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6th
International Conference on Operations and Supply Chain Management, Bali, 2014
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A STUDY ON KNOWLEDGE-RICH CRITICAL ELEMENTS
WITHIN SRI LANKAN MANUFACTURING SUPPLY
NETWORKS
L.D.C.S. Layangani
Department of Transport and Logistics Management, Faculty of Engineering, University of
Moratuwa, Sri Lanka, E-mail: [email protected]
Lee E J Styger
MBA Director, Sydney Business School, University of Wollongong, Australia, E-mail:
Amal S. Kumarage
Senior Professor, Department of Transport and Logistics Management, Faculty of Engineering,
University of Moratuwa, Sri Lanka, E-mail: [email protected]
ABSTRACT A supply network is the combination of elements that starts with the procurement of raw
materials from a supplier or a group of suppliers, and ends by delivering the finished
product to the end consumer. Some of these elements can be called Knowledge-rich critical
elements; because of the amount of knowledge presented and level of competency that a
given element contains within the network. The core objective of Supply Network
Management is to increase the market share and the performance of its supply network
members while increasing the operational efficiency and profitability of the company.
Failures should not occur within the network, because the smooth operation of a supply
network is important. Therefore, identification of the traits of individual supply network
elements is essential to reduce these potential failures. This research identifies the
Knowledge-rich critical elements within supply networks of Sri Lankan manufacturing
companies that will assist better decision making, concerning choices of supply network
configuration and management. The methodology supports the decision makers with
implementing risk mitigation plans to maintain a competitive advantage within their supply
network.
Keywords: Supply networks; Knowledge-rich critical elements; Risk profiles; Decision
making strategy
1. INTRODUCTION
Min and Zhou (2002) notes that Supply Chain Management contains business activities
that include purchase of raw materials and their conversion to finished products, value addition to
finished products and their distribution, promotion and the exchange of information between
different business manufacturers, distributors, third party logistics providers, and retailers who are
parts of the overall supply chain. This interaction generally follows the characteristics of a
network. Poor decision making in these supply networks can lead to poor management. An overall
operational improvement of a company’s supply network can be expected, when an appropriate
supply chain management strategy is applied (Chyan Yang and Yi-fen Su, 2009).
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Knowledge plays an important role in the modern business world and it is difficult to substitute as
it adds unique value to the company (Birou et al., 2005). As the amount of knowledge presented in
a subject increases to a level that is impossible to take out from the available individual element it
becomes rich with knowledge and it needs to be managed carefully to achieve success (Birou et
al., 2005).
Supply network elements can be varied, and it is therefore important to identify the
Knowledge-rich and critical elements to reduce potential negative impacts on companies.
Supply chain knowledge directly impacts on strategic decision making. Knowledge has to be
transferred to outside companies (i.e. raw material suppliers) in order to develop a good
relationship between the outside party and the company. Importantly, knowledge sharing reduces
the errors in business relationships. Kremic et al. (2006) specified that some functions in an
organization may not be transferred, because of unique data or technology that feeds the other
processes of the network. These kinds of supply network elements are the most critical elements
and are the core competencies in an organization. Supply networks frequently develop a
competitive advantage around their core competencies. Core competency is defined as the
essential or the "core" to the success of a company. The core-competency concept is important
when designing a supply chain; because, in an ideal supply chain, each process will perform as a
core-competency (Alvarenga & Malmierca, 2010).
1.1 Problem Statement
Limited knowledge in supply network elements can cause weak decision outcomes and
lead to failure. However, there has been little research into identification of Knowledge-rich
critical supply network elements, and there is a need for a comprehensive examination of these
elements to understand their characteristics and impact in industry. The decisions made about
these elements are critical for the better performance of a company. Therefore, the aim of this
study is to identify a list of Knowledge-rich critical elements within supply networks.
Key Research Question: What are the Knowledge-rich critical elements within supply networks?
Objective: To identify potential Knowledge-rich supply critical elements within Sri Lankan
manufacturing supply networks and to rank the identified supply network elements according to
knowledge content and the core competency concept.
2. BACKGROUND LITERATURE
Supply networks consist of several elements, where suppliers deliver raw materials to
manufactures directly or via a third party logistics provider and the finished products are sent to
end consumers via distributors, retailers and different types of customers. Intermediary parties are
typically served by third party logistics providers with different logistics services. Min and Zhou
(2002) point out two main business processes within the supply network; material management
(inbound logistics) and physical distribution (outbound logistics).
Johnson and Malucci (1999) indicated that inbound logistics cover from the purchase and
internal control of production materials, to planning and control of warehousing, shipping, and
distribution of end products. Whenever outbound logistics activities contain order receipt and
processing, inventory operations, storage and handling, outbound transportation, consolidation,
pricing, promotional support, returned product handling, and life cycle support (Bowersox and
Closs, 1996).
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According to Tanowitz et al. (2009) the most basic supply network elements are demand
and supply planning, sourcing materials and services, transferring raw materials to finished
products, storing and delivering of products through a logistics network, and managing returns of
finished products.
A way of organizing a supply chain was established in the supply chain operation reference
model or SCOR model (SCOR Model 10.0, 2010). The SCOR model has five functions that
include; plan, source, make, deliver and return which describe the smooth operation of a supply
network. The SCOR model was used in this research to describe how Knowledge-rich, supply
critical elements could have an impact on generating risks in a supply network. Furthermore, the
SCOR model was used as the framework to identify the elements of the supply network (Supply
Chain Council, 2010, Stephans, 2001, Lockamy & McCormack, 2004, and Yang & Su, 2009).
Figure 1: SCOR Model
Source: Supply Chain Council (2010, p.6).
Kremic et al. (2006) specified that some functions in an organization may not be
transferred, because of unique data or technology generated and feed the other
processes of the network. These kinds of supply network elements are known as the
most knowledge rich, critical elements and the control of these has to be kept within
the organization. Supply networks frequently develop competitive advantage around their core
competencies. Core competency is defined as essential or "core" to the success actions a company
is engaged in the best with and most cost-effective. Supply Chain Management is a core-
competent to most manufactures. The core-competent concept is very important when designing a
supply chain; because, in an ideal supply chain, each process will perform as a core-competent.
Supply network is critical to companies’ best performance (Alvarenga & Malmierca, 2010).
3. METHODOLOGY
A quantitative study was chosen to reach the targets of the study. A statistical analysis was
exploited to gain a comprehensive output and primary data for the study was gathered by a
questionnaire that was distributed to the targets.
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The questionnaire (Appendix 01) was designed around the supply chain operations
reference model. The supply network elements investigated in the research are the sub processes
of plan, source, make, deliver and return. The key questions were developed to investigate whether
the variables in each process are knowledge-rich or critical or the both disciplines. Conceptual
framework used is shown in the figure 2.
Figure 2: Conceptual Framework
When identifying the sample, manufacturing companies in Sri Lanka were selected as the
sample frame and data was collected from the manufacturers’ perspective. The sample selection
method was based on simple random sampling. When determining the sample size the equation
used by Israel (1992) for proportions was applied. Further, Israel (1992) indicated that a reliable
sample has a lower margin of error and high confidence level. However, the confidence level can’t
reach above 90% as most of the past researchers indicated that in the field of supply chain and
logistics confidence level is low (Wagener & Kemmerling, 2010). Therefore, 90% confidence
level and 5% margin of error were assumed for further calculations. The variability in the
proportion was 0.62 according to past research. Wagener & Kemmerling (2010) specified that a
proportion of 62% was present in a population related to logistics oriented surveys after 2003.
…………………………………. (1)
n = required sample size
t = confidence level at 90% (value of 1.645)
p = proportion of an attribute that is present in the population
m = margin of error at 5% (standard value of 0.05)
n= (1.645)² x 0.62(1-0.62)
0.05²
n=255
n= t² x p (1-p)
m²
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The sample size was 255 supply chain operations senior executives or managers from 77
selected manufacturing companies. Data was collected by a questionnaire via e-mail. Selecting the
respondents for the survey was also important in order to maintain the quality and standards of
survey findings. A dedicated approach was practiced to select respondents from each company,
because their responses were evaluated and the interpretations were entirely based on their
responses.
Identification of variables was a crucial component of the research study. The literature
survey provided the base to explore these variables and they were included into the questionnaire.
Pilot survey was carried out to test the appropriateness of the questionnaire. Factor analysis and
reliability analysis were performed to ensure the validity and the reliability of the selected
variables, and poorly designed questions were identified, which were then eliminated from the
questionnaire.
Table 1: Design of Questionnaire
Literature Finding for the questionnaire
Supply Chain Council (2008), SCOR
Model 9.0
Supply network elements
Chritensen at el. (2004), Lavastre et
al. (2011) and Srinivasan et al.
(2010)
Using Likert Scale Questionnaires
Alvarenga, CA & Malmierca, P
(2010)
Core competency in SCM
4. DATA ANALYSIS
Question one was designed to identify the critical elements in supply networks, as a Likert-
scale question while question two was designed as a Likert-scale question to sort out Knowledge-
rich elements in supply networks. Respondents could choose an option (7- Very much: 1-Very
Low) for each variable.
Out of 255, 168 completed questionnaires were received and accepted as complete
responses. These 168 respondents were managers and executives from the selected manufacturing
companies covering almost every sector of Sri Lankan manufacturers (Agriculture, food, apparel,
cement, tobacco, furniture, rubber, and etc.). The response rate of the questionnaire was 65.8%
and the margin of error was determined as 0.6 to 0.72.
Ridit analysis was first proposed by I. Bross (Bikash et.al, 2010) and it has been used to
study various business management and behavioral studies. It was used to discover Knowledge-
rich, supply critical elements in supply networks (Ho Wu, 2007). Variables were listed with Ridit
values, and sorted as the Knowledge-rich, supply critical elements. The initial mean Ridit was
always considered 0.5 in this definition, and values of Ridits below 0.5 were indicated as low
probabilities of being in a negative propensity (Beder and Heim, 1990). Therefore, Knowledge-
rich, supply critical elements were identified as elements with Ridit values lower than 0.5. Mean
Ridit value is a cumulative probability of the entire scale used in the survey (Bikash et.al, 2010).
The frequency fi was computed for each category of responses, where 1, 2..., i = n. Next,
the mid-point accumulated frequency Fi was worked out for each category of responses.
Fi=1/2 fi……….……………….…………………. (2)
Fi =1/2 fi …..…….……………..……………. (3)
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In order to calculate the Ridits (Ri) equation 4 was applied. (Annex 1)
Ri = Fi/ ………..……………..………………. (4)
After the completion of analysis of Ridits for each category of the scale, Ridit values rij for
each category of scale items were calculated. These values were used to estimate pi, mean Ridit
values, which were utilized to list the Knowledge-rich, supply critical elements.
r ij= Ri x i j / i……………..……………………. (5)
ij= …..….…….…….…………………. (6)
pi= …..……..………..…………………. (7)
4.1 Ranking Supply network elements according to the Ridit values
Table 2: Ranking Supply Network Elements
Supply Chain Elements SC Element is …….
Core competent to
the company
Supply Chain
Elements
SC Element
contains…….
knowledge of the
company
Mean Ridit (Pi) Mean Ridit (Pi)
Plan
Decision making 0.39 Decision making 0.36
Supply/Demand
planning
0.40 Supply/Demand
planning
0.42
Order entry processing
and Order fulfillment
0.49 Order entry processing
and Order fulfillment
0.46
Information technology 0.50 Information technology 0.49
Research and
Development
0.51 Mathematical analysis 0.51
Logistics and supply
chain technology
management
0.53 Logistics network
design
0.54
Logistics network design 0.54 Research and
Development
0.55
Mathematical analysis 0.56 Management of third
party logistics
providers
0.59
Management of third
party logistics providers
0.59 Logistics and supply
chain technology
management
0.59
Source
Quality Assurance 0.38 Quality Assurance 0.37
Procurement of raw
materials
0.42 Procurement of raw
materials
0.45
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Rate negotiation 0.44 Rate negotiation 0.46
Inventory Management 0.45 Inventory Management 0.48
Procurement of in-house
materials
0.48 Procurement of in-
house materials
0.49
Procurement of logistics
Services
0.52 Materials handling 0.54
Customs clearance 0.53 Procurement of
logistics Services
0.54
Materials handling 0.57 Customs clearance 0.55
Warehouse Management 0.60 Warehouse
Management
0.55
Warehousing 0.61 Warehousing 0.57
Make
Production 0.41 Production 0.40
Inventory ownership 0.46 Inventory Management 0.48
Inventory Management 0.47 Inventory ownership 0.49
Materials handling 0.50 Materials handling 0.50
Warehouse Management 0.56 Warehousing 0.56
Warehousing 0.60 Warehouse
Management
0.57
Deliver
Distribution control 0.43 Distribution control 0.43
Inventory Management 0.45 Inventory Management 0.45
Warehousing 0.47 Warehouse
Management
0.46
Shipment consolidation 0.48 Warehousing 0.48
Transportation
Management
0.50 Outbound
transportation
0.49
Outbound transportation 0.50 Transportation
Management
0.53
Inbound transportation 0.50 Shipment consolidation 0.53
Warehouse Management 0.53 Inbound transportation 0.54
Fleet Management 0.55 Freight forwarding 0.54
Freight forwarding 0.61 Fleet Management 0.55
Return
Customer service 0.34 Customer service 0.37
Inventory Management 0.50 Inventory Management 0.51
Reverse logistics 0.57 Waste Management 0.56
Waste Management 0.59 Reverse logistics 0.59
Mean Ridit values were used to identify the Knowledge-rich, supply critical elements.
Both Ridit values lower than 0.5 were sorted as Knowledge-rich, supply critical elements (Beder
and Heim, 1990).
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5. DISCUSSION According to the Ridit analysis applied to each function of the SCOR Model, certain
variables were categorized as Knowledge-rich, critical elements from the studied variables. Core
competency supply network elements in a company which are addressed as critical elements in
supply networks are illustrated in table 3. The SC elements’ mean values, which were over 0.5
were sorted as the critical SC elements.
Table 3: Critical Supply Network Elements
Plan Source Make Deliver Return
Decision Making Quality Assurance Production Distribution
Control
Customer
service
Supply/Demand
Planning
Procurement of
raw materials
Inventory
Ownership
Inventory
Management
Inventory
Management
Order entry
processing and
order fulfillment
Rate Negotiation Inventory
Management
Warehousing
Information
Technology
Inventory
Management
Material
handling
Shipment
Consolidation
Procurement of
in-house materials
Transportation
Management
Outbound
Transportation
Inbound
Transportation
Knowledge-rich supply network elements in a supply network were sorted according to the
Ridit value. The same concept used to sort the critical SC elements was used to sort the
knowledge-rich SC elements. Table 4 signifies the Knowledge-rich elements in each function of
the SCOR model.
Table 4: Knowledge-rich Supply Network Elements
Plan Source Make Deliver Return
Decision Making Quality
Assurance
Production Distribution
Control
Customer
service
Supply/Demand
Planning
Procurement of
raw materials
Inventory
Management
Inventory
Management
Order entry
processing and
order fulfillment
Rate Negotiation Inventory
Ownership
Warehouse
Management
Information
Technology
Inventory
Management
Materials
Handling
Warehousing
Procurement of
in-house
materials
Outbound
Transportation
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As per the tables 3 and 4, it was found that all critical SC elements are not knowledge-rich.
Outbound transportation in delivering and Inventory Management in the process of return are
critical but not knowledge-rich. As such Knowledge-rich, critical elements in a supply network
were identified, where both requirements; Knowledge-rich and critical were accomplished by the
variables indicated in the table 5.
Table 0: Knowledge-rich, Supply Critical Elements
Plan Source Make Deliver Return
Decision Making Quality
Assurance
Production Distribution
Control
Customer
service
Supply/Demand
Planning
Procurement of
raw materials
Inventory
Management
Inventory
Management
Order entry
processing and
Order fulfillment
Rate Negotiation Inventory
Ownership
Warehousing
Information
Technology
Inventory
Management
Materials
Handling
Outbound
Transportation
Procurement of
in-house
materials
Decision making, Supply/Demand Planning, Order Entry Processing and Order Fulfillment
and Information Technology are the knowledge-rich supply critical elements in the process of
planning. When considering the process sourcing; Quality Assurance, Procurement of Raw
Materials, Rate Negotiation, Inventory Management and Procurement of in-house materials are
the knowledge-rich supply critical elements. Production, Inventory Management, Inventory
ownership, and Material handling are the knowledge-rich supply critical elements in the making
process. As per the analysis Distribution control, Inventory Management, Warehousing and
Outbound transportation are the knowledge-rich supply critical elements in the process of
delivering. Customer service is the only knowledge-rich supply critical element in return process.
These sorted elements are the most important elements to a Sri Lankan manufacturing company,
where these should be carefully managed for a better performance.
6. CONCLUSIONS AND RECOMMENDATIONS From the work, it can be concluded that decision making, quality assurance, production,
distribution control, and customer service are the top ranked Knowledge-rich, critical elements
within SCOR model and within the context of the sample set of Sri Lankan manufacturing
companies. Importantly, all Knowledge-rich elements are not critical to the company and all
critical elements are not Knowledge-rich elements. The list of Knowledge-rich, supply critical
elements is useful for manufacturers to know the crucial elements, affecting the performance of
the company.
It has been found that the knowledge of Supply Chain Management is not sufficient among
supply network related employees in Sri Lanka. It is clear that decision making can be affected by
the lack of knowledge about these elements. This list of Knowledge-rich, critical elements are
identified to support the process of strategic decision making in Supply Chain Management.
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This list will assist decision makers in understanding the impact of Knowledge-rich,
critical supply network elements before making decisions. Decision making, supply and demand
planning, order entry processing and order fulfillment, and information technology are identified
as the most Knowledge-rich, critical elements in planning. Quality assurance, procurement of raw
materials, rate negotiation, inventory management, and procurement of in-house materials are
recognized as the most Knowledge-rich, critical elements in sourcing. In making; production,
inventory management, inventory ownership and materials handling are the most Knowledge-rich,
critical elements. Distribution control, inventory management, warehousing and outbound
transportation are identified as the most Knowledge-rich, critical elements in delivering while
customer service is identified as the most Knowledge-rich, critical element in returning. The list of
Knowledge-rich, critical elements assists the strategic decision making in Sri Lankan
manufacturing companies, while decreasing the risks associated with managing supply network.
7. LIMITATIONS The research provides a study on Knowledge-rich, supply critical elements in Sri Lankan
manufacturing companies and only within that region. Therefore, the results could be relative only
to manufacturing companies within the region. \
8. REFERENCES 1. Abebe, M., Elmuti, D., & Minnis, W., (2008). Longitudinal assessment of an integrated industrial
supply chain. Supply Chain Management: An International Journal, Vol. 13, No. 2, pp. 151 - 159,
(Emerald Publication).
2. Allen, I.E., & Seaman, C.A., (2007). Likert Scales and Data Analyses, Viewed 17 October, 2012,
http://mail.asq.org/quality-progress/2007/07/statistics/likert-scales-and-data-analyses.html
3. Aqua Management Consulting Group. Five Essential elements of Integrated Supply Chain. Aqua MCG
Leadership Review.
4. Armstrong, J.S., & Overton, T.S. (1977). Journal of Marketing Research, Vol. 14, pp. 396-402.
5. Beder, J.H., & Heim, R.C. (1990). On the use of Ridit analyses. Vol. 55, No. 4, pp. 603-616.
6. Berenson, M.L., Krehbiel, T.C., & Levine, D.M. (2002). Basic Business statistics, concepts and
application (8th ed.). Pearson Education.
7. Beverley, H. (1998). Trent Focus for Research and Development in Primary Health Care: An
Introduction to Qualitative Research. Trent Focus.
8. Birou, L., Christensen, W.J., & Germain, R. (2005). Build to order and Just in time as predictors of
applied supply chain knowledge and market performance. Journal of operations management:
Sciencedirect, Vol. 23, pp. 470-481.
9. Bowersox, D.J., & Closs, D.J. (1996). Logistical management: The integrated supply chain process.
New York, NY: McGraw-Hill.
10. Croom, S., Giannakis, M. & Romano, P. (2000). Supply chain management: an analytical framework
for critical literature review. European Journal of Purchasing & Supply Management, Vol. 6, pp. 67-
83.
11. Dodd, S., Lancaster, G.A. & Williamson, P.R. (2004). Design and analysis of pilot studies:
recommendations for good practice. Journal of Evaluation in Clinical Practice, Vol. 10, pp. 307-312.
12. Field, A. (2005). Factor analysis using SPSS. pp 1-14.
13. Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance Measures and metrics in a supply
chain. International Journal of Operations & Production Management: Emerald Publication, Vol.21,
No.1, pp. 71-87.
14. Hundley, V., & Teijlingen, E. (2002). The importance of pilot studies. Nurs Stand, Vol.19-25, No.
16(40), pp. 33-36.
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International Conference on Operations and Supply Chain Management, Bali, 2014
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15. Industry Canada (2006), Logistics and Supply Chain Management (SCM), Key Performance Indicators
(KPI) Analysis.
16. Israel, G.D. (1992). Determining Sample Size.
17. Kemmerling, R., & Wagner, S.M. (2010). Handling Non Responses in Logistics Research. Journal of
Business Logistics, Vol. 31, No. 2, pp.357.
18. Lineback, J.F., & Thompson, K.J. (2010). Conducting Non response Bias Analysis for Business
Surveys. Office of Statistical Methods and Research for Economic Programs.
19. Lockamy, A., & McCormack, K. (2004). Linking SCOR planning practices to supply chain
performance: an explorative study. International Journal of Operations & Production Management,
Vol. 24, No. 12, pp. 1192-1218.
2 0 .Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers
& Industrial Engineering , Vol.43, pp.231-249.
21. Mundy, D., (2002). A question of response rate. Science Editor, Vol. 25, No. 01.
22. Ospina, S. (2004). Qualitative Research. SAGE Publications.
23. SCOR Model 10.0 (2010). Supply chain Operations Reference Model Version 10.0. Supply chain
council.
24. SCOR Model 9.0 (2008). Supply chain Operations Reference Model Version 9.0. Supply chain council.
25. Stephens, S. (2001). Supply chain operations reference model (ed. 5.0): a new tool to improve supply
chain efficiency and achieve best practice. Information Systems Frontiers, Vol. 2, No. 4, pp. 471-476.
26. Su, Y., & Yang, C. (2009). Relationship between benefits of ERP systems implementation and its
impacts on firm performance of SCM. Vol. 22, No. 6, pp. 722-752.
27. Sukamolson, S. (2010).Fundamentals of quantitative research. Language Institute.
28. Wu, C.H. (2007). On the Application of Grey Relational Analysis and RIDIT Analysis to Likert- Scale
Surveys. International Mathematical Forum, Vol. 02, No. 14, pp. 675 – 687.
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Appendices
Appendix 01-Questionnaire