a study on knowledge-rich critical elements within … · 6th international conference on...

6th

International Conference on Operations and Supply Chain Management, Bali, 2014

1

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:

[email protected]

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).

mailto:[email protected]



6th


2

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).

6th


3

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.

6th


4

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

6th


5

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)

6th


6

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

6th


7

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


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


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).

6th


8

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


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


Ownership

Warehouse

Management

Information

Technology

Inventory

Management

Materials

Handling

Warehousing

Procurement of

in-house

materials

Outbound

Transportation

6th


9

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


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


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.

6th


10

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.

http://mail.asq.org/quality-progress/2007/07/statistics/likert-scales-and-data-analyses.html

6th


11

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.

6th


12

Appendices

Appendix 01-Questionnaire

a study on knowledge-rich critical elements within … · 6th international conference on...

Documents