A Study on the Supply Chain Performance of Manufacturing Industries in Union Territory of Puducherry...

7/31/2019 A Study on the Supply Chain Performance of Manufacturing Industries in Union Territory of Puducherry India.

1/9

A STUDY ON THE SUPPLY CHAIN PERFORMANCE OF MANUFACTURING

INDUSTRIES IN UNION TERRITORY OF PUDUCHERRY, INDIA.

C. Ganesh Kumar

Ph. D Research ScholarDepartment of Management Studies,

School of Management,

Pondicherry University,Puducherry- 605014, INDIA

Mobile: +91-97861-47867

E-Mail ID:[email protected]

Dr. T. NambirajanProfessor

Department of Management Studies,School of Management,

Pondicherry University,

Puducherry- 605014, INDIA

Mobile: +91-94433-84550

E-Mail ID: [email protected]

ABSTRACT

The purpose of this research work is to empirically test the relationships among supply

chain performance and business demographical variables.Data for the study were collected from asample of 255 SMEs and large scale manufacturing enterprises operating within the Union

Territory of Puducherry, India. The research variables were tested using chi-square test along with

correspondence analysis, analysis of variance (ANOVA) and canonical correlation.Based on thechi-square analysis, Supply chain performance has significant association with types of industry

and nature of industry. Finally the result indicates that there is a 12% of the variance shared

between supply chain performance and business demographical variables.

Key words: Supply Chain Performance, Business Demographical and Manufacturing Industry
mailto:[email protected]:[email protected]


2/9

1. INTRODUCTION

Globalization and intensive world-wide competition along with the technological advancements

create an entirely new business environment for the manufacturing organizations. Initially,

manufacturing companies have accomplished massive productivity gains through the

implementation of lean production in response to this intensifying competition. The waste has

eliminated from many different local operations for the sake of better productivity. Currently such

type of massive productivity improvements for many manufacturing organizations is very limited.

Instead, there is a huge improvement potential to reduce the inefficiencies caused by the poor

performance of the suppliers, unpredictable customer demands, and uncertain business

environment.

An integrated supply chain has a clear advantage on the competitiveness of the individual

companies. As a result, the chain-chain competition has started to take over the enterprise-

enterprise competition, although many enterprise-enterprise competitions do exist particularly in

the less developed economies (Koh et al., 2006).

The forward-looking enterprises today are dynamic; they collaborate with suppliers, customers and

even with competitors; share information and knowledge aiming to create a collaborative supply

chain that is capable of competing if not leading the particular industry. Hence, gaining

competitive edge under such a cut-throat environment becomes increasingly difficult.

2. LITERATURE REVIEW

The relationship between financial and non-financial measures of organizational

performance has long been discussed in organization and strategy literature. York and Miree

(2004) argue that non-financial performance such as improved quality, innovativeness and resource

planning should actually reduce costs, and thus have a positive effect on measures of financial

performance. Increased quality helps SMEs and large scale enterprises to retain current customers

and create greater customer loyalty, which in return may increase market share and organizational

performance (Rust et al., 1994).

A number of prior studies demonstrate positive relationship between operational performance

dimensions such as product quality, (Larson and Sinha, 1995) innovation and R&D (Prajogo and

Sohal, 2001 ;) employee performance (Fuentes-Fuentes et al., 2004). Increase in operational

performance may lead to high levels of organizational performance related to SCM in terms of

increased sales, organization-wide coordination and supply chain integration.


3/9

It is generally recognized that it is difficult to select a single measure of firm performance. The

literature lists several quantitative objectives that can be set to guide performance over a period of

time, as well as qualitative objectives (Hunger and Wheelen, 1993;). It has been argued that as

there are obvious difficulties in obtaining quantitative measures, there is a strong a priori case that

qualitative measures should be included in assessments of performance (Chakravarthy, 1986).

Therefore, the subjective approach has been used extensively in empirical studies, based on

executives perceptions of performance, having been justified by several writers.

3. RESEARCH METHODOLOGY

Based on the literature review, a set of eight supply chain performances items were identified,

These performance variables included improvement in lead time, improvement in inventory turns,

improvement in level of inventory write off, improvement in time to market (Product development

cycle), improvement of defect rate, improvement in order item fill rate, improvement in stock out

situation and improvement in set-up times. The questionnaire was developed and pre-tested to

ensure reliability and validity of the response. Data for this study was collected using a

questionnaire that was distributed to 255 SMEs and large scale manufacturing enterprises

operating in Union Territory of Puducherry in India. The sample was selected using simple random

sampling by lottery method from the database of Department of Industry and Commerce,

Government of Puducherry.

Respondents were asked to rate the supply chain performance of their organization over the past 3

year on five-point scales ranging from 1 = very low to 5 = very high .

Apparently, this research work is to investigate the impact of business demographical variables on

the supply chain performance so the supply chain performance variables are factored into two

factors using principle component analysis and then the two supply chain factors are segmented

into three clusters of manufacturing enterprises using k-mean cluster method.

4. RESULTS AND DISCUSSION

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 Supply chain performance variables. It can be noticed that

the high supply Chain performance units will display an improved overall better performance. In

this section, the characteristics of supply chain performance segments are identified through chi-

square test along with correspondence analysis and analysis of variance (ANOVA).


4/9

To understand the characteristics of these three supply chain performance segments, their

association with various business demographic related variables are analyzed. The chi-square test

is applied to test the significance of associations. The chi-square values along with their level of

significance are given in the following table.

TABLE 1.1: CHI-SQUARE TEST VALUE FOR VARIOUS VARIABLES

From the chi-square test it is found that Type of Industry, Number of Employees, Total

Capital Invested, Supply Chain Position, Side of Supply Chain, Type of Goods Produced, Type of

Business Organization, Type of Ownership, Type of Listing, kind of Manufacturing Manufacturing

Pattern, Type of process, Annual turnover, Market Coverage, Area of Market Business years and

Software Usage have no significant association with supply chain performance segments, while

there is a significant association between supply chain performance segments with Type of

Industry and Nature of Industry.

4.1 Relationship between Supply Chain Performance and Business Demographic Variables

The business demographic variables considered for the study are Type of Industry, Total

Capital Invested, Supply Chain Position, Side of Supply Chain, Type of Goods Produced, Type of

Variable

Chi-

Square

value

Sig.

Value

Type of Industry 26.163 0.045

Number of Employees 10.072 0.434

Total Capital Invested 2.824 0.831

Supply Chain Position 6.445 0.375

Nature of Industry 11.717 0.020Side of Supply Chain 5.130 0.077

Type of Goods Produced 1.943 0.379

Type of Business Organization 4.577 0.599

Type of Ownership 1.975 0.922

Type of Listing 9.988 0.125

What kind of Manufacturing 3.784 0.436

Manufacturing Pattern 6.434 0.376

Type of process 9.877 0.130

Annual Sales 12.374 0.261

Market Coverage 0.516 0.972

Area of Market 12.094 0.147

Business years 2.346 0.885

Software Usage 0.985 0.077


5/9

Business Organization, Type of Ownership, kind of Manufacturing, Type of process, Annual

turnover, Business years, Number of Employees, Nature of Industry, Type of Listing,

Manufacturing Pattern, Market Coverage, Area of Market and Software Usage of the

manufacturing units.

4.1.1 Association between Type of Industry and Supply Chain performance

The chi-square value as 26.163 and significant value as 0.045 which clearly indicates that

there is significant association between Type of Industry and Supply Chain performance of

Manufacturing units.

TABLE 1.2: ANOVA FOR TYPE OF INDUSTRY AND PERFORMANCE

Supply Chain Performance F Sig. Lead Time and Inventory 1.659 0.084

Responsiveness 0.997 0.450

It is observed from table 1.2 that there is no significant difference among the groups of

manufacturing units categorized on the basis of Type of Industry in respect of Lead Time and

Inventory and Responsiveness.

FIG. 1.1 TYPE OF INDUSTRY AND SUPPLY CHAIN PERFORMANCE -

CORRESPONDENCE DIAGRAM

The association between the type of industries categories and supply chain performance

segments can be identified by using correspondence analysis. The formed associations can

be seen from the diagram. Highly supply chain performance units belong to electronics,

Electronics, Building materials, Plastic, textiles and other types of industries, while the

Moderate supply chain performance units belong to Automobile, Agriculture, Furniture,


6/9

food and the Low supply chain performance units are associated with Chemical, metal and

pharmaceuticals industries.

4.1.2 Association between Nature of Industry and Supply Chain performance

The chi-square value as 11.717 and significant value as 0.020 which clearly indicates that

there is significant association between Nature of Industry and Supply Chain performance of

Manufacturing units.

TABLE 1.3: ANOVA FOR NATURE OF INDUSTRY AND PERFORMANCE

Supply Chain Performance F Sig. Lead Time and Inventory 1.573 0.209

Responsiveness 5.265 0.006

It is observed from table 1.3 that there is no significant difference among the groups of

manufacturing units categorized on the basis of Nature of Industry group with respect to Lead

Time and Inventory, while there is a significant difference among the groups in respect of

Responsiveness.

TABLE 1.4: DUNCAN TABLE FOR NATURE OF INDUSTRY AND RESPONSIVENESS

PERFORMANCE

Nature of

Industry N

Subset for alpha =

0.05

1 2

Medium Scale 94 3.2660

Small Scale 115 3.6217

Large Scale 46 3.6304

Sig. 1.000 0.951

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

among Nature of Industry with respect to responsiveness performance. Duncan table (Table 1.4)

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 1.3, Significant value is 0.006). This means that there is a significant

difference among groups of manufacturing units categorized on the basis of Nature of Industry

with respect to responsiveness performance.


7/9

FIG. 1.2 NATURE OF INDUSTRY AND SUPPLY CHAIN PERFORMANCE -

CORRESPONDENCE DIAGRAM

The association between the groups of units categorized based on Nature of Industry and

supply chain performance segments can be identified by using correspondence analysis.

The formed associations can be seen from the diagram. Highly supply chain performance

units belong to large scale industry group, Moderate supply chain performance units belong

to medium scale industry group, and Low supply chain performance units are associated

with small scale industry segment.


8/9

TABLE 1.5: CANONICAL CORRELATION BETWEEN SUPPLY CHAIN

PERFORMANCE AND BUSINESS DEMOGRAPHICAL VARIABLES

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

Roy's largest root .135259 2 252 17.0427 0.0000 uLawley-Hotelling trace .135818 4 500 8.4886 0.0000 a Pillai's trace .119702 4 504 8.0213 0.0000 a

Wilks' lambda .880365 4 502 8.2557 0.0000 eStatistic df1 df2 F Prob>F

Tests of significance of all canonical correlations

0.3452 0.0236

Canonical correlations:(Standard errors estimated conditionally)

ind_nature .1143118 3.634695 0.03 0.975 -7.043665 7.272289

ind .2915122 .8097826 0.36 0.719 -1.303231 1.886256v2

response 1.438354 3.835322 0.38 0.708 -6.114727 8.991435leadtime -1.486049 4.288398 -0.35 0.729 -9.931395 6.959296

u2

ind_nature 1.358312 .233479 5.82 0.000 .8985106 1.818113ind -.0851417 .0520174 -1.64 0.103 -.1875819 .0172986

v1

response .0009535 .2463666 0.00 0.997 -.4842279 .486135leadtime .6149735 .2754705 2.23 0.026 .0724764 1.157471

u1

Coef. Std. Err. t P>|t| [95% Conf. Interval]

A canonical correlation analysis was conducted using the two supply chain performance factors as

predictors of the two business demographical variables that was significant in the chi-square test to

evaluate the multivariate shared relationship between the two variable sets (i.e., supply chain

performance factors and business demographical). The analysis yielded two functions with

canonical correlations (r) of 0 .3452, and 0.0236 for each successive function. Collectively, the full

model across all functions was statistically significant using the Wilkss = 0.8804 criterion, F(4,

502) = 8.2557,p < 0.01. Because Wilkss represents the variance unexplained by the model, 1

yields the full model effect size in an r2 metric. Thus, for the set of three canonical functions, the r2

type effect size was 0.12, which indicates that the full model explained a substantial portion, about

12% of the variance shared between the variable sets that are two supply chain performance factors

and two business demographical variables.

5. CONCLUSION AND IMPLICATIONS

This paper has provided empirical justification for a relationship between of supply chain

performance and business demographic variables within the context of manufacturing in Union


9/9

Territory of Puducherry. Supply chain performances segments have significant association with

types of industry and nature of industry and also indicates that nature of industry have significant

difference with responsiveness performance.Finally the result indicates that there is a 12% of thevariance shared between two supply chain performance factors and two business demographical

variables. The analysis of the relationship between supply chain performance and business

demographical variables might directly influence the overall firm performance. Perhaps, the most

serious limitation of this study was its narrow focus on Puducherry manufacturing Enterprises, thus

precluding the generalization of findings to other emerging countries as well as other sectors such

as service and government sectors that may benefit from a sound SCM strategy. This researchpaper adds to the body of knowledge by providing new data and empirical insights.

REFERENCES

1. Burgess, K., Singh, P.J. and Koroglu, R. (2006), Supply chain management: a structuredliterature review and implications for future research, International Journal of Operations

& Production Management, Vol. 26 No. 7, pp. 703-29.2. Chakravarthy, B.S. (1986), Measuring strategic performance, Strategic Management

Journal, Vol. 7, pp. 437-58.

3. Chopra, S. and Meindl, P. (2001), Supply Chain Management, Prentice-Hall, EnglewoodCliffs, NJ.

4. Fuentes-Fuentes, M.M., Albacate-Saez, C.A. and Llorens-Montes, F.J. (2004), The impact

of environmental characteristics on TQM principles and performance, Omega, Vol. 32No. 6, pp. 425-42.

5. Gunasekaran, A., Patel, C. and McGaughey, E. (2004), A framework for supply chain

performance measurement, International Journal of Production Economics, Vol. 87, pp.333-47.

6. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data

Analysis, Prentice-Hall, Englewood Cliffs, NJ.

7. Hunger, J.D. and Wheelen, T.L. (1993), Strategic Management and Business Policy, 4thed., Addison-Wesley, Reading, MA.

8. Koh, S.C.L. and Tan, K.H. (2006), Operational intelligence discovery and knowledge

mapping approach in a supply network with uncertainty, Journal of ManufacturingTechnology Management, Vol. 17 No. 6, pp. 687-99.

9. Larson, P.D. and Sinha, A. (1995), The total quality management impact: a study of

quality managers perceptions, Quality Management Journal, Vol. 2 No. 3, pp. 53-66.

10.Prajogo, D.I. and Sohal, A.S. (2001), TQM and innovation: a literature review andresearch framework, Technovation , Vol. 21, pp. 539-58.

11. Tan, K.C. (2001), A framework of supply chain management literature, European

Journal of Purchasing & Supply Management , Vol. 7 No. 1, pp. 39-4812.York, K.M. and Miree, C.E. (2004), Causation or covariation: an empirical re-

examination of the link between TQM and financial performance, Journal of Operations

Management, Vol. 22, pp. 291-311.

A Study on the Supply Chain Performance of Manufacturing Industries in Union Territory of Puducherry...

Documents

Transcript of A Study on the Supply Chain Performance of Manufacturing Industries in Union Territory of Puducherry...