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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
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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.
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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.
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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
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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).
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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
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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
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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.
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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.
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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,
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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.
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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.
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CHAPTER 2
LITERATURE REVIEW
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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,
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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).
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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).
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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).
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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).
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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
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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
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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.
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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,
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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
β 2 = slope of Y with variable X2 holding variables X1, X3 and X4 constant
β 3 = slope of Y with variable X3 holding variables X1, X2 and X4 constant
β 4 = slope of Y with variable X4 holding variables X1, X2 and X3 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”
Independent Variable (X2):
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”
Independent Variable (X3):
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
after transformation 0.079 - 0.112 34 0.114 - 0.200 0.943 - 0.978 34 0.075 - 0.712
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
after transformation 0.100 - 0.341 34 0.000 - 0.200 0.442 - 0.976 34 0.000 - 0.629
Cash-to-cash cycle time
before transformation 0.241 - 0.458 34 0.000 0.207 - 0.734 34 0.000
after transformation 0.138 - 0.223 34 0.000 - 0.101 0.747 - 0.958 34 0.000 - 0.219
Return on working capital
before transformation 0.268 - 0.395 34 0.000 0.315 - 0.662 34 0.000
after transformation 0.124 - 0.238 34 0.000 - 0.200 0.844 - 0.940 34 0.000 - 0.064
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
Expected Count 7.1 1.5 1.5 10.0
% within No of SCMmeasures improvement(FY99-FY02)
33.3% 20.0% 20.0% 29.4%
21 to 34 Count 9 4 1 14
Expected Count 9.9 2.1 2.1 14.0
% within No of SCMmeasures improvement(FY99-FY02)
37.5% 80.0% 20.0% 41.2%
Total Count 24 5 5 34
Expected Count 24.0 5.0 5.0 34.0
% within No of SCMmeasures improvement(FY99-FY02)
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
Expected Count 4.1 3.2 2.6 10.0
% within No of SCMmeasures improvement(FY03-FY06)
21.4% 9.1% 66.7% 29.4%
11 to 20 Count 3 6 1 10
Expected Count 4.1 3.2 2.6 10.0
% within No of SCMmeasures improvement(FY03-FY06)
21.4% 54.5% 11.1% 29.4%
21 to 34 Count 8 4 2 14
Expected Count 5.8 4.5 3.7 14.0
% within No of SCMmeasures improvement(FY03-FY06)
57.1% 36.4% 22.2% 41.2%
Total Count 14 11 9 34
Expected Count 14.0 11.0 9.0 34.0
% within No of SCMmeasures improvement(FY03-FY06)
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
β 1 = slope of Y with variable X1 holding variables X2, X3 and X4 constant
β 2 = slope of Y with variable X2 holding variables X1, X3 and X4 constant
β 3 = slope of Y with variable X3 holding variables X1, X2 and X4 constant
β 4 = slope of Y with variable X4 holding variables X1, X2 and X3 constant
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
a. Dependent Variable: Y-Y changes in Market Capitalisation ('mil)
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
a. Dependent Variable: Y-Y changes in Market Capitalisation ('mil)
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
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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
Publish dataavailable
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
Publish dataavailable
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
Publish dataavailable
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
Publish dataavailable
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)
Shareholders' funds(RM'mil)
Marketcapitalization
(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
Company name / Coreactivities
BusinessSegments
Revenue(RM'mil)
PBT(RM'mil)
Shareholders' funds(RM'mil)
Marketcapitalization
(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
Company name / Coreactivities
BusinessSegments
Revenue(RM'mil)
PBT(RM'mil)
Shareholders' funds(RM'mil)
Marketcapitalization
(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.
Steel andfabrication
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.
Steel andfabrication
sector527.44 29.47 156.44 115.44
82
Company name / Coreactivities
BusinessSegments
Revenue(RM'mil)
PBT(RM'mil)
Shareholders' funds(RM'mil)
Marketcapitalization
(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.
Steel andfabrication
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
Company name / Coreactivities
BusinessSegments
Revenue(RM'mil)
PBT(RM'mil)
Shareholders' funds(RM'mil)
Marketcapitalization
(RM'mil)
28
Southern Steel Berhad isengaged in themanufacturing, sale andtrading of billets, steel bars,wire rods, steel wire meshand concrete wires and steelpipes.
Steel andfabrication
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.
Steel andfabrication
sector304.06 73.11 224.85 350.04
84
Company name / Coreactivities
BusinessSegments
Revenue(RM'mil)
PBT(RM'mil)
Shareholders' funds(RM'mil)
Marketcapitalization
(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.
Steel andfabrication
sector323.23 2.44 101.11 53.45
( Source: Annual Report of companies)