business units surveyed

(69.8%) are not utilizing the ERP/SCM/CRM Software System Application, while a

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shade under one-third of the business units (30.2 %) are utilizing the software system

application. Hence, it can be said that majority of the manufacturing business units in

UT of Puducherry are not utilizing the software system for their business.

4.4.18 Inbound and Outbound Logistics

Manufacturing business units may be engaged in outbound logistics if their

products are meant for direct consumption. Those units engaged in the manufacture of

industrial goods are said to be engaged in inbound logistics. The frequency

distribution of the manufacturing business units engaged in Outbound and inbound

logistics is portrayed in Table 4.18.

Table 4.18 Inbound and Outbound Logistics

Side of Supply Chain Frequency Percentage

Inbound 142 55.7

Outbound 113 44.3

Total 255 100.00

It can be inferred from Table 4.18 that majority of the business units (55.7%)

are engaged in inbound logistics, while the balance 44.3% of the manufacturing units

surveyed are engaged in outbound logistics. Hence, it can be said that majority of the

business units in UT of Puducherry are engaged in inbound logistics.

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4.5 SUPPLY CHAIN CONCERNS DIMENSION ANALYSIS

The supply chain concerns of manufacturing enterprises are studied with the

help of variables such as firm’s lack of leverage within the supply chain, suppliers’

geographical distance, lack of sophisticated information system, lack of interest

among suppliers or customers, competition from other supply chains, lack of

cooperation among supply chain members, customers’ geographical distance, lack of

ability in managing supply chain inventories and lack of trust among supply chain

members,

Each variable and its nature of relevance with supply chain concerns are

described in detail in the forthcoming sections.

4.5.1 PRIORITIES OF SUPPLY CHAIN CONCERNS

The opinion from the executives of the business units surveyed about various

supply chain concerns have been obtained in a five point scale, and the priority of

each of the supply chain concern have been ranked according to the mean values

assigned to them. This has been displayed in Table 4.19.

Table 4.19 Priorities of Supply Chain Concerns

Sl. no.

Supply Chain Concern Mean value

Rank

1 Antagonism from other supply chains 3.27 I 2 Inadequate urbane information system 3.14 II 3 Customers’ geographical distance 3.04 III 4 Inadequate concern from suppliers/customers 3.01 IV 5 Physical distance of Suppliers 2.96 V 6 Firm’s Inadequate Influence over its supply chain 2.88 VI 7 Inability to manage Supply chain inventories 2.87 VII 8 Non-existence of trust among supply chain Stake-holders 2.79 VIII 9 Insufficient support from supply chain members 2.77 IX

The above table reveals that competition from other supply chains has been

ranked as the topmost supply chain concern. This implies that the executives of

manufacturing units give more importance to competition from other supply chains.

This means that manufacturing units in Union Territory of Puducherry wish to build

competitive supply chain in their markets. The manufacturing units have not

displayed significant interest in lack of cooperation among supply chain members

because without cooperation among supply chain members, no manufacturing unit

can be successful. Hence, every manufacturing unit shall be maintaining good

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cooperation status with their supply chain members and this will not be a significant

supply chain issue for any manufacturing unit.

4.5.2 FACTORISATION OF SUPPLY CHAIN CONCERNS

Factor analysis was applied to condense the variables or items into minimum

number of manageable items or variables. Factor Analysis has been done with the two

statistical tests of Bartlett’s test and KMO test. The Kaiser-Meyer-Olkin (KMO) test

of sampling adequacy signifies the proportionate variance of variables or items which

may be caused through new factors. KMO value in excess of 0.50 reveals that factor

analysis is absolutely apt for the particular data set. KMO and Bartlett's Test results

are depicted in Table 4.20.

Table 4.20 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.773

Bartlett's Test of Sphericity

Approx. Chi-Square 445.538

DF 36

Sig. 0.000

The KMO value of 0.772 implies that the factor analysis applied for this data

is valid. The significance value being less than 0.01 implies that the value is

significant at 99 % level of confidence. The chi square value for Bartlett’s test of

Sphericity is 445.538. High Chi-square value denotes that the variables have been

aptly factored. Principal Component Analysis was used for extraction purpose, and

varimax rotation is used as the standard rotation. Factors having greater than one as

Eigen value are taken as reduced factors which now use as new factors for future

analysis. Hence the resultant three factors are extracted from nine original supply

chain concern variables. Variables have been grouped into three factors namely,

“supply chain coherence oriented concerns”, “geographical proximity oriented

concerns” and “competition oriented concerns”. Five variables constitute the first

factor while two variables constitute the other two factors.

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The variance and eigen value extracted through each factor of supply chain concerns

are shown in Table 4.21.

Table 4.21 Variance Explained by Factor of Supply Chain Concerns

Compo-

nent

Initial Eigen values Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 3.147 34.964 34.964 2.352 26.134 26.134

2 1.114 12.379 47.343 1.545 17.168 43.302

3 1.025 11.388 58.730 1.389 15.429 58.730

4 0.820 9.108 67.839

5 0.755 8.385 76.224

6 0.668 7.417 83.641

7 0.563 6.260 89.901

8 0.525 5.835 95.736

9 0.384 4.564 100.000

The reduced three factors explain the total variance of 58.7 percent which is

reasonably significant. Amongst the three supply chain concern factors, supply chain

coherence occupies the pivotal position, as this factor alone explains 26.1 percent of

total variance. This shows that the prime supply chain concerns of all manufacturing

firm is concern over the supply chain coherence, and manufacturing units vary from

each other primarily on the their supply chain coherence oriented concerns.

Supply chain coherence related concerns factor consists of statements related

to issues relating to supply chain components and inventories. This factor consists of

coordination of various stakeholders of supply chain with the manufacturing unit,

such as suppliers, manufacturers, distributors, warehouse, retailers and customers. In

the present era of globalization, the coordination of all these stakeholders is a complex

issue as the supply chain networks has become fairly complicated.

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Variables included on each supply chain concerns along with their loadings are shown

in Table 4.22.

Table 4.22 Factor Loadings for Supply Chain Concerns

Supply Chain Concerns Component

Supply Chain Coherence Oriented Concerns

1 2 3

Lack of trust among supply chain members 0.758

Inability in managing Supply chain inventories 0.725

Lack of cooperation among supply chain members 0.699

Lack of sophisticated information system 0.590

Firm’s lack of leverage within its supply chain 0.547

customers’ geographical distance 0.822 Geographical Proximity Oriented Concerns Suppliers’ geographical distance 0.753

Competition from other supply chains 0.878 Competition Oriented Concerns Lack of interest among suppliers or customers 0.532

4.5.2.1 Supply Chain Coherence Concerns

It can be inferred from the above table that Lack of trust among supply chain

members, Lack of ability in managing Supply chain inventories, Lack of cooperation

among supply chain members, Lack of sophisticated information system and Firm’s

lack of leverage within its supply chain are the most important constraints confronted

by manufacturing enterprises.

4.5.2.2 Geographical Proximity Concerns

This factor consists of two variables concerned with the proximity concerns

of both the suppliers and customers. Hence, it becomes absolutely indispensable to

clear the hurdle of proximity concerns to device a suitable supply chain policy.

It can further be noted from Table 4.22 that the geographical distance of

customers is the major constraint confronted by manufacturing enterprises, followed

by proximity problems of the suppliers. This emphasizes the strong need for

improving the infra-structure to eradicate this menace.

4.5.2.3 Competition Concerns

This factor consists of two variables namely, competition among the various

supply chain components and the lack of interest among supply chain components.

Table 4.22 reveals that competition among the supply chains have been the most

important constraint encountered by the manufacturing enterprises.

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4.5.3 RANKING OF SUPPLY CHAIN CONCERN FACTORS

By using factor analysis, the nine variables are grouped into three factor

concerns, labeled as “supply chain coherence”, “geographical proximity” and

“competition oriented concerns”. The mean values for the three supply chain concern

factors are displayed in Table 4.23.

Table 4.23 Strength of Supply Chain Concerns

Supply Chain Concerns Mean Rank Competition 3.14 I Geographical Proximity 3.00 II Supply Chain Coherence 2.89 III

Table 4.23 displays that among the three concerns studied, competition

oriented supply chain concerns appears to be the most important concern as the mean

value in respect of this concern is the highest, which is 3.14. This suggests that most

of manufacturing units consider competition related supply chain issues as the most

burning concern.

4.5.4 SEGMENTATION OF SUPPLY CHAIN CONCERNS

Manufacturing units may be grouped based on the three factors of supply

chain coherence, competition and geographical proximity oriented supply chain

concerns using Cluster analysis. Table 4.24 portrays the final cluster centers.

Table 4.24 Final Cluster Centers

Supply Chain Concerns

Cluster

1 2 3

Supply Chain Coherence 2.55(III) 3.51(I) 2.58(II)

Geographical Proximity 1.88(III) 3.68(I) 3.61(II)

Competition 2.80(II) 3.96(I) 2.58(III)

Average 2.41 3.71 2.91

It can be inferred from the above Table that manufacturing units may be

grouped into three segments. The first group is labeled as “low supply chain

concerned group”, as their average level of supply chain concerns is low when

compared to the other two groups. The second group can be designated as “high

supply chain concerned group”, because they have the highest mean value and rank

first among all supply chain concerns factors. The third group can be christened as

“moderate supply chain concerned group” as the mean value of this group in respect

of supply chain concerns is around the three mark which is in the central point of the

five-point scale.

74

Table 4.25, displaying the ANOVA values, portrays that all the three supply chain

concerns factors are playing strong role in bifurcating the manufacturing units into

three groups.

Table 4.25 ANOVA


Cluster Error

F Sig. Mean Square df Mean Square df

Supply Chain Coherence 26.221 2 0.450 252 58.285 0.000

Geographical Proximity 92.837 2 0.393 252 236.134 0.000

Competition 47.121 2 0.478 252 98.489 0.000

The significant difference in the mean scores of the three groups namely

“supply chain coherence”, “competition proximity” and “geographical proximity” is

depicted in Table 4.25. The significant difference among the mean scores shows that

these three clusters can be explained through the above mentioned three supply chain

concerns factors. A brief description of these three supply chain concerned groups is

given below.

4.5.4.1 Low Supply Chain Concerned Units

The supply chain concerns level of this group is lower in respect of the supply

chain coherence related factors and geographical proximity related factors, while

moderate in respect of competition related factors. The overall mean values of all the

three supply chain concerns factors are less than 3 in the 5 point rating scale. The

mean value in respect of supply chain coherence and geographical proximity related

factors is low, while the value is second in respect of competition related factors and

lowest in respect of the overall average. Though they occupy the second position in

competition-related factors, they still are labeled as “low supply chain concerned

group” as their concerns level in respect of supply chain coherence and geographical

proximity related factors is very low. Of the 255 manufacturing units surveyed, 93

constitute this group, which implies that more than one-third of the manufacturing

enterprises surveyed (37%) are “low supply chain concerned firms”.

4.5.4.2 High Supply Chain Concerned Units

The second segment of manufacturing units with respect to supply chain

concerned factors is labeled as “high supply chain concerned units”,. Manufacturing

units in this segment have high level of supply chain concerns. This group has very

high level of supply chain concerns in respect of all the three factors of supply chain

coherence, competition and geographical proximity related factors. The mean values

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of these supply chain concerns are revolving around the four mark in the five point

scale. They also rank first in all the supply chain concerned factors. The mean value

in respect of this group is higher than that of the other two groups. More than one-

third of the manufacturing enterprises surveyed (35%) constitute this segment.

4.5.4.3 Moderate Supply Chain Concerned Units

The average mean values in respect of the three supply chain concern factor of

this segment are 2.91. The mean value is revolving around the three mark in the five

point scale, which is the intermediate point. Further, this group has low level of

competition factor and moderate level of supply chain coherence and geographical

proximity related factors. Hence, this group of manufacturing units may be labeled as

“moderate supply chain concerned units”. Less than one-third of the manufacturing

enterprises surveyed (About 29%) come under this cluster.

The number of manufacturing firms comprising each of the three clusters is depicted

in Table 4.26.

Table 4.26 Number of Cases in each Cluster

Cluster

1 93 37%

2 89 35%

3 73 28%

Valid 255 100%

It can be inferred from the above table that a shade above one-third of the

manufacturing enterprises constitute the “highly supply chain concerned group” and

the “low supply chain concerned units” while a shade less than one-third of them

constitute the “Moderate supply chain concerned group”.

4.5.5 TESTING SUITABILITY OF SEGMENTATION USING

DISCRIMINANT ANALYSIS

The manufacturing units are grouped into three clusters based on their nature

of supply chain concerns. The three identified clusters are “highly supply chain

concerned cluster”, “low supply chain concerned cluster” and “moderate supply chain

concerned cluster”. Around 37 percent of the manufacturing units are “low supply

chain concerned units”, while 35 percent are “high supply chain concerned units” and

28 percent are “moderate supply chain units”. The next important task is to evaluate

whether the formation of clusters are genuine and differ significantly among each

other. sample constancy and classification of cluster reliability needs to be ascertained

to ensure that all the three supply chain concerns factors play a decisive role in

76

segregating the manufacturing units into three clusters. Discriminant Analysis may be

used for this purpose.

Table 4.27 depicts the equality of group means in respect of supply chain concerns.

Table 4.27 Tests of Equality of Group Means

Supply chain concerns Wilks' Lambda F df1 df2 Sig.

Supply Chain Coherence 0.684 58.285 2 252 0.000

Geographical Proximity 0.348 236.134 2 252 0.000

Competition 0.561 98.489 2 252 0.000

It can be observed from the above table that Wilks' lambda is very low for

geographical proximity related factor. This implies that there is a strong difference in

group between the three supply chain concerns under the stated geographical

proximity.

The mean values significantly differ between the three segments. Wilks’

Lambda for competition factors is high while there is no large degree of difference

between the first group and the third group in the mean values of competition.

Similarly, Wilks’ Lambda for supply chain coherence factors is high implying that

there is no high difference among the first and third group in the average values of

supply chain coherence.

The value of F ratio in accordance to the degrees of freedom is very

significant. Low value of significance implies that there is significant difference in

mean of supply chain concern level between the three groups. Based on the above two

facts it can be clearly observed that segmentation is good and there exists a significant

difference in group.

The next step is to ascertain the Eigen values and canonical correlation

coefficient. These calculated values are displayed in Table 4.28.

Table 4.28 Eigen and Canonical Correlation Values

Function Eigen value % of Variance Cumulative % Canonical Correlation

1 2.084a 72.3 72.3 0.822

2 0.798a 27.7 100.0 0.666

Large Eigen value implies that the mean values are largely dispersed while

small Eigen values implies low spread of the mean values. The Eigen value in respect

of the first discriminant function is more compared to the second function. For the

three clusters, two canonical correlations are formed along with two discriminant

functions. The canonical correlation gives the measure of association between

77

discriminant functions and the three supply chain concerns factors. The canonical

correlation among the first function and the three supply chain concerned factors is

high as 0.822, whereas canonical correlation for second function is 0.666.

Table 4.29 display results of the canonical correlations in the form of Wilks' Lambda

values. It can be observed from the table that the Wilks' Lambda value is significant.

Table 4.29 Wilks' Lambda

Test of Function(s) Wilks' Lambda Chi-square df Sig.

1 through 2 0.180 429.961 6 0.000

2 0.556 147.239 2 0.000

In Wilks’ lambda score of first function is 0.180 which implies that the mean

of the group is different in the first function that is a subset of geographical proximity

and in the second function scores is 0.556 which explains that mean of the group is

different but not the level of first function. The second function is the subset of

competition related factor and supply chain coherence related factor, the group

difference is a smaller amount. A chi-square score of Wilks’s lambda helps to

determine the df and significance level. The significance value is low in the first

function that is 0.000. It implies that means of the group in respect of the first function

is highly significant than the second function. The Chi-square value for the second

function is 147.239 and significant level is 0.000.

The Standardized beta values are shown in Table 4.30.

Table 4.30 Structure Matrix

Supply Chain Concerns factor Function

1 2

Geographical Proximity 0.915* -0.400

Competition 0.412 0.732*

Supply Chain Coherence 0.372 0.467*

It can be inferred from Table 4.30 that two functions can be formed from three

segments. The characteristics of these groups may be explained using these two

functions. The two domain functions of discriminant analysis with standardized beta

value is

Z1 = 0.915 * Geographical Proximity Concerns,

Z2 = 0.732 * Competition Concerns + 0.467* Coherence Concerns.

78

Territorial map of supply chain concerns is shown in Figure 4.2

Canonical Discriminant Function 2


Symbols used in territorial map: 1 -Low supply Chain Concerns,

2- High supply Chain Concerns, 3 -Moderate supply Chain Concerns

* Indicates a group Centroid

Figure 4.2 Territorial Map of Supply Chain Concerns

From the above Figure portraying the territorial map, it can be observed that

function 1, which is the function of geographical proximity factors, differentiates the

first group clusters between cluster 1 (low supply chain concerns) and cluster 3

(moderate supply chain concerns). The second group differentiates between cluster 3

(moderate supply chain concerns) and cluster 2 (high supply chain concerns). The

function 2 which is a function of competition factor and supply chain coherence factor

differentiates cluster 1 (low supply chain concerns) and cluster 2 (high supply chain

concerns).

79

Figure 4.3 portrays the Group centroids for supply chain concern clusters.

Figure 4.3 Group Centroids for Supply Chain Concern Clusters

The above figure depicting the group centroids, indicates that all the three

clusters are unique, containing dissimilar centroids group and dissimilar average

values. The cluster members are associated, distinct from other group of centroids.

The extent of correct classification is depicted in Table 4.31. It depicts the

degree of success based on the supply chain concerns factor.

Table 4.31 Extent of Correct Classification

Segmentation of Concerns

Predicted Group Membership Total

Low High Moderate

Count Low supply Chain 93 0 0 93

High supply Chain 1 87 1 89

Moderate supply Chain 0 4 69 73

% Low supply Chain 100 0 0 100

High supply Chain 1.1 97.8 1.1 100

Moderate supply Chain 0 5.5 94.5 100

The number of cases constituting the distinct clusters is displayed in Table

4.31. It can be inferred from the table that almost 100 % (93 cases) of low supply

chain concern segments are correctly classified and only 1 case is included in high

supply chain concerned units segment. In the case of high supply chain concerned

units segment, 97.8 % are correctly classified (87 cases), whereas in the moderate

supply chain concerned units, 94.5 % of the units are correctly classified. It can be

80

inferred from the aforesaid discussions that segmentation of manufacturing units has

led to a 97.6 % accurate classification.

4.5.6 CHARACTERISTICS OF SUPPLY CHAIN CONCERNS

In the previous section, supply chain concerns have been classified into three

categories namely high supply chain concerned units, moderate supply chain

concerned units and low supply chain concerned units on the basis of the three supply

chain concerns factors. It has also been noticed that the overall performance of the

high supply chain concerned units will be good and their supply chain concerns level

will be low. To make a strategic, tactical and operational decision in the firm, it is

necessary for any firm to understand the nature and characteristics of this supply

chain concerns with respect to the profile of manufacturing industries. In this section,

the characteristics of supply chain concerns are analyzed through chi-square test,

correspondence analysis, one way ANOVA, T-Test and canonical correlation.

The chi-square values along with their level of significance are shown in Table 4.32.

Table 4.32 Chi-Square Test for Profile of Manufacturing Industries Variables

Sl.no Variable

Chi-Square value

Sig. Value

Significance or not

1 Type of Industry 22.283 0.443 Not Significant 2 Number of Employees 22.763 0.012 Significant 3 Total Capital Invested 9.823 0.132 Not Significant 4 Supply Chain Position 11.830 0.066 Not Significant 5 Nature of Industry 16.070 0.003 Significant 6 Side of Supply Chain 0.829 0.661 Not Significant 7 Type of Goods Produced 4.587 0.117 Not Significant 8 Type of Business Organization 7.858 0.249 Not Significant 9 Type of Ownership 10.112 0.120 Not Significant

10 Type of Listing 24.059 0.001 Significant 11 Kind of Manufacturing 6.596 0.159 Not Significant 12 Manufacturing Pattern 17.811 0.007 Significant 13 Type of process 5.275 0.509 Not Significant 14 Annual Turnover 11.606 0.312 Not Significant 15 Market Coverage 9.686 0.046 Significant 16 Area of Market 21.180 0.007 Significant 17 Business years 1.468 0.962 Not Significant 18 Software Usage 7.332 0.026 Significant

To understand the characteristics of these three supply chain concerns

segments, association among the segments with profile of manufacturing industries

related variables are analyzed. The chi-square test is applied to test the significance of

associations. The chi-square values and significant value reveal that type of industry,

total capital invested, supply chain position, side of supply chain ,type of goods

81

produced, type of business organization, type of ownership, kind of manufacturing,

type of process, annual turnover and business years have no significant association

with supply chain concerns segments, while there is a significant association between

supply chain concerns segments and number of employees, nature of industry, type of

listing, manufacturing pattern, market coverage, area of market and software usage.

4.5.6 RELATIONSHIP BETWEEN SUPPLY CHAIN CONCERNS AND

PROFILE OF MANUFACTURING INDUSTRIES VARIABLES

Chi-square analysis shows significant association between supply chain

concerns segments with manufacturing industries profile variables like number of

employees, nature of industry, type of listing, manufacturing pattern, market

coverage, area of market and software usage. The forthcoming paragraphs shall throw

light on a detail analysis of nature of relationship among the profile of manufacturing

enterprises and supply chain concerns.

4.5.7.1 Number of Employees

To test the significance of association, chi-square test is applied. Table 4.32

reveals that the chi-square value is 22.763 and significant value as 0.012. This implies

that there is significant association among the number of employees of manufacturing

enterprises and supply chain concerns group.

This association is portrayed in the following Figure 4.4.

Figure 4.4 Employees and Concerns- Correspondence Diagram

Correspondence Analysis reveals the association between the number of

employees and the different segments of manufacturing enterprises segmented based

on supply chain concerns. It can be inferred from the above figure that the

82

manufacturing units with more than 1200 and 301-600 employees are associated with

the “Highly supply chain concerned units”, while those units with 101-300 and 601-

900 employees are associated with the “Moderate supply chain concerned units” and

the units employing less than 100 employees and 901-1200 employess category are

associated with the “Low supply chain concerned group”.

The relationship between number of employees and supply chain concerns is depicted

in Table 4.33.

Table 4.33 ANOVA for Number of Employees and Supply Chain Concerns

Supply Chain Concerns F Sig. Supply Chain Coherence 0.470 0.798 Geographical Proximity 1.667 0.143

Competition 1.607 0.159

The Analysis of variance is used to know the effect of supply chain concerns

factor on the manufacturing units categorized based on number of employees. It can

be observed from the Anova Table 4.33 that no significant difference has been found

among the manufacturing units grouped on the basis of number of employees, with

respect to supply chain coherence, geographical proximity and competition factors.

4.5.7.2 Nature of Industry

The chi-square value of 16.070 and significant value of 0.003 shown in

Table 4.32 clearly indicates existence of significant association between nature of

industry of manufacturing units and supply chain concerns segments.

The association between nature of industry and supply chain concerns is portrayed in

Figure 4.5.

Figure 4.5 Industry and Concerns- Correspondence Diagram

The association between the nature of industry and supply chain concerns

segments can be identified by using correspondence analysis. It can be observed from

83

the figure that the manufacturing units operating in small scale industry are associated

with the “Highly supply chain concerned group”, while the units operating in large

scale are associated with the “moderate supply chain concerned group” and the units

operating in medium scale are associated with the “Low supply chain concerned

group”.

The relationship between nature of industry and supply chain concerns is highlighted

in Table 4.34.

Table 4.34 ANOVA for Nature of Industry and Supply Chain Concerns

Supply Chain Concerns F Sig. Supply Chain Coherence 3.466 0.033 Geographical Proximity 7.936 0.000 Competition 8.802 0.000

The above table indicates that significant difference was found among the

units categorized based on nature of industry with respect to supply chain concern

factors of supply chain coherence, geographical proximity and Competition.

Mean values for coherence concerns of industry category are shown in Table 4.35.

Table 4.35 Mean Values for Coherence Concerns of Industry Category

Nature of Industry N

1 2 Medium Scale 94 2.74 Large Scale 46 2.84 Small Scale 115 3.03

The post hoc analysis is carried out with Duncan method to understand inter

group difference among nature of industry with respect to supply chain coherence.

Table 4.35 indicates that two homogeneous sub groups can be formed among the

three categories of manufacturing units grouped on the basis of nature of industry in

respect of their supply chain coherence factor. Both the homogeneous groups contain

large scale industry group and mean value of that industry category is 2.8 on supply

chain coherence. The mean values of medium scale industry segment and small scale

industry segment are 2.7 and 3.0 respectively. The difference in mean values between

medium scale industry group and small scale industry group is significant at 95%

level of confidence (Table 4.34, significant value is 0.033). This means that small

scale manufacturing units have high level of supply chain coherence concerns than

medium and large scale manufacturing units.

84

Mean values for geographical proximity of industry category are show in Table 4.36

Table 4.36 Mean Values for Geographical Proximity Concerns of Industry

Category


1 2 Medium Scale 94 2.67 Small Scale 115 3.16 Large Scale 46 3.28

Table 4.36 indicate that two homogeneous sub groups can be formed among

the three category of units grouped on the basis of geographical proximity. The mean

value in respect of geographical proximity is 2.7 for medium scale units, while the

mean in respect of small and large scale units are 3.2 and 3.3 respectively. The

difference in mean values between the first homogeneous group and second

homogeneous group is significant at 99 percent level of confidence (Table 4.35,

significant value is 0.000). This implies that large scale manufacturing units and small

scale manufacturing units have high level of geographical proximity concerns than the

medium scale manufacturing units.

Mean values for competition concerns of industry category are displayed in

Table 4.37.

Table 4.37 Mean Values for Competition Concerns of Industry Category


1 2 Medium Scale 94 2.89 Large Scale 46 2.98 Small Scale 115 3.39

Table 4.37 indicates that two homogeneous sub groups can be formed among

the three categories of units grouped on the basis of nature of industry in respect of

competition. The mean value in respect of competition among the medium scale, large

scale and small scale units segment are 2.9, 3.0 and 3.4 respectively. This confirms

that small scale manufacturing units have high level of competition concerns than the

other two groups of Industry.

4.5.7.3 Type of Listing

The value of chi-square is 24.059 and significant value is 0.001 as shown in

Table 4.32, which clearly indicates significant association between type of listing and

supply chain concerns of manufacturing units.

85

The association between type of listing and supply chain concerns is shown in

Figure 4.6.

Figure 4.6 Listing and Concerns -Correspondence Diagram

The association between the type of listing category and supply chain concerns

segments can be identified by using correspondence analysis. It can be noted that the

units whose shares are not listed are associated with the “Highly supply chain

concerned group”, while those units whose shares are listed in India and abroad are

associated with the “Moderate supply chain concerned group” and the units whose

shares are listed in India are associated with the “Low supply chain concerned group”.

The relationship between type of listing and supply chain concerns are shown in

Table 4.38.

Table 4.38 ANOVA for Type of Listing and Supply Chain Concerns


The above table indicates prevalence of significant difference (0.024) in mean

values of competition with respect to the units categorized based on type of listing,

while there is no significant difference (0.059) in mean values of geographical

proximity with respect to type of listing and no significant difference (0.055) in mean

values of supply chain coherence with respect to type of listing.

86

Mean values in respect of competition and units categorized based on listing of shares

are shown in Table 4.39.

Table 4.39 Mean Values for Competition Concerns of Listing Category

Type of Listing N

1 2

Listed only abroad 12 2.70

Listed in India and Abroad 33 2.78

Listed in India 64 3.16

Not listed 146 3.24

The mean value in respect of competition of those units listed in India and

Abroad, those units listed in India, those units listed only in abroad, and those units

not listed at all are 2.8, 3.2, 2.7 and 3.2 respectively. Mean values in respect of

competition significantly differ among the groups of units listed in abroad only and

those units not listed at all at 95 percent level of confidence (Table 4.38, significant

value is 0.024). This implies that those units not listed have high level of competition

concerns than the other type of listing units.

4.5.7.4 Market Coverage

The value of chi-square is 9.686 and significant value is 0.046 (as shown in

Table 4.32) which clearly indicates prevalence of significant association between

market coverage and supply chain concerns of manufacturing industries.

The association between market coverage and supply chain concerns is shown in

Figure 4.7.

Figure 4.7 Market Coverage and Concerns -Correspondence Diagram

87

The association between the manufacturing units grouped based on market

coverage and supply chain concerns can be identified by using correspondence

analysis. Those manufacturing units concentrating on domestic and international

markets are associated with the “High supply chain concerned Group”, while those

units concentrating on international market are associated with the “Moderate supply

chain concerned Group” and those units concentrating on domestic market are

associated with the “Low supply chain concerned Group”.

The relationship between market coverage category and supply chain concerns is

shown in Table 4.40.

Table 4.40 ANOVA for Market Coverage and Supply Chain Concerns


It is observed from the above table that there is no significant difference

among market coverage with respect to supply chain coherence, geographical

proximity and competition orientated supply chain concerns factors.

4.5.7.5 Area of Market

The value of chi-square is 21.180 and significant value is 0.007 shown in

Table 4.32 which clearly depicts significant association between the nature of market

and supply chain concerns of manufacturing units.

The association between area of market and supply chain concerns is shown in

Figure 4.8.

Figure 4.8 Area of Market and Concerns- Correspondence Diagram

88

It can be observed from the figure that those manufacturing units possessing market in

South India and Export market are associated with The “highly supply chain

concerned group” while those units possessing market in entire India and India and

Abroad are associated with the “moderate supply chain concerned group” while the

units having market within Pondicherry and Tamil Nadu are associated with the “low

supply chain concerned group”.

The relationship between area of market and supply chain concerns is displayed in

Table 4.41

Table 4.41 ANOVA for Area of Market and Supply Chain Concerns


The above ANOVA Table indicates prevalence of significant difference

among manufacturing units classified based on area of market in respect of the supply

chain concerns factors of supply chain coherence and competition.

Mean values for coherence concerns of area of market category are portrayed in

Table 4.42.

Table 4.42 Mean Values for Coherence Concerns of Area of Market

Area of Market N

1 2

Within Pondicherry and Tamil Nadu

89 2.77




Only export 4 3.95


the five categories of manufacturing units classified on the basis of area of market.

The difference in mean values between the group of units having market of within

Pondicherry and Tamil Nadu and the group of units having merely export market is

significant at 99 percent level of confidence (Table 4.41, significant value is 0.007).

This signifies that manufacturing units concentrating exclusively on export markets

have high level of supply chain coherence concerns than the other units.

89

Mean values in respect of the manufacturing units classified on the basis of area of

market, regarding competition concerns are displayed in Table 4.43.

Table 4.43 Mean Values of Competition Concerns for Area of Market

Area of Market N

1 2

Within Pondicherry and Tamil Nadu 89 3.00




Only export 4 4.12


the five categories of manufacturing units classified on the basis of area of market, in

respect of competition. The difference in mean values between the group of units

having market within Pondicherry and Tamil Nadu and those concentrating

exclusively on exports is significant at 99 percent level of confidence (table 4.41,

Significant value is 0.001). This signifies that those manufacturing units concentrating

exclusively on export markets have high level of competition concerns than the other

area of market units.

4.5.7.6 Software Usage

The value of chi-square being 7.332 and significance value of 0.026 inferred

from Table 4.32, clearly indicates prevalence of significant association between

software usage and supply chain concerns of manufacturing units.

The relationship between software usage and supply chain concerns is shown in

Table 4.44.

Table 4.44 Independent Samples Test for Software Usage and Concerns


Levene's Test for Equality of Variancest-test for Equality of MeansF Sig. t df Sig. (2-tailed)

Coherence 0.533 0.466 -.878 253 0.381 Geographical 3.420 0.066 1.076 253 0.283 Competition 1.223 0.270 -1.370 253 0.172

It can be observed from the above table that the value of significance is in

excess of 0.05 in respect of the group of units classified on the basis of software usage

regarding geographical proximity concerns, competition oriented concerns and

Coherence oriented concerns. This suggests that there is no significant difference in

the mean values of the different groups of manufacturing units classified based on

90

software usage regarding geographical proximity concerns, competition oriented

concerns and Coherence oriented concerns.

4.5.8 CANONICAL CORRELATION BETWEEN SUPPLY CHAIN

CONCERNS AND PROFILE OF MANUFACTURING INDUSTRIES

Canonical correlation is used to predict the shared relationship among two or

more set of variables. This analysis establishes the individual relationship among two

variables and also explores the overall relationship between two or more set of

variables. The forthcoming paragraphs discusses the canonical correlation between

two sets of variables. The first set of variables consist of supply chain concerns

factors namely supply chain coherence, geographical proximity and competitions,

while the second set consist of profile of manufacturing industries variables namely

number of employees, nature of industry,type of listing,manufacturing pattern, market

coverage, area of market and useage of software.

Canonical Correlations for supply chain concerns is displayed in Table 4.45.

Table 4.45 Canonical Correlation for Supply Chain Concerns

Coef. Std. Err. t P>|t| [95% conf. interval] U1

Coherence .2144754 .2888061 0.74 0.458 -.3542842 .783235 Geographical Proximity -.127993 .2093987 -0.61 0.542 -.5403721 .2843856 Competition -1.10723 .2451288 -4.52 0.000 -1.589975 -.624487

V1 Number of Employees .3690427 .1920978 1.92 0.056 -.0092646 .7473501 Nature of Industry .9870722 .3678742 2.68 0.008 .2626001 1.711544 Listing .2300085 .2083301 1.10 0.271 -.1802658 .6402828 Manufacturing Pattern .14388 .2061716 0.70 0.486 -.2621435 .5499035 Market coverage .3014798 .3633649 0.83 0.407 -.414112 1.017072 Market Area -.820725 .265067 -3.10 0.002 -1.342734 -.298716 software -.166431 .5466711 -0.30 0.761 -1.243017 .9101536

U2 Coherence .4400772 .3136598 1.40 0.162 -.1776279 1.057782 Geographical Proximity .8383347 .2274189 3.69 0.000 .3904679 1.286201 Competition -.453529 .2662238 -1.70 0.090 -.9778163 .070758

V2 Number of Employees .4838434 .2086291 2.32 0.021 .0729801 .8947066 Nature of Industry -.823076 .3995322 -2.06 0.040 - 1.609895 -.036259 Listing .3564255 .2262582 1.58 0.116 .0891557 .8020066 Manufacturing Pattern .0399343 .223914 0.18 0.859 -.4010302 .4808989 Market coverage .3412621 .3946348 0.86 0.388 -.4359111 1.118435 Market Area .3846481 .2878778 1.34 0.183 -.1822833 .9515794 software .2498602 .5937158 0.42 0.674 -.9193725 1.419093

U3 Coherence 1.327923 .4429044 3.00 0.003 .4556904 2.200156 Geographical Proximity -.577761 .3211276 -1.80 0.073 -1.210173 .0546506 Competition -.106550 .3759222 -0.28 0.777 -.8468723 .6337706

91

(Continued…) V3

Coef. Std. Err. t P>|t| [95% conf. interval] Number of Employees .5648546 .2945954 1.92 0.056 -.0153063 1.145015 Nature of Industry -.830553 .5641608 -1.47 0.142 -1.941582 .2804755 Listing -.379173 .3194887 -1.19 0.236 -1.008357 .2500113 Manufacturing Pattern .3197423 .3161786 1.01 0.313 -.3029232 .9424077 Market coverage -.065072 .5572455 -0.12 0.907 -1.162482 1.032338 Market Area -.277275 .4064988 -0.68 0.496 -1.077812 .5232622 software .8501017 .8383584 1.01 0.312 -.8009174 2.501121

Canonical correlations: 0.2955 0.2739 0.1977 Tests of significance of all canonical correlations Statistic df1 df2 F Prob>F Wilks' lambda 0.8112 21 704.058 2.5343 0.0002 a Pillai's trace 0 .201442 21 741 2.5399 0.0002 a Lawley-Hotelling trace 0 .217473 21 731 2.5234 0.0002 a Roy's largest root 0 .0956764 7 247 3.3760 0.0019 u

e = exact, a = approximate, u = upper bound on F

Two sets of data have been taken for this study. The first set contains the three

factors relating to supply chain concerns, while the second set consists of the seven

profile of manufacturing industry variables of number of employees, nature of

industry, type of listing, manufacturing pattern, market coverage, area of market and

software usage. Based on these two sets of data, Canonical Correlation has been

performed. The Canonical Correlation coefficient values in respect of these three

factors are 0.2955, 0 .2739 and 0.1977. Other results displayed in the above table such

as df1 value of 21, df2 value of 704, f value of 2.5343, Wilks’s λ value of 0.8112, and

p value of 0.002 which is less than 0.05, reveals that there is significant relationship

between the two sets of data. To predict the overall relationship between these two

sets of data, Wilk’s (λ) value should be deducted from one. From the three canonical

function set, the r2 value 0.1888. This implies that the entire canonical model explains

a considerable portion of about 18% of the variance. Hence, there is a decent positive

correlation between the two sets of data namely, the three supply chain concerns

factors and the seven variables relating to the profile of manufacturing enterprises.

92

4.6 SUPPLY CHAIN COMPETENCE DIMENSION ANALYSIS

This section studies the supply chain competence of manufacturing units in

UT of Puducherry in light of many variables such as the ability to enhance supply

chain’s position in terms of integrity, the ability to deliver high-quality services, the

ability to work with key suppliers, the ability to design low-pollution delivering

process, the ability to enhance supply chain’s position in terms of social

responsibility, the ability to make high quality products, the ability to respond to the

needs of key customers, the ability to manage supply chain inventory, the ability to

forecasting sales with greater accuracy, the ability to design low-pollution production

process, ability to fill orders with improved accuracy, the ability to issue notice on

shipping delays in advance, the ability to respond to a request in a timely manner, and

the ability to meet a delivery on promised date,

4.6.1 PRIORITIES OF SUPPLY CHAIN COMPETENCE

The executives of manufacturing units in Union Territory of Puducherry were

asked to rate their firm’ supply chain competence level in a five point rating scale,

ranging from very low to very high,. The mean values assigned to each of these

variables have been displayed in Table 4.46 and these variables have been ranked

according to their mean values.

Table 4.46 Priorities of Supply Chain Competence

Sl.no Supply Chain Competence Variables Mean Value Rank 1 Produce qualitative products 3.70 I 2 Catering wants of important customers 3.60 II 3 Work with important suppliers 3.58 III 4 Providing prompt response to requests 3.54 IV 5 Deliver qualitative services 3.52 V 6 Adhering to delivery schedule 3.50 VI 7 Honour orders promptly 3.31 VII 8 Effectively managing supply chain inventory 3.29 VIII 9 Accurate forecasting of demand 3.28 IX 10 Enhancing social responsibility of supply chain position 3.25 X 11 Designing pollution-free delivering process 3.18 XI 12 Enhancing integrity of supply chain position 3.15 XII 13 Designing pollution-free production process 3.09 XIII 14 Issuing prior notice about shipping delays 2.94 XIV

It can be observed from above table that manufacturing units in UT

Puducherry give utmost importance to the ability to make high quality products. This

implies that the manufacturing units are intentional to improve their supply chain

competence by improving the quality of the products they produce. It can be noted

93

that the units are showing least interest on the ability to issue notice on shipping

delays in advance.

4.6.2 FACTORISATION OF SUPPLY CHAIN COMPETENCE

Factor analysis was applied to condense the number of items or variables into

minimum number of manageable items or variables.

Results of KMO and Bartlett's Test are shown in Table 4.47.





Df 91

Sig. 0.000

It can be observed from the above table that the KMO value is 0.814 which

implies that the factor analysis applied for this data is valid. The significance value is

0.01 which means that the value is significant at 99 % confidence level while the chi-

square value for Bartlett’s test of Sphericity is 677.84.

The variance and Eigen value extraction of each factor (Supply Chain Competence)

are displayed in Table 4.48.

Table 4.48 Variance Explained by Factors of Supply Chain Competence

Component

Initial Eigen Values Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 3.914 27.955 27.955 2.854 20.387 20.387

2 1.422 10.156 38.112 1.868 13.341 33.728

3 1.163 8.309 46.421 1.777 12.693 46.421

4 1.006 7.185 53.606

5 .881 6.295 59.901

6 .838 5.988 65.888

7 .759 5.423 71.311

8 .728 5.200 76.511

9 .671 4.794 81.305

10 .636 4.543 85.848

11 .589 4.210 90.058

12 .543 3.877 93.936

13 .438 3.131 97.066

14 .411 2.934 100.000

Factors having eigen value in excess of one are taken as reduced factors .

These factors now assume the role of actual factors for further analysis. It can be

observed from the above table that the original 14 variables have been segregated into

three factors.

94

Results of Factor Loadings of Supply Chain Competence and the names assigned to

each of such factors are depicted in Table 4.49.

Table 4.49 Factor Loadings of Supply Chain Competence

Sl.no Supply Chain Competence Component

1 2 3

1 The ability to fill orders with improved accuracy 0.68

Quality and Services

2 The ability to respond to the needs of key customers

0.65

3 The ability to work with key suppliers 0.64

4 The ability to respond to a request in a timely manner

0.63

5 The ability to meet a promised delivery date 0.62

6 The ability to make high quality products 0.53

7 The ability to deliver high-quality services 0.50

8 The ability to design low-pollution delivering process

0.74

Design Effectiveness 9 The ability to enhance supply chain’s position in

terms of social responsibility 0.72

10 The ability to design low-pollution production process

0.65

11 The ability to issue advanced notice on shipping delays

0.68

Operations and

Distribution

12 The ability to forecasting sales with greater accuracy

0.67

13 The ability to enhance supply chain’s position in terms of integrity

0.62

14 The ability to manage supply chain inventory 0.43

It can be observed from the above table that three factors have been formed.

The first factor consists of seven variables, while the other two factors comprise of

three variables each. The first factor has been labeled as “quality and service

competence”, while the second factor has been designated as “design effectiveness

competence” and the third factor has been labeled as “operations and distribution

competence”.

The reduced three factors explain 46.42 percent of total variance which is

fairly significant. Among the three supply chain competence factors, quality and

services occupies pivotal position as it alone accounts for 20.38 percent of total

variance. This indicates that manufacturing firms attach paramount importance to

quality of services among the different supply chain competence variables.

95

It can be said that manufacturing units differ from each other based largely on

the level of their quality and services competence.

4.6.2.1 Quality and Service Competence

The first factor accommodates variables relating to quality aspects and provision

of services to stakeholders. Hence, this factor is designated as “Quality and Service

Competence”. This factor is the most important factor as it alone explains almost 20%

of variance. This suggests the importance attached by manufacturing firms to quality

of products and services to the customers, suppliers and other stakeholders of the

enterprise.

4.6.2.2 Design Effectiveness Competence

The next factor has been labeled as “Design Effectiveness Competence”. This is

the second most important factor as it accounts for 13.34% of the variance. The

variables comprising this factor relate to the designing of the manufacturing products

and processes related to supply chain, and hence this factor has been labeled as

“Design Effectiveness Competency”.

4.6.2.3 Operations and Distribution Competence

The third and final factor is labeled as “Operations and Distribution

Competence”. The statements loaded under this factor relate to the time element and

quality aspects of supply chain issues, and ways to improve the operational efficiency

of the manufacturing units. The statements loaded under this factor are the ability to

enhance supply chain’s position in terms of integrity and the ability to manage supply

chain inventory. Hence, this factor has been designated as “Operations and

Distribution Competence”.

4.6.3 RANKING OF SUPPLY CHAIN COMPETENCE FACTORS

Using factor analysis, the fourteen competence variables are grouped into

three factors namely, “Quality and Services competence”, “Design Effectiveness

Competence” and “Operations and Distribution competence”. Mean values assigned

to each of the three supply chain competence are portrayed in Table 4.50.

Table 4.50 Strength of Supply Chain Competence

Competence Mean Rank Design Effectiveness 3.17 I Operations and Distribution 3.16 II Quality and Services 3.10 III

96

It can be inferred from the above table that the mean value in respect of design

effectiveness competence is the highest. This implies that design effectiveness seems

to be the most dominant factor among manufacturing enterprises.

4.6.4 SEGMENTATION OF SUPPLY CHAIN COMPETENCE

Manufacturing units have been segregated depending on the similarities

exhibited by them regarding the three factors of quality and services competence,

design effectiveness competence, and operations and distribution competence oriented

supply chain competence. Cluster analysis is used for segmentation of manufacturing

units based on the degree of supply chain competence possessed by them. Final

cluster centers of supply chain competence are displayed in Table 4.51.


Supply Chain Competence

Cluster

1 2 3

Quality and Services 3.29(I) 2.61(III) 3.23(II)

Design Effectiveness 4.03(I) 2.34(III) 2.95(II)

Operations and Distribution 3.37(II) 2.46(III) 3.43(I)

Average 3.56 2.47 3.20

Manufacturing units surveyed are segmented into three groups. The first

segment is labeled as “high competence group” as the supply chain competence of the

units comprising this cluster is very high. The second segment is termed as “low

supply chain competence group” because their mean value is low and they are ranking

very low among all the supply chain competence factors. The third segment is

designated as “average supply chain competence group” as their mean is three, which

is exactly in the midpoint of the five point scale. Hence, this segment is referred to as

“moderate supply chain competence group”.

Anova results of supply chain competence clusters are displayed in Table 4.52.

Table 4.52 ANOVA

Supply Chain Competence Cluster Error


Quality and Services 10.131 2 0.282 252 35.892 0.000

Design Effectiveness 57.551 2 0.215 252 267.604 0.000

Operations and Distribution 21.410 2 0.305 252 70.268 0.000

The above table displaying the Anova values depicts that all the three supply

chain competence factors are playing strong role in bifurcating the manufacturing

units into three groups.

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The significant difference in the mean scores of all the three groups in respect

of the three supply chain competence factors namely quality and services competence,

design effectiveness competence and operations and distribution competence,

suggests that the three factors have aptly contributed to the grouping of manufacturing

enterprises into three clusters. Characteristics of the three clusters of “low supply

chain competence units”, “high supply chain competence units” and “moderate supply

chain competence units” are briefly explained in the forthcoming paragraphs.

4.6.4.1 Low Supply Chain Competence Units

The supply chain competence level of this group is the lowest among all the

three segments. Mean values for the three supply chain competence related factors of

quality and services, design effectiveness and operations and distribution for this

group is less than three in the five point scale, signifying that they rank the lowest in

quality and services, design effectiveness and operations and distribution related

competence, and lowest in the overall mean values in respect of all the three supply

chain competence factors.

Among the 255 manufacturing units surveyed in UT of Puducherry, 64 units

constitute this segment, implying that almost one-quarter of the units surveyed in UT

of Puducherry (25.1%) are low supply chain competence units.

4.6.4.2 High Supply Chain Competence Units

The second segment of manufacturing units with respect to supply chain

competence factors is termed as “high supply chain competence units” as

manufacturing units constituting this cluster command high level of supply chain

competence. This cluster has high level of supply chain competence on quality and

services and design effectiveness, while the segment is ranked second in respect of the

operations and distribution competence factor. Mean values in respect of this cluster

wobble at the highest level in the five point scale. They rank first in all the supply

chain competence factors. Almost 35% of manufacturing units surveyed in UT of

Puducherry constitute this segment.

4.6.4.3 Moderate Supply Chain Competence Units

The average means score value in respect of the three supply chain

competence factors for this segment is 3.20. As the mean value wobble around the

three mark in the five point scale, which is the intermediate level, this segment may

be treated as “average or moderate supply chain competence group”. This segment of

manufacturing units enjoy high level of operations and distribution competence and

98

moderate level of design effectiveness and quality and service effectiveness

competence. Almost 40 percent of manufacturing units surveyed in UT of Puducherry

constitute this segment.

Number of manufacturing firms constituting each cluster are displayed in

Table 4.53.


Cluster

1 89 35%

2 64 25%

3 102 40%

Valid 255 100%

It can be inferred from the above table that the highly supply chain

competence units group and the moderate supply chain competence units group

together account for three-fourth (75%) of the total business units surveyed.

4.6.5 TESTING SUITABILITY OF SUPPLY CHAIN COMPETENCE SEGMENTATION USING DISCRIMINANT ANALYSIS

The manufacturing business units are grouped into three clusters based on

their level of competence in supply chain management. The three identified clusters

are “highly supply chain competence units cluster”, “low supply chain competence

units cluster” and “moderate supply chain competence units cluster”. 25 percent of the

manufacturing units constitute the low supply chain competence units group, while 35

percent of the manufacturing units constitute the high supply chain competence units,

and 40 percent of the manufacturing units constitute the moderate supply chain

competence units.

The next important issue is to assess whether the segmentation is valid , and

whether each of the clusters significantly vary among each other, and whether the

three supply chain competence factors play a role in segregating manufacturing

enterprises into three clusters. For this purpose, sample stability and cluster

classification reliability has to be verified by Discriminant analysis. The equality of

group means in respect of supply chain competence can be inferred from Table 4.54.


Supply Chain Competence Wilks'

Lambda F df1 df2 Sig.

Quality and Services 0.778 35.892 2 252 0.000

Design Effectiveness 0.320 267.604 2 252 0.000

Operations and Distribution 0.642 70.268 2 252 0.000

99


design effectiveness factor. This implies that there is high difference in the clusters in

respect of design effectiveness competence factor. Mean values in respect of design

effectiveness differ significantly among the three segments. Wilks’ Lambda for

operations and distribution competence factor is high as there is no significant

difference among the first and third segments with respect to the average values of

operations and distribution. Similarly, the Wilks’ Lambda for quality and services

competence factors is relatively high, implying that there is not much difference

among the first and third segments in respect of the average value of quality and

services factor.


significant. Low significance value implies prevalence of significant difference in

supply chain competence level among the three groups. Based on the above two facts,

it can be concluded that the process of grouping has been completed aptly.

Eigen values and canonical correlation coefficient have been displayed in Table 4.55

Table 4.55 Eigen Values

Function Eigen value % of

Variance Cumulative %

Canonical Correlation

1 2.355a 88.1 88.1 0.838

2 0.318a 11.9 100.0 0.491

Eigen value in respect of the first discriminant function is very high compared

to the second function. For the three clusters, two canonical correlations are formed

along with two discriminant functions. The canonical correlation gives the measure of

association between discriminant functions and the three supply chain competence

factors. The canonical correlation among first function and three supply chain

competence factors is very high (0.838), but canonical correlation for the second

function is only 0.491. From Table 4.56, it can be inferred that both the canonical

correlations are significant.


Test of Function(s)

Wilks' Lambda

Chi-square df Sig.

1 through 2 0.226 373.150 6 0.000

2 0.759 69.296 2 0.000

100

Wilks’ lambda score in respect of the first function is quite low (0.226)

implying that the variables constituting this function (design effectiveness) play a

vital role in the grouping of manufacturing enterprises into three clusters. Variables

constituting the second factor (operations and distribution and quality and service)

seems to play a limited role in grouping manufacturing enterprises as the Wilks’

lambda score in respect of this factor is 0.759, which is quite high. However, the

significance values in respect of both the factors is 0.000, which implies that both the

factors and all the three variables constituting the two factors play a significant role in

grouping the units into three clusters. Since the Wilks’ lambda score is least in respect

of the first factor, it can be concluded that the first factor plays a significant role in

categorizing manufacturing enterprises into three clusters.

The Standardized beta values are depicted in Table 4.57.


Supply Chain Competence Function

1 2

Design Effectiveness 0.941* -0.337

Operations and Distribution 0.373 0.850*

Quality and Service 0.296 0.498*

It can be inferred from the above table that two functions can be formed from

the three clusters. The population characteristics may be explained through these two

functions. The two domain functions of discriminant analysis along with standardized

beta value are

Z1 = 0.941* Design Effectiveness Competence,

Z2 = 0.850 * Operations and Distribution Competence+ 0.498* Quality and

Service competence.

101

Territorial Map of Supply Chain Competence is displayed in Figure 4.9.

Canonical Discriminant Function 2 -8.0 -6.0 -4.0 -2.0 .0 2.0 4.0 6.0 8.0 ┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼ 8.0 ┼ 233 31 ┼ │ 223 31 │ │ 23 31 │ │ 23 31 │ │ 23 31 │ │ 23 31 │ 6.0 ┼ ┼ 23 ┼ ┼ ┼ ┼ 31 ┼ ┼ │ 23 31 │ │ 233 31 │ │ 223 31 │ │ 23 31 │ │ 23 31 │ 4.0 ┼ ┼ 23 ┼ ┼ ┼ 31 ┼ ┼ ┼ │ 23 31 │ │ 23 31 │ │ 233 31 │ │ 223 31 │ │ 23 31 │ 2.0 ┼ ┼ ┼ 23 ┼ ┼ 31 ┼ ┼ ┼ │ 23 31 │ │ 23 31 │ │ 23 31 │ │ 233 * 31 │ │ 223 31 │ 0.0 ┼ ┼ ┼ ┼ 23 ┼ 31 ┼ ┼ ┼ ┼ │ 23 31 * │ │ * 23 31 │ │ 23 31 │ │ 23 31 │ │ 231 │ -2.0 ┼ ┼ ┼ ┼ 21 ┼ ┼ ┼ ┼ │ 21 │ │ 21 │ │ 21 │ │ 21 │ │ 21 │ -4.0 ┼ ┼ ┼ ┼ 21 ┼ ┼ ┼ ┼ │ 21 │ │ 21 │ │ 21 │ │ 21 │ │ 21 │ -6.0 ┼ ┼ ┼ ┼ ┼21 ┼ ┼ ┼ ┼ │ 21 │ │ 21 │ │ 21 │ │ 21 │ │ 21 │ -8.0 ┼ 21 ┼ ┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼ -8.0 -6.0 -4.0 -2.0 .0 2.0 4.0 6.0 8.0


Symbols used in territorial map:

1-High Competence, 2-Low Competence,

3- Moderate Competence and *- Indicates a group Centroid

Figure 4.9 Territorial Map of Supply Chain Competence

From the above figure, it can be inferred that Design Effectiveness forming

part of the first function plays a significant role in formation of first and second

clusters namely, “High Supply Chain Competence Units” and “Low Supply Chain

Competence Units”. The variable of Operations and Distribution factor comprising

the second function plays a significant role in formation of first and third clusters

namely, “High supply Chain Competence Units” and Moderate supply Chain

Competence Units”, while the other variable of “Quality and Services Competence

factor” comprising the second function plays a significant role in formation of the

second and third clusters of “Moderate Supply Chain Competence Units” and “Low

Supply Chain Competence Units”.

102

Group centroids of supply chain competence clusters are portrayed in Figure 4.10.

Figure 4.10 Group Centroids for Supply Chain Competence Clusters

The above group centroids figure indicates that the entire three clusters are

unique clusters containing dissimilar centroids group and dissimilar average values.

The components of each cluster are homogeneous, whereas the clusters are

heterogeneous in character.

Degrees of success based on the supply chain competence are depicted in Table 4.58.


Segmentation of Competence

Predicted Group Membership

Total High Competence

Units

Low Competence

Units

Moderate Competence

Units

Count High Competence Units 89 0 0 89

Low Competence Units 1 60 3 64

Moderate Competence Units 0 2 100 102

% High Competence Units 100 0 0 100

Low Competence Units 1.6 93.8 4.7 100

Moderate Competence Units 0 2.0 98 100

103

The above table displays the number of cases constituting each cluster and the

percentage of proper classification and unclassification of the items. It can be

observed that 100 percent of high supply chain competence units segments are

correctly classified as only 1 case is included into low supply chain competence units

segment. In the case of low supply chain competence units segment, 60 cases

accounting for 93.8 percent are correctly classified. In the case of moderate supply

chain competence units, 98.0 percent of the units are properly classified. Hence, it can

be concluded that segmentation of manufacturing enterprises in UT of Puducherry

based on supply chain competence is correct by more than 97.6%.

4.6.6 CHARACTERISTICS OF SUPPLY CHAIN COMPETENCE In the previous section, the business units have been classified based on supply

chain competence into three categories namely “high supply chain competence units”,

“moderate supply chain competence units” and “low supply chain competence units”.

It is obvious that high supply chain competence units will have a very high improved

overall performance. The characteristics of the supply chain competence clusters are

studied using chi-square test along with correspondence analysis, T-test, analysis of

variance (ANOVA) and canonical correlation. The chi-square test values along with

their level of significance have been portrayed in Table 4.59.

Table 4.59 Chi-Square Test for Profile of Manufacturing Industries

S. No Variable Chi-Square

Value Sig.

Value Significance or not

1. Type of Industry 22.880 0.443 Not Significant 2. Number of Employees 20.017 0.029 Significant 3. Total Capital Invested 15.458 0.017 Significant 4. Supply Chain Position 9.098 0.168 Not Significant 5. Nature of Industry 4.543 0.337 Not Significant 6. Side of Supply Chain 1.379 0.502 Not Significant 7. Type of Goods Produced 0.034 0.983 Not Significant 8. Type of Business Organization 12.959 0.044 Significant 9. Type of Ownership 4.627 0.592 Not Significant 10. Type of Listing 14.799 0.022 Significant 11. Kind of Manufacturing 6.706 0.152 Not Significant 12. Manufacturing Pattern 1.423 0.964 Not Significant 13. Type of process 4.640 0.591 Not Significant 14. Annual Sales 20.180 0.028 Significant 15. Market Coverage 9.140 0.058 Not Significant 16. Area of Market 16.298 0.038 Significant 17. Business years 9.888 0.129 Not Significant 18. Software Usage 8.639 0.013 Significant

104

To understand the characteristics of these three supply chain competence

segments, association among the segments with various manufacturing profile related

variables are analyzed. The chi-square test is applied to test the significance of

association. The chi-square values displayed in Table 4.59 reveal that manufacturing

enterprises grouped on the basis of type of industry, nature of industry, supply chain

position, side of supply chain ,type of goods produced, type of ownership, kind of

manufacturing, manufacturing pattern, type of process, market coverage, and business

years have no significant association with supply chain competence based segments,

whereas there is significant association between supply chain competence segments

and manufacturing units grouped on the basis of number of employees, total capital

invested type of business organization, type of listing, annual sales, Area of market

and software usage.

4.6.7 RELATIONSHIP BETWEEN SUPPLY CHAIN COMPETENCE AND PROFILE OF MANUFACTURING INDUSTRIES


competence segments and the units grouped on the basis of the variables namely,

number of employees, total capital invested, type of business organization, type of

listing, annual sales, Area of market and software usage. The relationship of

manufacturing enterprises grouped on the basis of different profile variables and their

supply chain competence is discussed at length in the forthcoming paragraphs.

4.6.7.1 Number of Employees

To test the significance of association, chi-square test is applied. The chi-

square value is 20.017 and significant value as 0.029 are shown in Table 4.59. This

suggests that there is significant association among the units categorized on the basis

of number of employees and supply chain competence of manufacturing enterprises.

105

The association between number of employees and supply chain competence is

displayed in Figure 4.11.

Figure 4.11 Employees and Competence -Correspondence Diagram

The above figure portrays the association between manufacturing units

grouped on the basis of number of employees and the three supply chain competence

clusters. It can be inferred from the above figure that manufacturing units employing

301-600, 901-1200 and More than 1200 employees are associated with the “Highly

supply chain competence units”, while manufacturing units employing less than 100

are associated with the “Moderate supply chain competence units” and the units

employing 101-300 and 601-900 employees are associated with “Low supply chain

competence units”.

The relationship between number of employees category and supply chain

competence is displayed in Table 4.60.

Table 4.60 ANOVA for Number of Employees and Supply Chain Competence

Supply Chain Competence F Sig.

Quality and Services 1.576 0.167

Design Effectiveness 1.486 0.195

Operations and Distribution 2.464 0.033

It can be observed from the above anova table that significant difference

prevail among the units categorized based on number of employees in respect of

quality and services, design effectiveness and operations and distribution competence

factors.

106

4.6.7.2 Total Capital Invested

To test the significance of association between the units grouped on the basis

of capital invested and the three clusters segmented on the basis of supply chain

competence, chi-square test is conducted. The chi-square value is 15.458 and value of

significance is 0.017 are shown in Table 4.59. which clearly indicates significant

association between the units categorized based on total capital invested and supply

chain competence of manufacturing enterprises.

The association between total capital invested and supply chain competence has been

portrayed in Figure 4.12.

Figure 4.12 Total Capital Invested and Competence -Correspondence Diagram

It can be observed from the above figure that those manufacturing units which

have invested 1-50 crores are associated with the “Highly supply chain competence

units”, while those units with capital investment of less than 50 lakhs are associated

with “Moderate supply chain competence units”, and those units with capital

investment of more than 50 crores are associated with “Low supply chain competence

units”.

The relationship between total capital invested category and supply chain competence

factor is exhibited in Table 4.61.

Table 4.61 ANOVA for Total Capital Invested and Competence

Supply Chain Competence F Sig. Quality and Services 0.668 0.573 Design Effectiveness 2.312 0.077


107

It can be observed from the above anova table that there is no significant

difference among manufacturing units categorized based on capital investment with

respect to quality and services, design effectiveness and operations and distribution

related supply chain competence.

4.6.7.3 Type of Business Organization

To test the significance of association, chi-square test is conducted. The chi-

square value is 12.959 and significant value as 0.044 are shown in Table 4.59. which

clearly indicates that there is significant association between the units categorized

based on type of business organization and supply chain competence.

The association between manufacturing units categorized on the basis of type of

business organization and supply chain competence segments is portrayed in

Figure 4.13.

Figure 4.13 Business Organization and Competence -Correspondence Diagram

It can be observed from the above figure that manufacturing firms using the

Public Limited Company form of organization are associated with the “Highly supply

chain competence units” while those units using the Private Limited Company and

Sole Proprietorship forms of organization are associated with “Moderate supply chain

competence units” and those units using the Partnership form of organization are

associated with the “Low supply chain Competence Group”.

108

The relationship between manufacturing units categorized on the basis of type of

business organization and the three supply chain competence clusters is shown in

Table 4.62.

Table 4.62 ANOVA for Business Organization and Competence



It is observed from the above table that no significant difference exist among

the units categorized on the basis of type of business organization with respect to

quality and services and operations and distribution, while there is a significant

difference among such groups with respect to design effectiveness competence.

Mean values in respect of design effectiveness competence of manufacturing

enterprises categorized on the basis of business organization used is displayed in

Table 4.63.

Table 4.63 Mean Values for Design Competence of Business Organization

Type of Business Organization N

1 2 Partnership 70 2.97 Sole proprietorship 41 3.10 Private Limited Company 115 3.21 Public Limited Company 29 3.59

The above table indicates that two homogeneous sub groups can be formed

among the four categories of manufacturing enterprises grouped on the basis of

business organization in respect of design effectiveness. The mean values of

manufacturing units using the Partnership, Sole proprietorship and Private Limited

company forms of organization are 2.97, 3.10 and 3.21 respectively. It can be

observed that the units using Partnership, Sole proprietorship and Private Limited

company forms of organization comprise one group, while the units using the Public

sector company form of organization constitute the other group. This categorization

has been conducted based on mean values at 99 percent confidence level This implies

that manufacturing units using Public Limited company form of organization have

high level of design effective competence than those units using Sole Proprietorship,

Partnership and Private Limited Company forms of organisation.

109

4.6.7.4 Type of Listing

To test the significance of association, chi-square test is carried out. The chi-

square value is 14.799, and value of significance is 0.022 are shown in Table 4.59.

which clearly indicates significant association between the units categorized on the

basis of type of listing and the three supply chain competence clusters.

The association between manufacturing units categorized on the basis of type of

listing and supply chain competence clusters is portrayed in Figure 4.14.

Figure 4.14 Listed and Competence -Correspondence Diagram

It can be observed from the above figure that those manufacturing units which

have got their shares listed in India and abroad are associated with “High supply chain

competence units”, while those units whose shares have been listed in India are

associated with “Moderate Supply Chain Competence Units” and the units whose

shares have not been listed are associated with “Low Supply Chain Competence

Units”.

The relationship between manufacturing units categorized based on type of listing and

supply chain competence factor is shown in Table 4.64.

Table 4.64 ANOVA for Type of Listing and Supply Chain Competence Supply Chain Competence F Sig. Quality and Services 0.485 0.693 Design Effectiveness 3.594 0.014


It is observed from the above table that no significant difference prevails

among the units categorized on the basis of type of listing with respect to quality and

110

services, while there is a significant difference among the units categorized on the

basis of type of listing and design effectiveness and operations and distribution related

supply chain competence.

Mean values in respect of design effectiveness competence of the units categorized on

the basis of type of listing is displayed in Table 4.65.

Table 4.65 Mean Values for Design Competence of Listing Category

Type of Listing N

1 2 Listed only abroad 12 3.02 Not listed 146 3.10 Listed in India 64 3.14 Listed in India and Abroad

33

3.59

The mean values of the business units listed only abroad, not listed at all, and

listed only in India, comprising of a homogeneous group, are 3.02, 3.10 and 3.14

respectively while the units categorized on the basis of shares listed in India and

abroad have a higher mean than the other three categories. This suggests that business

units whose shares are listed in India and Abroad have high level of design effective

competence than the other three categories of manufacturing enterprises.

Mean values for operations and distribution of type of listing category are depicted in

Table 4.66.

Table 4.66 Mean Values for Operations Competence of Listing Category

Type of Listing N

1 2 Not listed 146 3.01 Listed in India 64 3.32 Listed in India and Abroad 33 3.40 Listed only abroad 12 3.43

The mean value of the units not listed and Listed in India are 3.01 and 3.32

respectively and the mean value of the units listed only in abroad and listed in India

and Abroad is 3.40 and 3.43 respectively. Hence, two groups can be formed based on

the mean values. The first group consists of manufacturing units whose shares have

been listed only in India and not at all listed, while the second group consists of units

whose shares have been listed only in abroad and listed in India and abroad. It can be

noted that those manufacturing units which have not got their shares listed and whose

shares have been listed in India alone have low level of operations and distribution

competence than the other groups of enterprises.

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4.6.7.5 Annual Sales

To test the significance of association, chi-square test has been conducted. The

chi-square value as 20.180 and significant value as 0.028 are shown in Table 4.59.

which clearly indicates prevalence of significant association between manufacturing

units categorized based on annual sales and supply chain competence.

The association between manufacturing units categorized based on annual sales and

supply chain competence segments is displayed in Figure 4.15.

Figure 4.15 Annual Sales and Competence- Correspondence Diagram

It can be observed from the above figure that manufacturing units with an

annual turnover of 1-3 crores and 3-6 crores are associated with the “Highly supply

chain competence units”, while those units with annual turnover of less than 1 crore

and 10-50 crores are associated with “Moderate supply chain competence units” and

those units with annual turnover of 6-10 crores are associated with “Low supply chain

competence units”.

The relationship between annual sales category and supply chain competence factor is

highlighted in Table 4.67.

Table 4.67 ANOVA for Annual Sales and Supply Chain Competence

Supply Chain Competence F Sig.

Quality and Services 0.945 0.452

Design Effectiveness 3.611 0.004


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It is observed from the above table that no significant difference prevails

among the units categorized based on annual sales with quality and services related

competence. However, there is a significant difference among the units categorized

based on annual sales in respect of design effectiveness and operations and

distribution related competence.

Mean values of different manufacturing units with different annual turnover and

design effectiveness factor is displayed in Table 4.68.

Table 4.68 Mean Values for Design Effectiveness of Annual Turnover Category

Annual Turnover N

1 2 3 1 Crores to 3 Crores 74 2.96 Less than 1 Crore 55 3.05 3 Crores to 6 Crores 41 3.11 6 Crores to 10 Crores 34 3.42 10 Crores to 50 Crores 32 3.42 More than 50 Crores 19 3.57

The above table indicates that three homogeneous sub groups can be formed

among the six groups of units with different levels of annual turnover based on design

effectiveness. The mean values of units with annual turnover of 1 crore to 3 crores,

less than 1 crore and 3 crores to 6 crores are 2.96, 3.05 and 3.11 respectively. The

mean value of units with annual turnover of 6 crores to 10 crores and 10 crores to 50

crores is 3.42 and 3.42 respectively. The mean value of units with annual turnover of

more than 50 crores is 3.57. This signifies that units with annual turnover of more

than 50 crores have high level of design effectiveness competence than other units

with different levels of annual turnover.

Mean values for operations and distribution competence of annual turnover category

is shown in Table 4.69.

Table 4.69 Mean Values for Operations and Distribution of Annual Turnover

Annual Turnover N

1 2 1 Crores to 3 Crores 74 3.03 3 Crores to 6 Crores 41 3.06 6 Crores to 10 Crores 34 3.08 Less than 1 Crore 55 3.23 10 Crores to 50 Crores 32 3.32 More than 50 Crores 19 3.55

Mean values of manufacturing units with annual turnover of 1 crore to 3

crores, 3 crores to 6 crores, 6 crores to 10 crores, Less than 1 crore and 10 crores to 50

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crores are 3.0, 3.1, 3.0, 3.2 and 3.3 respectively. The mean value of the category of

manufacturing units with annual turnover of more than 50 crores is 3.6. The

difference in mean values between the two groups is significant at 95 percent level of

confidence (Table 4.67, Significant value is 0.026). This signifies that those

manufacturing units with annual turnover of More than 50 crores have high level of

operations and distribution competence than the other group of manufacturing units.

4.6.7.6 Area of Market To test the significance of association the chi-square test is carried out. The

chi-square value as 16.298 and significant value as 0.038 are shown in Table 4.59.

which clearly indicates the existence of significant association between area of market

and supply chain competence of manufacturing Industries.

The association between the units categorized on the basis of area of market and

supply chain competence segments is displayed in Figure 4.16.

Figure 4.16 Area of Market and Competence- Correspondence Diagram

It can be observed from the above figure that those manufacturing units

enjoying market of the whole of India and both the national and international markets

are associated with the “Highly supply chain competence units”, while those units

possessing market in Puducherry and Tamil Nadu are associated with “Moderate

supply chain competence units” and those units possessing market in South India are

associated with “Low supply chain competence units”.

114

The relationship between area of market category and supply chain competence factor

is shown in Table 4.70.

Table 4.70 ANOVA for Area of Market and Supply Chain Competence



It is observed from the above table that there is no significant difference

among the manufacturing units segmented based on area of market with respect to

quality and services, operations and distribution and design effectiveness competence

factor.


The chi-square value as 8.639 and significant value as 0.013 are shown in

Table 4.59. which clearly indicates significant association between manufacturing

units categorized based on software usage and supply chain competence.

The relationship between manufacturing units categorized based on software usage

and supply chain competence factor has been displayed in Table 4.71.

Table 4.71 Independent Samples Test for Software Usage and Supply Chain

Competence

Supply Chain Competence Levene's Test for Equality of

Variances t-test for Equality of

Means F Sig. t df Sig.

Quality and Services 12.632 0.000 0.174 119.08 0.862 Design Effectiveness 1.554 0.214 2.656 253 0.008 Operations and Distribution 0.125 0.724 2.130 253 0.034

It can be observed from the above table that there is no significant difference

among the units categorized based on software usage and quality and services

oriented competence, while significant relationship exist in respect of such groups

regarding Design Effectiveness oriented competence and operations and distribution

oriented competence .


COMPETENCE AND PROFILE OF MANUFACTURING INDUSTRIES

Canonical correlation was applied to predict the shared relationship among

two or more set of variables. This analyis result provides individual relationship that

is between two variable and also provide overall relashionship that is between two or

more set of variables. The following section describes canonical correlation between

two sets of varaibles.First set of varaible is supply chain competence factors namely

115

quality and service competence, design effectiveness competence and operations and

distribution competence and second set of variable is profile of manufacturing

industry variables namely number employes, total capital, type of organization, type

of listing,annual sales, area of market and useage of software are used.

Canonical Correlations in respect of supply chain competence with regard to different

profile characteristics of manufacturing units is displayed in Table 4.72 .

Table 4.72 Canonical Correlation of Supply Chain Competence Coef. Std. Err. t P>|t| [95% conf. interval] U1

Quality -.9999181 .4012591 -2.49 0.013 -1.790137 -.2096996 Design 1.026804 .283937 3.62 0.000 .4676336 1.585975 Operations .7329715 .3522367 2.08 0.038 .039295 1.426648

V1 Employees .4362894 .2102167 2.08 0.039 .0222996 .8502792 Capital -.114036 .2897084 -0.39 0.694 -.6845725 .4565005 Organization .1254188 .2758852 0.45 0.650 -.417895 .6687327 Type of listing .1046589 .2150684 0.49 0.627 -.3188856 .5282033 Annual sales .1401398 .1967936 0.71 0.477 -.2474153 .5276948 Mark area .1355039 .2279061 0.59 0.553 -.3133223 .5843302 Software -.5586552 .5663026 -0.99 0.325 -1.673902 .5565915

U2 Quality 1.223069 .5342726 2.29 0.023 .1709008 2.275238 Design .75180961 .3780595 1.99 0.048 .0072792 .49634 Operations -1.173543 .4689998 -2.50 0.013 -2.097167 -.2499196

V2 Employees -.5378403 .2799016 -1.92 0.056 -1.089064 .0133832 Capital .0679975 .385744 0.18 0.860 -.6916665 .8276614 Organization .2978718 .3673385 0.81 0.418 -.4255455 1.021289 Type of listing .53563981 .2863616 1.87 0.063 -.0283056 1.099585 Annual sales .4779563 .2620288 1.82 0.069 -.0380695 .9939822 Mark area -.1031655 .3034547 -0.34 0.734 -.7007734 .4944424 Software .1185035 .7540266 0.16 0.875 -1.366437 1.603444

U3 Quality -1.073647 .8762032 -1.23 0.222 -2.799195 .6519019 Design .4553084 .6200147 0.73 0.463 -.7657161 1.676333 Operations -.9470926 .7691563 -1.23 0.219 -2.461829 .5676433

V3 Employees .3974773 .4590365 0.87 0.387 -.5065252 1.30148 Capital .6537611 .6326172 1.03 0.302 -.5920821 1.899604 Organization -.1709151 .6024325 -0.28 0.777 -1.357314 1.015484 Type of listing .5816817 .4696308 1.24 0.217 -.3431846 1.506548 Annual sales -.3920336 .4297254 -0.91 0.362 -1.238312 .454245 Mark area -.3478136 .4976636 -0.70 0.485 -1.327886 .6322589 Software .3360041 1.236598 0.27 0.786 -2.099287 2.771295

Canonical correlations: 0.2878 0.2201 0.1363 Tests of significance of all canonical correlations Statistic df1 df2 F Prob>F Wilks' lambda .856537 21 704 1.8577 0.0113 a Pillai's trace .14984 21 741 1.8551 0.0114 a Lawley-Hotelling trace .160134 21 731 1.8581 0.0112 a Roy's largest root .0902787 7 247 3.1855 0.0030 u

116


factors relating to supply chain Competence, while the second set consists of the

seven profile of manufacturing industry variables of number of employees, type of

listing, area of market, types of organization, annual turnover, total capital invested

and software usage. Based on these two sets of data, Canonical Correlation has been

performed. The Canonical Correlation coefficient values in respect of these three

factors are 0.29, 0 .22 and 0.14. Other results displayed in the above table such as df1

value of 21, df2 value of 704, f value of 1.858, Wilks’s λ value of 0.8565, and p value

of 0.0113, which is less than 0.05, reveals that there is significant relationship

between the two sets of data. To predict the overall relationship between these two

sets of data, Wilk’s (λ) value should be deducted from one. From the three canonical

function set, the r2 value is 0.1435. This implies that the entire canonical model

explains a considerable portion of about 14% of the variance. Hence, there is a decent

positive correlation between the two sets of data namely, the three supply chain

Competence factors and the seven variables relating to the profile of manufacturing

enterprises.

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4.7 SUPPLY CHAIN PRACTICES DIMENSION ANALYSIS

The supply chain practices of manufacturing units have been studied with the

help of variables such as holding safety stock, close partnership with customers, third

Party Logistics (3PL), just in time (JIT) supply, supply chain benchmarking,

subcontracting, E-procurement, many suppliers, close partnership with suppliers,

strategic planning, outsourcing, and few suppliers. Each variable and its nature of

relevance with supply chain practices are discussed at length in the forthcoming

sections.

4.7.1 PRIORITIES OF SUPPLY CHAIN PRACTICES

The executives of manufacturing units in Union Territory of Puducherry were

asked to rate their firm’ supply chain practices implementation level in a five point

rating scale, ranging from 1 denoting not at all implemented to five denoting fully

implemented. The mean values assigned to each of these variables and the ranking of

such variables based on their mean values is displayed in Table 4.73.

Table 4.73 Priorities of Supply Chain Practices

Sl.no Supply Chain Practices Mean Value Rank 1 Close partnership with customers 3.61 I 2 Close partnership with suppliers 3.54 II 3 Holding safety stock 3.42 III 4 Just in time (JIT) supply 3.41 IV 5 Strategic planning 3.38 V 6 Many suppliers 3.27 VI 7 Few suppliers 3.19 VII 8 Supply chain benchmarking 3.15 VIII 9 Subcontracting 2.95 IX

10 Outsourcing 2.93 X 11 Third Party Logistics (3PL) 2.82 XI 12 E-procurement 2.82 XII

It can be inferred from the above table that manufacturing units give more

importance to close partnership with customers. This implies that firms prefer the

ready availability of their products to their customers, while they accord least interest

on E-procurement.

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4.7.2 FACTORISATION OF SUPPLY CHAIN PRACTICES

With an endeavour to reduce the variables relating to Supply Chain Practices

into minimum number of manageable factors, Factor Analysis have been conducted

and results of KMO and Bartlett's Test of factor analysis have been portrayed in

Table 4.74.

Table 4.74 KMO and Bartlett's test




df 66

Sig. 0.000

The KMO value of 0.796 depicts that the factor analysis applied for this data

is valid. The significant value is 0.01 which means the value is significant at 99 %

level of confidence while the chi square value for Bartlett’s test of Sphericity is

700.22.

The variance and Eigen value explanation of each factor (Supply Chain Practices) are


Table 4.75 Variance Explained By Factor of Supply Chain Practices

Component

Initial Eigen Values Rotation Sums of Squared

Loadings

Total % of

Variance Cumulative

% Total % of Variance Cumulative %

1 3.652 30.433 30.433 2.561 21.338 21.338

2 1.690 14.087 44.520 2.237 18.643 39.980

3 1.242 10.346 54.866 1.786 14.886 54.866

4 .838 6.981 61.847

5 .776 6.467 68.314

6 .724 6.035 74.349

7 .600 5.001 79.350

8 .589 4.907 84.257

9 .546 4.550 88.807

10 .521 4.342 93.149

11 .466 3.886 97.035

12 .356 2.965 100.000

Factors possessing Eigen value in excess of one are taken as reduced factors

which shall be considered as the factors for further analysis. Three factors have been

extracted from the twelve variables relating to supply chain practices. These three

factors explain 54.86 percent of total variance which is quite significant. Of the three

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factors, the factor labeled as “procurement practices” is the most important factor as it

alone explains 21.33 percent of the total variance in the supply chain practices. This

implies that manufacturing enterprises accord maximum importance to procurement

practices. Variables included on each supply chain practices along with their loadings

have been displayed in Table 4.76.

Table 4.76 Factor Loading of Supply Chain Practices

Sl.no

Supply Chain Practices Variables Component Supply chain

Practices Factors 1 2 3

1 E-procurement 0.739

Procurement Practices

2 Third Party Logistics (3PL) 0.714

3 Outsourcing 0.684

4 Subcontracting 0.669

5 Few suppliers 0.591

6 Strategic planning 0.686 Strategic Planning and Lean Practices 7 Supply chain benchmarking 0.679

8 Many suppliers 0.664

9 Holding safety stock 0.517

10 Close partnership with suppliers 0.810 Close Partnership

Practices 11 Close partnership with customers 0.736

12 Just in time (JIT) supply 0.617

It can be inferred from the above table that five variables are constituting the

first factor, while four variables constitute the second variable and three variables

constitute the third factor. The first factor may be labeled as “Procurement Practices”,

while the second factor has been designated as “Strategic Planning and Lean

Practices” and the third factor has been labeled as “Close Partnership Practices”.

4.7.2.1 Procurement Practices

The most important practice that any enterprise must accord paramount

importance is the procurement aspect. The quality of output will depend solely on the

quality of inputs used by the enterprise. Similarly, the ability of any enterprise to

honor its contracts within the prescribed time will largely depend on the timely

availability of the needed inputs. Hence, procurement logistics take part a vital role in

influencing the effectiveness of practices of any enterprise. Since all statements

relating to procurement practices constitute this factor, the factor has been labeled as

“Procurement Practices”.

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4.7.2.2 Strategic Planning and Lean Practices

It is outmost importance for any manufacturing enterprise to minimize, if not

totally eliminate, the waste involved in the manufacturing process. Similarly, it is

equally important to ensure that the manufactured products are free from

manufacturing defects. Latest concepts such as six sigma and delphi method assist the

manufacturing enterprise to achieve an almost defect-free manufacturing process.

Hence, it can be said that it is absolutely indispensable for manufacturing enterprises

to focus on its lean practices to improve its operational effectiveness and efficiency.

Strategic Planning will positively contribute to the manufacturing enterprises in

achieving the desired level of lean practices. Capacity of the manufacturing

enterprises to maintain adequate inventory levels and to have multiple sources for its

inventory will have a positive impact on its effective practices.

4.7.2.3 Close Partnership Practices

Customers and Suppliers are the most important stakeholders of any business.

Close association with suppliers is indispensable for capacitating the manufacturing

enterprises to get good quality of inputs at affordable cost and within the required

time. Similarly, good relation with customers is also vital. Business enterprises must

be able to honor its commitments towards its customers by making timely deliveries

of the required quantity of products with strict compliance to the desired quality

norms. In fact, customers are the boss of any business and no business can grow and

prosper without good repute among customers. Hence, it becomes absolutely

important for any manufacturing enterprises to maintain good partnership with

customers and suppliers. Hence, all statements related to Inter-personal relations with

customers and suppliers have been grouped under this factor, which has been labeled

as “Close Partnership Practices”,

4.7.3 RANKING OF SUPPLY CHAIN PRACTICES FACTORS

By using factor analysis, eleven variables relating to supply chain practices

have been factored into three groups of practices namely, “Procurement Practices”,

“Strategic Planning and Lean Practices” and “Close Partnership Practices” based on

variables loaded under each factor. The mean values in respect of these three supply

chain practices factors have been displayed in Table 4.77.

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Table 4.77 Strength of Supply Chain Practices

S.No Supply chain Practices Mean Rank 1 Close Partnership Practices 3.52 I 2 Strategic Planning and Lean Practices 3.30 II 3 Procurement Practices 2.88 III

Among the three practices factors of procurement practices, strategic planning

and lean practices and close partnership practices, close partnership practices seems to

be the most strong factor as the mean value in respect of this factor is the highest.

Mean value of 3.52 in respect of the close partnership practices of manufacturing

industries indicates that this factor is most crucial for manufacturing units.

4.7.4 SEGMENTATION OF SUPPLY CHAIN PRACTICES

Based on the similarities of manufacturing units regarding the three factors of

procurement practices, strategic planning and lean practices and close partnership

practices, they may be segmented into clusters using Cluster Analysis.

The final cluster centers have been displayed in Table 4.78.


Supply Chain Practices

Cluster

1 2 3

Procurement Practices 2.83(II) 1.95(III) 3.67(I)

Strategic Planning and Lean Practices 2.91(II) 2.87(III) 4.11(I)

Close Partnership Practices 2.66(III) 4.02(II) 4.12(I)

Average 2.8 2.95 3.97

It can be observed from the above table that manufacturing units can be

classified into three clusters. The first cluster may be designated as “moderate

practices group” as the mean values of firms constituting this cluster in respect of

supply chain practices is moderate when compared with the other two groups. The

second cluster is labeled as “partnership practices group” because the mean values in

respect of the units constituting this cluster is high in respect of the “close partnership

practices factor”, while the mean in respect of the other two practices factors are very

low. The third cluster may be labeled as “High Supply Chain Practices Group” as the

units constituting this cluster enjoy high mean that is around the four mark, which is

quite high in the five point scale.

The next issue is to assess whether the three supply chain practices factors play a

significant role in classifying manufacturing units in UT of Puducherry into three

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clusters. For this purpose, ANOVA values have been ascertained and shown in

Table 4. 79.

Table 4.79 ANOVA

Supply Chain Practices Cluster Error


Procurement 55.942 2 0.419 252 133.497 0.000

Strategic Planning and Lean 42.136 2 0.460 252 91.567 0.000

Close Partnership 61.686 2 0.323 252 190.947 0.000

It can be inferred from the above table that all the three Supply Chain

Practices Factors play a significant role in bifurcating manufacturing units into three

clusters. The mean scores of these three clusters significantly differ and this serves as

a testimony to the fact that these three distinct clusters could be explained with the

help of these supply chain practices factors. A brief description about these three

clusters is discussed in the forthcoming paragraphs.

4.7.4.1 Moderate Supply Chain Practices Units

The supply chain practices level of the units constituting this group is average

regarding all the three supply chain practices of procurement practices, strategic

planning and lean practices and close partnership practices. Mean values in respect of

these three factors hover around the three mark in the five point scale, suggesting that

the standard of supply chain practices of the units constituting this cluster is moderate.

These units have moderate procurement practices and strategic planning and lean

practices and hence have occupied the moderate level in the overall situation

pertaining to supply chain practices. About 39% of manufacturing units surveyed (101

out of the total 255) possess an average standard as far as supply chain practices is

concerned and hence these units constitute this group.

4.7.4.2 Partnership Practices Units

The second cluster of manufacturing units with respect to supply chain

practices factors has been designated as “partnership practices units”. Mean values in

respect of units constituting this group regarding Partnership practices factor is quite

high whereas their mean regarding the other two supply chain practices factors of

procurement practices and strategic planning and lean practices. 27% of the

manufacturing units surveyed constitute this cluster.

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4.7.4.3 High Supply Chain Practices Units

The mean values in respect of the units constituting this group regarding the

three supply chain practices factors are 3.97. As the mean value hover around the four

mark in the five point scale, the units constituting this group may be designated as

“High supply chain practices group”. Almost 34 percent of the manufacturing units

surveyed constitute this segment.

Number of manufacturing firms constituting each cluster are displayed in the

following Table 4.80.


Cluster

1 101 39%

2 68 27%

3 86 34%

Valid 255 100%

It can be inferred from the above table that almost three-quarter of

manufacturing units surveyed (73%) possess high and moderate standards as far as

supply chain practices are concerned.

4.7.5 TESTING SUITABILITY OF SUPPLY CHAIN PRACTICES SEGMENTATION USING DISCRIMINANT ANALYSIS

The above discussion pointed out that manufacturing units have been

classified into three clusters based on the standard maintained by them regarding

supply chain practices. These clusters have been designated as “Moderate Supply

Chain Practices Units”, “Partnership Supply Chain Practices Units” and “High Supply

Chain Practices Units”. 39 percent of the manufacturing units have moderate supply

chain practices standard and hence they have been designated as “moderate supply

chain practices units”. 27 percent of the manufacturing units possess high standard as

far as partnership practices alone is concerned and hence this cluster has been

designated as “Partnership oriented supply chain practices units”. Finally, 34 percent

of the manufacturing units possess high supply chain practices standard and hence

they have been designated as “high supply chain practices units”.

The next important issue is to assess whether the segmentation is valid , and

whether each of the clusters significantly vary among each other, and whether the

three supply chain Practices factors play a role in segregating manufacturing

enterprises into three clusters. For this purpose, sample stability and cluster

classification reliability has to be verified through Discriminant analysis.

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The equality of group means in respect of supply chain practices can be

inferred from the following Table 4.81.


Supply Chain Practices Wilks' Lambda F df1 df2 Sig.

Procurement Practices 0.486 133.497 2 252 0.000

Strategic Planning and Lean Practices

0.579 91.567 2 252 0.000

Close Partnership Practices

0.398 190.947 2 252 0.000

It can be observed from the above table that the Wilks' lambda value is very

small in respect of the close partnership practice which implies that there is a very

strong group difference among the three clusters grouped on the basis of close

partnership practices factor. Mean values in respect of this factor were significantly

different among the three segments. Wilks’ Lambda for procurement practices factor

is high suggesting that there is no significant difference among the first and third

cluster regarding procurement practices. Similarly, the value of Wilks’ Lambda in

respect of the strategic planning and lean practices factor is relatively high suggesting

that there is no significant difference among the first and third segment regarding

strategic planning and lean practices.

The value of F ratio in accordance to the df is highly significant. Low

significance value indicates that significant difference prevail among the groups

regarding supply chain practices.

The above mentioned two points justify the accuracy of segmentation of

manufacturing enterprises into three clusters based on supply chain practices as

significant group difference exist among these three clusters.

The Eigen values and canonical correlation coefficient values are displayed in

Table 4.82.



Variance Cumulative

% Canonical

Correlation

1 1.727 54.5 54.5 0.796

2 1.443 45.5 100.0 0.768

It can be inferred from the above table that two canonical correlations can be

formed along with two discriminant functions. The canonical correlation gives the

measure of association between discriminant functions and the three supply chain

practices factors. The canonical correlation among first function and three supply

125

chain practices factors is high that is 0.796, while the canonical correlation value in

respect of the second function is 0.768. Hence, it can be said that both the canonical


The values of Wilks’ Lambda in respect of the two functions are displayed in the

following Table 4.83.


Test of Function(s)

Wilks' Lambda

Chi-square df Sig.

1 through 2 0.150 475.944 6 0.000

2 0.409 224.149 2 0.000

It can be inferred from the above table that the Wilks’ lambda score in respect

of the first function (procurement practices) is 0.150 which implies that the variables

constituting this function play a vital role in the grouping of manufacturing enterprises

into three clusters. The variables constituting the second factor (strategic planning and

lean practices and close partnership practices) seems to play a limited role in grouping

manufacturing enterprises as the Wilks’ lambda score in respect of this factor is

0.409, which is quite high. However, the significance values in respect of both the

factors is 0.000, which implies that both the factors and all the three variables

constituting the two factors play a significant role in grouping the units into three

clusters. Since the Wilks’ lambda score is least in respect of the first factor, it can be

concluded that the first factor plays a significant role in categorizing manufacturing

enterprises into three clusters.

Standardized beta values are shown in Table 4.84


Supply Chain Practices Function

1 2

Procurement Practices .817* 0.501

Strategic Planning and

Lean Practices -0.303 0.790*

Partnership Practices 0.086 0.704*



functions.

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The two domain functions of discriminant analysis along with standardized beta value

are-

Z1 = .817* Procurement Practices

Z2 = 0.790 * Strategic Planning and Lean Practices + 0.704* Close

Partnership Practices.

Territorial map of supply chain practices has been portrayed in Figure 4.17.

Canonical Discriminant Function 2 -8.0 -6.0 -4.0 -2.0 .0 2.0 4.0 6.0 8.0 ┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼ 8.0 ┼ ┼ │ │ │ │ │ │ │ │ │3 │ 6.0 ┼133 ┼ ┼ ┼ ┼ ┼ ┼ ┼ ┼ │ 1133 │ │ 1133 │ │ 1133 │ │ 11333 │ │ 11133 │ 4.0 ┼ ┼ 1133 ┼ ┼ ┼ ┼ ┼ ┼ 33┼ │ 1133 33322│ │ 1133 333222 │ │ 11333 333222 │ │ 11133 333222 │ │ 1133 333222 │ 2.0 ┼ ┼ ┼ 1133 ┼ ┼ ┼ ┼333222 ┼ ┼ │ 11333 * 333222 │ │ 11133 333222 │ │ 1133 333222 │ │ 1133 3333222 │ │ 1133 3332222 │ .0 ┼ ┼ ┼ ┼ 1133333222 ┼ ┼ ┼ ┼ │ 11222 │ │ * 12 │ │ 12 * │ │ 12 │ │ 12 │ -2.0 ┼ ┼ ┼ ┼ 12 ┼ ┼ ┼ ┼ │ 12 │ │ 12 │ │ 12 │ │ 12 │ │ 12 │ -4.0 ┼ ┼ ┼ ┼ 12 ┼ ┼ ┼ ┼ │ 12 │ │ 12 │ │ 12 │ │ 12 │ │ 12 │ -6.0 ┼ ┼ ┼ ┼ 12 ┼ ┼ ┼ ┼ ┼ │ 12 │ │ 12 │ │ 12 │ │ 12 │ │ 12 │ -8.0 ┼ 12 ┼ ┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼ -8.0 -6.0 -4.0 -2.0 .0 2.0 4.0 6.0 8.0

Canonical Discriminant Function 1 Symbols used in territorial map 1 Low Practice Company 2 Partnership Practice 3 High Practice Company * Indicates a group Centroid Figure 4.17 Territorial Map of Supply Chain Practices

It can be inferred from the above figure that procurement practices forming

part of the first function plays a significant role in formation of the first and second

clusters namely low practice firms and the partnership practice firms. Variables of

high supply chain practice and partnership practices comprising the second function

play a dominant role in the formation of first and third clusters namely, low practice

firms and high supply chain practices firms respectively, as well as the third and

127

second clusters namely high supply chain practices units and partnership practice

firms respectively.

Group centroids for supply chain practices clusters are shown in Figure 4.18.

Figure 4.18 Group Centroids for Supply Chain Practices Clusters

The above group centroids figure indicates that the entire three clusters are




The extent of correct classification is shown in Table 4.85. It depicts the degree of

success based on the supply chain practices factor.


Segmentation of Practice

Predicted Group Membership Total

High Practice Low Practice Moderate Practice

Count Moderate Practice Units 98 1 2 101

Partnership Practice Units 0 67 1 68

High Practice Units 3 3 80 86

% Moderate Practice Units 97.0 1 2 100

Partnership Practice Units 0 98.5 1.5 100

High Practice Units 3.5 3.5 93 100



observed that 97 percent of moderate practice units segments are correctly classified,

128

while In the partnership practice units segment,67 cases accounting for 98.5 percent

are correctly classified, while a mere 1 and 3 cases are included into high supply chain

practices units and moderate practice units respectively. In the case of high supply

chain practices units, the percent of correct classification is 93.0. Hence, it can be

concluded that segmentation of manufacturing enterprises in UT of Puducherry based

on supply chain practices is correct by more than 96.1 percent.

4.7.6 CHARACTERISTICS OF SUPPLY CHAIN PRACTICES In the previous section, Supply chain practices have been classified into three

categories namely “moderate supply chain practices units”, “partnership practices

units”, and “high supply chain practices units”, on the basis of their supply chain

practices factors. It is obvious that the high supply chain practices units will occupy

the top position as far as overall performance is concerned. In the forthcoming

paragraphs, the characteristics of supply chain practices segments are identified

through chi-square test along with correspondence analysis, T-test, analysis of

variance (ANOVA) and canonincal correlation.

The chi-square test values along with their level of significance have been displayed

in Table 4.86.

Table 4.86 Chi-Square Test Value for Profile of Manufacturing Industries Variables


value Sig. Value Significance or not

1. Type of Industry 51.021 0.000 Significant 2. Number of Employees 41.274 0.000 Significant 3. Total Capital Invested 21.720 0.000 Significant 4. Supply Chain Position 4.165 0.654 Not Significant 5. Nature of Industry 17.326 0.002 Significant 6. Side of Supply Chain 0.815 0.665 Not Significant 7. Type of Goods Produced 2.286 0.319 Not Significant 8. Type of Business Organization 21.102 0.002 Significant 9. Type of Ownership 11.432 0.076 Not Significant 10. Type of Listing 11.500 0.074 Not Significant 11. What kind of Manufacturing 5.874 0.209 Not Significant 12. Manufacturing Pattern 12.606 0.050 Not Significant 13. Type of process 10.655 0.000 Significant 14. Annual Sales 17.270 0.069 Not Significant 15. Market Coverage 19.243 0.001 Significant 16. Area of Market 43.802 0.000 Significant 17. Business years 8.577 0.199 Not Significant 18. Software Usage 12.709 0.002 Significant

129

To understand the characteristics of these three supply chain practices

segments, association among the three clusters with various manufacturing units

categorized based on their profile are analyzed. The chi-square test is applied to test

the significance of associations. From the chi-square test it is found that significant

association between supply chain practices clusters with manufacturing enterprises

grouped on the basis of type of industry, nature of industry, number of employees,

total capital invested, type of business organization, type of process, market coverage,

area of market and software usage.

4.7.7 RELATIONSHIP BETWEEN SUPPLY CHAIN PRACTICES AND

PROFILE OF MANUFACTURING INDUSTRIES


practices clusters with different groups of manufacturing enterprises categorized

based on type of industry, nature of industry, number of employees, total capital

invested, type of business organization, type of process, market coverage, area of

market and software usage. The forthcoming sections shall discuss at length, the

relationship between the clusters formed based on supply chain practices factors and

the units grouped on the basis of their profile of manufactiring industries.

4.7.7.1 Type of Industry

To test the significance of association, chi-square test is applied. The chi-square

value as 51.021 and significant value as 0.000 (Table 4.86). This suggests that there is

significant association among the units clustered based on supply chain practices and

the units categorized based on type of Industry.

The association between manufacturing units categorized based on type of industry

and supply chain practices segments are portrayed in Figure 4.19.

Figure 4.19 Industry and Practices- Correspondence Diagram

130

The association between manufacturing enterprises categorized based on

nature of industry and supply chain practices clusters can be identified by using

correspondence analysis. It can be inferred from the above figure that manufacturing

units belonging to electronics, chemical, textile and other types of industries are

associated with “Highly supply chain practices units”, while the units belonging to

plastic, food, furniture and pharmaceutical industries are associated with the

“partnership practice units”, and the units belonging to metal, agriculture, automobile

and building material industries are associated with moderate supply chain practices

units.

The relationship between the units categorized on the basis of type of industry and

supply chain practices factor is depicted in Table 4.87.

Table 4.87 ANOVA for Type of Industry and Procurement Practices

Supply Chain Practices F Sig. Procurement Practices 2.997 0.001 Strategic Planning and Lean Practices

1.481 0.139

Close Partnership 2.105 0.021

It can be observed from the above table that significant difference is observed among

the units categorized on the basis of type of industry with respect to procurement

practices and close partnership practices.

Mean values regarding procurement practices in respect of the units categorized on

the basis of nature of industry is displayed in Table 4.88.

Table 4.88 Mean values for Procurement Practices of Industry Category

Type of Industry N

1 2 3

Food 25 2.31

Furniture 7 2.46


Plastic 24 2.68

Agriculture 12 2.83

Textile 15 2.83

Chemical 37 2.91

Automobile 20 3.00

Metal 27 3.00


Electronics 36 3.06

Others 22 3.56

131


group difference among type of Industry with respect to procurement Practices. The

above table indicates that three homogeneous sub groups can be formed among

manufacturing enterprises belonging to twelve types of Industry based on

procurement practices. The difference in mean values among the three homogenous

groups (first group consisting of food, furniture, building materials, plastic,

agriculture, textile, chemical, automobile and metal segment industry group; second

group consisting of pharmaceutical and electronics industries; third group consisting

of other types of industries) is significant at 99 percent level of confidence

(Table 4.87, significant value is 0.001). This implies that there is a significant

difference among the units categorized on the basis of type of industry with respect to

procurement practices.

Mean values regarding partnership practices of manufacturing units categorized on

the basis of type of industry is depicted in Table 4.89.

Table 4.89 Mean Values for Close Partnership Practices of Industry Category

Type of Industry N

1 2 3

Agriculture 12 2.91

Automobile 20 3.03


Metal 27 3.30

Furniture 7 3.57

Food 25 3.57

Electronics 36 3.57


Chemical 37 3.65

Others 22 3.78

Plastic 24 3.79

Textile 15 3.88

the difference in mean values among three homogenous group, (first group

consisting of agriculture, automobile, building materials, metal, furniture, food,

electronics and pharmaceutical industries; second group consisting of chemical

industries; third group consisting of plastic, textile and other type of industries) is

significant at 99 percent level of confidence (Table 4.87, significant value is 0.021).

this implies that significant difference exists among the units categorized on the basis

of type of industry with respect to close partnership practices.

132

4.7.7.2 Number of Employees The chi-square value as 41.274 and significant value as 0.000(Table

4.86)which clearly indicates the prevalence of significant association between the

units categorized on the basis of number of employees and supply chain practices of

manufacturing units.

The association between the units categorized on the basis of number of employees

and supply chain practices segments are shown in Figure 4.20.

Figure 4.20 Employees and Practices- Correspondence Diagram

It can be observed from the above figure that manufacturing enterprises

employing 601-900 and 901-1200 are associated with highly supply chain practices

units, while those units employing less than 100 are associated with partnership

practice units, and those units employing 101-300 and 301-900 are associated with

moderate supply chain practices units.

The relationship between the units grouped on the basis of number of employees and

supply chain practices factor has been displayed in Table 4.90.

Table 4.90 ANOVA for Number of Employees and Supply Chain Practices

Supply Chain Practices F Sig.

Procurement Practices 2.414 0.037

Strategic Planning and Lean

Practices

1.878 0.099


133


the units grouped on the basis of number of employees with respect to strategic

planning and lean practices, while significant difference exist with respect to

procurement practices and close partnership practices.

Mean values regarding close partnership practices of manufacturing units categorized

on the basis of number of employees are depicted in Table 4.91.

Table 4.91 Mean values for Close Partnership Practices of Employees Category

Number of

Employees N

1 2

More than 1200 7 3.04

100-300 61 3.26

300-600 24 3.27

900-1200 18 3.37

600-900 17 3.50

Less 100 128 3.72

It can be observed that two homogenous groups can be formed, first group

consisting of units with number of employees More than 1200, 100-300,300-600,900-

1200 and 600-900, while the second group consist of units with Less than 100

employees. The mean values in respect of these two groups significantly differ. This

implies that significant difference prevails among units grouped based on number of

employees with respect to close partnership practices.

4.7.7.3 Total Capital Invested

The chi-square value as 21.720 and significant value as 0.000 (Table 4.86)

which clearly indicates significant association between the units grouped on the basis

of capital invested and supply chain practices of manufacturing enterprises.

The association between the units grouped on the basis of the units grouped on the

basis of capital invested and supply chain practices clusters has been displayed in

Figure 4.21.

134

Figure 4.21 Capital Invested and Practices- Correspondence Diagram

It can be inferred from the above figure that manufacturing enterprises with

capital investment of 1-50 crores are associated with Highly supply chain practices

units, while those units with capital investment of less than 1 crore are associated with

partnership practice units, and the units with capital investment of more than 50 crores

are associated with moderate supply chain practices units.

The relationship between the manufacturing units categorized on the basis of capital

invested and supply chain practices factor has been displayed in Table 4.92.

Table 4.92 ANOVA for Capital Invested and Supply Chain Practices

Supply Chain Practices F Sig. Procurement Practices 4.812 0.003 Strategic Planning and Lean Practices 1.962 0.120



the manufacturing units categorized on the basis of capital invested with respect to

strategic planning and lean practices, close partnership and significant difference exist

among the manufacturing units categorized on the basis of capital invested with

respect to procurement practices.

135

Mean values regarding procurement practices of the units categorized on the basis of

capital invested has been displayed in Table 4.93.

Table 4.93 Mean Values for Procurement Practices of Capital Invested Category

Total Capital Invested N

1 2


More than 50 Lakhs to 1 Crores 73 2.90


More than 1 crores to 50 crores 60 3.12

It can be inferred from the above table that two homogenous groups can be

formed based on the difference in mean values. The first group consists of units with

investment of Less than 50 Lakhs and 50 Lakhs to 1 Crores, while the second group

consists of units with investment of More than 50 Crores and 1 crore to 50 crores is

significant at 99 percent level of confidence (Table 4.92, Significant value is 0.003).

This implies that significant difference exist among units grouped on the basis of total

capital invested in respect of procurement practices.

4.7.7.4 Nature of Industry


which clearly indicates significant association between nature of industry and supply

chain practices of manufacturing units.

The association between units grouped on the basis of nature of industry and supply

chain practices segments are shown in Figure 4.22.

Figure 4.22 Industry and Practices- Correspondence Diagram

136

It can be inferred from the above figure that units operating as large scale

industry are associated with highly supply chain practices units, while the units

operating as small scale industry are associated with Partnership practice units, and

the units operating as medium scale industry are associated with Moderate supply

chain practices units.

The relationship between the units categorized based on nature of industry and supply

chain practices factor has been displayed in Table 4.94.

Table 4.94 ANOVA for Nature of Industry and Supply Chain Practices


8.050 0.000


It can be observed from the above table that significant difference exist among

the units grouped on the basis of nature of industry with respect to strategic planning

and lean practices, while no such difference exist with regard to procurement

practices and close partnership.

Mean values for strategic planning and lean practices of industry category are shown

in Table 4.95.

Table 4.95 Mean Values for Strategic Planning and Lean of Industry Category


1 2



Large Scale 46 3.70

It can be inferred that two homogenous groups can be formed based on the

difference in mean values. The first group consists of manufacturing units operating

as small scale and medium scale industry units, while the second group consists of

units operating as large scale industry. Significant difference prevails among these

two groups at 99 percent confidence level (Table 4.94, Significant value is 0.000).

Manufacturing units operating as large scale industry possess high level of strategic

planning and lean practices compare to the units operating as small and medium scale

industry.

137


The chi-square value as 21.102 and significant value as 0.002

(Table 4.86)which clearly indicates existence of significant association between the

units grouped on the basis of type of business organization and supply chain practices

of manufacturing units.

The association between type of business organization category and supply chain

practices segments are shown in Figure 4.23.

Figure 4.23 Business Organization and Practices- Correspondence Diagram

It can be inferred from the above figure that manufacturing units using the

Public and private company forms of organization are associated with Highly supply

chain practices units, while those units using the Sole Proprietorship form of

organization are associated with the partnership practice units, and those units using

the Partnership form of organization are associated with Moderate supply chain

practices units.

The relationship between the units grouped on the basis of type of business

organization and supply chain practices factor are shown in Table 4.96.

Table 4.96 ANOVA for Business Organization and Supply Chain Practices

Supply Chain Practices F Sig. Procurement Practices 3.329 0.020 Strategic Planning and Lean Practices 5.253 0.002


It is observed from the above table that significant difference exist among the

group of units categorized based on type of business organization with respect to

138

strategic planning and procurement practices, while no such significant difference

exist regarding lean practices and close partnership practices.

Mean values for strategic planning and lean practices of business organization

category are shown in Table 4.97.

Table 4.97 Mean Values for Strategic Planning and Lean of Business

Organization Category


1 2


Partnership 70 2.77

Private Limited 115 3.00

Public Limited 29 3.09

Based on difference in the mean values, two homogenous group may be

formed. The first group consists of units using the sole proprietor and partnership

forms of organization, while the second group consists of units using the private

limited company and public limited company form of organization.Significant

difference exist among the three groups at 99 percent confidence level (Table 4.96,

Significant value is 0.002). This implies that significant difference exists among units

using different forms of organization in respect of strategic planning and lean

practices.

Mean values for procurement practices of business organization category are shown

in Table 4.98.

Table 4.98 Mean Values for Procurement of Business Organization

Type of Business Organization

N

1 2 3

Partnership 70 3.02




Based on the difference in mean values, three homogenous groups may be formed.

The first group consists of manufacturing units using sole proprietorship and

partnership forms of organization, while the second group consists of manufacturing

units using private limited company and third group is public limited company forms

of organization. Significant difference exists at 99 percent confidence level

(Table 4.96, significant value is 0.020). This implies that significant difference exist

139

among groups of units categorized based on type of business organization in respect

of procurement practices. Units using the private limited company and public limited

company form of organization are having high level of procurement practices

compare to those manufacturing units using the sole proprietor and partnership form

of organization.

4.7.7.6 Type of Process

The chi-square value as 10.655 and significant value as 0.000 (Table 4.86) which

clearly indicates significant association between the units categorized based on type

of process and supply chain practices of manufacturing units.

The association between type of process category and supply chain practices

segments has been displayed in Figure 4.24.

Figure 4.24 Process and Practices- Correspondence diagram

It can be inferred from the above figure that firms using continuous process

are associated with “Highly supply chain practices units”, while units using the job

order process are associated with “partnership practice units” and those units using

batch and repetitive assemble process are associated with “Moderate supply chain

practices units”.

The relationship between manufacturing units grouped on the basis of type of process

and supply chain practices factor have been displayed in Table 4.99.

Table 4.99 ANOVA for Type of Process and Supply Chain Practices


4.242 0.006


140


groups of units categorized based on type of process group with respect to strategic

planning and procurement practices, while no such significant difference exist in

respect of lean practices and close partnership.



which clearly indicates existence of significant association between manufacturing

units grouped on the basis of market coverage and their supply chain practices.

The association between market coverage category of manufacturing units and supply

chain practices segments are shown in Figure 4.25.

Figure 4.25 Market Coverage and Practices- Correspondence Diagram

It can be inferred from the above figure that manufacturing units concentrating

on both the domestic and international markets are associated with the “Highly supply

chain practices units” while the units concentrating on domestic market alone are

associated with “partnership practice units” and those units concentrating on

international market are associated with “Moderate supply chain practices units”.

The relationship between market coverage category of manufacturing units and

supply chain practices factor is shown in Table 4.100.

Table 4.100 ANOVA for Market Coverage and Supply Chain Practices


1.561 0.212


141


units categorized based on market coverage with respect to procurement practices and

lean and close partnership practices.

Mean values for procurement practices of market coverage category have been

portrayed in Table 4.101.

Table 4.101 Mean Values for Procurement of Market Coverage Category

Market Coverage N

1 2


Both 66 3.03


Based on the difference in mean values, two homogenous groups can be

formed. The first group comprises of manufacturing units concentrating on domestic

Market, while the second group consists of manufacturing units concentrating on

international market. Hence, significant difference exists among the two groups of

units categorized based on market coverage in respect of procurement practices. It can

be said that the manufacturing units concentrating on international market have high

level of procurement practices compare to the units concentrating on domestic market.

Mean values for close partnership practices of market coverage category have been

displayed in Table 4.102.

Table 4.102 Mean Value for Close Partnership of Market Coverage Category

Market Coverage N

1 2


Both 66 3.35


Based on the difference in mean values, two homogenous groups can be

formed. The first group consists of units concentrating on International Market, while

the second group consists of manufacturing units concentrating on Domestic Market.

This implies that significant difference exist among manufacturing units grouped on

the basis of market coverage with respect to close partnership practices. It can be

observed that those manufacturing firms concentrating on domestic market command

higher level of performance with respect to close partnership practices compare to

those units concentrating on international market.

142

4.7.7.8 Area of Market


which clearly indicates that there is significant association between manufacturing

enterprises grouped on the basis of area of market and their supply chain practices.

The association between area of market category of manufacturing enterprises and

supply chain practices segments are shown in Figure 4.26.

Figure 4.26 Area of Market and Practices -Correspondence diagram

It can be noted from the above figure that units possessing both southern

region, India and abroad are associated with “Highly supply chain practices units”

while the units possessing regional markets within Pondicherry and Tamil Nadu are

associated with “Partnership practice units”, while those units enjoying merely

national market are associated with “Moderate supply chain practices units”.

The relationship between area of market category of manufacturing enterprises and

supply chain practices factor is shown in Table 4.103.

Table 4.103 ANOVA for Area of Market and Supply Chain Practices


1.561 0.185



the group of units categorized based on area of market with respect to strategic

planning and lean practices, while significant difference exist in respect of

procurement practices and close partnership practices.

143

Mean values for close partnership practices of area of market category of

manufacturing units have been displayed in Table 4.104.

Table 4.104 Mean Values for Close Partnership of Area of Market Category

Area of Market N

1 2




Within Pondicherry and Tamil Nadu 89 3.76

Only export 4 4.00

Based on differences in mean values, two homogenous groups may be formed.

The first group consists of manufacturing units concentrating entirely on India,both

India and abroad, southern India, within Pondicherry and Tamil Nadu, while the

second group consists of those units with market in exclusively foreign market. The

difference is significant at 99 percent confidence level (Table 4.2.31, Significant value

is 0.003). This implies that significant difference exist among manufacturing

enterprises grouped on the basis of area of market in respect of Close Partnership

practices. Those manufacturing units possessing market in the exclusively foreign

market are possessing high level of close partnership practices compare to the units

having market in the Entire country, India and abroad and southern India, within

Pondicherry and Tamil Nadu.



(Table 4.86) which clearly indicates that there is significant association between

manufacturing enterprises categorized based on software usage and their supply chain

practices. Table 4.105 displays the T-test results in respect of manufacturing units

categorized on the basis of software usage.

144

The relationship between software usage category of manufacturing enterprises and

supply chain practices has been portrayed in Table 4.105

Table 4.105 Independent Samples Test for Software Usage and Supply Chain

Practices

Supply Chain Practices Levene's Test for Equality

of Variances t-test for Equality of Means

F Sig. t df Sig. Procurement Practices 1.083 0.299 3.178 253 0.002 Strategic Planning and Lean Practices .012 0.913 3.292 253 0.001 Close Partnership 3.131 0.078 -0.940 253 0.348

The difference in mean value of groups of units using software and the other

units in respect of procurement practices and strategic planning and lean practices is

significant as the value of significance is less than 0.05, whereas there is no such

significant difference in respect of close partnership practices as the significance

value exceeds 0.05 (Table 4.105)


PRACTICES AND PROFILE OF MANUFACTURING INDUSTRIES


two or more set of variables. Results of the analysis explain the individual relationship

existing between two variables and also provide overall relationship that exist

between two or more set of variables. The following section describes canonical

correlation between two sets of variables. First set of variables are supply chain

practices consisting of variables such as procurement practices, Strategic Planning

and Lean Practices and close partnership practices, while the second set of variables

consist of the profile of manufacturing units namely number of employees, type and

nature of industry,capital invested, nature of industry ,nature of business organization

used,process type, market coverage, area of market and useage of software.

Canonical Correlations in respect of supply chain competence with regard to different

profile characteristics of manufacturing units are displayed in Table 4.106.

Table 4.106 Canonical Correlation for Supply Chain Practices

Coef. Std. Err. t P>|t| [95% conf. interval] U1

Procurement .5214024 .1772393 2.94 0.004 .1723567 .8704482 Strategic .7752797 .1984831 3.91 0.000 .3843975 1.166162 Partnership -.7932785 .1751769 -4.53 0.000 -1.138263 -.4482942

V1 Type of Industry .0445531 .0447891 0.99 0.321 -.0436522 .1327585 Employees .0844925 .1486655 0.57 0.570 -.2082815 .3772665 Total capital .0985151 .190248 0.52 0.605 -.2761493 .4731795 Nature of Industry .2544278 .2617928 0.97 0.332 -.2611331 .7699888

145

(Continued…) Coef. Std. Err. t P>|t| [95% conf. interval] Organization Type .3288796 .1879647 1.75 0.081 -.0412881 .6990474 Type of Process .1984334 .1919635 1.03 0.302 -.1796095 .5764763 Mark cover -.2320836 .2589003 -0.90 0.371 -.7419483 .2777811 Mark area .3868071 .1882355 2.05 0.041 .0161059 .7575083 Software -.4379668 .3930709 -1.11 0.266 -1.21206 .3361265

U2 Procurement .8008544 .3046558 2.63 0.009 .2008813 1.400827 Strategic -1.070841 .3411717 -3.14 0.002 -1.742727 -.3989555 Partnership -.3259113 .3011108 -1.08 0.280 -.9189032 .2670806

V2 Coef. Std. Err. t P>|t| [95% conf. interval] Type of Industry .0680523 .0769878 0.88 0.378 -.0835635 .2196681 Employees .323174 .2555404 1.26 0.207 -.1800739 .8264219 Total capital .3837212 .3270164 1.17 0.242 -.2602878 1.02773 Coef. Std. Err. t P>|t| [95% conf. interval] Nature of Industry -1.410223 .4499943 -3.13 0.002 -2.296418 -.5240276 Organization Type -.2078498 .3230916 -0.64 0.521 -.8441294 .4284298 Type of Process -.3379501 .3299652 -1.02 0.307 -.9877663 .3118661 Mark cover .2839423 .4450225 0.64 0.524 -.5924617 1.160346 Mark area .3783767 .3235572 1.17 0.243 -.2588199 1.015573 Software .6555085 .6756478 0.97 0.333 -.6750769 1.986094

U3 Procurement -.7817143 .4543193 -1.72 0.087 -1.676427 .1129983 Strategic .4047841 .5087739 0.80 0.427 -.5971685 1.406737 Partnership -.8680609 .4490329 -1.93 0.054 -1.752363 .016241

V3 Type of Industry -.17920 08 .1148085 -1.56 0.120 -.4052986 .046897 Employees .3778822 .3810758 0.99 0.322 -.3725886 1.128353 Total capital -.5062829 .4876647 -1.04 0.300 -1.466664 .4540983 Nature of Industry .4151982 .6710562 0.62 0.537 -.9063446 1.736741 Organization Type -.2304485 .4818118 -0.48 0.633 -1.179303 .7184064 Type of Process -.2991491 .4920622 -0.61 0.544 -1.26819 .6698924 Mark cover .4115708 .6636419 0.62 0.536 -.8953708 1.718512 Mark area .2194907 .4825062 0.45 0.650 -.7307317 1.169713 Software .7632995 1.007563 0.76 0.449 -1.220942 2.747541

Canonical correlations: 0.4025 0.2478 0.1691 Tests of significance of all canonical correlations Statistic df1 df2 F Prob>F Wilks' lambda .764054 27 710.328 2.5394 0.0000 a Pillai's trace .251996 27 735 2.4963 0.0000 a Lawley-Hotelling trace .288173 27 725 2.5793 0.0000 a Roy's largest root .193321 9 245 5.2626 0.0000 u

146


factors relating to supply chain practices, while the second set consists of nine profile

of manufacturing industries variables of number of employees, type and nature of

industry,capital invested, nature of industry ,nature of business organization

used,process type, market coverage, area of market and useage of software. Based on

these two sets of data, Canonical Correlation has been performed. The Canonical

Correlation coefficient values in respect of these three factors are 0.4025, 0.2478 and

0.1691. Other results displayed in the above table such as df1 value of 27, df2 value

of 710, f value of 2.54, Wilks’s λ value of 0.7641, and p value of 0.001, which is less

than 0.05, reveals that there is significant relationship between the two sets of data. To

predict the overall relationship between these two sets of data, Wilk’s (λ) value should

be deducted from one. From the three canonical function set, the r2 value is 0.24. This

implies that the entire canonical model explains a considerable portion of about 24%

of the variance. Hence, there is a decent positive correlation between the two sets of

data namely, the three supply chain Practices factors and the nine variables relating to

the profile of manufacturing enterprises.

147

4.8 SUPPLY CHAIN PERFORMANCE DIMENSION ANALYSIS

The supply chain performance of manufacturing units are studied with the

help of variables namely, Improvement in order item fill rate, Improvement of defect

rate, Improvement in set-up times, Improvement in Lead time, Improvement in Time

to market, Improvement in stock out situation, Improvement in level of inventory

write off and Improvement in inventory turns. Each variable and its nature of

relevance with Supply Chain Performance are discussed at length in the subsequent

sections.

4.8.1 PRIORITIES OF SUPPLY CHAIN PERFORMANCE

The executives of the manufacturing units in Union Territory of Puducherry

were asked to rate their firm’ supply chain performance level in a five point rating

scale, ranging from very low to very high. The mean values assigned to each of these

variables and the ranking of such variables according to their importance (as inferred

from the mean values) have been displayed in Table 4.107.

Table 4.107 Priorities of Supply Chain Performance

Sl.no Supply chain performance variables Mean value Rank 1 Improvement in inventory turns 3.56 I 2 Order item fill rate 3.49 II 3 Improvement in set-up times 3.49 III 4 Stock out situation 3.27 IV 5 Lead time improvement 3.24 V 6 Time to market (Product development cycle) 3.19 VI 7 Improvement of defect rate 3.10 VII 8 Change in level of inventory write off 3.08 VIII

It can be infer from the above table that the manufacturing units give more

importance to improvement in inventory turns. This signifies the intention of the units

to rotate their inventory in order to minimize the inventory cost. However, the

manufacturing units show least interest for change in level of inventory write off.

4.8.2 FACTORISATION OF SUPPLY CHAIN PERFORMANCE

Factor analysis was applied to condense the number of items or variables into

minimum number of manageable items or variables.

Results of KMO and Bartlett's Test are shown in Table 4.108





df 28

Sig. 0.000

148

Table 4.108 portrays that the KMO value is 0.768 which depicts that the factor

analysis is helpful for this data set. The value of chi-square for Bartlett’s test of

Sphericity is 298.897 and the value of significant is 0.000.

The variance and Eigen value explanation of each factor (Supply Chain performance)

have been shown in Table 4.109.

Table 4.109 Variance Explained By Factor of Supply Chain Performance

Component


Loadings

Total % of

Variance Cumulative

% Total % of Variance Cumulative %

1 2.677 33.463 33.463 2.109 26.360 26.360

2 1.095 13.693 47.156 1.664 20.796 47.156

3 1.035 12.943 60.099

4 .822 10.277 70.376

5 .637 7.957 78.333

6 .619 7.739 86.071

7 .604 7.547 93.618

8 .511 6.382 100.000

Factors having eigen value in excess of one are taken as reduced factors.

These factors now assume the role of actual factors for further analysis. It can be

observed from the above table that Factor Analysis has extracted two factors from

eight variables relating to supply chain performance. The extracted two factors alone

explain 47.156 % of total variance which is quite significant.

The results of Factor Loadings of Supply Chain Performance and the names assigned

to each of such factors are shown in Table 4.110.

Table 4.110 Factor Loading of Supply Chain Performance

Sl.no Supply chain performance Component Supply chain

performance factor 1 2

1 Improvement of defect rate 0.764

Lead Time and Inventory

Performance

2 Change in level of inventory write off 0.706

3 Lead time improvement 0.662

4 Improvement in inventory turns 0.482

5 Stock out situation 0.390

6 Time to market (Product development cycle) 0.374

7 Improvement in set-up times 0.828 Responsiveness Performance 8 Order item fill rate 0.748

149

Of the eight variables studied, six variables are accommodated in first factor

and the remaining two other variables are placed in second factor. The first factor has

been label as “Lead Time and Inventory Performance”, while the second factor has

been designated as “Responsiveness Performance”.

Lead Time and Inventory Performance occupies the important position as this

factor alone explains 26.36 % of total variance. This shows that primary supply chain

performance of all manufacturing unit is lead time and inventory performance, and

manufacturing units vary with each other primarily based on their nature of Lead time

and inventory performance.

4.8.2.1 Lead Time and Inventory Performance The efficiency of performance of any enterprise is strongly determined by its

capacity to manage its inventory and lead time in its various operations. Successful

enterprises invariably manage their inventory effectively. Similarly enterprises which

are minimizing their lead time in operations shall be operating with utmost efficiency.

Since all statements relating to Inventory management and lead time have been

grouped in this factor, this factor has been labeled as “Lead Time and Inventory

Performance Factors”.

4.8.2.2 Responsiveness Performance

The efficiency of operations of any enterprises is immensely affected by the

set-up time needed for its workers to actually start their work during their shift time.

Lower the set up time, maximum shall be the efficiency, as the time lost for set-up

time shall be minimum. Similarly, higher the fill up rate, maximum shall be the

operational efficiency. The factor comprising of these two statements has been aptly

labeled as ‘”Responsiveness Performance Factor”.

4.8.3 RANKING OF SUPPLY CHAIN PERFORMANCE FACTORS

By using factor analysis, the eight supply chain performance variables are

grouped into two factors, and these factors are labeled as lead time and inventory

performance, and responsiveness performance, based on variables loaded under each

factor. Mean values for these supply chain performance factors are displayed in

Table 4.111.

Table 4.111 Strength of Supply Chain Performance

Supply Chain Performance Mean Rank Responsiveness 3.49 I Lead Time and Inventory 3.25 II

150

Of the two supply chain performance factors of lead time and inventory

performance and responsiveness performance, manufacturing units have expressed a

stronger opinion about the latter factor. The mean in respect of this factor is 3.49,

which indicates that this performance factor is highly important for manufacturing

units.

4.8.4 SEGMENTATION OF SUPPLY CHAIN PERFORMANCE

Manufacturing units have been grouped based on their similarities among

these two factors namely lead time and inventory performance and responsiveness

performance oriented supply chain performance. Cluster analysis has been applied to

segment manufacturing units based on on the level of supply chain performance they

possess on lead time and inventory performance and responsiveness performance.

Final cluster centers have been displayed in Table 4.112.


Supply Chain Performance Cluster

1 2 3

Lead Time and Inventory Performance 2.84(III) 2.95(II) 3.80(I)

Responsiveness Performance 2.12(III) 3.45(II) 4.23(I)

Average 2.48 3.2 4.02

It can be observed from the above table that manufacturing units have been

grouped into three clusters. The first cluster is designated as “low performance group”

as the mean value regarding the supply chain performance in respect of this group is

low when compared to the other two groups. The second group has been labeled as

“moderate performance group” as they have a moderate mean value in the middle of

the five point scale. In the third group, mean value in respect of the two performance

factors is as high as four in the five point scale, and hence this group can be referred

to as “high supply chain performance group”.

ANOVA values in respect of the three clusters regarding the supply chain

performance factors are shown in Table 4.113.

Table 4.113 ANOVA

Supply Chain Performance

Cluster Error

F Sig. Mean Square

df Mean

Square df

Lead Time and Inventory

22.931 2 0.239 252 96.046 0.000

Responsiveness 68.347 2 0.211 252 323.200 0.000

151

It can be inferred from the above table that all the three supply chain

performance factors are playing strong role to bifurcate the manufacturing units into

three groups.

A brief description about the three supply chain performance groups of low

supply chain performance units, moderate supply chain performance units and high

supply chain performance units is given below.

4.8.4.1 Low Supply Chain Performance Units

The supply chain performance level of this group is low in respect of all the

supply chain performance related factors of lead time and inventory performance and

responsiveness performance as the mean values are below the three mark in the five

point scale. This cluster ranks the lowest in terms of the lead time and inventory

performance and responsiveness performance, as well as the overall average. 18% of

the 255 manufacturing units surveyed (46 units) constitute the group of low

performing units.

4.8.4.2 Moderate Supply Chain Performance Units

The second cluster of manufacturing units with respect to supply chain

performance factors is designated as “Moderate supply chain performance Units”.

This group has moderate level of supply chain performance on lead time and

inventory performance and responsiveness performance factor, as the mean value of

this group in respect of supply chain performance hover around the three mark in the

five point scale, which is quite moderate. 46% of the 255 manufacturing units

surveyed constitute this moderate group.

4.8.4.3 High Supply Chain Performance Units

The average means score values in respect of the three supply chain

performance factors of this segment is 4.02. Since the mean hover around the four

mark in the five point scale, this group can be designated as “high supply chain

performance group”. This group has high level of lead time and inventory

performance and responsiveness performance. 36 percent of the manufacturing units

surveyed constitute this group.

152


Table 4.114.


Cluster

1 46 18%

2 117 46%

3 92 36%

Valid 255 100%

It can be inferred from the above table that “high supply chain performance

units” group and “moderate supply chain performance units” group account for more

than three-fourth of manufacturing units surveyed (82 percent).

4.8.5 TESTING SUITABILITY OF SUPPLY CHAIN PERFORMANCE SEGMENTATION USING DISCRIMINANT ANALYSIS

The manufacturing units are segregated into three clusters based on their

supply chain performance. The three identified clusters are low supply chain

performance units clusters, moderate supply chain performance units cluster and high

supply chain performance units cluster. 46 percent of the manufacturing units

constitute “moderate supply chain performance units”, while 18 percent of the

manufacturing units make up the “low supply chain performance units”, and 36

percent of the manufacturing units may constitute the “high supply chain performance

units”. The next important issue is to assess whether the segmentation is valid , and

whether each of the clusters significantly vary among each other, and whether the two

supply chain performance factors play a role in segregating manufacturing enterprises

into three clusters. For this purpose, sample stability and cluster classification

reliability has to be verified through Discriminant analysis. The equality of group

means in respect of supply chain performance can be inferred from Table 4.115.


Supply Chain Performance Wilks'

Lambda F df1 df2 Sig.

Lead Time and Inventory 0.567 96.046 2 252 0.000

Responsiveness 0.280 323.200 2 252 0.000

It can be inferred from the above table that Wilks' lambda is low for

responsiveness performance. This implies that there is strong difference in the clusters

in respect of responsiveness performance. The mean values in respect of

responsiveness performance differ significantly among the three segments. The mean

values in respect of responsiveness performance differ significantly among the three

segments. Wilks’ Lambda for lead time and inventory factors is high as there is no

153

significant difference among the first and third segments with respect to the mean

values of lead time and inventory. Similarly, Wilks’ Lambda for responsiveness

performance factors is relatively low implying absence of high difference among the

first and third segments in the mean values of responsiveness performance.


significant. Low significance value implies prevalence of significant difference in

supply chain performance level among the three groups. Based on the above two

facts, it can be concluded that the process of grouping has been completed accurately.

Eigen values and canonical correlation coefficient have been displayed in

Table 4.116.



Variance Cumulative

% Canonical

Correlation

1 3.394 94.8 94.8 0.879

2 0.197 5.5 100.0 0.406

Eigen value in respect of the first discriminant function is very high compared

to the second function. From the three clusters, two canonical correlations are formed

along with two discriminant functions. The canonical correlation gives the measure of

association between discriminant functions and the two supply chain Performance

factors. The canonical correlation among first function and three supply chain

performance factors is very high (0.879), but canonical correlation for the second

function is only 0.406. It can be inferred from Table 4.56 that both the canonical


Wilks Lambda values are displayed in Table 4.117.


Test of Function(s)

Wilks' Lambda

Chi-square Df Sig.

1 through 2 0.190 417.495 4 0.000

2 0.835 45.228 1 0.000

Wilks’ lambda score in respect of the first function is quite low (0.190)

implying that the variables constituting this function (responsiveness performance)

play a vital role in the grouping of manufacturing enterprises into three clusters. The

variables constituting the second factor (lead time and inventory performance) seems

to play a limited role in grouping manufacturing enterprises as the Wilks’ lambda

score in respect of this factor is 0.835, which is quite high. However, the significance

154

values in respect of both the factors is 0.000, which implies that all the two factors

constituting and play a significant role in grouping the units into three clusters. Since

the Wilks’ lambda score is least in respect of the first factor, it can be concluded that

the first factor plays a significant role in categorizing manufacturing enterprises into

three clusters.

Standardized beta values have been portrayed in Table 4.118


Supply Chain Performance Function

1 2

Responsiveness 0.861* -0.509

Lead Time and Inventory 0.421 0.907*



functions. The two domain functions of discriminant analysis along with standardized

beta value are

Z1 =0.861* Responsiveness, Z2 = 0.9.7* Lead Time and Inventory Performance.

Territorial map of supply chain performance has been portrayed in Figure 4.27.


-


155

Symbols used in territorial map

1 - Low supply Chain Performance,

2 -Moderate supply Chain Performance, 3- Highly supply Chain Performance

and *Indicates a group centroid

Figure 4.27 Territorial Map of Supply Chain Performance

It can be inferred from the above figure that responsiveness performance

forming part of the first factor plays a significant role in the formation of first and

second clusters of low supply chain performance and moderate supply chain

performance respectively and the second and third clusters of moderate supply chain

performance and high supply chain performance. Furthermore, responsiveness

performance forming part of the second function plays a significant role in the

formation of the first and third clusters of low supply chain performance and high

supply chain performance.

Group centroids for supply chain performance clusters are shown in Figure 4.28.

Figure 4.28 Group Centroids for Supply Chain Performance Clusters The above group centroids figure indicates that the entire three clusters are




156

The extent of correct classification is shown in Table 4.119. It explains the degree of

success based on the supply chain performance factor.


Performance Cluster


Total

Low

Performance

Moderate

Performance

High

Performance

Count Low Performance Units 46 0 0 46

Moderate Performance Units 0 117 0 117

High Performance Units 0 1 91 92

% Low Performance Units 100.0 .0 .0 100

Moderate Performance Units .0 100.0 .0 100

High Performance Units .0 1.1 98.9 100



observed from the above table that 99.6% of original grouped cases are correctly

classified. 100 percent of low supply chain performance segments and moderate

supply chain performance segments are correctly classified. Furthermore, 98.9 percent

of the high supply chain performance units have been correctly classified. Hence, it

can be said that 98.9 percent of the overall segments are properly segregated. It can be

concluded based on the above discussion that classification of manufacturing units on

the basis of supply chain performance is 99.6% correct.

4.8.6 CHARACTERISTICS OF SUPPLY CHAIN PERFORMANCE

In the previous section, supply chain performance have been classified into

three categories namely low supply chain performance units, moderate supply chain

performance units and high supply chain performance units on the basis of their two

supply chain performance factors. It is most obvious that high supply chain

performance units will display better overall performance. In this section, the

characteristics of supply chain performance segments are discussed using chi-square

test along with correspondence analysis, analysis of variance (ANOVA) and

canonical correlation.

157

Chi-square values along with their level of significance have been portrayed in

Table 4.120.



value Sig. Value Significance or not

1. Type of Industry 26.163 0.245 Significant 2. Number of Employees 10.072 0.434 Not Significant 3. Total Capital Invested 2.824 0.831 Not Significant 4. Supply Chain Position 6.445 0.375 Not Significant 5. Nature of Industry 11.717 0.020 Significant 6. Side of Supply Chain 5.130 0.077 Not Significant 7. Type of Goods Produced 1.943 0.379 Not Significant 8. Type of Business Organization 4.877 0.599 Not Significant 9. Type of Ownership 1.975 0.922 Not Significant 10. Type of Listing 9.988 0.125 Not Significant 11. What kind of Manufacturing 3.784 0.436 Not Significant 12. Manufacturing Pattern 6.434 0.376 Not Significant 13. Type of process 9.877 0.130 Not Significant 14. Annual Sales 12.374 0.261 Not Significant 15. Market Coverage 0.516 0.972 Not Significant 16. Area of Market 12.094 0.147 Not Significant 17. Business years 2.346 0.885 Not Significant 18. Software Usage 0.985 0.077 Not Significant

To understand the characteristics of these three supply chain performance

segments, association among the segments with various manufacturing profile related

variables are analyzed. The chi-square test is applied to test the significance of

association. The chi-square values displayed in the above table reveal that there is a

significant association between manufacturing firms categorized based on type of

Industry and nature of industry and their supply chain performance.

4.8.7 RELATIONSHIP BETWEEN SUPPLY CHAIN PERFORMANCE AND PROFILE OF MANUFACTURING INDUSTRIES


performance segments with the profile attributes of manufacturing enterprises namely,

type of Industry and nature of industry.

The forthcoming section shall throw light on the significant relationship

between manufacturing units categorized based on nature of industry and nature of

industry and their supply chain performance.

4.8.7.1 Type of Industry

The chi-square value as 26.163 and significant value as 0.045 (Table 4.120).

This suggests that there is significant association between manufacturing firms

categorized based on type of Industry and their supply chain performance.

158

The association between type of industry category of manufacturing enterprises and

supply chain performance segments are shown in Figure 4.29.

Figure 4.29 Industry and Performance -Correspondence Diagram

It can be inferred from the above figure that those manufacturing units

grouped on the basis of electronics, Building materials, Plastic, textiles and other

types of industries are associated with the “Highly supply chain performance units”,

while those manufacturing units belonging to Automobile, Agriculture, Furniture and

food industries are associated with “Moderate supply chain performance units” and

those units belonging to Chemical, metal and pharmaceuticals industries are

associated with the “Low supply chain performance units”.

The relationship between type of industry category of manufacturing enterprises and

supply chain performance factor has been displayed in Table 4.121.

Table 4.121 ANOVA for Type of Industry and Supply Chain Performance

Supply Chain Performance F Sig. Lead Time and Inventory 1.659 0.084 Responsiveness 0.997 0.450

It can be observed from the above table that no significant difference exist

among the groups of manufacturing units categorized based on type of Industry with

respect to lead time and inventory and responsiveness.

4.8.7.2 Nature of Industry The chi-square value as 11.717 and significant value as 0.02 (Table 4.120)

which clearly indicates the prevalence of significant association between

manufacturing enterprises categorized based on nature of industry and their supply

chain performance.

159

The association between nature of industry category of manufacturing units and

supply chain performance segments are shown in Figure 4.30.

Figure 4.30 Industry and Performance -Correspondence Diagram

It can be inferred from the above figure that those manufacturing units

operating as large scale industry are associated with the “Highly supply chain

performance units” while those units operating as medium scale industry are

associated with the “Moderate supply chain performance units” and those

manufacturing units operating as small scale industry are associated with the “Low

supply chain performance units”.

The relationship between nature of industry category of manufacturing enterprises and

supply chain performance factor has been depicted in Table 4.122

Table 4.122 ANOVA for Nature of Industry and Supply Chain Performance

Supply Chain Performance F Sig.

Lead Time and Inventory 1.573 0.209

Responsiveness 5.265 0.006

It can be observed from the above table that no significant difference exist

among the groups of manufacturing units categorized on the basis of nature of

Industry with respect to lead time and inventory, while significant difference exist

with respect to responsiveness performance.

160

Mean values in respect of responsiveness performance of firms categorized based on

nature of industry have been displayed in Table 4.123.

Table 4.123 Mean Values for Responsiveness Performance of Industry Category

Nature of Industry

N

1 2



Large Scale 46 3.63


group difference among manufacturing units grouped on the basis of Nature of

Industry with respect to responsiveness performance. The above table indicates that

two homogeneous sub groups can be formed among the three groups of

manufacturing units categorized on the basis of nature of industry in respect of

responsiveness performance. The difference in mean values among the two

homogenous groups of “Medium Scale industry group”, and “Small Scale and Large

Scale industry group” is significant at 99 percent level of confidence(Table 4.122,

Significant value is 0.006). This implies that significant difference exist among

groups of manufacturing units categorized on the basis of Nature of Industry with

respect to responsiveness performance.


PERFORMANCE AND PROFILE OF MANUFACTURING

INDUSTRIES VARIABLES


two or more set of variables. Results of the analysis explain the individual relationship

existing between two variables and also provide overall relationship that exist

between two or more set of variables. The following section describes canonical

correlation between two sets of variables. First set of variables are supply chain

Performance factors namely lead time and Inventory and responsiveness performance,

while the second set of variables consist of profile of manufacturing enterprises

namely, nature of industry and type of Industry.

161

Canonical Correlations in respect of supply chain performance with regard to

different profile characteristics of manufacturing units are displayed in Table 4.124.

Table 4.124 Canonical Correlation for Supply Chain Performance

Coef. Std. Err. t P>|t| [95% conf. interval] U1 Lead time 1.600611 1.335328 1.20 0.232 -1.029114 4.230336 Response -.1049088 .99755 -0.11 0.916 -2.069432 1.859614 V1 Type of Industry .2899692 .2385193 1.22 0.225 -.1797581 .7596966 Nature of Industry .1381909 1.07059 0.13 0.897 -1.970172 2.246554 u2 Lead time .5733108 1.602221 0.36 0.721 -2.58202 3.728641 Response -1.265777 1.196931 -1.06 0.291 -3.622951 1.091397 V2 Type of Industry -.090257 .2861924 -0.32 0.753 -.6538692 .4733552 Nature of Industry 1.356091 1.284569 1.06 0.292 -1.173673 3.885854 Canonical correlations: 0.3452 0.0236 Tests of significance of all canonical correlations Statistic df1 df2 F Prob>F Wilks' lambda 0.8804 4 502 8.255 0.0000 e Pillai's trace .110840 4 504 8.0213 0.0000 a Lawley-Hotelling trace .139014 4 500 8.4886 0.0000 a Roy's largest root .133306 2 252 17.0427 0.0000 u

The above table depicts the results of Canonical Correlation performed on the

basis of the two sets of data. The Canonical Correlation coefficient values in respect

of these two factors are 0.0799 and 0.0667 respectively. Other results displayed in the

above table such as df1 value of 4, df2 value of 502, f value of 0.684, Wilks’s λ value

of 0.8804, and p value of 0.001, which is less than 0.05, reveals that there is

significant relationship between the two sets of data. To predict the overall

relationship between these two sets of data, Wilk’s (λ) value should be deducted from

one. From the three canonical function set, the r2 value is 0.12. This implies that the

entire canonical model explains a considerable portion of about 12% of the variance.

Hence, there is a decent positive correlation between the two sets of data namely, the

three supply chain performance factors and the two variables relating to the profile of

manufacturing enterprises.

162

4.9 ORGANIZATIONAL PERFORMANCE DIMENSION ANALYSIS

The organizational performance of manufacturing units has been studied in

this section with the help of variables such as Improvement in market share, sales

growth, overall product quality, overall competitive position, return on sales, profit

margin, return on investment and average selling price. each variable and its nature of

relevance with organizational performance are discussed in detail in the forthcoming

sections.

4.9.1 PRIORITIES OF ORGANIZATIONAL PERFORMANCE

The executives of the manufacturing units in Union Territory of Puducherry

were asked to rate their firm’ organizational performance level in a five point rating

scale, ranging from very low to very high, and the mean value assigned to each of

these variables and the ranking of such variables according to their importance (as

inferred from the mean values) are shown in Table 4.125.

Table 4.125 Priorities of Organizational Performance

Sl.no Organizational Performance Variables Mean Value Rank 1 Overall product quality 3.55 I 2 Sales growth 3.27 II 3 Return on sales 3.26 III 4 Profit margin 3.21 IV 5 Return on investment. 3.20 V 6 Overall competitive position 3.15 VI 7 Average selling price 3.11 VII 8 Market share 2.99 VIII

It can be inferred from the above table that manufacturing units give more

importance to overall product quality to enhance their organizational performance.

This implies that the manufacturing units have realized that superior quality of

products can alone enable them to attain excellence in the market. It can further be

noted that the manufacturing units have shown least interest in the maintenance of

market share for the sake of achieving excellence in the market.

4.9.2 FACTORIZATION OF ORGANIZATIONAL PERFORMANCE

Factor analysis was applied to condense the variables or items into minimum

number of manageable items or variables. Factor Analysis has been done with the two

statistical tests of Bartlett’s test and KMO test. The Kaiser-Meyer-Olkin (KMO) test

of sampling adequacy signifies the proportionate variance of variables or items which

may be caused through new factors. KMO value in excess of 0.50 reveals that factor

analysis is absolutely apt for the particular data set.

163

KMO and Bartlett's Test results are depicted in Table 4.126.





df 28

Sig. 0.000

The KMO value of 0.832 implies that the factor analysis applied for this data

is valid. The significance value being less than 0.01 implies that the value is

significant at 99 % level of confidence. The chi square value for Bartlett’s test of

Sphericity is 298.897. High Chi-square value denotes that the variables have been

aptly factored. Principal Component Analysis was used for extraction purpose, and

varimax rotation is used as the standard rotation. Factors having greater than one as

Eigen value are taken as reduced factors which now use as new factors for future

analysis.

The variance and Eigen value explanation of each factor of organizational

performance have been displayed in Table 4.127.

Table 4.127 Variance Explained By Factor of Organizational Performance

Component


Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 3.590 44.880 44.880 2.317 28.961 28.961

2 0.952 11.903 56.783 2.226 27.821 56.783

3 0.807 10.092 66.874

4 0.730 9.119 75.994

5 0.610 7.629 83.623

6 0.525 6.564 90.187

7 0.458 5.726 95.912

8 0.327 4.088 100.000

Factors having Eigen value in excess of one are taken as reduced factors

which shall become the new kind of factors for further analysis. Using factor analysis,

the variables have been categorized into two factors. These two factors have been

labeled as Financial Performance and Marketing Performance. These two factors

explain 56.783 % of total variance which is quite significant.

164

Variables included on each organizational performance along with their loadings have

been displayed in Table 4.128.

Table 4.128 Factor Loading of Organizational Performance

Sl.no Organizational Performance Component Organizational Performance

Factor 1 2

1 Return on investment. 0.844

Financial Performance 2 Return on sales 0.781

3 Profit margin 0.606

4 Average selling price 0.502

5 Overall product quality 0.486

6 Sales growth 0.853 Market Performance

7 Market share 0.769

8 Overall competitive position 0.558

It can be observed from the above table that two factors have been formed by

integrating four variables into each of the two factors. The first factor has been

labeled as “Marketing Performance”, while the second factor has been designated as

“Financial Performance”.

The organizational performance of any enterprise depends on the efficiency

with which it applies its financial resources in profitable avenues so as to enhance the

financial efficiency of the enterprise. In addition, it is inevitable for enterprises to

have effective marketing machinery, to ensure that the marketing efficiency of the

enterprise is at the optimum level.

4.9.2.1 Financial Performance

The financial performance of an enterprise can be judged from the financial

results, which may be inferred from its financial statements of Profit and Loss

Account and Balance Sheet. Parameters like Return on Investment (ROI), Return on

Sales, Profit Margin, and Contribution indicate the financial and Operational results

of the enterprise. The financial performance of the 255 manufacturing units studied

has been represented in Table 4.128, which also contains the variables of factor with

the item loadings.

4.9.2.2 Marketing Performance

The next important aspect of performance of enterprises is marketing

performance. It is absolutely indispensable for any manufacturing enterprise to

effectively market its products to maintain its market share as well as capture new

markets. In this Endeavour, quality of the products manufactured plays a remarkable

165

role. The marketing efficiency of the business unit can be observed from the trends in

its turnover and market share. The different variables included in Marketing

Performance are sales growth, market share, overall competitive position and overall

product quality.

4.9.3 RANKING OF ORGANIZATIONAL PERFORMANCE FACTORS

By using factor analysis, the eight variables indicating the organizational

performance of a manufacturing unit are factored into two categories, namely

“Financial Performance” and “Marketing Performance” based on variables loaded

under each factor.

Mean values for the two organizational performance factors have been displayed in

Table 4.129.

Table 4.129 Strength of Organizational Performance

Organizational Performance Mean Rank Marketing Performance 3.2382 I Financial Performance 3.1941 II

It can be inferred from the above table that manufacturing units consider both

financial performance and marketing performance as equally important for boosting

their efficiency.

4.9.4 SEGMENTATION OF ORGANIZATIONAL PERFORMANCE

Cluster Analysis has been employed to group manufacturing units studied into

clusters based on their resemblance to the two factors of financial performance and

marketing performance.

Final cluster centers have been displayed in Table 4.130.


Organizational Performance Cluster

1 2

Financial Performance 2.65(II) 3.74(I)

Marketing Performance 2.69(II) 3.79(I)

Average 2.67 3.77

Based on the data displayed in the above table, manufacturing enterprises have

been clustered into two groups. The first cluster may be designated as “Average

performance organization group” as the mean value of the components of this cluster

is moderately low when compared to the other cluster. The second cluster can be

labeled as “High Performance organization group” as the mean value indicating the

166

organisational performance of the components of this group hover around the four

mark, which is high in the five point scale.

The ANOVA values in respect of the three clusters of manufacturing enterprises

regarding the two organizational performance factors have been shown in

Table 4.131.

Table 4.131 ANOVA

Organizational

Performance

Cluster Error

F Sig. Mean

Square df

Mean

Square df

Financial Performance 76.528 1 0.299 253 256.064 0.000

Marketing Performance 77.953 1 0.311 253 250.800 0.000

The ANOVA values suggest that the two organizational performance factors

play a significant role in bifurcating the manufacturing units into three clusters. The

mean values of these three groups of manufacturing enterprises in respect of

organizational performance factors differ significantly. A brief description of the two

clusters of “Average Performance Units” and “High Performance Units” clusters have

been discussed in the forthcoming sections.

4.9.4.1 Average Performance Units

The organizational performance level of manufacturing units comprising of

this group is moderate. The mean values in respect of financial and marketing

performance of manufacturing firms comprising this group hover around the three

mark in the five point scale, which suggest that the performance level of this group of

units is quite moderate. A shade over half of the manufacturing units surveyed (128

out of 255) constitute this group. This shows that 50% of the manufacturing units are

“Average Performance Units”.

4.9.4.2 High Performance Units

The mean in respect of the manufacturing units comprising this segment

regarding the two performance factors is 3.77. As the mean value hover around the

four mark in five point scale, which is quite high, this group of manufacturing firms

have been classified as “High performance group”. This group of manufacturing units

exhibit high level of financial and marketing efficiency. Almost 50 percent of the

manufacturing units constitute this group.

167


Table 4.132.


Cluster

1 128 50%

2 127 50%

Valid 255 100%

The above table displays the number of manufacturing units present in each

cluster. It is worthy to note that both the groups consist of almost identical number of

manufacturing units.

4.9.5 TESTING SUITABILITY OF ORGANIZATIONAL PERFORMANCE SEGMENTATION USING DISCRIMINANT ANALYSIS

The manufacturing units are segregated into two clusters based on the level of

organizational performance of manufacturing units. The two identified clusters are

average performance units’ cluster, and high performance units cluster. Almost equal

number of firms constitutes each of these two clusters. The next important task is to

evaluate whether the formation of clusters are genuine and differ significantly among

each other. sample constancy and classification of cluster reliability needs to be

ascertained to ensure that all the organizational performance factors play a decisive

role in segregating the manufacturing units into two clusters. Discriminant Analysis

may be used for this purpose.

Table 4.133 depicts the equality of group means in respect of organizational

performance.


Organizational performance

Wilks' Lambda

F df1 df2 Sig.

Financial Performance 0.497 256.064 1 253 0.000

Marketing Performance 0.502 250.800 1 253 0.000


financial performance factor. This implies that there is a strong difference in group

between the organizational performance under the stated financial performance. The

mean values in respect of financial and marketing performance significantly differ

among the two segments.

Wilks’ Lambda for financial performance and marketing performance factors

is high. Hence, it can be said that there is no big difference among the first and second

segments regarding the mean values in respect of financial and marketing

168

performance likewise Wilks’ Lambda for financial performance and marketing

performance factors is comparatively high.


significant. Low value of significance implies that there is significant difference in

mean of organizational performance level between the two groups. Based on the

above two facts it can be clearly observed that segmentation is good and there exists a

significant difference in group.

The next step is to ascertain the Eigen Values and Canonical correlation coefficient.

These calculated values are displayed in Table 4.134.



Variance Cumulative

% Canonical

Correlation

1 1.607 100.0 100.0 0.785

Large Eigen value implies that the mean values are largely dispersed while

small Eigen values implies low spread of the mean values. The Eigen value in respect

of the first discriminant function is more compared to the second function. For the

three clusters, two canonical correlations are formed along with two discriminant

functions. The canonical correlation gives the measure of association between

discriminant functions and the two organizational performance factors. The canonical

correlation among the first function and the two organizational performance factors is

high as 0.822, whereas canonical correlation for second function is 0.666.

Table 4.135 display results of the Wilks' Lambda values in respect of the two

organizational performance factors. It can be observed from the table that the Wilks'

Lambda value is significant. The canonical correlation value is as high as 0.785.

Large Eigen value implies that the mean values are largely dispersed while small

Eigen values implies low spread of the mean values. The canonical correlation gives

the measure of association between discriminant functions and the two organizational

performance factors. It can be observed from Table 4.135 that the Wilks' Lambda

value is significant.


Test of Function(s) Wilks' Lambda Chi-square df Sig.

1 0.384 241.451 2 0.000

It can be observed from the above table that the Wilks' Lambda value is

significant. Wilks' lambda value in respect of the single discriminant function formed

169

is 0.384 which shows that means of the groups is different regarding financial and


The Chi-square value of Wilks' lambda and the df value and the corresponding

significance value of 0.000 suggest that the process of clustering the manufacturing

units into two groups has been done aptly and both financial and marketing factors

have contributed ideally to the grouping of manufacturing enterprises into two

clusters.

Standardized beta values are shown in Table 4.136.


Organizational Performance

Function

1

Financial Performance 0.794

Marketing Performance 0.785

The above matrix table shows that a solitary function have been formed from

the two clusters. The characteristics of the units comprising the groups can be

explained through this solitary function. The single function derived from

discriminant analysis along with standardized beta value is

Z1 = 0.794* Financial Performance + 0.785* Marketing Performance.

The extent of correct classification of the manufacturing units has been displayed in

Table 4.137. Degrees of success based on the organizational performance factor are



Organizational

Performance Cluster


Total Low Performance

Moderate Performance

High Performance

Count Average Performing Organization

128 0 128 128

High Performing Organization

0 127 127 0

% Average Performing Organization

100.0 0.0 100.0 100.0

High Performing Organization

0.0 100.0 100.0 0.0



observed that 100% of average performing organization segments is precisely

classified and 100% of the high performing organization units have been exactly

170

classified. This implies that the classification of manufacturing units on the basis of

organizational performance is 100% accurate.

4.9.6 CHARACTERISTICS OF ORGANIZATIONAL PERFORMANCE In the previous section, manufacturing units have been classified into two

categories namely “Average performance units” and “high performance unit on the

basis of their organizational performance. It is obvious that the organizational

performance of the “high performance units” will be quite high. The forthcoming

sections shall throw light on the characteristics of clusters formed based on

organizational performance of manufacturing units.

Chi-square test values along with their level of significance have been displayed in

Table 4.138.


S. No Variable Chi-

Square value

Sig. Value Significance

or not

1. Type of Industry 13.516 0.261 Not Significant

2. Number of Employees 10.741 0.057 Not Significant

3. Total Capital Invested 17.828 0.000 Significant

4. Supply Chain Position 2.184 0.535 Not Significant

5. Nature of Industry 9.841 0.007 Significant

6. Side of Supply Chain 2.184 0.535 Not Significant

7. Type of Goods Produced 1.742 0.187 Not Significant

8. Type of Business Organization 7.165 0.007 Significant

9. Type of Ownership 1.975 0.578 Not Significant

10. Type of Listing 7.164 0.067 Not Significant

11. kind of Manufacturing 2.146 0.342 Not Significant

12. Manufacturing Pattern 1.417 0.702 Not Significant

13. Type of process 4.886 0.182 Not Significant

14. Annual turnover 15.892 0.007 Significant

15. Market Coverage 15.379 0.000 Significant

16. Area of Market 16.385 0.003 Significant

17. Business years 4.555 0.207 Not Significant

18. Software Usage 6.932 0.008 Significant To understand the characteristics of these two clusters segmented based on

organizational performance, association among the segments with various

manufacturing profile related variables are analyzed. The chi-square test is applied to

test the significance of association. The chi-square values displayed in the above table

reveal that manufacturing enterprises grouped on the basis of total capital invested,

nature of industry, type of business organization, annual turnover, market coverage,

171

area of market and years of existence of the units have significant association with

organizational performance segments.

4.9.7 RELATIONSHIP BETWEEN ORGANIZATIONAL PERFORMANCE AND PROFILE OF MANUFACTURING INDUSTRIES

Chi-square analysis shows significant association between organizational

performance segments and the units grouped on the basis of the variables namely,

total capital invested, nature of industry, type of business organization, annual

turnover, market coverage, area of market and years of existence of the business firm.

Hence in this section has probing in a detail analysis of nature of relationship among

significant manufacturing industries profile variables and organizational performances

are discussed in detail.

4.9.7.1 Total Capital Invested The chi-square value as 17.828 and significant value as 0.000 (Table 4.138).

This reveals that there is significant association among manufacturing units grouped

based on total capital invested and their organizational performance.

The relashionship between capital invested category of manufacturing units and

organizational performance factor is displayed in Table 4.139.

Table 4.139 ANOVA for Capital Invested and Organizational Performance

Organizational Performance F Sig. Financial Performance 6.144 0.000 Marketing Performance 9.196 0.000

It is observed from the above table that significant difference exists among the

groups of manufacturing units categorized on the basis of total capital invested in

respect of financial performance and marketing performance.

Mean values for financial performance of capital invested category are shown in

Table 4.140.

Table 4.140 Mean Values for Financial Performance of Capital Invested Category


1 2




More than 1 Crores to 50 Crores 60 3.39

Post hoc analysis is carried out with Duncan method to understand inter group

difference among total capital Invested with respect to financial performance. The

above table indicates that two homogeneous sub groups can be formed among the

172

four groups of manufacturing units categorized on the basis of total capital invested in

respect of financial performance. The first group consist of manufacturing units with

capital investment of less than 50 Lakhs, while the second group shall consist of

manufacturing units with capital investment of 50 Lakhs to 1 Crore, More than 50

Crores and 1 Crore to 50 Crores. The mean values in respect of these two groups

significantly differ at 99 percent level of confidence (table 4.139, Significant value is

0.000). This implies that the two groups formed based on capital investment, differ

significantly with respect to financial performance.

Mean values for marketing performance of total capital invested are shown in

Table 4.141.

Table 4.141 Mean Values for Marketing Performance of Total Capital Invested


1 2




More than 1 Cr to 50 Cr 60 3.49

It can be observed from the above table that two groups can be formed out of

the manufacturing units with four levels of capital investment. The first roup shall

consist of units with capital investment of less than 50 lakhs, while the second group

shall consist of units with capital investment of 50 Lakhs to 1 Crore, more than 50

Crores and 1 Crore to 50 Crores. The mean values in respect of these two groups

significantly differ at 99 percent level of confidence (Table 4.139, Significant value is

0.000). Hence, it can be said that there is significant difference in the units grouped on

the basis of capital investment with respect to marketing performance.

4.9.7.2 Nature of Industry The chi-square value as 9.841 and significant value as 0.007 (Table 4.138)

which clearly indicates existence of significant association between nature of Industry

and organizational performance of manufacturing units.

The relationship between industry category of manufacturing units and organizational

performance factor is shown in Table 4.142.

Table 4.142 ANOVA for Nature of Industry and Organizational Performance

Organizational performance F Sig. Financial Performance 8.985 0.000 Marketing Performance 10.757 0.000

173


groups of manufacturing units categorized on the basis of nature of industry with

respect to financial performance and marketing performance.

Mean values for financial performance of industry category of manufacturing firms

have been displayed in Table 4.143.

Table 4.143 Mean Values for Financial Performance of Industry category


1 2 3



Large Scale 46 3.54

The difference in mean values among the three homogenous groups of

medium scale, small scale and large scale group” is significant at 99 percent level of

confidence (Table 4.142, Significant value is 0.000). This implies that significant

difference exist among groups of manufacturing units categorized on the basis of

nature of Industry with respect to financial performance.

Mean values for marketing performance of industry category of manufacturing firms

are displayed in Table 4.144.

Table 4.144 Mean Value for Marketing Performance of Industry category


1 2



Large Scale 46 3.70

Two groups have been formed based on the mean values assigned to

marketing performance. The first group consists of manufacturing units operating in

medium scale and small Scale, while the second group consist of units operating in

large Scale. This implies that significant difference exist among the units grouped on

the basis of nature of industry with respect to marketing performance.



(Table 4.138) clearly indicates that there is significant association between type of

business organization and organizational performance of manufacturing units.

174

The relationship between type of business organization category of manufacturing

enterprises and organizational performance factor has been portrayed in Table 4.145.

Table 4.145 ANOVA for Business Organization and Organizational Performance



the groups of manufacturing units categorized on the basis of type of business

organization in respect of financial performance, while significant difference exist

regarding marketing performance.

Mean values for marketing performance of type of business organization category of

manufacturing enterprises are displayed in Table 4.146.

Table 4.146 Mean Values for Marketing Performance of Business Organization


1 2 3


Partnership 70 3.13



It can be inferred from the above table that significant difference exist among

the groups of manufacturing units categorized on the basis of type of business

organization in respect to marketing performance.

4.9.7.4 Annual Sales The chi-square value as 15.892 and significant value as 0.007 which clearly

indicates existence of significant association between annual sales and organizational

performance of manufacturing units.

The relationship between annual sales category of manufacturing enterprises and

organizational performance factor is highlighted in Table 4.147.

Table 4.147 ANOVA for Annual Sales and Organizational Performance



groups of manufacturing units categorized on the basis of annual sales in respect of

financial performance and marketing performance.

175

Mean values for financial performance of annual sales category of manufacturing

enterprises are portrayed in Table 4.148.

Table 4.148 Mean Values for Financial Performance of Annual Sales Category

Annual Sales N

1 2 3

Less than 50 Lakhs to 1 Crore 55 2.92

More than 1 Crore to 3 Crores 74 3.13






groups of manufacturing units categorized on the basis of annual sales in respect to

financial performance.

Mean values for marketing performance of annual sales category of manufacturing

enterprises are depicted in Table 4.149.

Table 4.149 Mean Values for Marketing Performance of Annual Sales Category

Annual Sales N

1 2 3

Less than 50 Lakhs to 1 Crore 55 2.92

More than 1 Crore to 3 Crores 74 3.02






groups of manufacturing units categorized on the basis of annual sales with respect to




clearly indicates significant association between manufacturing enterprises grouped

on the basis of market coverage and their organizational performance.

176

The relationship between market coverage category of manufacturing enterprises and

organizational performance factor is portrayed in Table 4.150.

Table 4.150 ANOVA for Market Coverage and Organizational Performance



groups of manufacturing units categorized on the basis of market coverage with

respect to financial performance and marketing performance.

Mean values for financial performance of market coverage category of manufacturing

units are highlighted in Table 4.151.

Table 4.151 Mean Value for Financial Performance of Market Coverage

Market Coverage N

1 2


Both 66 3.37


Two homogenous groups can be formed based on the response of the units

regarding financial performance. The first group shall consist of units concentrating

on domestic market and both domestic and international market, while the other group

shall consist of units with only international market. Significant difference exists

among the units categorised based on market coverage with respect to financial

performance at 99 percent level of confidence (Table 4.150, Significant value is

0.006).

Mean values for marketing performance of market coverage category of

manufacturing enterprises are displayed in Table 4.152.

Table 4.152 Mean Value for Marketing Performance of Market Coverage

Market Coverage N

1 2



Both 66 3.62

Two homogenous groups can be formed based on the response of

manufacturing enterprises regarding marketing performance. The first group shall

consist of manufacturing enterprises enjoying merely domestic market and Both-

domestic and International Market, while the second group consist of units enjoying

177

purely international market. There is significant difference among the units

categorized based on market coverage with respect to marketing performance at 99

percent level of confidence (Table 4.150, significant value is 0.000).

4.9.7.6 Area of Market The chi-square value as 16.385 and significant value as 0.003 (Table 4.138)

which clearly indicates the presence of significant association between manufacturing

enterprises grouped on the basis of area of market and their organizational

performance.

The relationship between area of market category of manufacturing enterprises and

organizational performance factor is displayed in table 4.153.

Table 4.153 ANOVA for Area of Market and Organizational Performance

Organizational Performance F Sig.

Financial Performance 4.491 0.002

Marketing Performance 10.393 0.000


groups of manufacturing units categorized on the basis of area of market with respect

to financial performance and marketing performance.

Mean values for financial performance of area of market category of manufacturing

industry are depicted in Table 4.154.

Table 4.154 Mean Values for Financial Performance of Area of Market

Area of Market N

1 2

Within Pondicherry and Tamil Nadu

89 2.96




Only export 4 3.81

The difference in mean values among the two homogenous groups of Within

Pondicherry and Tamil Nadu, Entire India, Southern Region, India and abroad and

Only export group” is significant at 99 percent level of confidence (Table 4.153,

Significant value is 0.002).

178

Mean values for marketing performance of area of market category are shown in

Table 4.155.

Table 4.155 Mean Values for Marketing Performance of Area of Market

Area of Market N

1 2 3

Within Pondicherry & Tamil Nadu 89 2.93




Only export 4 4.18

The difference in mean values among the three homogenous groups regarding

marketing performance is significant at 99% level of significance. The first group

shall consist of manufacturing units concentrating on Entire India, South India and

Within Pondicherry and Tamil Nadu, while the second group consists of units

focusing on India and abroad, while third group consist of those units concentrating

on only export market. Hence, it can be said that significant difference exist among

manufacturing enterprises grouped on the basis of area of market in respect to


4.9.7.7 Software Usage The chi-square value as 6.932 and significant value as 0.008 (Table 4.138)

clearly indicates existence of significant association between manufacturing units

categorized based on software usage and their organizational performance.

The relationship between software usage category of manufacturing enterprises and

organizational performance factor is highlighted in Table 4.156.

Table 4.156 Independent Samples Test for Software Usage and Organizational

Performance

Organizational

Performance

Levene's Test for Equality of

Variances

t-test for Equality of

Means

F Sig. t df Sig. (2-tailed)

Financial Performance 1.125 0.290 2.049 253 0.042

Marketing Performance 0.070 0.791 4.101 253 0.000

The difference in mean value of groups of units using software and the other

units in respect of both marketing and financial performance is significant, which is

indicated by the significance value being less than 0.05 in both cases.

179

4.9.7.8 CANONICAL CORRELATION BETWEEN ORGANIZATIONAL

PERFORMANCE AND PROFILE OF MANUFACTURING

INDUSTRIES

Canonical correlation was applied to predict the shared relationship

among two or more set of variables. Results of the analysis explains the individual

relationship existing between two variables and also provide overall relationship that

exist between two or more set of variables. The following section describes canonical

correlation between two sets of variables. First set of variables are organisational

performance (financial and marketing performance), while the second set of variables

consist of the variables relating to profile of the units surveyed such as capital

invested, nature of industry,business organization, annual sales, market coverage, and

area of market.

Canonical Correlations for organizational performance are shown in Table 4.157.

Table 4.157 Canonical Correlation for organizational performance

Coef. Std. Err. T P>|t| [95% conf. interval] U1 Financial .1669379 .2435787 0.69 0.494 -.3127532 .646629 Marketing 1.164463 .2400939 4.85 0.000 .691635 1.637292 V1 Capital .1724907 .1914868 0.90 0.369 -.2046133 .5495946 Nature of Industry -.1605726 .2542105 -0.63 0.528 -.6612013 .3400562 Organization Type .135366 .1936326 0.70 0.485 -.2459639 .5166959 Annual sales .2543938 .1368668 1.86 0.064 -.0151446 .5239321 Mark cover .1825937 .261822 0.70 0.486 -.3330249 .6982123 Mark area .3749628 .1885188 1.99 0.048 .0037038 .7462218 u2 Financial -1.644574 .6874132 -2.39 0.017 -2.99833 -.290819 Marketing 1.139689 .6775785 1.68 0.094 -.1946983 2.474077 V2 Capital -.7687164 .5404024 -1.42 0.156 -1.832957 .2955238 Nature of Industry 1.156218 .7174175 1.61 0.108 -.2566261 2.569063 Organization Type .2795381 .5464584 0.51 0.609 -.7966284 1.355704 Annual sales -.0540176 .3862573 -0.14 0.889 -.8146925 .7066573 Mark cover .5606077 .7388984 0.76 0.449 -.8945402 2.015756 Mark area -.2961867 .5320264 -0.56 0.578 -1.343931 .7515581 Canonical correlations: 0.3931 0.1498 Tests of significance of all canonical correlations Statistic df1 df2 F Prob>F Wilks' lambda .826515 12 494 4.1148 0.0000 e Pillai's trace .176952 12 496 4.0120 0.0000 a Lawley-Hotelling trace .205706 12 492 4.2170 0.0000 a Roy's largest root .182759 6 248 7.5541 0.0000 u

e = exact, a = approximate, u = upper bound on F

180

Two sets of data have been taken for this study. The first set contains the two

factors relating to organizational performance, while the second set consists of the six

profile of manufacturing industry variables. Based on these two sets of data,

Canonical Correlation has been performed. The Canonical Correlation coefficient

values in respect of these two factors are 0.3931 and 0.14981 respectively. Other

important results inferred from the above table relate to df1 value of 12, df2 value of

494, F value of 4.1148, Wilks’s λ value of 0.8265, and p value of 0.001, which is less

than 0.05, reveals that there is significant relationship between the two sets of data. To

predict the overall relationship between these two sets of data, Wilk’s (λ) value should

be deducted from one. From the two canonical function set, the r2 value is 0.1735.

This implies that the entire canonical model explains a considerable portion of about

17% of the variance. Hence, there is a decent positive correlation between the two sets

of data namely, the two organizational performance factors and the six variables

relating to the profile of manufacturing enterprises.

181

4.10 CAUSAL MODEL AND HYPOTHESES TESTING

4.10.1 INTRODUCTION TO STRUCTURAL EQUATION MODELING (SEM)

SEM framework has been used to test the proposed conceptual model. SEM

consists of two components. The first component relates to the using of Measurement

Model or confirmatory factor analysis (CFA) which is employed to identify the items of

each construct or variable and also evaluate reliability and validity of each variable or

construct. The second component relates to structural model or path analysis, which is

employed to examine the causal relationship among constructs or variables. Since the

validity and reliability results of the data have been dealt in detail in the chapter on

research methodology, this section shall confine to the sub models of CFA and path

analysis utilizing the LISREL 8.72 software. LISREL (Linear Structural Relations)

software was developed by Joreskog and Sorbomin 1989 to use the SEM to explore the

relationships among latent and observed variables.

chi-square goodness of fit results may be used to test whether the data available

fit into the proposed conceptual model with the estimated model. There are three kinds

of fit index measures. The absolute fit measures evaluates the overall conceptual model

fit, while incremental fit measures assesses the conceptual model with null model, and

parsimonious fit measures assesses the minimum number of estimate needed to attain a

model fit.

Goodness of fit index of chi-square is the widely used fit test that estimates

variation among the observed data covariance matrix with estimated or fitted

covariance matrix. However, as chi-square goodness of fit test is sensitive to sample

size and subject to type II error, some researchers like Joreskog and Sorbomin (1989)

suggested that chi-square test results have to be inferred with caution, for which

Normed chi-square (χ2/df) is employed. The value of this Normed chi-square should

not exceed 3 (Hair et el 2010).

Many other fit indexes are available to test the validity of SEM results. Some of

such indexes are Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI),

Comparative Fit Index (CFI), Normed Fit Index (NFI), Root Mean Square Residual

(RMR) and Root Mean Square Error of Approximation (RMSEA). GFI specifies

comparative quantity of co-variance and variance collectively explained through the

proposed model. GFI value should be in the range of 0 to 1.

182

AGFI is a kind of goodness of fit index which indicates desirable value of

degree of freedom, while NFI compares the proposed conceptual model and null model

of the study. CFI evaluates the absolute fit index of the proposed model with

independence model. The value of GFI, AGFI, CFI, and NFI should range from 0.80 to

0.89 to render the model as absolutely acceptable and if the value exceeds 0.90, the

model shall be considered as very good fit (Hair et al., 2010). RMSEA estimates the

error in the population and it is based on degree of freedom. The value of RMSEA

should not exceed 0.08 (Hair et al., 2010).

4.10.2 PROPOSED CONCEPTUAL MODEL

The proposed conceptual model has been portrayed in Figure 4.31. The five

constructs presented in the conceptual model are employed to test the causal

relationship among supply chain management components, supply chain performance

and organizational performance of manufacturing firms located in UT of Puducherry.

Figure: 4.31 Conceptual Model

4.10.3 SUB- CONFIRMATORY FACTORY ANALYSIS (CFA) OR

MEASUREMENT MODEL

CFA or measurement model in respect of each dimension namely, supply chain

concerns, supply chain competence, supply chain practices, supply chain performance

and organizational performance of manufacturing enterprises are shown from

Figure 4.32 to Figure 4.38.

183

Figure 4.32 CFA of Supply Chain Concerns

Figure : 4.33 CFA of Supply Chain Practices

184

Figure 4.34 CFA of Supply Chain Performance

Figure 4.35 CFA of Organizational Performance

185

Figure: 4.36 CFA of Supply Chain Competence

186

Figure: 4.37 CFA of Supply Chain Management Components

187

Figure 4.38 CFA of Supply Chain Management Components and Supply Chain

Performance

188

4.10.4 OVERALL CFA OR MEASUREMENT MODEL

Measurement model for all constructs are tested and validated through CFA.

The overall measurement model has been portrayed in Figure 4.39, displaying the

reliability of the observed items and scale used to measure the unobserved constructs or

latent variables.

Figure: 4.39 Overall Tested Measurement Model

189

Results of overall Confirmatory Factor Analysis (CFA) are depicted in Table 4.158.

Table 4.158 Results of Overall CFA (Measurement Model)

Table Results of Measurement Model (Confirmatory Factor Analysis)

Results of Validity and Reliability Test Value

VARIABLES Factor estimat

e

t - value

Error variance

R2

Composite Reliability

Average Variance Extracted

(AVE)

SUPPLY CHAIN CONCERNS

0.73

0.47

Con1: Lack of sophisticated information system 0.46 14.29 1.15 0.15 Con2: Lack of ability in managing Supply chain inventories 0.41 12.55 1.21 0.12 Con3: Lack of cooperation among supply chain members 0.59 17.88 0.91 0.28 Con4: Lack of trust among supply chain members 0.60 18.03 0.99 0.27 Con5: Lack of interest among your suppliers or customers 0.69 20.23 0.86 0.35 Con6: Competition from other supply chains 0.57 17.49 0.99 0.25 Con7: Your firm’s lack of leverage within your supply chain 0.67 19.94 0.93 0.33 Con8: Your suppliers’ geographical distance 0.72 20.50 1.03 0.34 Con9: Your customers’ geographical distance 0.67 20.12 1.17 0.28

SUPPLY CHAIN COMPETENCE

Com1 : The ability to fill orders with improved accuracy 0.61 22.47 1.03 0.27

0.84

0.46

Com2 :The ability to forecasting sales with greater accuracy 0.39 15.17 0.83 0.15 Com3 :The ability to issue notice on shipping delays in advance 0.41 16.10 0.97 0.15 Com4 :The ability to respond to a request in a timely manner 0.45 17.00 0.71 0.22 Com5 :The ability to make high quality products 0.52 19.82 0.98 0.22 Com6 :The ability to deliver high-quality services 0.50 18.88 0.86 0.22 Com7 :The ability to respond to the needs of key customers 0.59 22.16 0.88 0.29 Com8 :The ability to work with key suppliers 0.47 17.79 0.88 0.20 Com9 :The ability to manage supply chain inventory 0.58 22.11 0.89 0.27 Com10 :The ability to meet a delivery on promised date 0.41 15.78 0.87 0.16 Com11 :The ability to enhance supply chain’s position in terms of integrity 0.44 17.13 0.77 0.20 Com12 :The ability to enhance supply chain’s position in terms of social 0.48 18.51 0.96 0.19 Com13 :The ability to design low-pollution production process 0.53 20.19 0.91 0.23 Com14 :The ability to design low-pollution delivering process 0.58 22.05 0.81 0.30

SUPPLY CHAIN PRACTICES

Par1: Close partnership with suppliers 0.55 22.63 1.13 0.21

0.73

0.55

Par 2:Close partnership with customers 0.54 21.40 1.10 0.21 Par3:Just in time (JIT) supply 0.50 20.22 1.15 0.18 Par4:Strategic planning 0.59 23.76 0.97 0.26 Par5:Supply chain benchmarking 0.74 28.94 0.94 0.37 Par6:Many suppliers 0.74 29.29 1.16 0.32 Par7:Holding safety stock 0.69 27.52 0.93 0.34 Par8:Subcontracting 0.55 22.35 1.11 0.21 Par9:E-procurement 0.66 25.99 1.18 0.27 Par10:Outsourcing 0.66 25.92 1.06 0.29 Par11:Third Party Logistics (3PL) 0.57 22.61 1.18 0.22 Par12:Few suppliers 0.18 7.54 1.53 0.02

SUPPLY CHAIN PERFORMANCE

Per1 : Improvement in Lead time 0.53 18.55 0.74 0.27

0.75

0.54

Per2 : Improvement in inventory turns 0.41 14.88 0.66 0.20 Per3 : Improvement in level of inventory write off 0.54 18.93 0.86 0.25 Per4 : Improvement in Time to market (Product development cycle) 0.55 19.91 1.04 0.23 Per5 : Improvement of defect rate 0.57 20.07 0.81 0.29 Per6 : Improvement in order item fill rate 0.44 16.17 0.90 0.18 Per7 : Improvement in stock out situation 0.52 18.65 1.11 0.19 Per8 : Improvement in set-up times 0.48 17.63 0.85 0.21

ORGANIZATIONAL PERFORMANCE

Op1: Market share 0.66 24.24 0.85 0.34

0.75

0.49

Op2:Sales growth 0.58 21.86 0.67 0.33 Op3:Profit margin 0.76 27.69 0.50 0.53 Op4:Overall product quality 0.71 26.49 0.59 0.46 Op5:Overall competitive position 0.57 21.72 0.87 0.28 Op6:Average selling price 0.53 20.40 0.60 0.32 Op7: Return on investment. 0.74 27.66 0.64 0.46 Op8: Return on sales 0.55 21.03 0.82 0.27

190

Results of goodness of fit test for CFA model are shown in Table 4.159.

Table 4.159 Results of Goodness of Fit Test for Confirmatory Factor Analysis

Model

Normed Chi-

squre (ᵡ2/df )

P-

Value GFI AGFI CFI NFI RMESA

Study model 2.25 0.00 0.87 0.85 1.00 1.00 0.070

Recommended

value Less than 3 >0.05

0.8-

0.9

0.8-

0.9

0.8-

0.9

0.8-

0.9

Less than

0.080

The above table highlights the CFA or measurement model results. It can be

inferred from the above table that the values of various goodness of fit indices are well

within the desired limits. The normed chi-square is 2.25, GFI is 0.87, AGFI is 0.85,

NFI is 1.00, RMSEA is 0.070 and CFI is 1.00. Furthermore and more importantly, the

factor loadings in respect of all the items included in the model exceed 0.5 and are

highly significant at 0.05 level of significance. Hence, these results suggest that there is

no need for any modifications in the model and the available data aptly fits into the

proposed conceptual model.

4.10.5 STRUCTURAL MODEL OR PATH ANALYSIS

Structural model or path analysis is employed to estimate the strength of the

causal relationship among unobserved or latent variables of dependent and independent

variables. The sub models and overall model proposed in the proposed research is

discussed at length in the following paragraphs.

4.10.5.1 Relationships between Supply Chain Concerns, Supply Chain Performance

and Organizational Performance

The strength of relationships among supply chain concerns, supply chain

performance and organizational performance have been portrayed in Figure 4.40.

Results for the proposed structural model have been shown in Table 4.160 and

Table 4.161.

191

Figure: 4.40 Sub-Concept Model 1

Results of Goodness- of-Fit Test for Sub-Concept Model 1 have been displayed in

Table 4.160.

Table 4.160 Results of Goodness- of-Fit Test for Sub-Concept Model 1

Model

Normed Chi-squre

(ᵡ2/df )

P-Value GFI AGFI CFI NFI RMESA

Study model 1.96 0.00 0.95 0.94 1.00 1.00 0.061

Recommended value

Less than 3

Greater than 0.05

0.8-0.9

0.8-0.9 0.8-0.9

0.8-0.9

Less than 0.80

The above table highlights the results of various goodness of fit indices. The

value of normed chi-square is 1.96, RMESA is 0.061, GFI is 0.95, AGFI is 0.94, NFI is

192

1.00 and CFI is 1.00. It can be seen that the values of all the indices fall within the

desired limits for SEM analysis. This confirms that the available data set aptly fits into

the proposed structural model.

Results of the Sub-Concept Model 1 have been shown in Table 4.161.

Table 4.161 Results of Sub-Concept Model 1

Independent Variable

Dependent Variable

Path coefficient

Standard Error

T-value

P-value

R 2

S C concerns SC performance 0.17 0.015 11.05 0.00 0.029

S C concerns organizational performance

0.22 0.019 11.09 0.00

0.44 SC performance

organizational performance

0.53 0.074 8.09 0.00

Figure 4.40 portrays three causal relationships. These relationships are between

supply chain concern and supply chain performance, supply chain concerns and

organizational performance, and between supply chain performance and organizational

performance. From Table 4.161, the beta value, error value and t-value corresponding

to the first causal relationship between supply chain concerns and supply chain

performance are 0.17, 0.015 and 11.05 respectively. This proves the point that supply

chain concerns have a positive causal relationship with supply chain performance.

The beta value, error value and t-value in respect of the second causal

relationship between supply chain concerns and organizational performance are 0.22,

0.019 and 11.09 respectively. This serves as a testimony to the point that supply chain

concerns have a positive causal relationship with organizational performance. With

respect to the third causal relationship, the beta value, error value and t-value

corresponding to the relationship between supply chain performance and organizational

performance are 0.53, 0.074 and 8.09 respectively. This serves as a proof to the point

that supply chain performance has a positive causal relationship with organizational

performance.

4.10.5.2 Relationships between Supply Chain Competence, Supply Chain

Performance and Organizational Performance

Strength of relationships among supply chain competence, supply chain

performance and organizational performance have been portrayed in Figure 4.41.

Results for the proposed structural model are shown in Table 4.162 and Table 4.163.

193

Figure 4.41 Sub-Concept Model 2

Results of Goodness of Fit Test in respect of the Sub-Concept Model 2 have been


Table 4.162 Results of Goodness of Fit Test for Sub-Concept Model 2

Model

Normed Chi-squre

(ᵡ2/df )


Study model 2.16 0.00 0.93 0.92 1.00 1.00 0.068 Recommended value

Less than 3 Greater

than 0.05 0.8-0.9

0.8-0.9

0.8-0.9

0.8-0.9

Less than 0.80

The above table provides details about the values of goodness of fit indices. The

value of normed chi-square is 2.16, RMESA is 0.068, GFI is 0.93, AGFI is 0.92, NFI is

1.00 and CFI is 1.00. It can be inferred that all the results fall within the generally

acceptable limit. This confirms that the available data set aptly fits into the proposed

structural model.

194



Independent

Variable

Dependent

Variable

Path

coefficient

Standard

Error

T-

value

P-

value R 2

S C Competence SC Performance 0.54 0.021 25.25 0.00 0.29

S C Competence Organizational

Performance

0.19 0.027 6.99 0.00

0.42

SC Performance Organizational

Performance

0.53 0.070 7.55 0.00

Figure 4.41 portrays three causal relationships namely, relationship between

supply chain competence and supply chain performance, relationship between supply

chain competence and organisational performance, and relationship between supply

chain performance and organisational performance. It can be inferred from Table 4.163

that the beta value, error value and t-value corresponding to the first causal relationship

between supply chain competence and supply chain performance are 0.54, 0.021 and

25.25 respectively. This serves as a testimony to the point that supply chain competence

has a positive causal relationship with supply chain performance.

Taking the second causal relationship, the beta value, error value and t-value

corresponding to the relationship between supply chain competence and organizational


competence has a positive causal relationship with organizational performance. With

respect to the third causal relationship, the beta value, error value and t-value



chain performance has a positive causal relationship with organizational performance.

4.10.5.3 Relationships between Supply Chain Practices, Supply Chain

Performance and Organizational Performance

Strength of relationships among supply chain practices, supply chain

performance and organizational performance have been portrayed in Figure 4.42, while

Results in respect of the proposed structural model have been displayed in Table 4.164

and Table 4.165.

195





Model Normed Chi-squre (ᵡ2/df )



Less than 3 Greater

than 0.05 0.8-0.9

0.8-0.9

0.8-0.9

0.8-0.9

Less than 0.80

The above table portrays the goodness of fit indices values. The values of

normed chi-square are 2.80, RMESA is 0.084, GFI is 0.92, AGFI is 0.90, NFI is 1.00

196

and CFI is 1.00. It can be well observed that the results fall within the generally

accepted limits. This confirms that available data set aptly fits with the proposed

structural model.




Dependent Variable

Path coefficient

Standard Error

T-value

P-value

R 2

S C Practices SC Performance 0.55 0.020 27.65 0.00 0.30 S C Practices Organizational

Performance 0.22 0.027 7.94 0.00

0.45 SC Performance


0.53 0.084 6.25 0.00

Figure 4.42 portrays three causal relationships namely, the relationship between

supply chain practices and supply chain performance, supply chain practices and

organisational performance, and supply chain performance and organisational

performance. It can be inferred from Table 4.165 that the beta value, error value and t-

value corresponding to the relationship between supply chain practices and supply

chain performance are 0.55, 0.020 and 27.65 respectively. This proves the point that

supply chain practices has a positive causal relationship with supply chain performance.


corresponding to the relationship between supply chain practices and organizational

performance are 0.22, 0.027 and 7.94 respectively. This serves as a testimony to the

point that supply chain practices have a positive causal relationship with organizational

performance. With respect to the third causal relationship, the beta value, error value

and t-value corresponding to the relationship between supply chain performance and

organizational performance are 0.53, 0.084 and 6.25 respectively. This clearly suggests

that supply chain performance has a positive causal relationship with organizational

performance.

4.10.5.4 Relationships between Supply Chain Concerns, Supply Chain

Competence, Supply Chain Practices and Supply Chain Performance

Strength of relationships among supply chain concerns, supply chain

competence, supply chain practices, and supply chain performance have been portrayed

in Figure 4.43. Results for the proposed structural model are shown in Table 4.166 and

Table 4.167.

197





Model

Normed Chi-squre

(ᵡ2/df )



Less than 3 Greater

than 0.05 0.8-0.9

0.8-0.9

0.8-0.9

0.8-0.9

Less than 0.80

The above table displays the values of various goodness of fit indices. The

normed chi-square is 2.40, RMESA is 0.074, GFI is 0.86, AGFI is 0.84, NFI is 1.00,

and CFI is 1.00. It can be inferred from the above values that the results fall within the

198

generally accepted limits. This confirms that the available data set aptly fits into the

proposed structural model.



Independent

Variable

Dependent

Variable

Path

coefficient

Standard

Error

T-

value

P-

value R 2

S C concerns SC performance 0.079 0.017 4.103 0.00 0.39

S C competence SC performance 0.35 0.024 14.58 0.00

SC practices SC performance 0.33 0.024 13.82 0.00


supply chain concerns and supply chain performance, supply chain competence and

supply chain performance, and supply chain practices and supply chain performance.

From Table 4.167, the beta value, error value and t-value corresponding to the first

causal relationship between supply chain concern and supply chain performance are

0.079, 0.017 and 4.103 respectively. This serves as testimony to the point that supply

chain concerns has a positive causal relationship with supply chain performance.


corresponding to the relationship between supply chain competence and supply chain


chain competence has a positive causal relationship with supply chain performance. In

respect of the third causal relationship, the beta value, error value and t-value

corresponding to the relationship between supply chain practices and supply chain

performance are 0.33, 0.024 and 13.85 respectively. This provides adequate proof to the

point that supply chain practices have a positive causal relationship with supply chain

performance.


Competence, Supply Chain Practices and Organizational Performance

Strength of relationships among supply chain concerns, supply chain competence,

supply chain practices and organizational performance are shown in Figure 4.44.

Results for the proposed structural model are shown in Table 4.168 and Table 4.169.

199


200



Table 4.168 Results of goodness- of-Fit Test for Sub-Concept Model 5

Model

Normed Chi-squre

(ᵡ2/df )


Study model 2.33 0.00 0.87 0.86 1.00 1.00 0.076

Recommended value

Less than 3

Greater than 0.05

0.8-0.9

0.8-0.9 0.8-0.9

0.8-0.9

Less than 0.80

The above table displays the values in respect of the different goodness of fit

indices. The values of Normed chi-square are 2.33, RMESA is 0.076, GFI is 0.87,

AGFI is 0.86, NFI is 1.00, and CFI is 1.00. These values clearly suggest that the results

fall within the generally accepted limits. This confirms that the available data set aptly

fits into the proposed structural model.




Dependent Variable

Path coefficient

Standard Error

T-value P-

value R 2

S C concerns Organizational Performance

0.27 0.018 14.80 0.00 0.38

S C competence Organizational Performance

0.33 0.024 14.03 0.00

SC Practices Organizational Performance

0.24 0.024 10.16 0.00


supply chain concerns and organizational performance, supply chain competence and

organizational performance, and between supply chain practices and organizational

performance. Table 4.169 provides the beta value, error value and t-value

corresponding to the first causal relationship between supply chain concerns and

organizational performance as 0.27, 0.018 and 14.80 respectively. This proves the point

that supply chain concerns have a positive causal relationship with organizational

performance.


corresponding to the relationship between supply chain competence and organizational


chain competence has a positive causal relationship with organizational performance. In

respect of the third causal relationship, the beta value, error value and t-value

201

corresponding to the relationship between supply chain practices and organizational


chain practices have a positive causal relationship with organizational performance.


Competence, Supply Chain Performance and Organizational Performance

Strength of relationships among supply chain concerns, supply chain competence,

supply chain performance and organizational performance have been portrayed in

Figure 4.45. Results for the proposed structural model have been displayed in

Table 4.170 and Table 4.171.


202

Results of Goodness of Fit Test in respect of the Sub-Concept Model 6 is shown in

Table 4.170.


Model

Normed

Chi-squre

(ᵡ2/df )

P-Value GFI AGFI CFI

NFI RMESA

Study model 1.94 0.00 0.91 0.89 1.00 1.00 0.061

Recommended

value

Less than

3

Greater

than

0.05

0.8-

0.9 0.8-0.9

0.8-

0.9

0.8-

0.9 Less than

0.80

Table 4.170 contains details about values of goodness of fit indices. The values

of normed chi-square (ϰ2/df) are 1.96, RMESA is 0.061, GFI is 0.91, AGFI is 0.89, NFI

is 1.00 and CFI is 1.00. These values indicating the results relating to the validity of

model, fall within the generally acceptable limits. This confirms that the available data

set aptly fits into the proposed structural model.

Results of the Sub-Concept Model 6 are shown in Table 4.171.

Table 4.171 Result of Sub-Concept Model 6

Independent

Variable

Dependent

Variable

Path

coefficient

Standar

d Error

T-

value

P-

value R2

S C concerns SC Performance 0.17 0.016 10.83 0.00 0.32

S C competence SC

Performance

0.53 0.021 25.09 0.00

S C concerns Organizational

Performance

0.26 0.018 14.52 0.00 0.49

S C competence Organizational

Performance

0.20 0.026 7.85 0.00

SC

Performance

Organizational

Performance

0.47 0.067 7.01 0.00

Figure 4.45 portrays five causal relationships namely, the relationship between

supply chain concerns and supply chain performance, supply chain competence and

supply chain performance, supply chain concerns and organizational performance,

supply chain competence and organizational performance and between supply chain

203

performance and organizational performance. Table 4.171 provides details about the

beta value, error value and t-value corresponding to the first causal relationship

between supply chain concern and supply chain performance as 0.17, 0.016 and 10.83

respectively. This shows that supply chain concerns have a positive causal relationship

with supply chain performance.



performance are 0.53, 0.021 and 25.09 respectively. This shows that supply chain

competence have a positive causal relationship with supply chain performance. With

regards to the third causal relationship, the beta value, error value and t-value

corresponding to the relationship between supply chain concerns and organizational


concerns have a positive causal relationship with organizational performance.

With regards to the fourth causal relationship, the beta value, error value and t-

value corresponding to the relationship between supply chain competence and

organizational performance are 0.20, 0.026 and 7.85 respectively. This shows that

supply chain competence have a positive causal relationship with organizational

performance. With regard to the fifth causal relationship, the beta value, error value and

t-value corresponding to the relationship between supply chain performance and


supply chain performance has a positive causal relationship with organizational

performance.

4.10.5.7 Relationships between Supply Chain Competences, Supply Chain

Practices, Supply Chain Performance and Organizational Performance

Strength of relationships among supply chain competence, supply chain practices,

supply chain performance, and organizational performance have been portrayed in

Figure 4.46. Results for the proposed structural model have been indicated in

Table 4.172 and Table 4.173.

204


205

Results of Goodness of Fit Test in respect of the Sub-Concept Model 7 are shown in

Table 4.172.


Model

Normed

Chi-squre

(ᵡ2/df )


Study model 2.38 0.00 0.90 0.88 1.00 1.00 0.074

Recommended

value

Less than

3

Greater

than

0.05

0.8-

0.9 0.8-0.9

0.8-

0.9

0.8-

0.9 Less than

0.80

The above table contains details about the values of different goodness of fit

indices. The values in respect of normed chi-square are 2.38, RMESA is 0.074, GFI is

0.90, AGFI is 0.88, NFI is 1.00 and CFI is 1.00. These values indicating the results of

validity of the model, fall well within the generally accepted limits. This confirms that

the available data set aptly fits into the proposed structural model.



Independent

Variable

Dependent

Variable

Path

coefficient

Standar

d Error

T-

value

P-

value R2

S C competence SC Performance 0.33 0.024 14.15 0.00 0.39

S C practices SC

Performance

0.37 0.022 16.58 0.00

S C competence Organizational

Performance

0.13 0.029 4.35 0.00 0.45

S C practices Organizational

Performance

0.17 0.029 5.84 0.00

SC

Performance

Organizational

Performance

0.48 0.078 6.18 0.00


supply chain competence and supply chain performance, supply chain practices and

supply chain performance, supply chain competence and organizational performance,

supply chain practices and organizational performance and between supply chain

206

performance and organizational performance. From Table 4.173, the beta value, error

value and t-value corresponding to the first causal relationship between supply chain

competence and supply chain performance are 0.33, 0.024 and 14.15 respectively. This

shows that supply chain competence has a positive causal relationship with supply

chain performance. With regard to the second causal relationship, the beta value, error

value and t-value corresponding to the relationship between supply chain practices and

supply chain performance are 0.37, 0.022 and 16.58 respectively. This shows that

supply chain practices have a positive causal relationship with supply chain

performance.

With regard to the third causal relationship, the beta value, error value and t-



supply chain competence has a positive causal relationship with organizational

performance.

With regard to the fourth causal relationship, the beta value, error value and t-

value corresponding to the relationship between supply chain practices and


supply practices have a positive causal relationship with organizational performance.

With regard to the fifth causal relationship, the beta value, error value and t-value



performance has a positive causal relationship with organizational performance.

4.10.5.8 Relationships between Supply Chain Concerns, Supply Chain Practices,

Supply Chain Performance and Organizational Performance

Strength of relationships among supply chain concerns, supply chain practices,

supply chain performance and organizational performance have been displayed in

Figure 4.47. Results for the proposed structural model are shown in Table 4.174 and

Table 4.175.

207


208

Results of Goodness of Fit Test in respect of the Sub-Concept Model 8 are shown in

Table 4.174.


Model

Normed Chi-squre

(ᵡ2/df )


Study model 2.40 0.00 0.90 0.88 1.00 1.00 0.074

Recommended value

Less than 3

Greater than 0.05

0.8-0.9

0.8-0.9 0.8-0.9

0.8-0.9

Less than 0.80

The above table contains details about the values of different goodness of fit

indices. The values in respect of normed chi-square (ϰ2/df) are 2.40, RMESA is 0.074,

GFI is 0.90, AGFI is 0.88, NFI is 1.00 and CFI is 1.00. These values indicating the

results of validity of the proposed model, fall well within the generally accepted limits.

This confirms that the available data set aptly fits into the proposed structural model.




Dependent Variable

Path coefficient

Standard Error

T-value P-value R2

S C concerns SC Performance 0.036 0.016 2.23 0.00 0.30 S C practices SC

Performance 0.54 0.021 25.23 0.00


0.22 0.020 10.97 0.00 0.49

S C practices Organizational Performance

0.16 0.028 5.51 0.00

SC performance


0.51 0.080 6.47 0.00


supply chain concern and supply chain performance, supply chain practices and supply

chain performance, supply chain concern and organizational performance, supply chain

practices and organizational performance and between supply chain performance and

organizational performance. From Table 4.175, the beta value, error value and t-value

corresponding to the first causal relationship between supply chain concern and supply

chain performance are 0.036, 0.016 and 2.23 respectively. This shows that supply chain

concerns have a positive causal relationship with supply chain performance.

With regard to the second causal relationship, the beta value, error value and t-

value corresponding to the relationship between supply chain practices and supply

chain performance are 0.54, 0.021 and 25.23 respectively. This shows that supply chain

209

practices have a positive causal relationship with supply chain performance. With

regard to the third causal relationship, the beta value, error value and t-value

corresponding to the relationship between supply chain concerns and organizational


concern have a positive causal relationship with organizational performance.

With regard to the fourth causal relationship, the beta value, error value and t-

value corresponding to the relationship between supply chain practices and


supply chain practices have a positive causal relationship with organizational

performance. With regard to the fifth causal relationship, the beta value, error value and

t-value corresponding to the relationship between supply chain performance and


supply chain performance has a positive causal relationship with organizational

performance.

4.10.5.9 Cumulative Structural Equations Model

SEM has been employed to estimate the strength of the causal relationship

among unobserved or latent variables of supply chain concerns, supply chain

competence, supply chain practices, supply chain performance and organizational

performance and Figures 4.48 and 4.49 portrays these relationships while the results for

the proposed structural model are shown in Table 4.176 and Table 4.177.

Figure 4.48 Full Structural Model 1

210

Figure 4.49 Full Structural Model 2

211

Results of Goodness of Fit Test of overall structural model are shown in

Table 4.176.

Table 4.176 Results of Goodness- of-Fit Test for Overall Structural Model

Model

Normed Chi-squre

(ᵡ2/df )


Study model 2.25 0.00 0.90 0.88 1.00 1.00 0.070

Recommended value

Less than 3

Greater than 0.05

0.8-0.9

0.8-0.9 0.8-0.9

0.8-0.9

Less than 0.80

The above table displays the values of different goodness of fit indices. The

values in respect of normed chi-square are 2.25, RMESA is 0.070, GFI is 0.90, AGFI is

0.88, NFI is 1.00 and CFI is 1.00. These values revealing the results in respect of

validity of the proposed model, fall well within the generally accepted limits. This

confirms that the available data set aptly fits into the proposed overall structural model.

Results of overall structural model have been portrayed in Table 4.177.

Table 4.177 Result of Overall Structural Model


Dependent Variable

Covariance/ Beta

Standard Error

T-value

P-value

R2

S C concerns S C competence 0.08 0.02 4.59 0.00 - S C competence S C practices 0.54 0.02 28.91 0.00 - S C practices S C concerns 0.31 0.02 16.8 0.00 - S C concerns SC Performance 0.075 0.017 4.40 0.00

0.39 S C competence SC Performance 0.35 0.023 14.96 0.00

S C practices SC Performance 0.34 0.024 14.27 0.00


0.24 0.019 12.30 0.00 0.50

S C competence Organizational Performance

0.17 0.028 5.98 0.00

S C practices Organizational Performance

0.090 0.029 3.15 0.00

S C Performance Organizational Performance

0.45 0.075 5.97 0.00

Figure 4.49 portrays three association and seven causal relationships. The three

associations explored are the association between supply chain concerns and supply

chain competence, supply chain competence and supply chain practices and the

association between supply chain concerns and supply chain practices.

From Table 4.177, the covariance value, error value and t-value corresponding

to the first association between supply chain concern and supply chain competence are

0.08, 0.02 and 4.59 respectively. This serves as proof to accept and support the

hypothesis that supply chain concerns are associated with supply chain competence.

212

Taking the second association, the covariance value, error value and t-value

corresponding to the association between supply chain competence and supply chain

practices are 0.54, 0.02 and 28.91 respectively. This serves as testimony to accept and

support the hypothesis that supply chain competence is associated with supply chain

practices.

Taking the third association, the covariance value, error value and t-value

corresponding to the association between supply chain concern and supply chain

practices are 0.31, 0.02 and 16.82 respectively. This serves as a statistically significant

evidence to accept and support the hypothesis that supply chain concerns are associated

with supply chain practices.

The next seven causal relationships explored using the overall conceptual model

are the relationship between supply chain concern and supply chain performance,

supply chain competence and supply chain performance, supply chain practices and

supply chain performance, supply chain concern and organizational performance,

supply chain competence and organizational performance, supply chain practices and

organizational performance and the between supply chain performance and

organizational performance.

Taking the first causal relationship, the beta value, error value and t-value

corresponding to the relationship between supply chain concern and supply chain

performance are 0.075, 0.017 and 4.40 respectively. This serves as a statistically

significant evidence to accept and support the hypothesis that supply chain concerns

have a positive causal relationship with supply chain performance.



performance are 0.35, 0.023 and 14.96 respectively. This serves as adequate and

significant statistical evidence to accept and support the hypothesis that supply chain

competence has a positive causal relationship with supply chain performance.

Taking the third causal relationship, the beta value, error value and t-value

corresponding to the relationship between supply chain practices and supply chain

performance are 0.34, 0.024 and 14.27 respectively. This serves as a testimony to the

point that there is statistically significant evidence to accept and support the hypothesis

that supply chain practices have a positive causal relationship with supply chain

performance. With regard to the fourth causal relationship, the beta value, error value

and t-value corresponding to the relationship between supply chain concern and

213

organizational performance are 0.24, 0.019 and 12.30 respectively. This serves as a

testimony to the point that there is statistically significant evidence to accept and

support the hypothesis that supply chain concerns have a positive causal relationship

with organizational performance.

With regard to the fifth causal relationship, the beta value, error value and t-


organizational performance are 0.17, 0.028 and 5.98 respectively. This serves as a


support the hypothesis that supply chain competence has a positive causal relationship

with organizational performance. With regard to the sixth causal relationship, the beta

value, error value and t-value corresponding to the relationship between supply chain

practices and organizational performance are 0.090, 0.029 and 3.15 respectively. This

serves as a testimony to the point that there is statistically significant evidence to accept

and support the hypothesis that supply chain practices have a positive causal

relationship with organizational performance.

With regard to the seventh and final causal relationship, the beta value, error

value and t-value corresponding to the relationship between supply chain performance

and organizational performance are 0.45, 0.075 and 5.97 respectively. This serves as a


support the hypothesis that supply chain performance has a positive causal relationship

with organizational performance.

Overall measurement results depicts that higher levels of supply chain practices

and competence will contribute to the enhancement of supply chain performance of the

organization while a better supply chain performance will result in the improvement of

organizational performance of manufacturing enterprises.

4.10.6 STRUCTURAL EQUATIONS OF CONCEPTUAL MODEL

Supply chain performance = 0.075*concerns + 0.35*competence + 0.34* practices

--- (5.1)

Organizational Performance = 0.45*performance + 0.24*concerns+ 0.17*

competence + 0.090*practices --- (5.2)

The first structural equation explores the casual relationship among supply

chain performance and supply chain management components. supply chain

performance act as exogenous or dependent variable for supply chain management

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components namely supply chain concerns, supply chain competence and supply chain

practices, which act as exogenous or independent variables.

Table 4.177 highlights that 39% of supply chain performance is contributed by

the three supply chain management components.

The first linear equation displays the extent of influence exerted by critical

supply chain management components on supply chain performance. Results reveal

that 35% of supply chain performance is dependent on supply chain competence, while

34% of supply chain performance is dependent on supply chain practices and finally

7.5% of supply chain performance is dependent on supply chain concerns. Furthermore,

the significance value is 0.000, suggesting that the result is absolutely reliable. These

results suggests that any manufacturing undertaking endeavouring to enhance their

supply chain performance should give paramount priority to improve their supply chain

competence followed by supply chain practices dimensions.

The second structural equation explores the impact exerted on organizational

performance by the supply chain management components and supply chain

performance. organizational performance act as exogenous or dependent variable for

supply chain performance and the supply chain management components of supply

chain concerns, supply chain competence and supply chain practices. These four

variables assume the role of exogenous or independent variables for organizational

performance.

Table 4.177 highlights that 50% of organizational performance is contributed by

the three supply chain management components and supply chain performance.

The second linear equation displays the degree of influence exerted by critical

supply chain management components and supply chain performance on organizational

performance. Results reveals that 45% of organizational performance is dependent on

supply chain performance, while 24% of organizational performance is dependent on

supply chain concerns, 17% of organizational performance is dependent on supply

chain competence, and finally 9% of organizational performance is dependent on

supply chain practices. The value of significance being less than 0.05 signifying that the

results are absolutely reliable. The aforesaid points suggest that the organizational

performance of manufacturing enterprises will improve if their supply chain

performance is improved. Hence, enterprises endeavouring to improve their

organizational performance should focus on continuously assessing and improving their

supply chain performance.

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4.10.7 DIRECT, INDIRECT AND TOTAL PATH EFFECT OF OVERALL

STRUCTURAL MODEL

The following equations derived from results of overall structural model

portrayed by Figure 4.48 can be used to assess the degree of direct, indirect and total

effects of critical components of supply chain Management on supply chain

performance and organizational performance of manufacturing enterprises.

4.10.7.1 Impact of Supply Chain Competence on Supply Chain Performance

Results of direct, indirect and total path relationship between supply chain

competence and supply chain performance are 0.24, 0.006, 0.18 and 0.43, while the

total effect (both direct and indirect) of supply chain competence on supply chain

performance is 0.43.

4.10.7.2 Impact of Supply Chain Concerns on Supply Chain Performance


concerns and supply chain performance are 0.08, 0.028, 0.11 and 0.22 and the total

effect (both direct and indirect) of supply chain concerns on supply chain performance

is 0.22.

Direct path: Supply chain concerns Supply chain performance = 0.08

Indirect path: Supply chain concerns Supply chain competence

Supply chain performance = 0.08 x0.35 = 0.028

Supply chain concerns Supply chain practices

Supply chain Performance = 0.31 x0.34 = 0.11

Total Path = Direct path + Indirect path= 0.08+0.028+ 0.11= 0.22

Direct path: Supply chain competence Supply chain performance = 0.24

Indirect path: Supply chain competence Supply chain concerns


Supply chain competence Supply chain practices



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4.10.7.3 Impact of Supply Chain Practices on Supply Chain Performance


practices and supply chain performance are 0.34, 0.19, 0.025 and 0.56, while the total

effect (both direct and indirect) of supply chain practices on supply chain performance

is 0.56.

4.10.7.4 Impact of Supply Chain Competence on Organizational Performance


competence and organizational performance are 0.17, 0.019, 0.049 and 0.24 while the

total effect (both direct and indirect) of supply chain competence and organizational


4.10.7.5 Impact of Supply Chain Concerns on Organizational Performance


concerns and organizational performance are 0.24, 0.014, 0.028 and 0.28, while the

Direct path: Supply chain concerns organizational performance = 0.24

Indirect path: Supply chain concerns Supply chain competence

Organizational performance = 0.08 x0.17 = 0.014

Supply chain concerns Supply chain practices

Organizational Performance = 0.31 x0.09 = 0.028

Total Path = Direct path + Indirect path= 0.24+0.014+ 0.028 = 0.28

Direct path: Supply chain competence organizational performance = 0.17

Indirect path: Supply chain competence Supply chain concerns


Supply chain competence Supply chain practices



Direct path: Supply chain practices Supply chain performance = 0.34

Indirect path: Supply chain practices Supply chain competence


Supply chain practices Supply chain concerns



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total effect (both direct and indirect) of supply chain concerns and organizational


4.10.7.6 Impact of Supply Chain Practices on Organizational Performance


practices and organizational performance are 0.09, 0.092, 0.074 and 0.26, while the

total effect (both direct and indirect) of supply chain practices and organizational


Results of total, direct and indirect path analysis using structural model have been

portrayed in Table 4.178.

Table 4.178 Total, Direct and Indirect Path Analysis Result of Structural Model

Path Total Path Coefficient

Direct Path

Coefficient

Indirect Effects

Concerns Supply Chain Performance 0.22 0.08 0.03 0.11

Competence Supply Chain Performance 0.43 0.24 0.006 0.18

Practices Supply Chain Performance 0.56 0.34 0.19 0.03

Concerns Organizational Performance 0.28 0.24 0.014 0.028

Competence Organizational Performance 0.24 0.17 0.019 0.049

Practices Organizational Performance 0.26 0.09 0.092 0.074

From the above table, it can be inferred that supply chain concern directly

affects supply chain performance by 8% while this degree of effect reduces to 2.8% in

case of influence through supply chain competence and 11% in case of influence

through supply chain practices. Hence, the total impact (aggregate of direct and indirect

effect) of supply chain concern on supply chain performance is 22%.

It can further be noted that supply chain competence directly affects supply

chain performance by 35% while this level of effect is 0.6% in case of influence

Direct path: Supply chain practices organizational performance = 0.09

Indirect path: Supply chain practices Supply chain competence


Supply chain practices Supply chain concerns



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through supply chain concern and 18% in case of influence through supply chain

practices. The total impact (aggregate of direct and indirect effect) of supply chain

competence on supply chain performance is 43%.

Furthermore, it can be noted that supply chain practices directly affects supply

chain performance by 34% while this level of effect is 19% in case of influence through

supply chain competence and 2.5% in case of influence through supply chain concern.

The total effect (aggregate of direct and indirect effect) of supply chain practices on

supply chain performance is 56%.

It can further be noted that supply chain concern directly affects organizational

performance by 24% while this level of effect is 1.4% in case of influence through

supply chain competency and 2.8% in case of influence through supply chain practices.

The total effect (aggregate of direct and indirect effect) of supply chain concern on

organizational performance is 28%.

It can further be noted that supply chain competence directly affects

organizational performance by 17% while this level of effect is 1.9% in case of

influence through supply chain concern and 4.9% in case of influence through supply

chain practices. The total impact (aggregate of direct and indirect effect) of supply

chain concern on organizational performance is 24%.

It can further be noted that supply chain practices directly affects organizational

performance by 9% while this level of effect is 9.2% in case of influence through

supply chain competency and 7.4% in case of influence through supply chain practices.

The total impact (aggregate of direct and indirect effect) of supply chain practices on

organizational performance is 26%.

Findings based on SEM have been displayed in Table 4.179. It can be inferred

from the SEM results that all the 10 formulated hypotheses stands accepted at 0.01

significance level.

219

Results of total path analysis using structural model have been portrayed in Table

4.179.

Table 4.179 Total Path Analysis Result of Structural Model

Path Coefficient t-value Sig Level Hypotheses

Concerns Competence 0.08 4.59 Less than 0.01 Supported

Competence Practices 0.54 28.91 Less than 0.01 Supported

Practices Concernss 0.31 16.8 Less than 0.01 Supported

Concerns Performance 0.08 4.40 Less than 0.01 Supported

Competence Performance 0.35 14.96 Less than 0.01 Supported

Practices Performance 0.34 14.27 Less than 0.01 Supported

Concerns Organizational Performance 0.24 12.30 Less than 0.01 Supported

Competence Organizational Performance 0.17 5.98 Less than 0.01 Supported

Practices Organizational Performance 0.09 3.15 Less than 0.01 Supported

Performance Organizational Performance 0.45 5.97 Less than 0.01 Supported

The above table depicts that supply chain concern and supply chain competence

have 8% association, while supply chain competence and supply chain practices have

54% association and supply chain concern and supply chain practice have 31%

association. Furthermore, 8% of variance in supply chain performance is explained by

supply chain concern, while 35% of variance in supply chain performance is explained

by supply chain competence, and 34% of variance in supply chain performance is

explained by supply chain practice.

Further, 24% of variance in organizational performance is explained by supply chain

concern, while 17% of variance in organizational performance is explained by supply

chain competence, 9% of variance in organizational performance is explained by

supply chain practices, and finally, 45% of variance in organizational performance is

explained by supply chain performance.

4.10.8 DISCUSSION OF STRUCTURAL EQUATION MODEL AND

HYPOTHESES TESTING RESULTS

This research work is a first of kind conducted in Indian context to testing the

casual relationship between vital components of supply chain management and its

impact on supply chain performance and organizational performance through SEM

framework. From the results, the linkage of supply chain management in Indianised

context can be very well understood.

220

Generally, success of any firm depends on continuous updating of latest

concepts and trends of best practices existing in the market and development of

required competence and implementation of effective practices to improve the overall

performance of the organization.

To conclude, results of the hypotheses testing using SEM shows that all the ten

hypothesized relationships were significant at the 0.01 level and that the available data

aptly fit into the proposed conceptual model. It should however be noted that statistical

significance and fitness of data set into model will not make any research work

complete in all aspects. Results of the study need to be compared with results of earlier

studies undertaken at different countries with different cultural background and

different supply chain dimensions, to arrive at meaningful inferences. This shall lead to

good indulgent of the subject matter under examination and detection of some new

causal relationships.

4.10.8.1 First Set of Hypotheses of Supply Chain Management Components

H1: Supply chain practices and supply chain concerns are associated

H2: Supply chain practices and supply chain competencies are associated.

H3: Supply chain concerns and supply chain competencies are associated.

Using Path Analysis of SEM, it has established that there is a significant

association between supply chain concerns and supply chain competence, supply chain

competence and supply chain practices, and supply chain concerns and supply chain

practices. This study has yielded consistent results with the study of Chow et al. (2008)

according to whom there is significant association between supply chain concern and

supply chain practice among manufacturing enterprises in the US, while the same study

has revealed that there is significant association between supply chain concern and

supply chain competence among manufacturing enterprises in the country of Taiwan

and a US-based study by Tan (2002) has established that there is significant association

between supply chain concern and supply chain practices.

4.10.8.2 Second Set of Hypotheses of Supply Chain Performance

Structural model results show that there is significant and positive impact of

supply chain concerns, supply chain competence and supply chain practices on supply

chain performance.

The next causal relationship explores the relationship between supply chain

concern and supply chain performance, the relationship between supply chain

competence and supply chain performance and the relationship between supply chain

221

practices and supply chain performance, the relationship between supply chain concern

and organizational performance, relationship between supply chain competence and

organizational performance, relationship between supply chain practices and

organizational performance and the relationship between supply chain performance and


H4: The level of supply chain concern positively influences the degree of

supply chain performance

H5: The level of supply chain competence positively influences the degree of

supply chain performance

H6: The level of supply chain practice positively influences the degree of supply

chain performance.

It has been found using path analysis of SEM that supply chain concerns, supply

chain competence and supply chain practices exert significant effect on supply chain

performance of the manufacturing enterprises.

Hypotheses test result shows that there is positive and significant effect of

supply chain management components on supply chain performance of manufacturing

enterprises in UT of Puducherry. Similar results have been the outcome of various

research studies conducted in different countries with different cultural context

(Ellinger et al.,2012., Vanichchinchai, and Igel., 2011., Hsu et al., 2007., Bayraktarn et

al., 2009., Sukati et al., 2012., Vijayasarathy.,2010., Cook et al 2011. Kristal et al.,

2010., Trkman et al., 2010.,Bhatnagar, and Sohal, 2005.)

The results of this research is consistent with results of a global study conducted

by Hsu et al (2008) which revealed that there is significant relationship between supply

chain competence and supply chain performance. Bayraktar et al. (2009) have found

that there is positive relationship between supply chain practices and supply chain

performance of Turkish manufacturing enterprises. Sukti et al. (2012) have also found

out the existence of the same relationship among Malaysian manufacturing enterprises

and Sukti et al. (2009) has indicated that supply chain practices exerts 13% impact on

supply chain performance of Malaysian manufacturing firms considering supply chain

strategy as an intermediate variable. On other hand Vijayasarathy (2010) has found on

the basis of a global study, that supply chain competence exerts a significant effect on

the supply chain performance of the enterprises.

Koh at el. (2007) has indicated that supply chain practices exert a significant effect on

supply chain performance of Turkish manufacturing enterprises and Qrunfleh and

222

Tarafdar (2012) have found that supply chain practices exert a significant effect on

supply chain performance of US firms and Sundram et al (2011) has indicated that 14%

of supply chain performance is dependent on the supply chain practices that the supply

chain components exert a positive impact on the supply chain performance of

manufacturing enterprises at global level.

It can be inferred from the above discussion that this study has yielded supporting

results with many studies undertaken on manufacturing firms of Malaysian firms. Li et

al (2005) and Paulraj (2006) have found at different levels in different countries.

4.10.8.3 Third Set of Hypotheses of Organizational Performance.

Structural model results show that there is significant and positive impact of

supply chain concerns, supply chain competence and supply chain practices on

organizational performance and supply chain performance having highly significant

and positive impact on organizational performance.

H7: The level of supply chain concern positively influences the degree of


H8: The level of supply chain competence positively influences the degree of


H9: The level of supply chain practice positively influences the degree of

organizational performance

H10: The level of supply chain performance positively influences the


Hypotheses test result shows that there is positive and significant effect of

supply chain management components and supply chain performance on organizational

performance of manufacturing industries in UT of Puducherry and similar kind of

results are found in research of different researcher in conducted in different cultural

context (Hsu et al., 2007., Ou et al., 2010., Bayraktar et al., 2009., Tan, 2002.,Kristal

et al., 2010., Richey Jr et al 2009., Koh et al., 2007., Qrunfleh et al., 2012., Li et al

2006., Kannan and Tan, 2005., Wong, and Wong., 2011., and Chow et al., 2006.)

It has further been found using path analysis of SEM that supply chain

concerns, supply chain competence and supply chain practices exert significant effect

on organisational performance of the manufacturing enterprises.

Ellinger et al (2012) has found out that supply chain competence exerts a

significant effect on organizational performance of manufacturing firms at the global

level and Lockamy III and McCormack (2004) has found that a supply chain practice

223

exerts indirect impact on organizational performance of US manufacturing firms and

Vanichchinchai et al (2010) has indicated that a supply chain component exerts a

significant effect on organizational performance of Thai manufacturing firms.

Huo (2012) has found that supply chain components exerts a significant effect

both directly and indirectly, on organizational performance of Chinese manufacturing

firms and Hsu et al (2007) has found that supply chain competence and practices exerts

a significant effect on organizational performance of global manufacturing firms and

Ou et al (2010) has found that supply chain components exert a significant effect on

organizational performance of Taiwan manufacturing firms.

Lin et al (2004) have found that supply chain components exert a significant

effect on organizational performance of Taiwan and Hong kong manufacturing firms

and Cook et al (2010) has found that 17.4% of organizational performance of North

American manufacturing enterprises depends on supply chain practices and Spekman et

al (2002) has found that 37% of organizational performance of US and European

manufacturing enterprises depend on supply chain competence.

It can be observed from the above discussion that this study has yielded

supporting results with the studies conducted on manufacturing enterprises in different

countries with different context.

Finally, path analysis using SEM reveal that supply chain performance exerts a

significant and positive impact on the organizational performance.

Bhatnagar and Sohal (2005) has found that supply chain performance exert a

positive impact on the organizational performance of Asian manufacturing enterprises.

Qrunfleh and Tarafdar (2012) has found the same result on US firms, while Yusuf et al

(2012) has got the same result on UK manufacturing enterprises. Hence, it can be

inferred that results of this study is in consistence with many studies conducted at

different countries. The overall results indicate that the supply chain performance of

manufacturing enterprises is significantly influenced by supply chain competence and

supply chain practices, while organizational performance of the manufacturing

enterprises is significantly influenced by supply chain performance.

4.11 CONCLUSIONS

Supply chain components are constituted by many variables such as supply

chain concerns, supply chain competence, supply chain practices and supply chain

performance. These variables differ among different manufacturing enterprises. This

research has tried to analyze how and why these variables are different among different

224

segments of manufacturing enterprises. This study has attempted to use segmentation

approach to understand the characteristics of the manufacturing enterprises which has

not been made in previous researches of this area. In this study manufacturing

enterprises are segmented based on different supply chain management variables and

the characteristics of every segment are inferred by using different statistical tools.

Finally conceptual model is proposed and empirically tested using SEM based

on data collected from the manufacturing enterprises. Wing S. Chow et al (2006), based

on a survey on manufacturing enterprises of US and Taiwan, has found that supply

chain management practices is dependent on situations and regions and it may vary

from country to country based on the nature of supply chain existing in that country.

This research has identified that supply chain performance strongly influences

the organizational performance of the manufacturing firms, while the supply chain

performance of the manufacturing firms is strongly influenced by supply chain

competence and supply chain practices of the manufacturing firms. Hence,

manufacturing firms concentrating on improving their supply chain competence and

supply chain practices can significantly improve their performance as the former

impacts the latter indirectly through their impact on supply chain performance. Hence,

managers should concentrate on improving the supply chain competence and supply

chain practice to enhance the efficiency of their firms.

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