A STUDY ON THE RELATIONSHIP AMONG SUPPLY CHAIN MANAGEMENT COMPONENTS, SUPPLY CHAIN PERFORMANCE AND ORGANIZATIONAL PERFORMANCE OF MANUFACTURING INDUSTRIES IN UNION TERRITORY OF PUDUCHERRY

A STUDY ON THE RELATIONSHIP AMONG SUPPLY CHAIN MANA GEMENT

COMPONENTS, SUPPLY CHAIN PERFORMANCE AND ORGANIZATI ONAL

PERFORMANCE OF MANUFACTURING INDUSTRIES IN

UNION TERRITORY OF PUDUCHERRY

C. GANESH KUMAR

1

C. GANESH KUMARResearch Scholar

Department of Management StudiesPondicherry University

Mail: [email protected]

CONTENTS1. Introduction

2. Review of Literature and Conceptual Framework

3. Research Methodology

4. Results and Discussions

4.1 Profile of Manufacturing Industries

4.2 Dimension Level Analysis

4.2.1 Supply Chain Concerns4.2.1 Supply Chain Concerns

4.2.3 Supply Chain Competence

4.2.4 Supply Chain Practices

4.2.5 Supply Chain Performance

4.2.6 Organizational Performance

4.3 Casual Model and Hypotheses Testing

5. Summary of Findings

6. Conclusion

3

1. 1 Introduction

Today’s competition is moving from “among organizations” to“between supply Chains” (Koh et al., 2006) So there is a need for businessexecutives and managers to have a knowledge of essential components ofsupply chain management and the impact of those components on theorganizational performance .

This knowledge will help the manager to give emphasis on thosecomponentsto improvetheproductivityandefficiencyof theorganization

4

componentsto improvetheproductivityandefficiencyof theorganization(Chow et al., 2006).

Thus, the following research question are explored in this researchwork:

What is the impact of important components of SCMon theperformance of the supply chain per se and also on the organizationalperformance?

Country Japan United States Korea IndiaRatio of logistics cost with GDP (In %) 8.7 8.5 16.5 12.3

1.2 Challenges and Opportunities of supply chain in India

�Taxation structure drives firm’s location decisions

� Poor state of logistics infrastructure –Both Warehouse and Transportation are in the

hand of unorganized sector.

�Large number of customers at the bottom of the economic pyramid.

5

70 % of population are in Rural

37 % People are below poverty line

� Purchasing Power Parity(PPP) is $ 4.4 trillion, third in the world by IMF

� Amul, the Shakti project of Uniliver and the Dabbawalas of Mumbai are cases in point

where Indian firms have come up with unique solutions to supply chain challenges

in India.

�Firms like Asian Paints and Marico industries have achieved higher levels of profitability

and growth than their competitors because of their superior supply chain capabilities

1.3 Objectives of the Study

• To study the relationship between supply chain managementcomponents and supply chain performance of manufacturingenterprises.

• To study the impact of supply chain management componentsand supply chain performanceon organizationalperformanceand supply chain performanceon organizationalperformanceof manufacturing enterprises

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2. REVIEW OF LITERATURE

S.no Authors Objectives Variables and Constructs used

Methodology Findings

1 Chow et al(2008)

To study the association of supplychain management components and organizational performance through

Supply chain concerns, Supply chain competence and organizational performance

Perceptions of middle-line managers in the US and Taiwan

Supply chain competencies have positive effects on organizational performance.and

7

performance through structural equation modeling

and Supply chain practices and competencies are significantly associated in both the US and Taiwan.

S.no Authors Objectives Variables and Constructs used

Methodology Findings

2 Koh et al (2007)

To study the impact of supply chain management practices onperformance of SMEs in Turkey

SCM practices, Operational performance, organizational performance

Data collected using a self-administered questionnaire in the manufacture industries within the city of Istanbul in Turkey

High levels of SCM practices have a high level of operational performance and High level of SCM practices have high level of organizational performance.

3 Tan(2006) To investigates the contemporary

Supply Chain Management

Empirical survey in US, The lack of consensus

The supply chain practices affected

Cont…

8

the contemporary practices and concerns of supply chain management and relate it to firm’s performance in United States

Management Practices, Supply Chain Management Concerns and performance measure

The lack of consensus regarding a valid cross industry measure of organizational performance, in this study was operational zed by senior management’s perceptions of a firm’s performance in comparison to that of major competitors.

practices affected overall product quality and customer service levels.

S.no Authors Objectives Variables and Constructs used Methodology Findings

4 Rohit Bhatnagarand Amrik S. Sohal(2008)

Impact of location decisions on the competitiveness of global supply chains.

Qualitative factors in plant location, Supply chain uncertainty, Manufacturing practices, Supply chain performance measures

Empirical survey in developed country,regression analysis

The impact of the location decision framework comprising location factors, supply chain uncertainty, and manufacturing practices, is outlined for a variety of performance measures

5 C.-C. HSU et To study the Operations capability and Survey instrument Direct influence of

Cont…

9

5 C.-C. HSU et (2007)

To study the supply chain management practices mediate the relationship between operations capability and firm performance.

Operations capability and supply chain management practices, Total quality management, Just-in-time capability and firm performance

Survey instrument and data collection,structural equation modeling

Direct influence of operations capability and supply chain management practices on firm performance and on the need to examine operations capability in a broader, supply chain context.


6 ErkanBayraktaret (2009)

To empirically tested the causal links among supply chain management (SCM) and information system s (IS) practices, SCM –IS related inhibiting factors and operational performance

SCM Practices, SCM –IS related inhibiting factors and operational performance

Data collected using a self-administered questionnaire , a sample of 203 manufacturing SMEs operating within the greater metropolitan area of Istanbul

SCM and IS practices positively and significantly influenced the operational performance

Cont…

10

area of Istanbul in Turkey

7 Suhong Li et al (2005)

To Develop and validation of a measurement instrument forstudying supply chain management practices

SCM practices and Performance outcomes

Scale development, Empirical scale refinement and validation, Assessment of construct validities

conceptualize and develop measures of SCM practicesand a parsimonious measurement instrument.


8 Michael Tracey et al (2005)

To empirically test the impact of supply-chain management (SCM) capabilities on business performance

Outside-in capabilities, Inside-out capabilities, Spanning capabilities and organizational performance

Data collected using a questionnaire , a sample of 555 manufacturing in US

This study found both direct and indirecteffects of outside-in, inside-out, and spanning capabilities on the perceived product value, customer loyalty, marketperformance, and financial performance

9 VijayR. Kannanaand Keah Choon

To study the linkage of Just in time, total

Just in time, Quality management, Supply chain management practices

Empirical survey of 556 manufacturing in

linkages exist between JIT, TQM, and SCM.

Cont…

11

and Keah Choon Tanb (2004)

of Just in time, total quality management, and supply chainPractices and its impact onbusiness performance

Supply chain management practices and business performance

556 manufacturing in US

JIT, TQM, and SCM.While some companies may understand the inherent relationshipsbetween the three and actively exploit their synergy,those that do not maybe inadvertently achieving thebenefits of synergy


10 Chinho Lin et al (2004)

The impact of supply chain qualitymanagement on organizational performance

Quality management practices, Supplier participation and Organizational performance

SEM was used to empirically test theProposed hypotheses.

organizational performance can be optimizedwhen the organization considers its suppliersas important trading partners and members ofthe value chain.

11 Keah-Choon Tan et al (1999)

To determine qualitymanagement, supply base management,

Total quality management practices, Supply base management practices, Customer relations practices and

Performance measures Questions were designed

This study support this notion and confirm that all three

Cont…

12

base management, and customer relations practices can impact corporatePerformance

Customer relations practices and Performance measures

were designedusing a seven point Likert scale, sample is 313

confirm that all three major componentsof a supply chain, suppliers, manufacturers, and customers, must be effectivelyintegrated in order to achieve financial and growth objectives


12 Archie LockamyIII and Kevin McCormack(2004)

To study the relationship between supply-chainmanagement practices and supply chain performance

Supply-chainmanagement practices and supply chain performance

Data collected using a questionnaire , a sample of 55 manufacturing in US

Collaboration variables werefound to have a direct impact on SC performance in the PLAN, SOURCE, and MAKEareas of the SCOR Model

13 Suhong Li et al (2004)

To study the impact of supply chain management practices on

Supply chain management practices , competitiveadvantage and organizational performance

Instrument development methods for SCM practices include

do organizations withhigh levels of SCM practices have high levels of competitive

Cont…

13

competitiveadvantage and organizational performance

four phases: (1) item generation, (2) pre-pilot study,(3) pilot study, and (4) large-scale data analysis.

advantage; (2) do organizations with high level of SCMpractices have high levels of organizational performance;


14 Inda Sukati et al (2010)

To study of supply chain management practices: an empiricalInvestigation on consumer goods industry in malaysia.

Supply chainManagement practices, Supply ChainResponsiveness and Competitiveadvantage

Data collected using a questionnaire , a sample of200 consumer goods industry in malaysia

supply chainmanagement practices is term of strategic supplier partnership, customer relationship and information sharing arerelated to supply chain responsiveness in term of operation system responsiveness, logistic processresponsiveness and

Cont…

14

supplier network responsiveness.

15 Chin S. Ou et al (2012)

To examine the relationships among supply chainmanagement (SCM) practices and their impacts on firm financial and non-financial performance

Supply chainmanagement (SCM) practices and firm performance

Data collected using a questionnaire , manufacturing industry in Taiwan

external customer-firm-supplier relationmanagement positively impacts firm internal contextual factors, which in turn have positive effects onfirm performance.

S.no Authors Objectives Variables and Constructs used Methodology Findings18 Wai Peng Wong

et al (2011)To investigate how supply chain management (SCM)practices and knowledge management (KM) capabilities affect firm performance

Supply chain management (SCM)practices and knowledge management (KM) capabilities and firm performance

three-phase statistical analysis which comprised phase one(convergent validity, reliability, and discriminantvalidity), phase two (mediated regression analysis)and phase three (path analysis) was used to analyze the data

he implementation of SCM practices willinteract with KM capabilities to influence firm performance.

19 Veera PandiyanKaliani Sundram

To explore the effects of different

Supply chain management (SCM)practices and supply chain

The study employed the quantitative

The empirical results of PLS indicate that

Cont…

15

Kaliani Sundramet al (2011)

effects of different dimensions of supply chainmanagement practices (SCMP) on supply chain performance (SCP) in the electronics industry in Malaysia

practices and supply chain performance

the quantitative method whereconvenience sampling and self-administrated survey questionnaires were sent to 125 electronicsfirms in Malaysia. The research framework was tested using variance-based structural equationmodel, the partial least squares (PLS) method.

of PLS indicate that six of the seven dimensions of SCMP have asignificant positive effect on SCP. Furthermore, agreed vision and goals shows a greater influence thanother dimensions of SCMP.


20 Sang M. Lee et al (2011)

To examine supply chain (SC) innovation for improvingorganisational performance in the healthcare industry

Supply chain (SC) innovation and organisational performance

The proposed research model and hypotheses were tested usingstructural equation modeling based on data collected from 243 hospitals.

The results of the study support that organisational performance is positively associatedwith constructs of each SC innovation factor.

21 Alexander Ellingeret al

The relationship between supply

Supply chain management (SCM) competency and firm

SCM competency is assessed with

Firms recognized by peers and

Cont…

16

Ellingeret al (2012)

between supply chain management (SCM) competency and firm performance

(SCM) competency and firm performance

is assessed with data from the expert opinion element ofGartner Supply Chain Group (formerly AMR Research)’s Supply Chain Top 25 rankings;

by peers and experts for superior SCM competency exhibit higher levels of customersatisfaction and shareholder value than their respective industry averages

S.no Authors Objectives Variables and Constructs used Methodology Findings22 R. Glenn Richey

Jr et al(2009)To examine moderators impacting supply chain integration barriers

Supply chainintegration and firm performance

survey was developed to measure levels of supply chain integrationdrivers, barriers to supply chain integration, and firm performance. The measures were validatedusing EFA, and the responses analyzed using multiple

barriers to supply chainintegration can actually increase the firm’s ability to achieve firm performance as the firm is required tomake greater efforts to overcome those barriers and develop effective supply chain linkages.

Cont…

17

using multiple regression

23 Lori S. Cook et al (2010)

OTHER REVIEW

To examine the relationships between specific supply chainpractices and organizational performance

Supply chainpractices and organizational performance

regression analysis and the relative weightsmethod to analyze a set of survey data from respondents within the non-academic, North Americanmembership of the Institute of Supply Management.

The results show that the supply chain role for a company makes a difference in terms ofthe specific supply chain practices that lead to better performance.

�Different supply chain components are studied separately

�There are studies on impact of supply chain components on Organizational

performance

�There are studies on the linkage supply chain performance and Organizational

performance

2.1 Research Gap

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�There is no study on impact of supply chain components on supply chain

performance and in turn its impact on organizational performance.

2.2 Conceptual Framework

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H1 Supply chain competencies and supply chain concerns are associated.

H2 Supply chain practices and supply chain competencies are associated.

H3 Supply chain practices and supply chain concerns are associated.

H4 The level of supply chain concerns positively influences thedegree of supply chain performance

2.4 HYPOTHESES

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.

H7 The level of supply chain concerns positively influences the degree of organizational

performance.

HypothesesCont…

performance.

H8 The level of supply chain competence positively influences the degree of

organizational performance

H9 The level of supply chain practice positively influences the degree of organizational

performance

H10 The level of supply chain performance positively influences the degree of

organizational performance

� Research design- Descriptive

� Nature of data :Supply chain concerns, competence,practices, performance, Organizational performance andProfile of manufacturing industries.

� Source of data- Primary data

� Data collection instrument : Structured Questionnaire3.1Sampling Design

3.RESEARCH METHODOLOGY

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� Sample unit = Manufacturing Industries in Union Territoryof Puducherry.

� Sample technique = Simple randomsampling

� Sample frame: Dept of Industry and commerce

� Sample size = 255 (Israel, 2009)Where, Z=1.96 ,σ = 8.12, e = 1

= 253.29

.

3.2 Research tools used : �Simple Mean,

�Chi-square test,

�Independent Sample T-Test,

�Analysis of Variance (ANOVA),

�Factor Analysis,

�Cluster Analysis,

�Discriminate Analysis,

�Correspondence Analysis,

�Canonical correlation,

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�Structural Equation Modeling (SEM)- Confirmatory Factor Analysis (CFI)

and Path or Structural Model.

3.3 Software packages-

� SPSS,

� LISREL,

� Microsoft-Excel and

� STATA

Items were generated

Comprehensive review of Literature

• CONTENT

VALIDITY

PHASE I

(PRE PILOT

3.4 Development and Purification of the Questionnaire

23

Based on their feedback,

redundancies and ambiguousness

were removed from the

questionnaire

Interview with Practitioners and

Academicians

VALIDITY(PRE PILOT

STUDY)

Final survey

Final Questionnaire

Initial Reliability

Pilot Study with 30 sample

•Reliability,

Assumptions

and

Construct

validity

PHASE

II

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Assessment of construct validity

Checking Assumptions

Final Reliability 1. Normality

2. Homogeneity

3. Multicollinearity

4. Linearity

1. Convergent Validity

2. Discriminate Validity

3. Uni dimensionality

3.5 Reliability Test for Data Collection Instrument

S.no Variables

Cronbach’s- Alpha value

Pilot Study

1 Supply Chain Concerns 0.6863

2 Supply Chain Competence 0.8447

3 Supply Chain practices 0.6990

4 Supply Chain Performance 0.7524

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4 Supply Chain Performance 0.7524

5 Organizational Performance 0.7234

3.6 Checking Assumptions

�Normality,

�Homogeneity,

�Multicollinearity and

�Linearity

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CON COM PRA PER OP

N 255 255 255 255 255

Normal Parametersa Mean 2.9708 3.3521 3.2062 3.3034 3.2162

Std. Deviation 0.69022 0.55976 0.65503 0.60684 0.70241

Most Extreme Differences

Absolute 0.069 0.066 0.066 0.059 0.061

Positive 0.069 0.066 0.066 0.059 0.054

Negative -0.067 -0.053 -0.041 -0.038 -0.061

Kolmogorov-Smirnov Z 1.099 1.061 1.046 .947 .979

Asymp. Sig. (2-tailed) 0.179 0.210 0.224 0.332 0.294

3.7 Normality

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Asymp. Sig. (2-tailed) 0.179 0.210 0.224 0.332 0.294

Constructs Levene Statistic df1 df2 Sig.

CON 1.952 11 243 0.054

COM 1.204 11 243 0.285

PRA 1.502 11 243 0.131

PER 0.956 11 243 0.488

OP 1.449 11 243 0.152

3.8 Homogeneity

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OP 1.449 11 243 0.152

Model

UnstandardizedCoefficients

Standardized Coefficients

t Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF1 (Constant) 0.171 0.284 .603 0.547

CON 0.188 0.055 0.184 3.420 0.001 0.919 1.088

COM 0.236 0.074 0.188 3.167 0.002 0.760 1.316

PRA 0.150 0.066 0.140 2.278 0.024 0.707 1.415

3.9 Multicollinearity

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PER 0.368 0.069 0.318 5.362 0.000 0.759 1.318

a. Dependent Variable: OP

Equation

Model Summary

R Square F df1 df2 Sig.

Linear 0.035 6.438 1 253 0.002

Logarithmic 0.016 4.068 1 253 0.024

Inverse 0.008 2.087 1 253 0.150

Quadratic 0.022 6.859 2 252 0.034

Cubic 0.017 5.035 3 251 0.021

Compound 0.027 6.925 1 253 0.021

Power 0.019 4.873 1 253 0.042

S 0.011 2.911 1 253 0.089

3.10 Linearity

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Growth 0.023 6.925 1 253 0.022

Exponential 0.024 6.925 1 253 0.035

Logistic 0.024 6.925 1 253 0.045

3.11 Construct Validity

�Convergent Validity

�Discriminate Validity

�Unidimensional validity

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3.12 Assessment of Composite Reliability

CONSTRUCT INDICATORS RELIABILITY (α) CONSTRUCT RELIABILIY

(ρc)

Supply Chain Concerns 9 0.7153 0.75

Supply Chain Competence

14 0.8595 0.84

Supply Chain practices 12 0.7458 0.73

Supply Chain Performance

8 0.7694 0.75

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PerformanceOrganizational Performance

8 0.7854 0.75

3.13 Assessment of Unidimensionality and Convergent Validity

CONSTRUCT INDICATORS X2(DF) p-VALUE Goodness of Fit

Index (GFI)

RMR AVE

Supply Chain Concerns 9 81.49(27) 0.0000.93

0.052 0.47

Supply Chain Competence 14 206.97(77) 0.0000.90

0.054 0.46

Supply Chain Management

practices

12 353.58(54) 0.000

0.810.080 0.55

Supply Chain Performance 8 50.20(20) 0.0020.93

0.041 0.54

33

Supply Chain Performance 8 50.20(20)0.93

0.041 0.54

Organizational Performance 8 71.86(20) 0.0000.93

0.042 0.49

3.14 Discriminant Validity

CONSTRUCT VE SQUARE CORRELATION

Organizational Performance versus Supply Chain Performance 0.5950.5429

Organizational Performance versus Supply Chain practices 0.59

0.4489Organizational Performance versus Supply Chain Competence 0.53

0.2601Organizational Performance versus Supply Chain concern 0.46

0.3844Supply Chain Performance versus Supply Chain Competence 0.45

34

Supply Chain Performance versus Supply Chain Competence 0.45

0.5476Supply Chain Performance versus Supply Chain concern 0.51

0.1521Supply Chain Performance versus Supply Chain Practices 0.48

0.4489Supply Chain Practices versus Supply Chain concern 0.58

0.5476Supply Chain Practices versus Supply Chain Competence 0.47

0.1521Supply Chain concern versus Supply Chain Competence 0.51

0.1521

Capital Invested (in Rs.) Frequency Percent

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

CAPITAL INVESTED

4. RESULTS AND DISCUSSION 4. 1Profile of Manufacturing Industries

Questionnaire

35

More than 50 Crores 35 13.7

Total 255 100.00

Position Frequency Percent

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

POSITION IN SUPPLY CHAIN

SC_CON

SC_COM

SC_PRA

Confirmatory Factor Analysis(CFA)

Path Analysis

Factor Analysis Cluster Analysis Discriminate Analysis

Demographical Demographical

DATA ANALYSIS PROCESS

SC_PER

OP

ANOVA and T-test Chi-Squareand Correspondence analysis

Canonical Correlation

1Lack of sophisticated information system 1 2 3 4 5

2Lack of ability in managing Supply chain inventories 1 2 3 4 5

3Lack of cooperation among supply chain members 1 2 3 4 5

4

Very Low Low Moderate High Very High

1 2 3 4 5

4.2 Dimension Wise Analysis – Supply Chain Concerns

37

4Lack of trust among supply chain members 1 2 3 4 5

5Lack of interest among your suppliers or customers 1 2 3 4 5

6Competition from other supply chains 1 2 3 4 5

7Your firm’s lack of leverage within your supply chain 1 2 3 4 5

8Your suppliers’ geographical distance 1 2 3 4 5

9Your customers’ geographical distance 1 2 3 4 5

4.2.1 Priorities of Competition Oriented Concerns

SUPPLY CHAIN CONCERN Mean Value Rank

Competition from other supply chains 3.27 I

Lack of sophisticated information system 3.14 II

Customers’ geographical distance 3.04 III

Lack of interest among suppliers or customers 3.01 IV

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Suppliers’ geographical distance 2.96 V

Firm’s lack of leverage within its supply chain 2.88 VI

Lack of ability in managing Supply chain inventories 2.87 VII

Lack of trust among supply chain members 2.79 VIII

Lack of cooperation among supply chain members 2.77 IX

4.2.2 Factorisation of Supply Chain Concerns

Kaiser-Meyer-Olkin (KMO)Measure of Sampling Adequacy. 0.773Cronbach’s

alpha

Bartlett's Test of Sphericity Approx. Chi-Square 445.538 0.758DF 36

Sig. 0.000

Variance Explained by Factor

Component

Initial Eigen values Rotation Sums of Squared Loadings

Eigen % of Variance Cumulative % Total % of Variance Cumulative %1 3.147 34.964 34.964 2.352 26.134 26.134

39

1 3.147 34.964 34.964 2.352 26.134 26.1342 1.114 12.379 47.343 1.545 17.168 43.3023 1.025 11.388 58.730 1.389 15.429 58.7304 0.820 9.108 67.8395 0.755 8.385 76.2246 0.668 7.417 83.6417 0.563 6.260 89.9018 0.525 5.835 95.7369 0.384 4.264 100.000

Rotated Component MatrixSl.No Competition Oriented Concerns Component

1 2 31 Lack of trust among supply chain members

0.758

Supply Chain Coherence

2 Lack of ability in managing Supply chain inventories0.725

3 Lack of cooperation among supply chain members0.699

4 Lack of sophisticated information system 0.590

5 Your firm’s lack of leverage within your supply chain0.547

40

0.547

6 Your customers’ geographical distance0.822

Geographical Proximity7 Your suppliers’ geographical distance

0.753

8 Competition from other supply chains0.878

Competition9 Lack of interest among your suppliers or customers0.532

4.2.3 Segmentation of Supply Chain Concerns

FINAL CLUSTER CENTERS

Supply Chain ConcernsCluster

1 2 3Supply Chain Coherence

2.55(III) 3.51(I) 2.58(II)

Geographical Proximity1.88(III) 3.68(I) 3.61(II)

Competition2.80(II) 3.96(I) 2.58(III)

41

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

Average2.41 3.71 2.91

Cluster name and number of Units in each clusterLow Supply

Chain Concerned Units-93

High Supply Chain

Concerned Units-89

Moderate Supply Chain

Concerned Units-73

4.2.4 Testing Suitability of Segmentation using Discriminating Analysis

42

4.2.5 Association between Business Demographic Variables and Supply Chain Concerns Segmentation

Demographical VariablesChi-Square

valueSig. Value Significance or not

Nature of Industry 16.070 0.003 SignificantType of Industry 22.283 0.443 Not SignificantNumber of Employees 22.763 0.012 SignificantTotal Capital Invested 9.823 0.132 Not SignificantSupply Chain Position 11.830 0.066 Not SignificantSide of Supply Chain 0.829 0.661 Not SignificantType of Goods Produced 4.287 0.117 Not SignificantType of Business Organization 7.858 0.249 Not Significant

43

Type of Ownership 10.112 0.120 Not SignificantType of Listing 24.059 0.001 SignificantKind of Manufacturing 6.596 0.159 Not SignificantManufacturing Pattern 17.811 0.007 SignificantType of process 5.275 0.509 Not SignificantAnnual Turnover 11.606 0.312 Not SignificantMarket Coverage 9.686 0.046 SignificantArea of Market 21.180 0.007 SignificantBusiness years 1.468 0.962 Not SignificantSoftware Usage 7.332 0.026 Significant

P-Value is > 0.05

4.2.6 Nature of Industry and Supply Chain Concerns Correspondence Analysis

44

4.2.7 Relationship between 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

Nature of IndustryN

1 2Medium Scale 94 2.7426Large Scale 46 2.8435Small Scale 115 3.0313

Relationship between nature of industry and supply chain coherence

45

Small Scale 115 3.0313

Nature of IndustryN

1 2Medium Scale 94 2.6702Small Scale 115 3.1609Large Scale 46 3.2826

Nature of IndustryN

1 2Medium Scale 94 2.8989Large Scale 46 2.9891Small Scale 115 3.3957

Relationship between nature of industry and geographical proximity

Relationship between nature of industry and competition

4.2.8 Canonical correlation between selected demographical variables and supply chainConcerns

mark_cover ....3333444411112222666622221111 ....3333999944446666333344448888 0000....88886666 0000....333388888888 ----....4444333355559999111111111111 1111....111111118888444433335555manf_pattern ....0000333399999999333344443333 ....222222223333999911114444 0000....11118888 0000....888855559999 ----....4444000011110000333300002222 ....4444888800008888999988889999 listing ....3333555566664444222255555555 ....2222222266662222555588882222 1111....55558888 0000....111111116666 ----....0000888899991111555555557777 ....8888000022220000000066666666 ind_nature ----....8888222233330000777766668888 ....3333999999995555333322222222 ----2222....00006666 0000....000044440000 ----1111....666600009999888899995555 ----....0000333366662222555599991111 emply ....4444888833338888444433334444 ....2222000088886666222299991111 2222....33332222 0000....000022221111 ....0000777722229999888800001111 ....8888999944447777000066666666vvvv2222 comp_con ----....4444555533335555222299991111 ....2222666666662222222233338888 ----1111....77770000 0000....000099990000 ----....9999777777778888111166663333 ....000077770000777755558888 geo_con ....8888333388883333333344447777 ....2222222277774444111188889999 3333....66669999 0000....000000000000 ....3333999900004444666677779999 1111....222288886666222200001111 coh_con ....4444444400000000777777772222 ....3333111133336666555599998888 1111....44440000 0000....111166662222 ----....1111777777776666222277779999 1111....000055557777777788882222uuuu2222 software ----....1111666666664444333311118888 ....5555444466666666777711111111 ----0000....33330000 0000....777766661111 ----1111....222244443333000011117777 ....9999111100001111555533336666 mark_area ----....8888222200007777222255552222 ....222266665555000066667777 ----3333....11110000 0000....000000002222 ----1111....333344442222777733334444 ----....2222999988887777111166661111 mark_cover ....3333000011114444777799998888 ....3333666633333333666644449999 0000....88883333 0000....444400007777 ----....444411114444111111112222 1111....000011117777000077772222manf_pattern ....11114444333388888888 ....2222000066661111777711116666 0000....77770000 0000....444488886666 ----....2222666622221111444433335555 ....5555444499999999000033335555 listing ....2222333300000000000088885555 ....2222000088883333333300001111 1111....11110000 0000....222277771111 ----....1111888800002222666655558888 ....6666444400002222888822228888 ind_nature ....9999888877770000777722222222 ....3333666677778888777744442222 2222....66668888 0000....000000008888 ....2222666622226666000000001111 1111....777711111111555544444444 emply ....3333666699990000444422227777 ....1111999922220000999977778888 1111....99992222 0000....000055556666 ----....0000000099992222666644446666 ....7777444477773333555500001111vvvv1111 comp_con ----1111....111100007777222233331111 ....2222444455551111222288888888 ----4444....55552222 0000....000000000000 ----1111....555588889999999977775555 ----....6666222244444444888877775555 geo_con ----....1111222277779999999933332222 ....2222000099993333999988887777 ----0000....66661111 0000....555544442222 ----....5555444400003333777722221111 ....2222888844443333888855556666 coh_con ....2222111144444444777755554444 ....2222888888888888000066661111 0000....77774444 0000....444455558888 ----....3333555544442222888844442222 ....777788883333222233335555uuuu1111 Coef. Std. Err. t P>|t| [95% Conf. Interval]

46 e = exact, a = approximate, u = upper bound on F Roy's largest root ....0000999955556666777766664444 7777 222244447777 3333....3333777766660000 0000....0000000011119999 uuuuLawley-Hotelling trace ....222211117777444477773333 22221111 777733331111 2222....5555222233334444 0000....0000000000002222 aaaa Pillai's trace ....222200001111444444442222 22221111 777744441111 2222....5555333399999999 0000....0000000000002222 aaaa Wilks' lambda ....8888111111112222 22221111 777700004444....000055558888 2222....5555333344443333 0000....0000000000002222 aaaa Statistic df1 df2 F Prob>F

Tests of significance of all canonical correlations

0000....2222999955555555 0000....2222777733339999 0000....1111999977777777Canonical correlations: (Standard errors estimated conditionally) software ....8888555500001111000011117777 ....8888333388883333555588884444 1111....00001111 0000....333311112222 ----....8888000000009999111177774444 2222....555500001111111122221111 mark_area ----....2222777777772222777755551111 ....4444000066664444999988888888 ----0000....66668888 0000....444499996666 ----1111....000077777777888811112222 ....5555222233332222666622222222 mark_cover ----....0000666655550000777722221111 ....5555555577772222444455555555 ----0000....11112222 0000....999900007777 ----1111....111166662222444488882222 1111....000033332222333333338888manf_pattern ....3333111199997777444422223333 ....3333111166661111777788886666 1111....00001111 0000....333311113333 ----....3333000022229999222233332222 ....9999444422224444000077777777 listing ----....3333777799991111777733331111 ....3333111199994444888888887777 ----1111....11119999 0000....222233336666 ----1111....000000008888333355557777 ....2222555500000000111111113333 ind_nature ----....8888333300005555555533332222 ....5555666644441111666600008888 ----1111....44447777 0000....111144442222 ----1111....999944441111555588882222 ....2222888800004444777755555555 emply ....5555666644448888555544446666 ....2222999944445555999955554444 1111....99992222 0000....000055556666 ----....0000111155553333000066663333 1111....111144445555000011115555vvvv3333 comp_con ----....1111000066665555555500009999 ....3333777755559999222222222222 ----0000....22228888 0000....777777777777 ----....8888444466668888777722223333 ....6666333333337777777700006666 geo_con ----....5555777777777777666611112222 ....3333222211111111222277776666 ----1111....88880000 0000....000077773333 ----1111....222211110000111177773333 ....0000555544446666555500006666 coh_con 1111....333322227777999922223333 ....4444444422229999000044444444 3333....00000000 0000....000000003333 ....4444555555556666999900004444 2222....222200000000111155556666uuuu3333 software ....2222444499998888666600002222 ....5555999933337777111155558888 0000....44442222 0000....666677774444 ----....9999111199993333777722225555 1111....444411119999000099993333 mark_area ....3333888844446666444488881111 ....2222888877778888777777778888 1111....33334444 0000....111188883333 ----....1111888822222222888833333333 ....9999555511115555777799994444 mark_cover ....3333444411112222666622221111 ....3333999944446666333344448888 0000....88886666 0000....333388888888 ----....4444333355559999111111111111 1111....111111118888444433335555

Constructs Factors Analysis Cluster analysis Discriminant analysis

Supply Chain concerns

Coherence Low supply chain concerned unitsZ1 = 0.915 * Geographical Proximity, Z2 = 0.732 * Competition + 0.467*Supply Chain Coherence.

Geographical proximity High supply chain concerned units

Competition Moderate supply chain concerned units

Supply Chain Competence

Design Effectiveness Low supply chain competence unitsZ1 = 0.941* Design Effectiveness, Z2 = 0.850 * Operations and Distribution + 0.498* Quality and Service.

Quality and Services High supply chain competence units

Operations and Distribution Moderate supply chain competence units

5.1Summary of Factor, Cluster and Discriminant Analysis Results

47

units

Supply chain Practices

Procurement Moderate supply chain practices units

Z1 = .817* Procurement PracticesZ2 = 0.790 * Strategic Planning and Lean Practices + 0.704* Close Partnership Practices.

Strategic planning and lean Partnership practices unitsClose partnership High supply chain practices units


Lead Time and Inventory Low supply chain performance units Z1 =0.861* Responsiveness

Z2 = 0.9.7* Lead Time and Inventory PerformanceResponsiveness

Moderate supply chain performance unitsHigh supply chain performance units

Organizational Performance

Financial Performance Average performance units Z = 0.794* Financial Performance + 0.785* Marketing PerformanceMarketing Performance High performance units

Sl.no VariableSupply Chain

Concerns

Supply Chain

Competence

Supply Chain

Practices


OrganizationalPerformance

1 Type of Industry Not Sig Not Sig Sig Sig Not Sig2 Number of Employees Sig Sig Sig Not Sig Not Sig3 Total Capital Invested Not Sig Sig Sig Not Sig Sig4 Supply Chain Position Not Sig Not Sig Not Sig Not Sig Not Sig5 Nature of Industry Sig Not Sig Sig Sig Sig6 Side of Supply Chain Not Sig Not Sig Not Sig Not Sig Not Sig7 Type of Goods Produced Not Sig Not Sig Not Sig Not Sig Not Sig

8 Business Organization Not Sig Sig Sig Not Sig Sig

5.2 Chi-Square Analysis Results

48

8 Business Organization Not Sig Sig Sig Not Sig Sig9 Type of Ownership Not Sig Not Sig Not Sig Not Sig Not Sig10 Type of Listing Sig Sig Not Sig Not Sig Not Sig11 Kind of Manufacturing Not Sig Not Sig Not Sig Not Sig Not Sig12 Manufacturing Pattern Sig Not Sig Not Sig Not Sig Not Sig13 Type of process Not Sig Not Sig Sig Not Sig Not Sig14 Annual Turnover Not Sig Sig Not Sig Not Sig Sig15 Market Coverage Sig Not Sig Sig Not Sig Sig16 Area of Market Sig Sig Sig Not Sig Sig17 Business years Not Sig Not Sig Not Sig Not Sig Not Sig18 Software Usage Sig Sig Sig Not Sig Sig

* Sig- Significant at 0.05 level,* Not Sig- Not Significant

5.3 Summary of Correspondence Analysis Results

5.3.1 Supply Chain Concerns

Variables Highly ConcernsModerate Concerns

Low Concerns

Number of Employees More Than 1200 101-300 Less than 100

Nature of Industry Small scale Large scale Medium Scale

Type of ListingNot Listed Listed in India and

AbroadListed In India

Market Coverage Both International Domestic

Area of Market Southern Region Entire India Within Pondicherry

5.3.2Supply Chain Competence

49

5.3.2Supply Chain Competence

Variables Highly Competence Moderate Competence Low Competence

Number of Employees 301-600 Less 100 101-300

Total capital Invested 1 crores to 50 crores Less than 50 Lakhs More than 50 crores

Type of Business Organization

Public Limited Private Limited Partnership

Type of ListingListed in India and abroad

Listed in India Not listed

Annual Sales 1 crore to 3 crores 50 Lakhs to 1 crores 6 crores to 10 crores

Area of Market Entire India Within Pondicherry Southern Region

5.3.3 Supply Chain practices

Variables Highly Practice Moderate PracticePartnership

PracticeType of Industry Electronics Metal Food

Number of Employees601-900 101-300 Less than 100

Total capital Invested1crore to 50 crores 50 lakhs to 1 crore More than 50 crores

Nature of Industry Large Scale Medium Scale Small Scale

Business OrganizationPublic Limited Partnership Sole proprietor

Type of Process Continuous Batch Job order

50

Type of Process Continuous Batch Job orderMarket Coverage Both International DomesticArea of Market Southern region Entire India Pondicherry

5.3.4 Supply Chain Performance

Variables Highly Performance Moderate Performance Low Performance

Type of Industry Building Materials Automobile Pharmaceutical

Nature of Industry Large Scale Small Scale Medium Scale

5.4 SUMMARY OF ANOVA ANALYSIS RESULTS

51

5.5 Summary of Canonical Correlation Analysis Results

ConstructsFactors

variablesProfile variables

Canonical correlation

s

Shared Variance

Supply chain concerns Coherence Capital, Industry, Organization, sales, market coverage and area

0.30 18 %Geographical proximity 0.27

Competition 0.20Supply chain competence

Quality and Services Employees, Capital, Organization, listing, sales, Mark cover and software

0 .28 14 %Design Effectiveness 0.22Operations and Distribution

0.14

52

softwareDistributionSupply chain practices Quality and Services Industry, Employees,

capital, Industry, Organization, Process, Mark cover, Mark area and software

0 .40 24 %Design Effectiveness 0.25Operations and Distribution

0.17

Supply chain performance

Lead Time and Inventory

Type of Industry andNature of Industry

0.08 12 %

Responsiveness 0.07Organizational performance

Financial Performance Capital, Industry, Organization, sales, sales and sales

0 .39 17 %

Marketing Performance 0.15

4.3 Causal Analysis and Proposed Hypotheses Testing

53

4.3 Sub- Confirmatory Factory Analysis(CFA) Or Measurement Model

4.3.1 CFA of Supply Chain Concerns

54

4.3.2 CFA of Supply Chain Practices

55

4.3.3 CFA of Supply Chain Performance

56

4.3.4 CFA of Supply chain competence

57

4.3.5 CFA of Organizational Performance

58

4.3.6 OVERALL CONFIRMATORY FACTOR ANALYSIS (CFA) O R MEASUREMENT MODEL

59

4.3.7 Goodness of Fit Statistics

Goodness of fit indices

Absolute fit indices Incremental fit indices Parsimony fit indices

MODELNORMED CHI-

SQURE (�2/DF )

P-

VALUEGFI 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 0.8-0.90.8-0.9

Less than 0.080

60

indices1 Normed chi-square- (<4) Normed Fit Index (NFI)-

(>0.9)Parsimony Goodness of fit Index (PGFI)- (>0.9)

2 Goodness of fit Index (GFI)-(>0.9)

Comparative Fit Index (CFI)-(>0.9)

Parsimony Normed fit Index (PNFI)- (>0.9)

3 Root mean square error of Approximation (RMSEA) –(0.03-0.08)

Tucker Lewis Index (TLI)-(>0.9)

Adjusted Normed fit Index (ANFI)- (>0.9)

4.3.8 Results of Overall CFA (Measurement Model)

61

4.3.9 STRUCTURAL MODEL OR PATH ANALYSIS

4.3.9.1 Relationships between Supply Chain Concerns, Supply Chain Performance and Organizational Performance -sub conceptual model-1

62

4.3.9.2 Relationships between Supply Chain Competence, Supply Chain Performance and Organizational Performance -sub conceptual model-2

63

4.3.9.3 Relationships between Supply Chain Practices, Supply Chain Performance and Organizational Performance -sub conceptual model-3

64

4.3.9.4 Relationships between Supply Chain Concerns, Supply Chain Competence, Supply Chain Practices and Supply Chain Performance -sub conceptual model-4

65

4.3.9.5 Relationships between Supply Chain Concerns, Supply Chain Competence, Supply Chain Practices and Organizational Performance -sub conceptual model-5

66

4.3.9.6 Relationships between Supply Chain Concerns, Supply Chain Competence, Supply Chain Performance andOrganizational Performance- -sub conceptual model-6

67

4.3.9.7 Relationships between Supply Chain Competences, Supply Chain Practices, Supply Chain Performance and

Organizational Performance- -sub conceptual model-7

68

4.3.9.8 Relationships between Supply Chain Concerns, Supply Chain Practices, Supply Chain Performance and Organizational Performance - -sub conceptual model-8

69

4.3.10 Cumulative Structural Equations Model

70

Supply chain performance = 0.075*concerns + 0.35*competence + 0.34* practices

Organizational Performance = 0.45*performance + 0.24*concerns+ 0.17*competence + 0.090*practices

STRUCTURAL EQUATIONS OF CONCEPTUAL MODEL

71

4. 3.11 Total, Direct and Indirect Path Analysis Results of Structural Model

72

4. 3.12 Result of Overall Structural Model

Independent Variable

Dependent VariableCovariance/

BetaStandard

ErrorT-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

0.39S C competence SC Performance 0.35 0.023 14.96 0.00

SC practices SCPerformance 0.34 0.024 14.27 0.00

73

SC practices SCPerformance 0.34 0.024 14.27 0.00

S C concerns OrganizationalPerformance

0.24 0.019 12.30 0.00

0.50S C competence Organizational

Performance0.17 0.028 5.98 0.00

S C practices OrganizationalPerformance

0.090 0.029 3.15 0.00

S C performance OrganizationalPerformance

0.45 0.075 5.97 0.00

4.3.13 Findings of Structural Equation Model

74

6. CONCLUSION

• Supply chain management components of supply chain concerns, supplychain competence and supply chain practices are associatedwith each other.

• Supply chain components have positive impact on supply chainperformance and organizational performance of manufacturing enterprises.

• Supply chain performance exerts strong positive impact on organizationalperformance of manufacturing enterprises.

• Supply chain performance strongly influences the organizationalperformance of the manufacturing firms, while the supply chain

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performance of the manufacturing firms, while the supply chainperformance of the manufacturing firms is strongly influenced by supplychain competence and supply chain practices of the manufacturing firms.

• Hence, manufacturing firms concentrating on improving their supply chaincompetence and supply chain practices can significantly improve theirperformance as the former impacts the latter indirectly through their impacton supply chain performance. Hence, managers should concentrate onimproving the supply chain competence and supply chain practice toenhance the efficiency of their firms.

6.1 Contributions of this Research

�Consistent constructs with high level of reliability and validity

�Confirms the relationship between SCM and organizational performance.

�This research provides valuable inputs to strengthen academic thoughts andarguments regarding theory and proposition, methods of approaching theresearch issues.

�Valuableinputsto managementpractitioners

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�Valuableinputsto managementpractitioners

6.2 Limitations of the Study and Directions for Future Research

�The study covers only manufacturing enterprises and does not concentrate on thebusiness firms engaged in services sector

�The concept of supply chain management is highly complex and exhaustive.Supply chain issues may vary among firms engaged in the manufacture of differentproducts.

�The study has collected data from a single executive from each manufacturingenterprise.

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enterprise.

THANK YOU…

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A STUDY ON THE RELATIONSHIP AMONG SUPPLY CHAIN MANAGEMENT COMPONENTS, SUPPLY CHAIN PERFORMANCE AND ORGANIZATIONAL PERFORMANCE OF MANUFACTURING INDUSTRIES IN UNION TERRITORY OF PUDUCHERRY

Business

supply chain practices

supply chain concerns

supply chain competence

supply chain uncertainty

opportunities of supply

chain challenges

firms performance

performance of smes