SUPPLY CHAIN PERFORMANCE AND FINANCIAL ......to-cash cycle time, working capital or cash flow from operations with objective in maximize shareholders’ value. The supply chain manager,

i

SUPPLY CHAIN PERFORMANCE AND FINANCIAL

SUCCESS OF SELECTED COMPANIES ON BURSA

MALAYSIA

LOH SHYONG WOEI

CHARTERED ACCOUNTANT

MALAYSIAN INSTITUTE OF ACCOUNTANTS

1995

Submitted to the Graduate School of Business

Faculty of Business and Accountancy

University of Malaya, in partial fulfilment

of the requirements for the Degree of

Master of Business Administration

JUN 2008

ii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS iv

ABSTRACT v

LIST OF TABLES vi

LIST OF FIGURES vi

CHAPTER 1 : INTRODUCTION

1.0 Purpose and Significance of the Study 2

1.1 Research Questions / Objectives of the Study 7

1.2 Scope of the Study 8

1.3 Organisation of the Study 10

CHAPTER 2 : LITERATURE REVIEW

2.0 The Development of Supply Chain Management (SCM) 12

2.1 Supply Chain Management Frameworks 17

2.2 Companies’ financial success 22

2.3 Linking SCM practices with companies’ financial success 24

CHAPTER 3 : RESEARCH METHODOLOGY

3.0 Research Hypotheses 30

3.1 Selection of Measures 32

3.1.1 Dependent variable 34

3.1.2 Independent variables 35

3.2 Sampling Design 38

3.3 Data Collection Procedure 39

3.4 Data Analysis Techniques 40

iii

CHAPTER 4 : RESEARCH FINDINGS

4.0 Summary Statistics of Sample Profile 42

4.1 Analysis of Measures 45

4.2 Testing of the Hypotheses 46

4.2.1 Testing whether correlation exist between company

financial success and superior supply chain practices46

4.2.2 Testing the predictability of improvement in supply chain

practices has effect on market capitalization of companies50

4.3 Summary of Research Results 57

CHAPTER 5 : CONCLUSION AND RECOMMENDATIONS

5.0 Summary and Conclusion 61

5.1 Limitation of the Study 63

5.2 Suggestions for Future Research 64

5.3 Implications 65

REFERENCES 66

APPENDIX

App 1.0 List of Selected Companies 73

App 2.0 Selected Companies Profile 78

iv

ACKNOWLEDGEMENTS

I would like to take this opportunity to express my warm and sincere thanks to

Prof.Madya Dr.Abdul Latif Haji Saleh, my project supervisor, who has given me his

invaluable time, encouragement, understanding and constructive suggestions that had

made this report successful.

Special appreciation goes to my most beloved family members for their love, patience

and understanding during period of my study in pursuing the MBA degree. They have

always been wonderful to my life.

Once again, thank you to all of you.

v

ABSTRACT

The lack of links between supply chain operations and financial performance seems to be

related to the perception on the difficulty of translating supply chain operational

measures into financial targets. There is accuses being made that these operational

measures are set at functional level. With little academic research in this area, this study

represents an attempt to explore and to gain a preliminary insight into the linkage among

companies practicing superior supply chain management (SCM) and their financial

success (FS). This SCM-FS relationship can be used by companies to benchmark its

supply chain practices from a financial performance viewpoint. To explore the SCM-FS

relationship, superior supply chain practices companies and financial success companies

was defined and use cross-tabulation and chi-square analysis for statistical testing. A

financial success predictive model using the selected four supply chain measures was

developed and tested by multiple regression analysis. The results of this study reveal that

there is a linkage between companies which practices superior SCM and financial

success. Besides, the study also shown that more companies are adopting SCM practices

lately. On the other hand, the multiple regression results shown all the selected supply

chain performance measures can explain portion of the variability of the companies’

financial success. The implication of this study would undoubtedly be beneficial to SCM

practitioners and financial professional as it has provided empirical evidence that

company with superior SCM practices will associate with financial success. It is hoped

that this paper would not only increase our understanding of SCM, but also generate

more interest in this field from an empirical perspective, specifically in the Malaysian

context and contribute to the SCM literature library.

vi

LIST OF TABLES

NO. TITLE OF TABLE

1. Level 1 Metrics from SCOR model 33

2. Performance Attributes Definition 34

3. Summary of Dependent and Independent Variables 38

4. Selected companies business sectors 43

5. Revenues, shareholders funds and market capitalization 43

6. Profit before tax 44

7. Tests of Normality of Data 45

8. Financial success and market capitalization

cross tabulation (FY99 to FY02) 47

9. Chi Square test (FY99 to FY02) 47

10. Symmetric Measures (FY99 to FY02) 47

11. Financial success and market capitalization

cross tabulation (FY03 to FY06) 49

12. Chi Square test (FY03 to FY06) 49

13. Symmetric Measures (FY03 to FY06) 49

14. Model Summary 54

15. ANNOVA 54

16. Coefficients 54

17. Collinearity Diagnostics 55

18. Residuals Statistics 55

LIST OF FIGURES

NO. FIGURE TITLE

1. SPSS Output for Multi-collinearity testing 53

1

CHAPTER 1

INTRODUCTION

2

CHAPTER 1: INTRODUCTION

1.0 PURPOSE AND SIGNIFICANCE OF THE STUDY

Today business entities competes base on platform of customized products and services

and cost efficient productions. In addition, globalization and intensive world-wide

competition along with the technological advancements create an entirely new business

environment for competition as well as providing the opportunities to succeed.

As a result, many companies becoming more customers oriented in terms of reducing

response time to customer requests and improving quality. Companies focused on core

competencies and attempted to achieve competitive advantage by more effectively

managing purchasing activities and relationships with suppliers. This has shifted many

companies focus on supply chain processes to an ability to add customer value.

Organizations are increasingly faced with the reality that they cannot exist in isolation but

are one piece of a complex chain of business activity (Keah Choon Tan et al. 1999).

These forward-looking companies are dynamic and 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.

The supply chain concept is originated from the formation of a value chain network

consisting of individual functional entities committed to provide resources and

information to achieve the objectives of efficient management of suppliers as well as the

3

flow of parts (Lau and Lee, 2000). Supply chain management (SCM) includes a set of

approaches and practices to effectively integrate suppliers, manufacturers, distributors

and customers for improving the long-term performance of the individual firms and the

supply chain as a whole in a cohesive and high-performing business model (Chopra and

Meindl, 2001). As defined by the Council of Supply Chain Management Professionals,

SCM encompasses the planning and management of all activities involved in sourcing

and procurement, conversion and all logistics management activities as well as

coordination and collaboration with channel partners.

SCM and related strategies are crucially important to the success of a company. This is

because the cost and quality of goods and services sold are directly related to the cost and

quality of goods and services purchased. Therefore, supply chain policies such as

procurement and supplier selection have an important role in the SCM (Hartley and Choi,

1996; Degraeve et al. 2000). Lean practices to improve the internal processes of an

organization in line with the principles of just in time (JIT) supply are other highly

recognized practices in SCM (Burgess et al., 2006; Cigolini et al. 2004). Integration of

internal processes of the organization with the suppliers and customers forms the essence

of the whole idea behind SCM. With the widespread use of internet, web-based systems

enable organizations to form strong customer and supplier integration for inventory

management, demand forecasting, customer and supplier relationship management

(Frohlich and Westbrook, 2002). Responding proactively to the market and business

environment changes, can be facilitated by simultaneous development of supply chain

and the output/product of the chain (Ismail et al. 2006).

4

While interest in SCM is immense, it is clear that much of the knowledge about SCM

resides in narrow functional such as purchasing, logistics, IT and marketing. As

highlighted by Gardner et al. 2004, although the implications of SCM are fully

understood, little research has been devoted to find the links between operational

execution and financial improvements.

A number of operational metrics have also been developed over time. However, most of

the metrics developed are fragmented within and across organizations and are not linked

with companies’ financial performance.

Supply chain processes and activities must be linked with financial performance. In order

to develop such links, correlations between operational measures and financial measures

must be identified. The identification of these correlations would allow the senior

decision makers to translate financial targets into operational measures and middle

management to tie these measures to operational processes to be implemented at lower

levels of the organization. On the other hand, the knowledge of the impact of processes

on operational and financial measures can help supply chain executives assess the

viability and the financial impact of proposed changes.

Most senior decision makers now acknowledge that SCM is an essential contributor to

operational excellence. A survey carried out from period 1997 to 2000 by an international

study team staffed by researchers from Accenture, INSEAD and Stanford University and

report published in 2003 shown that the supply chain is “very important” or “critical” to

5

about 90 percent of the executive survey population. Another research conducted by

Caruso et al. (2004) validates the hypothesis that companies with superior supply chain

performance achieve better earnings per share, return on assets and profit margins than

their peers.

Despite the awareness of important of SCM practices, there is little empirical research in

how SCM practices impact on company financial success. This research is to explore the

causal linkage among SCM practices and financial success by means of empirical data.

By identify the linkage between supply chain measures and financial success of the firm,

it will reduce the perception gaps exist in operational aspect and financial aspect on

SCM. In addition, making the financial-supply chain management connection is a

fundamental prerequisite to achieving the breakthrough results anticipated from SCM

initiatives.

As indicated by the survey results, reducing cost of operations, improving inventory

turnover, reducing lead times, fulfilling customer satisfaction, increasing flexibility and

cross-functional communication appear to be the most important objectives to implement

SCM strategies (V.M. Rao Tummala, 2006). In order to develop and implement SCM

plans therefore needs the commitment and involvement by senior management of the

company and its operating business units. At the same time the responses by the survey

respondents indicated that not enough resources were allocated to implement and support

SCM initiatives in their divisions. In another survey, participants were asked whether

their return on investment had increased to expected levels after implementing

6

contemporary supply chain management (SCM) practices, 76% affirmative response to

that question clearly showed that effort focused on carefully managing supply chains

produced financial benefits for participating firms (Gunasekaran et al. 2004). This

implication for the connection will have involvement of the finance function in setting

supply chain metrics and monitoring performance and alignment among functions on

performance measures.

The chief financial officials (CFO) measure the company’s success in terms of growth in

revenue and earnings per share, effective and efficient in assets utilization such as cash-

to-cash cycle time, working capital or cash flow from operations with objective in

maximize shareholders’ value. The supply chain manager, on the other hand, looks at on-

time deliveries; inventory turns and forecast reliability and so on. Part of the reason why

operational measures are not being used by finance is that they do not fit in with the

normal accounting-financial language of the firm. The challenge is to provide the

translation of operations outcomes to financial measures. The finance function must be

the conjunction where all business factors converge. Companies seem to have operational

measurement systems in place, but not comprehensive measurement systems involving

both financial and non-financial measures. Measures seem to be often contradictory

partly due to the fact that supply chain goals are set at functional level and partly to the

different languages spoken by supply chain and finance executives.

7

1.1 RESEARCH QUESTIONS / OBJECTIVES OF THE STUDY

This study addresses the following research questions concerning the causal relationship

between superior supply chain performance achievement and financial success of

companies. There are:

Research question (RQ)

RQ1. Does companies with higher levels of SCM performance achievement will also be

financial success companies? and

To further explore, if there is evidence shown the existence of causal relationship

between superior supply chain practices and financial success then the following question

address.

RQ2. Which SCM performance measures have high correlations with companies’

financial success?

Therefore, the objective of the study is to explore the causal relationship between SCM

practices and financial success companies that measure by market capitalization which

can be used by senior decision maker to benchmark its supply chain practices from a

financial performance viewpoint. In addition, the identified SCM performance measures

that have high correlation with companies’ financial success will become primary

measures and focus in SCM. This enable company is to examine correctly the linkages

between a firm's supply base management and financial success.

8

It can therefore be proposed that firms reporting the highest levels of financial and

operational performance will be emphasizing not only internal quality initiatives, but also

initiatives relating to the management of all elements of their supply chain including

customers and suppliers, and the quality of delivered products.

In addition, this paper adds to the body of knowledge by providing new data and

empirical insights into the relationship between supply chain performance measures and

financial success of company.

1.2 SCOPE OF THE STUDY

The scope of the study is based on result of benchmarking study of financial information

extracted from public quoted companies in Malaysia. The objective of using

benchmarking in this research was to increase the knowledge about the SCM practices

that link to financial success and to enable the companies to learn from the best practice.

A number of measures have been proposed over time to measure supply chain efficiency.

Most “traditional” high-level corporate measures focus on financial impact and

outcomes. This seems mostly to be a consequence of the facts that financial reporting is

done on a regular basis, financial measures are readily available and relatively easy to

obtain from Companies Commission Malaysia for companies not quoted and from Bursa

Malaysia website for companies that were quoted in Malaysia. In addition, practicing

managers in business would agree that cost analysis is important in the management,

9

planning and control of their organizations. Because the metric of cost is easily

understood and routinely welcomed by management, cost-based performance measures

are attractive for use in SCM. Cost-based performance measures are compatible across

various processes and stages of the supply chain. Cost-based measures also provide direct

input into the capital budgeting processes used to justify investment in supply chain

improvement initiatives.

The first study we carried out is to identify is there a relationship exist between a

company’s financial success with the four supply chain performance measures chosen

namely (X1) revenue, (X2) cost of sales as percentage of revenue, (X3) cash to cash cycle

and (X4) return on working capital. We study this over two distinct periods, ie from

financial year 1999 to 2002 and from financial year 2003 to 2006. Two distinct time

periods were used to ascertain the gains or slips in supply chain performance with

improvements or deterioration in financial performance. In addition it can use for identify

and validate the relationship exist.

Next, we use regression analysis to identify which supply chain measures that has

strongly correlated with the company’s financial success representing by market

capitalization. The data use for the analysis using period from financial year 1999 to

2006. The identification of measures will enable senior decision maker to set the priority

in design the key performance measures for supply chain performances.

10

1.3 ORGANISATION OF THE STUDY

The paper is organized into five sections. This section outlines the purpose of the

research, significant of the study, research questions and scope of the study. Section 2

presents the literature review on the development of SCM and its frameworks,

companies’ financial success and the linkage between SCM practice and financial

success. This literature review helps to underpin the research framework in this paper and

set out the study’s hypotheses. Section 3 describes the methodology used by this research

which comprised of selection of measures, sampling design, data collection and data

analysis techniques employed. Sections 4 analyses the data collected such as description

of the general characteristics of the selected companies and followed by analysis of

measures and testing the hypothesis to assess the SCM practices that has impact on

companies’ financial success. The last section summarizes the key findings and draws

some conclusions related to the causal linkage between SCM practices and company

financial success. Besides, the implication of this study and suggestion for future research

is highlighted and followed by list out some limitation of this study.

11

CHAPTER 2

LITERATURE REVIEW

12

CHAPTER 2: LITERATURE REVIEW

2.0 THE DEVELOPMENT OF SUPPLY CHAIN MANAGEMENT (SCM)

In the 1980s, severe global competition imposed business entities to offer high quality

products at low costs while at the same time increasing design flexibility. Manufacturers

practiced the principles of just-in-time and total quality management (TQM) to improve

competitiveness. Companies implemented practices of benchmarking, process control

techniques, and training and development programs to built quality into products

(Ebrahimpour, 1985; Modarress and Ansari; 1989; Schroeder et al. 1992). Senior

management leadership on quality related matters, strategic quality planning, and

evaluation of information on quality also became part of the management agenda

(Benson et al. 1991; Saraph et al. 1989).

As competition in the 1990s intensified further, so did the challenges related with

delivering products or services to the right place at the right time at the lowest costs.

Manufacturing organizations began to recognize the potential benefits and importance of

strategic and cooperative buyer-supplier relationships. Organizations began to implement

strategic suppliers in resource management decisions (Morgan and Monczka, 1996).

Instead of relying on tools such as acceptance sampling to inspect the quality of incoming

materials and component parts, manufacturers purchased from qualified or certified

suppliers (Inman and Hubler, 1992). Many manufacturers adopted the concept of supply

base management to reduce costs by reducing inventory level and improving efficiency

throughout the supply chain (Watts and Hahn, 1993, Krause, 1997). In addition,

13

organizations focus more on customer driven corporate policies that sought to

simultaneously pursue objectives of customer satisfaction, quality and productivity

improvement and cost reduction. This simultaneous integration of customer

requirements, internal processes, and upstream supplier performance is commonly

referred to as supply chain management (Christopher, 1992).

Supply base management refers to how firms make use of their suppliers' processes,

technologies, and capabilities to improve competitive advantage (Farley, 1997), and how

the manufacturing, logistics, distribution and transportation functions are coordinated

within organizations (Lee and Billington, 1992). Many firms have reduced their supply

base so they can more effectively manage relationships with strategic suppliers (Tully,

1995). Buying firms are developing cooperative with suppliers and viewing suppliers as

virtual extensions of their firm (Copacino, 1996). As a result of increasing dependence on

suppliers, any shortcomings in supplier performance may present buying firms with

problems such as missed target delivery dates and inferior quality levels. For other

companies however, supplier with superior performance may participate earlier in the

product design process to provide more cost-effective design choices, develop alternative

conceptual solutions, select the best components and technologies, and help in design

assessment (Monczka et al., 1994). Emphasizing internal competencies requires greater

reliance on external suppliers to support non-core requirements, particularly in design and

engineering support (Prahalad and Hamel, 1990).

14

The supply chain comprises organizations and flows of goods and information between

organizations from raw materials to end-users (Handfield and Nichols, 2002). It is a

meta-organization built up by independent organizations that have established inter-

organizational relationships and integrated business processes across the borderlines of

the individual firms. Management of such an arrangement refers to Supply Chain

Management (SCM) with the objective of improving the overall profitability of the

activities and/or organizations involved.

SCM is implemented by integrating corporate functions using business processes within

and across companies (Council of Logistics Management 2003). SCM encompasses more

than the activities of any individual corporate function. However, frequently it is seen as

a synonym for logistics (Simchi-Levi, Kaminsky, and Simchi-Levi 2000), operations

management (APICS 2001), procurement (Monczka, Trent, and Handfield 1998), or a

combination of the three (Wisner, Leong, and Tan 2004). Many view the "supply chain"

as being composed of inbound materials, raw material inventories, manufacturing,

finished goods inventories and distribution and view these activities within the purview

of a single firm; others view the supply chain as these activities from point-of-origin to

point-of-consumption. Another perspective of SCM is based on the management of

relationships both between corporate functions and across companies (Ellram and Cooper

1993).

15

Lambert et al. (1998) offer the following definition of SCM: “SCM is the integration of

key business processes from end user through original suppliers that provides products or

services and information that add value for customers and other stakeholders”. These

processes include customary logistics activities such as warehousing, inventory control,

and transportation management, as well as non-traditional logistics activities like

purchasing, production support, packaging, and customer order processing. SCM is based

on the concept that integration across business operations is essential to customer

satisfaction, value creation, exceptional returns, and long-run competitive advantage.

SCM can be seen as an example of evolutionary and cumulative innovation, which is

often described as originating from internal programs aimed at improving overall

effectiveness (Saad et al. 2002). The focus is not only limited to increasing the internal

efficiency of organizations, but also has now been extended to include methods of

reducing waste and adding value across the entire supply chain (New and Ramsay, 1997;

Christopher, 1998; Harland et al. 1999). SCM has shifted the emphasis from internal

structure to external linkages and processes and is dependent on the interaction between

the organization and its external environment. It is seen as a set of practices aimed at

managing and coordinating the whole supply chain from raw material suppliers to end

customers (Harland, 1996; Vollman et al. 1997; Slack et al. 2001), which develop greater

synergy through collaboration along the whole supply chain (Lamming, 1993; New and

Ramsay, 1997).

16

This holistic approach is linked with the effective management of the interfaces between

all the organizations involved (Von Hipple, 1986), and the integration of both upstream

and downstream processes (Harland et al. 1999; Christopher and Juttner, 2000). This

major emphasis on co-ordination and integration is strongly linked to the development of

more effective and longer-term relationships between buyers and suppliers (Spekman et

al. 1998).

These new types of relationships are gradually alleged as a mean to utilize resources

better through the whole supply chain (Dubois and Gadde, 2000). In addition, it can

promote greater transparency in transactions, increased trust and commitment (Ali et al.

1997). There are successful examples of where SCM is delivering significant

performance improvements across the entire supply chain such as Amazon.com, Tesco,

Dell computers and Toyota (Houlihan, 1985; Burgess, 1988). It can also be an important

element in innovation in products, processes and organization (Edum-Fotwe et al. 2001).

Information can be more readily shared and knowledge identified, captured and

disseminated throughout the organizations in the chain (Mowery, 1988). This has led to

an increasing adoption of partnership approaches and inter-organizational alliances to

achieve significant mutual benefits involving sharing resources, information, learning and

other key assets (Akintoye et al. 2000).

17

2.1 SUPPLY CHAIN MANAGEMENT FRAMEWORKS

Since the mid 1990s, academics in the fields of logistics, marketing, and operations

management have attempted to describe SCM (Lambert et al. 2005).

In 1994, The Global Supply Chain Forum (GSCF) was formed by executives from a

group of multi-national companies and developed a definition of SCM. The GSCF

defines supply chain management as "the integration of key business processes from end

user through original suppliers that provides products, services, and information that add

value for customers and other stakeholders". Implementation is carried out throughout

three primary elements: the supply chain network structure, the supply chain business

processes, and the management components. The supply chain network structure is

comprised of the member firms with which key processes will be linked. The following

eight supply chain management processes are included in the GSCF framework:

Customer Relationship

Customer Service Management

Demand Management

Order Fulfilment

Manufacturing Flow Management

Supplier Relationship

Product Development and Commercialization

Returns Management

18

Customer Relationship Management provides the structure for how relationships with

customers are developed and maintained. Cross-functional customer teams tailor product

and service agreements to meet the needs of key accounts, and segments of other

customers (Croxton et al. 2001). Customer Service Management provides the firm's face

to the customer, a single source of customer information, and the key point of contact for

administering the product service agreements (Bolumole et al. 2003). Demand

Management provides the structure for balancing the customers' requirements with

supply chain capabilities, including reducing demand variability and increasing supply

chain flexibility (Croxton et al. 2002). Order Fulfilment includes all activities necessary

to define customer requirements, design a network, and enable the firm to meet customer

requests while minimizing the total delivered cost (Croxton 2003). Manufacturing Flow

Management includes all activities necessary to obtain, implement and manage

manufacturing flexibility and move products through the plants in the supply chain

(Goldsby and Garcia-Dastugue 2003). Supplier Relationship Management provides the

structure for how relationships with suppliers are developed and maintained. Cross-

functional teams tailor product and service agreements with key suppliers (Croxton et al.

2001). Product Development and Commercialization provides the structure for

developing and bringing to market new products jointly with customers and suppliers

(Rogers et al. 2004). Returns Management includes all activities related to returns and

custody (Rogers et al. 2002).

Customer relationship management and supplier relationship management draw the vital

links in the supply chain and the other six processes are coordinated through them. Each

19

of the eight processes is cross-functional and cross-firm. Each is broken down into a

series of strategic sub-processes, where the outline for managing the process is defined.

Every sub-process is described by a set of activities. Cross-functional teams are used to

define the structure for managing the process at the strategic level and implementation at

the operational level. While the management components that support the processes

planning and control, work structure, organization structure, product flow facility

structure, information flow, management methods, power and leadership structure, risk

and reward structure, and culture and attitude.

The second framework was developed by the Supply-Chain Council (SCC), a non-profit

organization founded by Pittiglio, Rabin, Todd, and McGrath (PRTM), a consulting

company, and AMR Research in 1996. The SCC originally had 69 member companies

and developed the Supply-Chain Operations References (SCOR) framework. Initially,

SCOR included four business processes: plan, source, make, and deliver (Supply-Chain

Council 1996), which are to be implemented within the firm and eventually connected

across firms in the supply chain. Return, the fifth process, was added in 2001 (Supply-

Chain Council 2001). By 2006, the SCC had over 800 members, and held conferences,

meetings, and retreats in many countries.

The objectives of the five SCOR processes are (Supply-Chain Council 2007, p. 7):

Plan - balances aggregate demand and supply to develop a course of action which

best meets sourcing, production, and delivery requirements.

Source - includes activities related to procuring goods and services to meet

planned and actual demand.

20

Make - includes activities related to transforming products into a finished state to

meet planned or actual demand.

Deliver - provides finished goods and services to meet planned or actual demand,

typically including order management, transportation management, and

distribution management.

Return - deals with returning or receiving returned products for any reason and

extends into post-delivery customer support.

Each of these processes is implemented in four levels of detail. Level 1 defines the scope

and content for the Supply Chain Operations Reference-model. Here basis of competition

performance targets are set. A company’s supply chain can be “configured-to-order” at

Level 2 from core “process categories.” Companies implement their operations strategy

through the configuration they choose for their supply chain. Level 3 defines a

company’s ability to compete successfully in its chosen markets, and consists of:

Process element definitions;

Process element information inputs and outputs;

Process performance metrics;

Best practices, where applicable;

System capabilities required to support best practices; and

Systems/tools

At this level, companies “fine tune” their operations strategy. Finally, Level 4 defines

practices to achieve competitive advantage and to adapt to changing business conditions

and companies implement specific SCM practices at this level. Each process is analyzed

and implemented around three components: business process reengineering,

21

benchmarking, and best practices analysis (Supply-Chain Council 2007, p. 1). SCOR

prescribes the use of business process re-engineering techniques to capture the current

state of a process and then determine the "to-be" state based on business process

templates for plan, source, make, deliver, and return. Benchmarking is used to determine

target values for operational performance metrics for the "to-be" state of the processes.

The third component, best practices analysis, aims to identify management practices and

software solutions used successfully by similar companies that are considered top

performers. The identification of the best business practices needed to support the "to-be"

state of the processes becomes the roadmap for implementation.

The third framework includes three business processes: customer relationship

management, product development management, and supply chain management

(Srivastava, Shervani, and Fahey 1999). The description of customer relationship

management includes many of the activities that conventionally are performed by the

marketing and sales functions such as developing and executing advertising programs. In

their description, product development management is the process where the need for

cross-functional interfaces is the most explicit. In fact, their description includes a sub-

process called "identifying and managing internal functional/departmental relationships"

(Srivastava, Shervani, and Fahey 1999). The third process, supply chain management,

focuses on the product flow from acquisition of materials from suppliers to

manufacturing, to order processing, to distribution to customer service management. This

process includes many of the activities that are part of the Council of Logistics

Management's definition of logistics. Srivastava and his colleagues focused on the role of

22

the marketing function in the three processes and did not address the role of other

corporate functions.

The forth framework was also developed in 1999 by Bowersox et al. (1999) based on

operational, planning and control, and behavioral. This framework was further developed

by Melnyk et al. (2000) to include eight business processes: plan, acquire, make, deliver,

product design/redesign, capacity management, process design/redesign, and

measurement.

The fifth framework was developed by Mentzer and his colleagues that focus on the

cross-functional interaction within a firm and on the relationships developed with other

supply chain members (Mentzer 2004; Mentzer 2001; Mentzer et al. 2001).

2.2 COMPANIES’ FINANCIAL SUCCESS

The view that the shareholder is the owner of the company and is therefore to whom a

business is ultimately responsible is not new. A firm's value can be increased in four

ways: increasing revenue, reducing operating cost, reducing working capital, and

increasing asset efficiency. In general, initiatives based on cost reductions and efficiency

improvements are easier to support. For example, if an initiative focuses on reducing

inventories and the same level of sales is achieved with lower inventory, then the benefits

from that initiative are easy to measure. However, long-term growth requires revenue

enhancement and managers need to focus on all four ways to increase value (Lambert et

23

al. 2005). Hence shareholder value planning direct management making decisions which

have the primary objective of increasing firm’s value by increasing returns to

shareholders' investment: such as capital appreciation of share price, dividend growth and

a positive net present value for future income streams.

Managers may adopt wealth-maximizing strategies in the bid to increase the value of the

firm’s share prices. Of importance in this respect are three main strategies. These are

operating, investment, and financial strategies. Operating strategies improved economic

efficiency, lower operating costs or through improvements in the efficient utilization of

resources thus leading to improved profitability. Investment strategies such as upgrading

in production capacity and technological process may give rise to an overall improvement

in the level of firm performance. Financial strategies are those that provide the choice of

equity or debt finance, since dividend payout and restructuring are financing models

which corporate managers can utilize in the bid to increase firm value. Thus, the adoption

of strategies should be positively related to shareholder wealth maximization.

Recent surveys indicated that many public and private companies have strong interests in

managing for value. Ramirez et al. (1991) surveyed Fortune 500 Chief Financial Officials

(CFOs) and managers and showed concerns about the market capitalization of the

companies. Trahan and Gitman (1995) surveyed Fortune 500 and Forbes 200 small

company CFOs and found out that CFOs have strong desires to know more about the

impact of financial decisions on stock value. In addition, small company CFOs wanted to

know more about impacts of cash flow on stock price. The survey by Davis (1996) also

24

showed that executives would like to focus on cash flow and performance measures. All

these surveys indicated that executives are strongly interested in creating shareholder

value by generating earnings and cash flow above their cost of capital used.

The CFO has the requisite financial expertise to link business processes, activities, and

tasks to key financial measures and takes an enterprise-wide perspective. Hence, their

objective is to achieve a financial success company by maximization shareholders’ value.

2.3 LINKING SCM PRACTICES WITH COMPANIES’ FINANCIAL

SUCCESS

Performance measurement is central to SCM. However, there appears to be a missing

link between the measurement of day-to-day supply chain operations and the overall

financial performance of the chain (Enrico Camerinelli et al. 2006). Several recent studies

have proposed sets of measures used to evaluate supply chain performance (Gunasekaran,

Patel, and McGaughey (2004), Banker, Chang, Janakiraman, and Konstans (2004), Otto

and Kotzab (2003), Gunasekaran and Tirtiroglu (2001), Beamon (1999)). While many of

the measures were non-quantifiable or non-public, such as customer satisfaction

measures or ability to meet deadlines, other measures that can be found include inventory

turnover, profit margin, and cash-to-cash cycle. As a result, quantitative performance

measures are often preferred to such qualitative evaluations because data are readily

available or because it has been used for a long time.

25

Financial markets reward supply chain performers in the long run. A survey result

reported in 2003 after joint studied by Accenture together with INSEAD and Stanford

University shown that supply chain leaders achieve a market capitalization Compounded

Annual Growth Rate (CAGR) premium of 7 to 26% above industry average. The results

above demonstrate that supply chain performance can be linked to the generation of

shareholder value. David Walters (1999) has identified the implications of shareholder

value planning for logistics decisions and belief that the shareholders’ return are

important has always been implicit objective manifested through financial objectives.

The survey by Uma V. Sridharan et al. (2005) has concluded that the supply chain

implementation issues can have a major impact on the value of firms. Another survey

also highlighted the fact that SCM initiatives alone cannot improve profitability and

market share. It is therefore imperative for managers to ensure their quality and

procurement implementation strategies, tactics, and measurements are correctly aligned

with strategies in the areas of finance, operations, marketing, new product development

and sales (Keah Choon Tan et al. 1999).

One of the methods which can guide CFO to judge his action in creating value to

shareholders is through benchmarking. Benchmarking is relevant in studying the supply

chain by measuring the company’s products, services, and processes and comparing them

against the relevant measures of successful firms (Christopher, 1998). Previous research

into supply chain benchmarking shown that it may lead to increase productivity of the

supply chain as a whole as managers compare their practices to the best in the field.

Stewart (1995) reported that Pittiglio, Rabin, Todd and McGrath (PRTM) generated a

26

comprehensive set of fact-based performance measures that can be used to accurately

describe a world-class supply chain of planning, sourcing, making, and delivering

activities. The benchmarking scheme covers four areas of performance measures which

are identified as the keys to unlocking supply chain excellence, delivery performance,

flexibility and responsiveness, logistics cost, and asset management. This is the first

known study that objectively links best practices employed with relative quantitative

performance achievements. Additionally, the study results describe relevant trend

information indicating the progress that companies have made towards improving their

supply chain operations. The PRTM’s concept of supply chain benchmarking has been

extended to be the supply chain operations reference (SCOR) model by the Supply Chain

Council (Stewart, 1997).

The concept of business process reengineering (BPR) was developed since the late 1980s.

In the beginning, companies rushed to capture the major perceived benefits to be gained

through reconfiguring businesses to meet shifting market demands and new strategic

imperatives. Early BPR efforts which were often based on information system design

techniques, were used to derive desired “to be” business processes, aimed at eliminating

non-value-added activities and improving the effectiveness of remaining activities. As a

result of BPR having a mainly internal viewpoint, these early efforts often left executives

confused about the real level of improvement. These concerns gave rise to the second

wave of re-engineering, centered on benchmarking which focuses on measuring the “best

of the best”. Gradually, benchmarking evolved to encompass “best practice” analysis,

combining quantitative metrics with qualitative practices and allowing correlation of

27

specific business practices to the resulting measurable outcome. A process reference

model approach allows management to be much more confident that the changes desired

in business process performance are the “right” changes, and that performance

improvements can be predicted, achieved and measured (Steward, 1997).

"SCOR mark supports and integrates seamlessly into the 'analyze phase' of the SCOR

model, resulting in a benchmark report highlighting where an organization stands against

selected peer groups," including hard to measure metrics such as flexibility and agility,

said Thomas Phelps, the council's chairman in 1997. "Our members are now able to use

the defined metrics in the SCOR model to set corporate strategy and accurately analyze

performance gaps."

Benchmarking can be defined as follows (American Productivity and Quality Center

(APQC),1993): Benchmarking is the practice of being humble enough to admit that

someone else is better at something, and being wise enough to learn how to match them

and even surpass them at it. The core of the current interpretation of benchmarking is:

Measurement, of own and the benchmarking partners' performance level, both for

comparison and for registering improvements.

Comparison, of performance levels, processes, practices, etc.

Learning, from the benchmarking partners to introduce improvements in your

own organization.

Improvement, which is the ultimate objective of any benchmarking study.

28

The firm’s success in the areas of cost control, operational flexibility, functional

integration, and information dissemination all hinge on the quality of its supply chain

management processes (Michael Tracey et al. 2005).

29

CHAPTER 3

RESEARCH METHODOLOGY

30

CHAPTER 3: RESEARCH METHODOLOGY

This section reports the research methodology including research hypotheses, selection of

measures, sampling design, data collection and data analysis techniques.

3.0 RESEARCH HYPOTHESES

The SCM framework developed in this study proposes that superior SCM practices have

a direct impact on the financial success of companies. SCM practices are expected to

increase a company financial success through market capitalization. A company is

defined financial success if it generated growth in market capitalization, the fundamental

objective for maximization of shareholders’ value. This leads to the following

hypotheses:

H1o. There is no relationship between company with superior SCM practices and

financial success.

H1A. There is relationship between company with superior SCM practices and financial

success.

Next, to identify which SCM measures have high correlation with financial success. This

leads to the following predictive model design.

Y i = β 0 + β 1X 1i + β 2X 2i + β 3X 3i + β 4X 4i + ε i

31

Where

Y i = financial success measure by market capitalization for observation i

β 0 = Y intercept

β 1 = slope of Y with variable X1 holding variables X2, X3 and X4 constant




ε i = random error in Y for observation i

X1= revenue ie proxy for supply-chain performance attributes “reliability,

responsiveness and flexibility”

X2 = Cost of Sales as % of Revenue ie proxy for supply-chain performance

attributes “Cost”

X3 = Cash-to-Cash Cycle Time ie proxy for supply-chain performance attributes

“Assets management”

X4 = Return on Working Capital ie proxy for supply-chain performance attributes

“Assets management”

To determine whether there is significant relationship between the dependent variable

and the set of explanatory variables, the null and alternative hypotheses are as follows:

H2B: β 1 = β 2 = β 3 = β 4 = 0 (no linear relationship between the dependent variable and

the explanatory variables)

H2A: At least one β j ≠ 0 (linear relationship between the dependent variable and at least

one of the explanatory variables)

32

3.1 SELECTIONS OF MEASURES

To construct and measure the above SCM practices, metrics and processes defined by the

Supply Chain Council’s Supply Chain Operations Reference Model (SCOR®) have been

used.

The Supply Chain Council’s SCOR® model represents one of the first trials to introduce

a common language and common practices in supply chain operations to allow results

comparison and benchmarking. The model sets out for its ability to become an industrial

standard for supply chain and logistics practitioners. SCOR is now recognized by the 800

member companies of the Supply Chain Council as an effective "toolkit" for companies

wanting to upgrade their supply chains for strategic advantage. SCOR is a cross-industry

model that contains standard process definitions and metrics, matching supply chain

processes against best practices.

The main advantages of the SCOR model are that it provides descriptions of standard

processes, a framework for the relationships among processes, performance metrics and

standard alignment to features and functionality. The framework can be effectively used

by finance executives to translate financial targets into operational metrics and by middle

management to tie these metrics to operational processes to be implemented at lower

levels of the organization. Also, the framework can help supply chain executives develop

an awareness of the impact of processes on operational and financial measures which can

be used to assess the viability and the financial impact of proposed changes. This

framework attempts to link operational metrics to shareholder value.

33

Below Table 1 shows the performance attributes and level 1 metrics from SCOR model.

The level 1 metrics are the calculations by which an implementing organization can

measure how successful they are in achieving their desire positioning within the

competitive market space.

Table 1: Level 1 Metrics from SCOR model

Level 1 Metrics

Supply-Chain Performance Attributes

Customer-Facing Internal-Facing

Reliability Responsiveness Flexibility Cost Assets

Perfect order Fulfillment √

Order Fulfillment Cycle Time √

Upside Supply Chain Flexibility √

Upside Supply Chain Adaptability √

Downside Supply Chin Adaptability √

Supply Chain Management Cost √

Cost of Goods Sold √

Cash-to-cash Cycle Time √

Return on Supply Chain Assts √

Return on Working Capital √

(Source: SCOR model)

From the table, each of the performance attributes is matched with one to several Level 1

metrics. For example, supply chain reliability is match with perfect order fulfilment while

supply chain responsiveness is match with order fulfilment cycle time. Each performance

attributes cover specific characteristics of a particular supply chain. In order to permit the

performance attributes to be analyzed and evaluated against other supply chain, Supply

Chain Council has defined the performance attributes as shown in Table 2 below.

34

Table 2: Performance Attributes Definition

Performance

Attribute

Performance Attribute Definition

Supply-chain delivery

reliability

The performance of the supply chain in delivering: the correct product,

to the correct place, at the correct time, in the correct condition and

packaging, in the correct quantity, with the correct documentation, to the

correct customer.

Supply-chain

responsiveness

The velocity at which a supply chain provides products to the customer.

Supply-chain

flexibility

The agility of a supply chain in responding to market place changes to

gain or maintain competitive advantage.

Supply-chain costs The costs associated with operating the supply chain.

Supply-chain asset

management

efficiency

The effectiveness of an organization in managing assets to support

demand satisfaction; this includes the management of all assets: fixed

and working capital.

(Source: SCOR model)

We used the following variables that were defined by SCOR’s model to measure the

correlation between superior supply chain performance and financial success of

companies.

3.1.1 Dependent variable

For this study, we defined financial success is where a company is able to generate

growth in market capitalization. Hence, the dependent variable is growth rate of market

capitalization. At the CFO level, supply chain solutions are of interest only to the extent

that they influence growth in market capitalization. Improvement in growth rate on

35

market capitalization will enable company to generate return to its shareholders over

years.

Dependent variable Y = Market Capitalization ie proxy for financial success

Marketcapitalization =

Number of ordinary sharesissued at each financial yearend

Xshare price at financialyear end

3.1.2 Independent variables

Independent Variable (X1):

Our first supply chain measure is growth in revenue. It is a proxy for supply-chain

performance attributes of reliability, responsiveness and flexibility. A key element of

successful supply base management involves downstream integration of customers as

well as the management of upstream suppliers. Each entity in the supply chain is a

supplier as well as a customer. When a customer driven corporate vision is implemented

simultaneously with effective supply base management practices, it can produce a

competitive edge in a number of different ways. These include increases in customer

satisfaction, market share and profits as result of practicing delivering the correct product

to the correct place at the correct time in the correct condition and packaging, in the

correct quantity, with the correct documentation to the correct customer. Through

practice of supply chain benchmarking, emerging as a leader in the industry would

provide a firm with the opportunity of increased sales. If an industry leader position is

still far reaching, benchmarking the supply chain performance against the best practice in

the industry would provide incentives for further improvement that will eventually lead to

increased sales. Revenue growth means companies able to provide customer satisfaction

of its products and services as result from effectiveness in managing supply base

36

management. The highest the growth means the company effective manage supply base

to satisfy customer satisfaction and increase market share and profits. Under our

hypothesis, we expect revenue to be positively related to financial success.

Independent variable X1 = Revenue ie proxy for supply-chain performance

attributes “reliability, responsiveness and flexibility”


The second supply chain measure is Cost of Sales as percentage of revenue (COS). This

is the proxy to measure supply chain costs. With appropriate strategic planning, it may be

anticipated that the utilization of resources will be optimized leading to cost savings. For

example, reduced cycle time in production could be materialized through reducing set-up

time and/or eliminating non value-added activities. With a shortened cycle time, more

orders could be processed, which would then result in improved efficiency and reduced

production cost per unit. In addition, the use of e-procurement tools could also shorten

order lead time and reduce ordering cost. Under our hypothesis we expect COS to be

negatively related to financial success. The lowest the percentage of the measure means

the more effective in manage supply base.

Independent variable X2 = Cost of Sales as % of Revenue ie proxy for supply-

chain performance attributes “Cost”


Next we focus on cash-to-cash cycle, a widely-used measure of supply chain

performance to represent asset utilization in working capital. This can be combined with

37

Inventory Conversion, Accounts Receivable Conversion and Accounts Payable

Conversion. Inventory Conversion is calculated as 365 times Inventory value divided by

Cost of sales and reflects the average length of time inventory turnover. Accounts

Receivable Conversion is calculated as 365 times Accounts Receivable divided by

Revenues and reflects the average length of time between sales and the cash collection.

Accounts Payable is calculated as 365 times Accounts Payable divided by Cost of Sales

and is a rough measure of how long it takes a firm to pay its suppliers. We expect cash-

to-cash cycle to be negatively related to financial success. SCM practice will not only

reduce inventory level, but will also free up warehouse space and reduce cash flow

(Mistry, 2006).

Independent variable X3 = Cash-to-Cash Cycle Time ie proxy for supply-chain

performance attributes “Assets management”

Cash-cash

Cycle Time =

Days Sales

Outstanding-

Days Payable

Outstanding+

Inventory Days

of Supply

Independent variable (X4):

Lastly, a measure concern with return on assets (ROI) that calculated by profit margin

divided by working capital. Management techniques that focus primarily on inventory

were that companies will have to pay higher prices for inputs and/or will cut selling

prices in order to move finished goods inventory. Therefore we used profit margin

calculated as earnings before interest, tax, depreciation and amortization (EBITDA).

While many measures of Profit could have been used – including Gross Profit, Operating

Profit, or Net Profit – we choose EBITDA since it represents cash flows to the firm

ignoring any financing or taxes. Working capital is amount invested in accounts

38

receivables and inventories less accounts payables. We expect return on working capital

to be positively related to financial success.

Independent variable X4 = Return on Working Capital ie proxy for supply-chain

performance attributes “Assets management”

Return on Working capital = EBITDA / Working capital

All the above variables and their relationships are summary below:

Table 3: Summary of Dependent and Independent Variables

Independent variables

( measures of SCM practices )

Dependent variables

(measures of companies’ financial success )

X1: Revenue

X2: Cost of sales as % of revenue

X3: Cash to cash cycle time

X4: Return on working capital

Y1: Market capitalization

3.2 SAMPLING DESIGN

The sample of the study was confined to selected companies quoted on Bursa Malaysia.

We have examined 160 companies quoted in the Industry Products of the main board of

Bursa Malaysia as at 24 September 2007. In this study, the following defined

characteristics were used for company selection. There are:

(1) The companies were quoted in the Industry Products sector;

(2) The companies were quoted on Bursa since financial year 1999

(3) The financial year end of the companies were 31 December; and

39

(4) The presentation of the Income Statement of each company follow the same

format

The reason for the above criteria set as explain below:

Selection criteria Reason for the criteria set

1 The companies were quoted

in the Industry Products

sector

To minimize the difference exist in business practices such

as legal, tax incentives, accounting requirement etc. which

is differ from Property or Construction sector. Hence, all

selected companies operate approximate to similar

business practices.

2 The companies were quoted

in Bursa since financial year

1999

This is to facilitate in comparative for two distinct periods

in data analysis.

3 Financial year end of the

companies were 31 December

To ensure consistence in comparative.

4 Presentation of the Income

Statement of each company

follow the same format

To ensure consistence in comparative.

On the basis of these selection criteria, a total of 34 companies were identified and

selected. The information on selected companies can be referred to Appendix 1 and 2.

3.3 DATA COLLECTION PROCEDURE

The data for this study was collected through Annual Reports of selected companies filed

with Bursa Malaysia web page covering from the financial years ended 31 December

1999 to 2006. The financial information extracted from the financial statements to

calculate the four SCM performance measures ie (X1) Revenue, (X2) Cost of Sales as

40

percentage of revenue, (X3) Cash-to-cash cycle time and (X4) Return on working capital

for each financial years. In order to calculate the company’s market capitalization,

number of shares issued and share price information of each company was also taken as

of each company’s financial year end as at 31 December. Share price of each company

was gathered through Bloomberg web page. The data collected was entered into

Microsoft Excel spreadsheet for further computation of market capitalization, cost of

sales as percentage of revenue, cash-to-cash cycle time, earnings before interest , tax and

amortization and working capital.

3.4 DATA ANALYSIS TECHNIQUES

Data was analyzed using the Statistical Package for the Social Sciences (SPSS) software.

Besides the normal descriptive analysis, the data was also tested with Chi-square test, t-

test, analysis of variance (ANOVA) and multiple regressions. Chi-square test employed

to test the correlation between categories of SCM performance measures and financial

success. While t-test and ANOVA were employed to determine the significance level of

predictability of the SCM performance measures to company financial success.

Collinearity diagnostics employed to test any problem with collinearity among

independent variables. To test the relationship between superior supply chain practices

and financial success of companies, data was rearrange into two distinguish period ie

from 1999 to 2002 and 2003 to 2006 in order to calculate the improvement of the

selective measures during these two period respectively. On the other hand, year to year

changes in measures used to test the predictability of the selected measures to financial

success for the period from 1999 to 2006.

41

CHAPTER 4

RESEARCH RESULTS

42

CHAPTER 4: RESEARCH RESULTS

This chapter presents the findings of the survey. It begins with a description of the

general characteristics of the selected companies and followed by analysis of the

measures and testing of the hypotheses. Finally a summary of the research results at the

end.

4.0 SUMMARY STATISTICS OF SAMPLE PROFILE

Based on the data collected, the characteristics of the sample companies were constructed

and shown in Table 4, 5 and 6. Table 4 list out the business sectors where the sample

companies’ core activities are engaged in. There were nine companies (26.5%) involved

in the plantation related product manufacturing such as oil palm and timber. Steel and

fabrication sector has a total seven companies (20.6%) and their products are steel

related, hardware, fasteners and galvanized products. There were five companies (14.7%)

involved in engineering product manufacturing and automotive product manufacturing

have three companies (8.8%). There were three companies engaged in building material

related products manufacturing. While packaging related product manufacturer, oil

related product manufacturer and food and beverage product related manufacturer has

two companies (5.9%) each respectively. Finally, there was a company engaged in

manufacture of pharmaceuticals related product.

43

Table 4: Selected companies business sectors

Number of companies %

Plantation related product manufacturers 9 26.5%

Steel and fabrication related product manufacturers 7 20.6%

Engineering product related manufacturers 5 14.7%

Automotive related product manufacturers 3 8.8%

Building materials related product manufacturers 3 8.8%

Packaging products related manufacturers 2 5.9%

Oil products related manufacturers 2 5.9%

Food and beverage product manufacturers 2 5.9%

Pharmaceuticals related product manufacturer 1 2.9%

Total 34 100.0%

(Sources: Annual Reports)

In terms of size of sample companies, refer table 5 and 6 below for their revenue,

shareholders’ funds, market capitalization and profit before tax reported in the annual

report for financial year ended 31 December 2006.

Table 5: Revenue, Shareholders’ funds and Market capitalization

Revenue Shareholders’ Funds Market Capitalization

RM' million Freq. % Cum % Freq. % Cum % Freq. % Cum %

0 - 500 19 55.9 55.9 25 73.5 73.5 23 67.7 67.7501 - 1,000 8 23.5 79.4 7 20.6 94.1 7 20.6 88.21,001 - 1,500 1 2.9 82.4 1 2.9 97.1 2 5.9 94.11,501 - 2,000 2 5.9 88.3 1 2.9 100.0 1 2.9 97.1> 2,000 4 11.8 100.0 - - 1 2.9 100.0Total 34 100.0 34 100.0 34 100.0( Sources : Annual Reports )

44

Table 6 : Profit Before Tax

RM' million Freq. %Cum

%

-100 to 0 6 17.7 17.71 to 200 23 67.7 85.3201 to 300 4 11.8 97.1> 300 1 2.9 100.0

Total 34 100.0(Sources : Annual Reports )

As shown in table 5, a total of nineteen companies (55.9%) reported revenue was less

than Ringgit Malaysia five hundred million. There were eight companies (23.5%) was

reported revenue between RM500 million to RM1 billion. Only seven companies

(20.6%) where their revenue was reported exceed RM1 billion.

The similar characteristic also shows in shareholders’ funds and market capitalization.

Almost thirty-two companies (94.1%) reported shareholders’ fund and thirty companies

(88.2%) reported market capitalization was less than one billion ringgit Malaysia

respectively.

Finally, an analysis of the profit before tax in table 6 reported six companies (17.7%)

were at loss situation. Most companies (67.7%) achieved profit before tax less than

RM200 million. Four companies (11.8%) achieved profit before tax between RM200 and

RM300 million.

Overall, the majority of the sample companies have the following characteristics where

the Revenue, Shareholders’ funds and Market capitalization was less than RM500 million

45

while Profit before tax was less than RM200 million and engaged primary in plantation

related products, steel and fabricated related products manufacturers.

4.1 ANALYSIS OF MEASURES

The major measures for the study were market capitalization, revenue, costs of sales as

percentage of revenue, cash-to-cash cycle time and return on working capital. Since many

statistical tests application assume data are normally distributed, it's a pre-require to

check the data is normally distribution and either to transforms the data or employ

nonparametric tests for data not normally distributed.

In this study, Descriptive data analysis and Explore procedure were used to test data

normality of each measure. If the results of the data shown not normal distributed, then

we engaged natural logarithmic for transformation.

Table 7: Tests of Normality of Data

Tests of Normality

Kolmogorov-Smirnov Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

Market capitalization

Before transformation 0.217 - 0.274 34 0.000 0.543 - 0.757 34 0.000

after transformation 0.075 - 0.129 34 0.161 - 0.200 0.963 - 0.988 34 0.246 - 0.962

Revenue

Before transformation 0.279 - 0.356 34 0.000 0.456 - 0.578 34 0.000


Cost of sales as % of Revenue

Before transformation 0.082 - 0.465 34 0.000 - 0.200 0.242 - 0.989 34 0.000 - 0.980


Cash-to-cash cycle time

before transformation 0.241 - 0.458 34 0.000 0.207 - 0.734 34 0.000


Return on working capital

before transformation 0.268 - 0.395 34 0.000 0.315 - 0.662 34 0.000


46

A low significance value (generally less than 0.05) indicates that the distribution of the

data differs significantly from a normal distribution. The Shapiro-Wilk test is displayed

for sample less than 50 cases. Table 7 above shown before transformation, the data not

normally distributed because both the significant value for Kolmogorov-Smirnov and

Shapiro-Wilk were less than 0.05. After transformation, the data for market capitalization

and revenue became normally distributed. The remaining three measures ie cost of sales

as % of revenue, cash-to-cash cycle time and return on working capital shown data

slightly better normal distributed than before transformation.

4.2 TESTING OF THE HYPOTHESES

In the following section, each of the proposed hypotheses is tested to identify any

significant differences and influences on the SCM practices to company financial

success. The results are illustrated as follows:

4.2.1 Testing whether correlation exist between company financial success and

superior supply chain practices

We use Chi-Square Analysis for this testing. The chi-square statistics (x2) is used to test

the statistical significance of the observed association in a cross tabulation. Here, the test

is conducted based on market capitalization and SCM practices from the data collected.

H1o. There is no relationship between company with superior SCM practices and

financial success.

H1A. There is relationship between company with superior SCM practices and financial

success.

47

(1) Period from financial year 1999 to financial year 2002.

Table 8: Financial Success -Market Capitalization (FY99-FY02) * No of SCM measuresimprovement (FY99-FY02) Cross tabulation

No of SCM measures improvement (FY99-FY02)

Total

0 or 1 SCMmeasures

improvement

2 SCMmeasures

improvement

3 or 4 SCMmeasures

improvement

FinancialSuccess -MktCapitalisation(FY99-FY02)

top 10 Count 7 0 3 10

Expected Count 7.1 1.5 1.5 10.0

% within No of SCMmeasures improvement(FY99-FY02)

29.2% 0.0% 60.0% 29.4%

11 to 20 Count 8 1 1 10



33.3% 20.0% 20.0% 29.4%

21 to 34 Count 9 4 1 14



37.5% 80.0% 20.0% 41.2%

Total Count 24 5 5 34



100.0% 100.0% 100.0% 100.0%

Table 9: Chi-Square Tests (FY99 to FY02)

Value dfAsymp. Sig. (2-

sided)

Pearson Chi-Square 5.942(a) 4 0.204

Likelihood Ratio 6.806 4 0.146

Linear-by-LinearAssociation

0.238 1 0.626

N of Valid Cases 34

a. 6 cells (66.7%) have expected count less than 5. The minimumexpected count is 1.47.

Table 10: Symmetric Measures (FY99 to FY02)Value Approx. Sig.

Nominal byNominal

Phi 0.418 0.204

Cramer's V 0.296 0.204

Contingency Coefficient 0.386 0.204

N of Valid Cases 34

a. Not assuming the null hypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

48

Inference:

The two-sided asymptotic significance of the chi square statistics is 0.204 in Table 9

which is greater than 0.10, so it’s safe to say that the difference are due to chance

variation, which implies that there are no relationship between financial success and

SCM success. While the chi square test is useful for determining whether there is a

relationship. It doesn’t tell us the strength of the relationship. Symmetric measures in

Table 10 attempt to quantify this. The significant values of all the three measures are

0.204 (p>0.05) indicating that no significant relationship. However, the values of all three

measures are below 0.5, so although the relationship is not due to chance, it is also not

very strong.

49

(2) Period from financial year 2003 to financial year 2006.

Table 11: Financial Success - Market Capitalization (FY03-FY06) * No of SCM measuresimprovement (FY03-FY06) Cross tabulation

No of SCM measures improvement (FY03-FY06)

Total

0 or 1 SCMmeasures

improvement

2 SCMmeasures

improvement

3 or 4 SCMmeasures

improvement

FinancialSuccess -MktCapitalisation(FY03-FY06)

top 10 Count 3 1 6 10



21.4% 9.1% 66.7% 29.4%

11 to 20 Count 3 6 1 10



21.4% 54.5% 11.1% 29.4%

21 to 34 Count 8 4 2 14



57.1% 36.4% 22.2% 41.2%

Total Count 14 11 9 34

Expected Count 14.0 11.0 9.0 34.0


100.0% 100.0% 100.0% 100.0%

Table 12: Chi-Square Tests (FY03 to FY06)Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 11.499(a) 4 0.021

Likelihood Ratio 10.918 4 0.028

Linear-by-LinearAssociation

4.457 1 0.035

N of Valid Cases 34

a. 8 cells (88.9%) have expected count less than 5. The minimum expectedcount is 2.65.

Table 13: Symmetric Measures (FY03 to FY06)Value Approx. Sig.

Nominal byNominal

Phi 0.582 0.021

Cramer's V 0.411 0.021

Contingency Coefficient 0.503 0.021

N of Valid Cases 34

a. Not assuming the null hypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

50

Inference:

The two-sided asymptotic significance of the chi square statistics is 0.021 in Table 12

which is less than 0.10, which implies that there is relationship between financial success

and SCM measures improvement. The symmetric measures in Table 13 shows the

significant values of all the three measures are 0.021 (p<0.05) indicating there is

significant relationship. The values of measures are above 0.5 (except Cramer’s V =

0.411), so the relationship is strong. Besides, compared to financial year 1999 to 2002,

total number of companies that have 3 or 4 SCM performance measures improvement has

increased from 5 companies to 9 companies in financial year 2003 to 2006. The same

happened for companies that have 2 SCM performance measures improvement for both

study periods. This indicated that SCM practices gaining more importance lately.

4.2.2 Testing the predictability of improvement in supply chain practices has effect

on market capitalization of companies

(1) Testing the appropriate use of multiple regression for predictability model

The residual analysis has been used to determine whether the multiple regression model

is appropriate to use. This is to determine whether there is a significant relationship

between the dependent variable and the set of explanatory variables. Because there is

more than one explanatory variable, the null and alternative hypotheses are as follows:

H2o: β1 = β2 = β3 = β4 = 0 (no linear relationship between the dependent variable and the

explanatory variables)

51

H2A: At least one βj ≠ 0 (linear relationship between the dependent variable and at least

one of the explanatory variables)

The null hypothesis is tested with an F test as shown in Anova summary table 15. From

the table, the p-value = 0.000 and less than 0.05, the decision rule is to reject H2o and

conclude that at least one of the explanatory variables is related to market capitalization.

Next, we like to know which of the explanatory variables is related to market

capitalization.

(2) Predictive Model

Linear regression was used to develop model for the predictability of financial success by

the four (4) independent variables.

Y i = β 0 + β 1X 1i + β 2X 2i + β 3X 3i + β 4X 4i + ε i

Where

Y i = financial success proxy by market capitalization for observation i

β 0 = Y intercept





ε i = random error in Y for observation i

X1= revenue ie proxy for performance attributes “reliability, responsiveness and

flexibility”

52

X2 = Cost of Sales as % of Revenue ie proxy for performance attributes “Cost”

X3 = Cash-to-Cash Cycle Time ie proxy for performance attributes “Assets”

X4 = Return on Working Capital ie proxy for performance attributes “Assets”

Linear Regression Assumptions testing

Significance levels of α = 0.05 and the Durbin-Watson was used to verify that residuals

were independent and normal probability plots were used to verify that residuals were

normally distributed.

Before we accepted the regression results as valid, we examined the degree of

multicollinearity and its effect on the results. To do so, we examined the Eigenvalue and

Condition Indices and make comparisons with the conclusion drawn from the variance

inflation factor (VIF) and tolerance values. Lastly, we checked for outliners (ie cases

falling at the outer ranges of the distribution that may be potentially biasing the results).

We used a threshold of three standard deviations for the residuals, which is appropriate

for our sample size of 238 to identify the outliers. All observations outside this range (3σ)

were considered outliers and were duly dropped from the regression.

53

SPSS outputs :

Observed Cum Prob

1.00.80.60.40.20.0

Exp

ecte

dC

um

Pro

b

1.0

0.8

0.6

0.4

0.2

0.0

Normal P-P Plot of Regression Standardized Residual

Dependent Variable: Y-Y changes in Market Capitalisatn ('mil)

Regression Standardized Residual

7.55.02.50.0-2.5

Re

gre

ss

ion

Sta

nd

ard

ize

dP

red

icte

dV

alu

e

6

4

2

0

-2

Scatterplot

Dependent Variable: Y-Y changes in Market Capitalisatn ('mil)

Figure 1: SPSS Outputs for multi-collinearilty testing

54

Table 14: Model Summary(b)

Model RR

SquareAdjustedR Square

Std. Errorof the

Estimate

Change Statistics

Durbin-Watson

Sig. FChange

RSquareChange

FChange df1 df2

1 .320(a) 0.102 0.087 165.12465 0.102 6.638 4 233 0.000 1.812

a. Predictors: (Constant), Y-Y changes in Return on working capital (%), Y-Y changes in Cash to cash cycle time (days),Y-Y changes in Revenue ('mil), Y-Y changes in Cost of sales (% of revenue)

b. Dependent Variable: Y-Y changes in Market Capitalisation ('mil)

Table 15: ANOVA(b)

Model Sum of Squares df Mean Square F Sig.

1 Regression 723,922.6 4 180,980.6 6.638 .000(a)

Residual 6,353,013.0 233 27,266.1

Total 7,076,935.6 237

a. Predictors: (Constant), Y-Y changes in Return on working capital (%), Y-Y changes in Cash to cash cycle time(days), Y-Y changes in Revenue ('mil), Y-Y changes in Cost of sales (% of revenue)

b. Dependent Variable: Y-Y changes in Market Capitalisation ('mil)

Table 16: Coefficients

Coefficients(a)

UnstandardizedCoefficients

Std.Coeff.

t Sig.

95% ConfidenceInterval for B Collinearity Statistics

BStd.

Error BetaZero-order Partial Tolerence VIF

Mo

del

1

(Constant) 8.512 11.243 0.757 0.450 -13.6 30.7

Y-Y changesin Revenue('mil)

0.158 0.031 0.317 5.039 0.000 0.1 0.2 0.976 1.024

Y-Y changesin Cost ofsales (% ofrevenue)

-37.486 20.873 -0.115 -1.796 0.074 -78.6 3.6 0.946 1.057

Y-Y changesin Cash tocash cycletime (days)

-0.007 0.013 -0.034 -0.538 0.591 -0.0 0.0 0.965 1.036

Y-Y changesin Return onworkingcapital (%)

0.002 0.025 0.004 0.065 0.948 -0.0 0.1 0.998 1.002

a. Dependent Variable: Y-Y changes in Market Capitalisation ('mil)

55

Table 17: Collinearity Diagnostics(a)

Model Dimension EigenvalueCondition

Index

Variance Proportions

Y-Ychanges in

Cost of sales(% of

revenue)

Y-Ychangesin Cashto cashcycletime

(days)

Y-Ychanges

in Returnon

workingcapital

(%) (Constant)

Y-Ychanges

inRevenue

('mil)

1 1 1.298 1.000 0.29 0.35 0.04 0.01 0.00

2 1.185 1.047 0.07 0.00 0.38 0.35 0.00

3 1.001 1.139 0.01 0.00 0.00 0.00 0.98

4 0.877 1.216 0.15 0.10 0.30 0.53 0.00

5 0.638 1.426 0.48 0.55 0.28 0.11 0.01


Table 18: Residuals Statistics(a)

Minimum Maximum Mean Std. Deviation N

Predicted Value -82.7118 353.7632 26.2179 55.26776 238

Residual -476.07394 1,248.13330 0.00000 163.72526 238

Std. Predicted Value -1.971 5.927 0.000 1.000 238

Std. Residual -2.883 7.559 0.000 0.992 238


Inference:

Testing the assumptions of linear programming:

Durbin-Watson statistic measures the correlation between each residual and the residual

for the time period immediately preceding the one of interest. If the residuals are not

correlated, the value of this statistic will be close to 2. From table 14, Durbin-Watson

statistic shown 1.812 and close to 2, therefore it can conclude that the assumption of the

regression model which requires the errors around the regression line be independent for

the residuals is not violated. From the scatter plot of residuals against predicted values

(Figure 1), there is no clear relationship between the residuals and the predicted values,

consistent with the assumption of linearity. The normal plot of regression standardized

56

residuals for the dependent variable also indicates a relatively normal distribution.

Therefore, based on the statistical analysis, it is safe to conclude that multi regression

model can be used.

Testing of multicollinearity among independent variables:

Table 16 and 17 displays statistics to show if there are any problems with collinearity

among independent variables. Collinearity (or multicollinearity) is the undesirable

situation where the correlations among the independent variables are strong. Eigenvalues

in Table 17 provide an indication of how many distinct dimensions there are among the

independent variables. When several eigenvalues are close to zero, the variables are

highly inter-correlated and small changes in the data values may lead to large changes in

the estimates of the coefficients. Condition indices in Table 17 are the square roots of the

ratios of the largest eigenvalue to each successive eigenvalue. A condition index greater

than 15 indicates a possible problem and an index greater than 30 suggests a serious

problem with collinearity. Tolerance shown in table 16 is a statistic used to determine

how much the independent variables are linearly related to one another (multicollinear).

When the tolerances are close to 0, there is high multicollinearity and the standard error

of the regression coefficients will be inflated. VIF, or the variance inflation factor in

Table 16 is the reciprocal of the tolerance. A variance inflation factor greater than 2 is

usually considered problematic, large VIF values are an indicator of multicollinearity.

Based on the collinearity diagnostics (Table 16 and 17) analysis, it is saved to conclude

that the problem with multicollinearity in our data can be ignored.

57

Results from the predictive model:

Table 14 shown all independent variables together explain 10.2% (R2) of the variance in

market capitalization, which is highly significant as indicated by the F-value in Table 15

(p-value < 0.05). An examination of the t-value in Table 16 indicates that Revenue

contributes to the prediction of market capitalization. The least square equation show

below:

Yi = 8.515 + 0.158X1i – 37.486X2i – 0.007X3i + 0.002X4i + εi

Where:

X1= revenue ie proxy for supply-chain performance attributes “reliability, responsiveness

and flexibility”

X2 = Cost of Sales as % of Revenue ie proxy for supply-chain performance attributes

“Cost”

X3 = Cash-to-Cash Cycle Time ie proxy for supply-chain performance attributes “Assets

management”

X4 = Return on Working Capital ie proxy for supply-chain performance attributes “Assets

management”

4.3 SUMMARY OF RESEARCH RESULTS

The research results can be summarized into two sections. Section one relates with

identified the relationship between company with superior SCM practices and financial

success. While section two is relates the predictability of improvement in SCM practices

has the effect on company financial success.

58

Research results for section one:

The results from cross tabulation between number of SCM measures improvement and

ranking of top financial success companies discover that the correlation analyzed by chi-

square statistics was not strong for financial years 1999 to 2002 and therefore we do not

reject the null hypothesis at the 0.05 level of significance. However, the correlation has

become stronger for subsequent financial years 2003 to 2006, this lead us to reject the

null hypothesis at the 0.05 level of significance. This imply that there is correlation exist

between superior SCM practices and financial success in second time period. Besides, the

result also shown that, companies which practices full scope of supply chain measures

tend to have chances to become a financial success companies in current business

environment compare to few years back.

Research results for section two:

The results from the multiple regression model shows that all the four SCM measures

explained 10.2% of the variation in market capitalization. Revenue and return on working

capital has positive correlation with market capitalization while cost of sales as % of

revenue and cash-to-cash cycle time shown negative correlation. This is expected

considering that lower cost of sales % of revenue and lower cash-to-cash cycle time

implies more cost effective in assets utilization. Among the four SCM measures,

Revenue (X1) is the SCM measures that highly contribute to the prediction of company

financial success as its p-value is significant at 0.05. The next measure probably is the

cost of sales as % of revenue (X2). The remaining two SCM measures ie cash-to-cash

59

cycle time (X3) and return on working capital (X4) seem statistically less contribute to the

prediction of company financial success. We have tested the assumptions underpin the

use of regression and satisfy the assumptions not violated. We also do have problem with

collinearity among independent variables in our predictive model.

60

CHAPTER 5

CONCLUSION

AND RECOMMENDATIONS

61

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS

The following section is organized in the following major sections: (5.1) Summary and

conclusions; (5.2) Limitation of the study; (5.3) Suggestion for additional research; and

lastly (5.4) Implication

5.0 SUMMARY AND CONCLUSION

This study represents an attempt to explore and to gain a preliminary insight into the

linkage among companies practice superior supply chain management and their financial

success. Recall our earlier research questions.

RQ1. Does companies with superior SCM performance achievement will also be

financial success companies? and

RQ2. Which SCM performance measures have high correlations with companies’

financial success?

Based on the analysis of the results of the study, our answer to the above research

questions can be summarized below.

Firstly, to answer the first research question and explore the SCM practices and financial

success relationship, we use cross-tabulation and tested by chi-square analysis. The

results of this study reveal that there is a linkage between companies which have superior

SCM practices and financial success. This can be seen by chi-square statistics shown in

62

the cross tabulation in table 12 for financial year 2003 to 2006 but not for financial year

1999 to 2002. This indicates that SCM is gaining more importance in recent years

(FY2003 to 2006) compared to previous years (FY1999 to 2002) as one of the

management tools to create shareholder value. Besides, the table also shown companies

which has 3 or 4 supply chain performance measures improvements tends to have 60%

chance to become one of the top 10 financial success companies in both of the time frame

of study. This empirical study support the previous studied by Chen et al. (2004) provide

evidence that strategic purchasing, an integral part of SCM, has a positive effect on the

company’s financial performance. Therefore, based on the sample study, we may

conclude that there is a relationship between companies practice superior supply chain

management and financial success and more companies are adopting full scope of the

SCM practices lately.

Second, in order to answer the subsequent research question, we have developed a

predictive model using the selected four supply chain performance measures as

independent valuables and financial success measured by market capitalization as

dependent variable. We use multi regression to test the degree of predictive of the

independent variables. Overall, the regression results show that all the four supply chain

performance measures can explain 10.2% of the variability of the company financial

success. Among the independent variables, revenue (X1) is the variable highly correlated

with financial success. The second variable will be the cost of sales as percentage of

revenue (X2) while the remaining two variables not so correlated as initial expected. This

indicates that revenue as a proxy for companies’ supply chain reliability, responsiveness

63

and flexibility is an important factor for management to consider when drafting out

business strategies. This is consistent with the current business environment which

provides customer satisfaction through reliability in delivery, responsiveness in changing

demand and flexibility in adopting changes in internal processes. By achieving these

objectives, management has to design and implement a system that can improve Level 1

Metric as defined by SCOR model ie perfect order fulfilment, lower order fulfilment

cycle time and flexibility in upside and downside supply chain adaptability. All these

activities consume financial resources and to success implement SCM practices,

company has to draw a balance between fulfilments of customer satisfaction with supply

chain costs. It is reflected from the result that cost of sales as percentage of revenue (X2)

is second variable that highly correlated with financial success.

5.1 LIMITATION OF THE STUDY

The findings of the study need to be interpreted with the following limitations in mind.

First, for practical reasons, only a limited number of control variables were included ie

revenue, Cost of sales, Cash-to-cash cycle time and Return on working capital. The above

study was use financial measures and omitted the non-financial measures. Performance of

the company should be judged using the financial and non-financial measures. Non-

financial measures such as order fulfilment rate, on-time deliveries, supplier rejection

rate, forecasting accuracy etc. By incorporating the non-financial measures will provide

complete SCM practices.

64

Secondly, further research involving other sectors and industries needs to be undertaken

in order to gain an in-depth understanding of the key factors associated with the

implementation of supply chain practices.

Thirdly, the findings from small sample size cannot be generalized for a larger

population.

Fourthly, they may also argue that the overall financial success of companies is

influenced by many other factors such as economy, exchange rates, geopolitical issues

and many of these are beyond the control but have significant impact to its financial

success of companies.

Lastly but not the least, this research was constrained by accessibility, resources and time.

5.2 SUGGESTIONS FOR FUTURE RESEARCH

As this study is limited to only 34 companies listed on main board of Bursa Malaysia, it

does not necessarily portray a good representation of all companies in Malaysia. Further

study with larger sample size in different industries is required to validate the trend. In

addition, in-depth face-to-face interview with companies’ supply chain practitioners and

financial professionals will provide additional information in relation to actual practicing

of the SCM especially in Malaysian company context. Lastly, a longitudinal studies

would also be useful in providing and exploring more information in SCM practices.

65

5.3 IMPLICATIONS

The findings of this study would undoubtedly be beneficial to SCM practitioners,

financial professional and company decision makers as it has provided empirical

evidence that company with superior SCM practices associated with financial success. It

is hoped that this paper would not only increase our understanding of SCM, but also

generate more interest in this field from an empirical perspective, specifically in the

Malaysian contest and contribute to the SCM literature library.

Besides, it is hoped to narrow the gap in the perceptions of the CFO about the role of

SCM practices not only for cost reduction techniques but as an enabler to contribute to

companies’ financial success. This linkage enables managers to navigate from activity

performance to shareholder value. For example, management can target areas for

additional training or reengineering efforts to improve performance and demonstrate the

resulting effect on shareholder value. In order to achieve the desire value drivers,

management need to develop new and innovative solutions not previously considered.

66

REFERENCES

Akintoye, A., McIntosh, G. and Fitzgerald, E. (2000), “A survey of supply chaincollaboration and management in the UK construction industry. Supply chainmanagement in construction – special issue”, European Journal of Purchasingand Supply Management, Vol. 6, pp. 159-168.

Ali, F., Smith, G. and Saker, J. (1997), “Developing buyer-supplier relationshipsin the automobile industry, a study of Jaguar and Nippondenson”, EuropeanJournal of Purchasing and Supply Management, Vol. 3 No. 1, pp. 33-42.

Banker, R.D., Chang, H., Janakiraman, S.N., and C. Konstans (2004), “Abalanced scorecard analysis of performance metrics,” European Journal ofOperational Research, 154(2), pp. 423-436.

Beamon, B.M., (1999), “Measuring supply chain performance,” InternationalJournal of Operations and Production Management, 19(3), pp.275-292.

Benson, P.G., Saraph, J.V. and Schroeder, R.G. (1991), ``The effects oforganizational context on quality: an empirical investigation'', ManagementScience, Vol. 37 No. 9, pp. 1107-24.

Bolumole, Yemisi A., A. Michael Knemeyer, and Douglas M. Lambert (2003),"The Customer Service Management Process," The International Journal ofLogistics Management, Vol. 14,No. 2, pp. 15-31.

Bowersox, Donald J., David J. Closs, and Theodore P. Stank (1999), 21" CenturyLogistics: Making Supply Chain Integration a Reality, Chicago, IL: Council ofLogistics Management.

Burgess, K., Singh, P.J. and Koroglu, R. (2006), “Supply chain management: astructured literature review and implications for future research”, InternationalJournal of Operations & Production Management, Vol. 26 No. 7, pp. 703-29.

Burgess, R. (1988), “Avoiding supply chain management failure: lessons frombusiness process reengineering”, International Journal of Logistics Management,Vol. 9, pp. 15-23.

Caruso, D. and Gazzi, Jill (2004) DDSN: Increase Profitability by MasteringDemand

Chen, I. and Paulraj, A. (2004b), “Towards a theory of supply chainmanagement: the constructs and measurements”, Journal of OperationsManagement, Vol. 22, pp. 119-50.

67

Christopher, M. (1992), Logistics and Supply Chain Management, PitmanPublishing, London.

Christopher, M. (1998), Logistics and Supply Chain Management – Strategies forReducing Cost and Improving Service, Financial Times, Prentice-Hall,Englewood Cliffs, NJ.

Christopher, M. and Juttner, U. (2000), “Developing strategic partnerships in thesupply chain: a practitioner perspective”, European Journal of Purchasing &Supply Chain Management, Vol. 2 No. 6, pp. 117-27.

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

Cigolini, R., Cozzi, M. and Perona, M. (2004), “A new framework for supply chainmanagement: conceptual model and empirical test”, International Journal ofOperations & Production Management, Vol. 24 No. 1, pp. 7-41.

Copacino, W.C. (1996), ``Seven supply-chain principles'', Traffic Management, Vol. 35No. 1, p. 60.

Croxton, Keely L. (2003), "The Order Fulfillment Process," The InternationalJournal of Logistics Management, Vol. 14, No. 1, pp. 19-33.

Croxton, Keely L., Douglas M. Lambert, Sebastian J. Garcia-Dastugue, and DaleS. Rogers (2002), "The Demand Management Process," The International Journalof Logistics Management, Vol. 13, No. 2, pp. 51-66.

Croxton, Keely L., Sebastidn J. Garcia-Dastugue, Douglas M. Lambert, and DaleS. Rogers (2001), "The Supply Chain Management Processes," The InternationalJournal of Logistics Management, Vol. 12, No. 2, pp. 13-36.

David Walters (1999), “The implications of shareholder value planning andmanagement for logistics decision making”, International Journal of PhysicalDistribution & Logistics Management, Vol. 29 No. 4, pp. 240-258.

Davis, H.A. (1996), “Cash flow and performance measurement: managing forvalue”, Financial Executives Research Foundation Executive Report, September.

Degraeve, Z., Labro, E. and Roodhooft, F. (2000), “An evaluation of vendorselection models from a total cost of ownership perspective”, European Journalof Operational Research, Vol. 125, pp. 34-58.

Douglas M. Lambert, Sebastian J. Garcia-Dastugue and Keely L. Croxton (2005), “AnEvaluation of Process-Oriented Supply Chain Management Frameworks”, Journal ofBusiness Logistics, Vol. 26, No. 1, pp. 25-51

68

Dubois, A. and Gadde, L. (2000), “Supply strategy and network effects –purchasing behaviour in the construction industry”, European Journal ofPurchasing & Supply Chain Management, Supply Chain Management inConstruction – special issue, No. 6, pp. 207-15.

Ebrahimpour, M. (1985), ``An examination of quality management in Japan:implications for management in the United States'', Journal of OperationsManagement, Vol. 5 No. 4, pp. 519-431.

Edum-Fotwe, F.T., Thorpe, A. and McCaffer, R. (2001), “Informationprocurement practices of key actors in construction supply chains”, EuropeanJournal of Purchasing & Supply Management, No. 7, pp. 155-64.

Enrico Camerinelli and Alessandra Cantu’ (2006), “Measuring the Value of theSupply Chain: A Framework”, Supply Chain Practice Vol. 8. No. 2. 2006, pp. 40-59.

Farley, G.A. (1997), ``Discovering supply chain management: a roundtable discussion'',The Performance Advantage, Vol. 7 No. 1, pp. 38-9.

Frohlich, M. and Westbrook, R. (2002), “Demand chain management inmanufacturing and services: web-based integration, drivers and performance”,Journal of Operations Management, Vol. 20, pp. 729-45.

Gardner, Daniel (2004) Supply Chain Vector Methods for Linking the Executionof Global Business Models with Financial Performance, J.Ross Publishing, USA

Goldsby, Thomas J. and Sebastidn J. Garcia-Dastugue (2003), "TheManufacturing Row Process," The International Journal of LogisticsManagement, Vol. 14, No. 2, pp. 33-52. Grdnroos,

Gunasekaran, A., Patel, C. and E. Tirtiroglu (2001), “ Performance metrics in asupply chain environment,” International Journal of Operations and ProductionManagement, 21(1/2), pp. 71-87.

Gunasekaran, A., Patel, C. and R.E. McGaughey (2004), “A framework forsupply chain performance measurement,” International Journal of ProductionEconomics, 87(3) pp. 333-347.

Handfield, R.B. and Nichols, E.L. (2002), Supply Chain Redesign: TransformingSupply Chains into Integrated Value Systems, Financial Times Prentice-Hall,Englewood Cliffs, NJ.

69

Harland, C.M. (1996), “Supply chain management relationships”, Chains,Networks, British Academy of Management, Vol. 7.

Harland, C.M., Lamming, R.C. and Cousins, P.D. (1999), “Developing theconcept of supply strategy”, International Journal of Operations & ProductionManagement, Vol. 19 No. 7, pp. 650-73.

Hartley, J.L. and Choi, T.Y. (1996), “Supplier development: customer as acatalyst of process change”, Business Horizons, Vol. 39 No. 4, pp. 37-40.

Houlihan, J.B. (1985), “International supply chain management”, InternationalJournal of Physical Distribution & Materials Management, Vol. 3 No. 4, pp. 22-8.

H.S. Ismail and H. Sharifi (2006), “A balanced approach to building agile supply chains”International Journal of Physical Distribution & Logistics Management Vol. 36 No. 6,pp. 431-444

Inman, R.A. and Hubler, J.H. (1992), ``Certify the process, not just the product'',Production and Inventory Management Journal, Vol. 33 No. 4, pp. 11-14.

Keah Choon Tan, Vijay R. Kannan, Robert B. Handfield and Soumen Ghosh (1999),“Supply chain management: an empirical study of its impact on performance”International Journal of Operations & Production Management, Vol. 19 No. 10, pp.1034-1052.

Krause, D.R. (1997), ``Supplier development: current practices and outcomes'',International Journal of Purchasing and Materials Management, Vol. 33 No. 2,pp. 12-19.

Lambert, D.M., Stock, J.R. and Ellram, L.M. (1998), Fundamentals of LogisticsManagement, Irwin/McGraw-Hill, Boston, MA.

Lamming, R. (1993), Beyond Partnership: Strategies for Innovation and LeanSupply, Prentice-Hall, New York, NY.

Lau, H.C.W. and Lee, W.B. (2000), “On a responsive supply chain informationsystem”, International Journal of Physical Distribution &Logistics Management,Vol. 30, pp. 598-610.

Lee, H.L. and Billington, C. (1992), ``Managing supply chain inventory: pitfalls andopportunities'', Sloan Management Review, Vol. 33 No. 3, pp. 65-73.

Michael Tracey, Jeen-Su Lim and Mark A. Vonderembse (2005), “The impact ofsupply-chain management capabilities on business performance”, Supply ChainManagement: An International Journal 10/3 pp 179–191

70

Mistry, J.J. (2006), “Origins of profitability through JIT process in supply chain”,Industrial Management & Data Systems, Vol. 105 No. 6, pp. 752-68.

Modarress, B. and Ansari, A. (1989), ``Quality control techniques in US firms: asurvey'', Production and Inventory Management Journal, Vol. 30 No. 2, pp. 58-62.

Monczka, R.M., Trent, R.J. and Callahan, T.J. (1994), ``Supply base strategies tomaximize supplier performance'', International Journal of Physical Distributionand Logistics, Vol. 24 No. 1, pp. 42-54.

Morgan, J. and Monczka, R.M. (1996), ``Supplier integration: a new level ofsupply chain management'', Purchasing, Vol. 120 No. 1, pp. 110-13.

Mowery, D.C. (1988), International Collaborative Ventures in US Manufacturing,Ballinger, Cambridge, MA.

New, S. and Ramsay, J. (1997), “A critical appraisal of aspects of the leanapproach”, European Journal of Purchasing & Supply Management, Vol. 3 No. 2,pp. 93-102.

Otto, A., and H. Kotzab (2003), “Does supply chain management really pay? Sixperspectives to measure the performance of managing a supply chain,” EuropeanJournal of Operational Research, 144(2), pp. 306-320.

Prahalad, C.K. and Hamel, G. (1990), ``The core competence of the corporation'',Harvard Business Review, Vol. 68 No. 3, pp. 79-91.

Ramirez, G.G., Waldman, D.A. and Lasser, D.J. (1991), “Research needs incorporate finance: perspectives from financial managers”, FinancialManagement, Vol. 20 No. 1, pp. 17-29.

Rogers, Dale S., Douglas M. Lambert, and A. Michael Knemeyer (2004), "TheProduct Development and Commercialization Process," The International Journalof Logistics Management, Vol. 15, No. 2, pp. 43-56.

Rogers, Dale S., Douglas M. Lamben, Keely L. Croxton, and Sebastian J.Garcia-Dastugue (2002), "The Retums Management Process," The InternationalJournal of Logistics Management, Vol. 13, No. 2, pp. 1-18.

Saad, M., Jones, M. and James, P. (2002), “A review of the progress towards theadoption of supply chain management (SCM) relationships in construction”,European Journal of Purchasing & Supply Management, Vol. 8, pp. 173-83.

71

Saraph, J.V., Benson, P.G. and Schroeder, R.G. (1989), ``An instrument formeasuring the critical factors of quality management'', Decision Sciences, Vol. 20No. 4, pp. 810-29.

Schroeder, R.G., Sakakibara, S., Flynn, E.J. and Flynn, B.B. (1992), ``Japaneseplants in US: How good are they?'', Business Horizons, Vol. 335 No. 4, pp. 66-72.

Slack, N., Chambers, S. and Johnston, R. (2001), Operations Management, 3rd

edition., Financial Times, Prentice-Hall, Eaglewood cliff, NJ.

Spekman, R.E., Kamauff, J.W. Jr and Myhr, N. (1998), “An empiricalinvestigation into supply chain management: a perspective on partnerships”,International Journal of Physical Distribution & Logistics Management, Vol. 28No. 8, pp. 630-50.

Srivastava, Rajendra K., Tasadduq A. Shervani, and Liam Fahey (1999),"Marketing, Business Processes, and Shareholder Value: An OrganizationallyEmbedded View of Marketing Activities and the Discipline of Marketing,"Journal of Marketing, Vol. 63, No. 4, pp. 168-179.

Stewart, G. (1995), “Supply chain performance benchmarking study reveals keysto supply chain excellence”, Logistics Information Management, Vol. 8 No. 2, pp.38-44.

Stewart, G. (1997), “Supply-chain operations reference model (SCOR): the firstcross-industry framework for integrated supply chain management”, LogisticsInformation Management, Vol. 10 No. 2, pp. 62-7.

Supply Chain Council (2007), Supply Chain Operations Reference Model: Overview ofSCOR Version 8.0, Supply Chain Council Inc.

Trahan, E.A. and Gitman, L.J. (1995), “Bridging the theory-practice gap: a surveyof chief financial officers”, Quarterly Review of Economics and Finance, Vol. 35No. 1, pp. 73-87.

Tully, S. (1995), ``Purchasing's new muscle'', Fortune, Vol. 20, p. 76.

Uma V. Sridharan, W. Royce Caines and Cheryl C. Patterson (2005),“Implementation of supply chain management and its impact on the value offirms”, Supply Chain Management: An International Journal 10/4, 313–318.

V.M. Rao Tummala, Cheryl L.M. Phillips and Melanie Johnson (2006),”Assessing supply chain management success factors: a case study”, Supply ChainManagement: An International Journal 11/2, pp.179–192

72

Vollman, T., Cordon, C. and Raabe, H. (1997), Supply Chain Management inMastering Management, FT Pitman, London, pp. 316-22.

Von Hipple, E. (1986), “Co-operation between rivals: informal know how trading”,Research Policy, Vol. 16 No. 5, pp. 21-8.

Watts, C.A. and Hahn, C.K. (1993), ``Supplier development programs: anempirical analysis'', International Journal of Purchasing and MaterialsManagement, Vol. 29 No. 2, pp. 11-17.

73

APPENDIX 1.0: LIST OF SELECTED COMPANIES

INDUSTRIAL PRODUCTS

Publish dataavailable

from 1999 to2006

FinancialYear Ended

31 December

SameIncome

Statementformat

1 Advance Synergy Bhd √ √

2 Adventa Bhd

3 Ajiya Bhd √

4 Aluminium Company of Malaysia Bhd √ √

5 Amalgamated Containers Bhd √

6 Amalgamated Industrial Steel Bhd √ √

7 Amsteel Corporation Bhd √

8 Ancom Bhd √

9 Ann Joo Resources Bhd √ √ √

10 APB Resources Bhd

11 APL Industries Bhd

12 APM Automotive Holdings Bhd √ √ √

13 Astino Bhd

14 Boon Koon Group Bhd

15 Boustead Heavy Industries Corp Bhd

16 Box-Pak (Malaysia) Bhd √ √

17 BP Plastics Holding Bhd

18 BSA International Bhd

19 C.I. Holdings Bhd √

20 Camerlin Group Bhd √

21 Can-One Bhd

22 CB Industrial Product Holding Bhd √ √ √

23 Cycle & Carriage Bintang Bhd

24 Cement Industries of Malaysia Bhd √ √

25 Century Bond Bhd

26 Chemical Company of Malaysia Bhd √ √ √

27 Chin Well Holdings Bhd √

28 Choo Bee Metal Industries Bhd √ √

29 Coastal Contracts Bhd

30 Cymao Holdings Bhd

31 Daibochi Plastic & Packaging Inds Bhd √ √ √

32 Delloyd Ventures Bhd √ √ √

33 DK Leather Corporation Bhd

34 Dolomite Corporation Bhd

35 Dominant Enterprise Bhd

36 DRB-Hicom Bhd √

74

INDUSTRIAL PRODUCTS


from 1999 to2006

FinancialYear Ended

31 December

SameIncome

Statementformat

37 Eksons Corporation Bhd

38 Encorp Bhd

39 Englotechs Holding Bhd

40 Eonmetall Group Bhd

41 EP Manufacturing Bhd √ √

42 ESSO Malaysia Bhd √ √ √

43 Evergreen Fibreboard Bhd

44 Evermaster Group Bhd √

45 Facb Industries Incorporated Bhd √

46 Favelle Favco Bhd

47 FCW Holdings Bhd √

48 Goh Ban Huat Bhd √ √ √

49 Gopeng Bhd √

50 Guh Holdings Bhd

51 Heveaboard Bhd

52 Hexza Corporation Bhd √

53 Hiap Teck Venture Bhd

54 HIL Industries Bhd √ √ √

55 Hirotako Holdings Bhd √ √ √

56 Ho Wah Genting Bhd √ √ √

57 Hume Industries (M) Bhd √

58 Industrial Concrete Products Bhd √

59 Ingress Corporation Bhd

60 Integrated Rubber Corporation Bhd

61 Jadi Imaging Holdings Bhd

62 Java Incorporated Bhd

63 Jaya Tiasa Holdings Bhd √

64 Johore Tin Bhd

65 Keck Seng (M) Bhd √ √ √

66 Kia Lim Bhd √ √ √

67 Kian Joo Can Factory Bhd √ √

68 Kim Hin Industry Bhd √ √ √

69 Kinsteel Bhd

70 KKB Enginring Bhd √ √

71 KNM Group Bhd

72 Kossan Rubber Industries Bhd √ √

73 Kramat Tin Dredging Bhd √ √

74 KYM Holdings Bhd √

75

INDUSTRIAL PRODUCTS


from 1999 to2006

FinancialYear Ended

31 December

SameIncome

Statementformat

75 Lafarge Malayan Cement Bhd

76 LB Aluminium Bhd √

77 Lcth Corporation Bhd

78 Leader Steel Holdings Bhd √

79 Leader Universal Holdings Bhd √ √ √

80 Leweko Resources Bhd

81 Linear Corporation Bhd √ √

82 Lingui Development Bhd √

83 Lion Corporation Bhd √

84 Lion Industries Corporation Bhd

85 Luster Industries Bhd

86 Magni-Tech Industries Bhd √

87 Malaysia Aica Bhd

88 Malaysia Smelting Corporation Bhd √ √ √

89 Malaysia Steel Works (KL)Bhd

90 Malaysia AE Models Holdings Bhd √

91 Megan Media Holdings Bhd

92 Melewar Indusial Group Bhd

93 Mentiga Corporation Bhd √ √ √

94 Metrod (M) Bhd √ √ √

95 Mieco Chipboard Bhd √ √

96 Minho (M) Bhd √ √ √

97 MOL.Com Bhd √

98 MP Technology Resources Bhd

99 Muda Holdings Bhd √ √ √

100 Mycron Steel Bhd

101 Narra Industries Bhd

102 NWP Holdings Bhd √

103 Nylex (M) Bhd √

104 Octagon Consolidated Bhd √

105 OKA Corporation Bhd

106 Ornapaper Bhd

107 Ornasteel Holdings Bhd

108 P.A. Resources Bhd

109 P.I.E. Industrial Bhd √ √

110 Pan Malaysia Corporation Bhd √ √ √

111 Paos Holdings Bhd

112 Petronas Gas Bhd √

76

INDUSTRIAL PRODUCTS


from 1999 to2006

FinancialYear Ended

31 December

SameIncome

Statementformat

113 PNE PCB Bhd √

114 Poly Tower Ventures Bhd

115 Press Metal Bhd √ √ √

116 Prestar Resources Bhd √ √ √

117 Priceworth Wood Products Bhd

118Perusahaan Sadur Timah M'sia(Perstima)Bhd

√

119 Rohas-Euco Industries Bhd √ √ √

120 Rubberex Corporation (M) Bhd √ √

121 Sanbumi Holdings Bhd

122 Sapura Industrial Bhd

123 Scientex Incorporated Bhd √

124 Scomi Group Bhd

125 Seal Incorporated Bhd √

126 Shell Refining Co (F.O.M.) Bhd √ √ √

127 Sindora Bhd √ √ √

128 Sinora Industries Bhd √ √ √

129 Sitt Tatt Bhd √

130 SKP Resources Bhd

131 Southern Acids (M) Bhd √

132 Southern Steel Bhd √ √ √

133 Subur Tiasa Holdings Bhd √

134 Success Transformer Corp Bhd

135 Supermax Corporation Bhd √ √

136 Ta Ann Holdings Bhd √ √ √

137 Tasek Corporation Bhd √

138 Tekala Corporation Bhd √

139 Tenggara Oil Bhd

140 Thong Guan Industries Bhd √ √ √

141 Titan Chemicals Corp. Bhd

142 Tong Herr Resources Bhd √ √ √

143 Top Glove Corporation Bhd

144 TSH Resources Bhd √ √ √

145 UAC Bhd √ √

146 Uchi Technologies Bhd √ √

147 United U-Li Corporation Bhd

148 V.S Industry Bhd √

149 Versatile Creative Bhd

77

INDUSTRIAL PRODUCTS


from 1999 to2006

FinancialYear Ended

31 December

SameIncome

Statementformat

150 Wah Seong Corporation Bhd

151 Weida (M) Bhd

152 Wembley Industries Holdings Bhd √ √

153 White Horse Bhd √ √

154 Wijaya Baru Global Bhd √ √

155 WTK Holdings Bhd √ √ √

156 YE Chiu Metal Smelting Bhd √ √

157 YI-Lai Bhd

158 YLI Holdings Bhd √

159 YTL Cement Bhd √

160 Yung Kong Galvanising Industries Bhd √ √ √

Notes: Selected companies were those shaded. There were 34 companies in total.

(Source: Annual Reports of companies)

78

APPENDIX 2.0: SELECTED COMPANIES PROFILE

Financial Year ended 31 December 2006

Company Name / Coreactivities

BusinessSegments

Revenue(RM'mil)

PBT(RM'mil)

Shareholders' funds(RM'mil)

Marketcapitalization

(RM'mil)

1

Ann Joo Resources Berhadmain business is iron andsteel, which is engaged in themanufacture and trading ofhardware, steel and ironproducts, building andconstruction materials of allkinds and operations of steelmill and steel services centre.

Steel andfabrication

sector1,453.27 136.46 635.71 519.22

2

APM Automotive HoldingsBerhad principle activitiesare manufactures and sellsautomotive components andaccessories.

Automotivesector

899.82 80.08 536.54 461.66

3

CB Industrial ProductHolding Berhad principleactivities is engaged inmanufacturing and trading ofpalm oil equipment and itsrelated products,commissioning andcontracting works for palmoil mills.

Plantationsector

228.81 33.66 142.32 591.51

4

Chemical Company ofMalaysia Berhad actiities isengaged in the manufactureand marketing of fertilizers,chlor-alkali and coagulantproducts, pharmaceuticalsand healthcare products, andthe marketing of a range ofchemicals.

Pharmaceuticals sector

110.06 138.37 738.46 1,288.97

5

Daibochi Plastic andPackaging Industries Bhdis engaged in manufacturingand marketing of flexiblepackaging materials andproperty development.

Packagingsector

209.97 7.18 104.59 40.23

79

Company name / Coreactivities

BusinessSegments

Revenue(RM'mil)

PBT(RM'mil)



(RM'mil)

6

Delloyd Ventures Berhadprinciple activities isengaged in themanufacturing and trading ofautomotive parts andaccessories.

Automotivesector

189.98 13.07 254.89 168.83

7

Esso Malaysia Berhadprincipal activities are themanufacturing and marketingof petroleum products inPeninsular Malaysia.

Oil sector 9,336.44 7.22 633.81 804.60

8

Goh Ban Huat Berhadprincipally engaged in themanufacturing and trading ofceramic wares, ceramicformers and pipes.

Buildingmaterials

sector43.17 (2.65) 166.06 71.83

9

HIL Industries Berhad isprincipally engaged inmanufacture and sale ofindustrial and domesticmolded plastic products.

Engineeringsector

81.33 11.31 183.82 104.68

10

Hirotako Holdings Berhadis principally engaged in themanufacture and sale of seatbelts, car airbag modules,steering wheels, noise andheat reduction materials, aswell as insulator parts formotor vehicles.

Automotivesector

143.56 20.65 150.23 102.96

11

Ho Wah Genting Berhadinvolves the manufacture ofwires and cables, moldedpower supply cord sets andcable assemblies forelectrical and electronicdevices and equipment.

Engineeringsector

213.31 (13.73) 65.11 60.67

12

Keck Seng (Malaysia)Berhad is principallyengaged in the cultivation ofoil palm, processing andmarketing of refined palm oilproducts.

Plantationsector

819.85 80.00 1,039.57 820.73

80


BusinessSegments

Revenue(RM'mil)

PBT(RM'mil)



(RM'mil)

13

Kia Lim Berhad is engagedin the manufacturing ofbricks and roofing tiles.

Buildingmaterials

sector50.75 3.78 30.60 24.78

14

Kim Hin Industry Berhadis engaged in themanufacture and sale ofceramic floor, homogeneousand monoporosa tiles.

Buildingmaterials

sector245.53 18.56 425.40 198.30

15

Leader Universal HoldingsBerhad engagedmanufacturing and sales oftelecommunication cableswhich mainly used for thetelecommunication andinformation technologysectors.

Engineeringsector

2,365.02 65.76 371.23 248.78

16

Malaysia SmeltingCorporation Berhad isengaged in the smelting oftin concentrates and tin-bearing materials.

Engineeringsector

1,637.70 64.68 302.35 558.75

17

Mentiga CorporationBerhad is principallyengaged in timber extractionand trading in timber-relatedproducts.

Plantationsector

11.71 (4.37) 24.52 60.60

18

Metrod (Malaysia) Berhadis engaged in themanufacturing and marketingof electrical conductivity-grade copper wires, rods,strips and flat copperwinding wire systems.

Engineeringsector

1,999.79 40.99 194.13 167.40

19

Minho (M) Berhad isengaged in kiln drying andchemical preservativetreatment; manufacturing,exporting and dealing inmolded timber and its relatedproducts products.

Plantationsector

364.00 23.64 144.57 53.83

81


BusinessSegments

Revenue(RM'mil)

PBT(RM'mil)



(RM'mil)

20

Muda Holdings Bhd isprincipally engaged in papermilling and paper packagingand manufacture ofcorrugated cartons,honeycomb paper products,paperbags and laminatedpaper.

Packagingsector

613.45 (13.22) 366.70 45.58

21

Pan Malaysia Corp Berhadprincipal activities includemanufacturing, marketingand distribution ofconfectionery and cocoa-based and other foodproducts.

Food sector 236.55 (66.35) 336.79 193.34

22

Press Metal Berhad. TheGroup's principal activitiesare manufacturing andmarketing aluminiumproducts. Other activitiesinclude contracting andfabricating aluminium andstainless steel products,developing property,recycling waste, operating,maintaining and constructingwaste recycling project,trading waste treatment andrecycling equipment.


sector663.46 27.79 216.70 92.92

23

Prestar Resources Berhadis engaged in the slitting,shearing and sales of steelsheets and coils; manufactureand supply of carbon steelpipes ; general hot-dipgalvanising and coating ofmetal products and threadeitems; import and trading ofsteel materials and generalhardware products.


sector527.44 29.47 156.44 115.44

82


BusinessSegments

Revenue(RM'mil)

PBT(RM'mil)



(RM'mil)

24

Rohas-Euco IndustriesBerhad is engaged in thedesign and fabrication ofsteel structures for high-tension transmission towers,microwave towers andsubstation structures, as wellas the manufacture ofpressed steel sectional watertank panels and provision ofother fabrication andinstallation works.


sector182.83 31.30 125.42 96.42

25

Shell Refining CompanyBerhad whose principalactivities consist of refiningand manufacturing ofpetroleum products.

Oil sector 10,886.84 325.39 1,939.81 3,150.00

26

Sindora Berhad is primarilyengaged in operations of oilpalm plantations, palm oilmilling and rubber estate.Through its subsidiaries, theCompany is also engaged intimber logging, processingand sale of sawn timber,timber doors, laminatedtimber scantling and tradingof wood products; bulkmailing and printingservices; contract blendingand packing of tea andproducing carbonated drinks,and provision of seatransportation and relatedservices.

Plantationsector

205.64 14.49 165.05 113.28

27

Sinora Industries Berhad isengaged in log extractioncontracting and operation ofoil palm plantations. Plantation

sector3.90 (1.20) 30.91 100.00

83


BusinessSegments

Revenue(RM'mil)

PBT(RM'mil)



(RM'mil)

28

Southern Steel Berhad isengaged in themanufacturing, sale andtrading of billets, steel bars,wire rods, steel wire meshand concrete wires and steelpipes.


sector2,353.28 63.28 624.14 532.18

29

Ta Ann Holdings Berhad isengaged in timber concessionlicensee, trading of logs,manufacture and sale ofsawn timber, veneer,plywood and other timberproducts, and reforestationand plantation, whichincludes oil palm andreforestation (tree planting).

Plantationsector

636.96 164.60 625.92 1,974.60

30

Thong Guan IndustriesBerhad principally engagedin the manufacturing andtrading of plastic-basedproducts, manufacturing andtrading of consumer foodproducts, such as tea, coffee,biscuits, snack food andcurry powder. The Otherssegment is engaged in themanufacturing and trading ofproducts, such as high-density monofilament ropes,polypropylene string, paperserviette, cologne papertowel, rubber band, drinkingstraw and machinery.

Food sector 469.32 26.71 174.75 151.49

31

Tong Herr ResourcesBerhad is engaged in themanufacture and sale ofstainless steel fasteners,including nuts, bolts, screwsand all other threaded items.


sector304.06 73.11 224.85 350.04

84


BusinessSegments

Revenue(RM'mil)

PBT(RM'mil)



(RM'mil)

32

TSH Resources Berhad isengaged in the marketing anddistribution of cocoa beans,operation of oil palmplantations, manufacture andsale of crude palm oil andpalm kernel, and generationand supply of electricityfrom a biomass plant andmanufactures and sells ofsawn timber and downstreamwood products.

Plantationsector

624.65 64.44 435.40 575.30

33

WTK Holdings Berhad isengaged in extraction andsale of timber, manufactureand sale of plywood, veneerand sawn timber; trading oftapes, foil, papers andelectrostatic dischargeproducts, and manufacturing,conversion of aluminumfoils, flexible packaging,metallised and electrostaticdischarge products,manufacture and sale ofadhesive and gummed tapes.

Plantationsector

704.07 155.96 897.31 1,253.61

34

Yung Kong GalvanisingIndustries Berhad isprincipally engaged in themanufacture and sale ofgalvanized and coated steelproducts.


sector323.23 2.44 101.11 53.45

( Source: Annual Report of companies)

Documents

SUPPLY CHAIN PERFORMANCE AND FINANCIAL ......to-cash cycle time, working capital or cash flow from operations with objective in maximize shareholders’ value. The supply chain manager,