effective supply value chain based on competence success

Research paper

Effective supply value chain based oncompetence success

Gulgun Kayakutlu

Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey, and

Gulcin BuyukozkanDepartment of Industrial Engineering, Galatasaray University, Istanbul, Turkey

AbstractPurpose – This paper seeks to propose a managerial decision framework for different levels of supply chain, by addressing the strategic importance ofcompetence values in supply chain effectiveness.Design/methodology/approach – A conceptual framework for supply chain effectiveness is defined in levels of supply chain targets, knowledgemanagement dynamics, competence levels and competence success attributes. Analysis of literature in the areas of competence management,knowledge management, supply chain and value chain management resulted in defining the factors of the model. Surveys of industrial practices wereused to validate the choice of factors. The analytical network process (ANP) is used to determine the most beneficial competence success attributes in acase study performed for three companies that participate in different stages of the textile supply chain.Findings – Individual competence in continuous learning and networking, as well as innovativeness of the team are found to be the three mostimportant competence attributes in supply chain effectiveness.Research limitations/implications – The case study is executed in the regional textile industry. New case studies in other industries will help improvethe framework. Further international surveys can improve the detail level of factors used.Practical implications – The study creates awareness of knowledge management dynamics and competence management for companies which arein need of innovation to improve their supply chain competitiveness.Originality/value – The proposed decision framework is one of the first efforts to consider the importance of competence in supply chain success. TheANP method is used to offer an accurate analysis of interdependent factors observed in management of knowledge dynamics and competence levels.

Keywords Competences, Knowledge management

Paper type Research paper

1. Introduction

Supply chains play a significant role in the global knowledge

based economy. Effectiveness of the value fostered has been

the main focus of all parties in a supply chain. Yet,

measurement of the effectiveness has been problematic for

the policy makers when knowledge based attributes have been

as influential as the cost-down or physical assets (Kinder,

2003; Walters, 2006). Two new dimensions are to be taken

into consideration: the significant value of innovation and the

opportunities provided by collaboration. Collaboration needs

to be used in data and knowledge sharing as well as in the

integration of plans and decisions (Queseda et al., 2008). The

value of innovation, on the other hand, is created by the

ability to renew business models and access competencies in

the chain (Leavy, 2005). Acute awareness of the need for

emphasis on competence management and development is

now attributable to the whole value chain (Chan and Lee,

2005). That is why organisational competence in the chain is

to be explored in interaction with team and individual success

factors of each partner. All three levels of competence are to

be seen as viable resources of integrated business strategies.Key components of any knowledge based supply chain

system are the people who create and use the knowledge

(Barson et al., 2000), and organisations need to learn from

these internal resources as much as from the external

environment. In this context, there have been numerous in-

depth research studies run by psychologists, sociologists and

epistemologists, each with its own view (Harzallah and

Vernadat, 2001; Lee et al., 2009; Feldman, 2003). In

addition, studies run by knowledge management researchers

The current issue and full text archive of this journal is available at

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Supply Chain Management: An International Journal

15/2 (2010) 129–138

q Emerald Group Publishing Limited [ISSN 1359-8546]

[DOI 10.1108/13598541011028732]

The authors would like to express deep gratitude to the industrial expertsfrom Kipas Iplik A.S., Karagozlu Tekstil A.S. and Catalys Inc. for theirunlimited support in evaluation of the framework. The authors would liketo thank the Editor and anonymous reviewers for their valuable commentsand suggestions, which were helpful in improving the paper. The authorsalso acknowledge Ellen Saunders for her help in improving the linguisticquality of the paper.

129

are focused on technological processes and/or intellectual

capital measurement (Hicks et al., 2006) ignoring the

multidisciplinary interactions. With the spread of globalcompetition, the need for collaboration is increased

(Blenkhorn and Fleisher, 2005); and this fact requires the

supply value chain players to review their goals byreconsidering the interaction of different levels of

competence issues with a knowledge-based vision (Lindgrenet al., 2003). Advanced implementation of virtual teams

facilitates the technological collaboration and leads to

fundamental changes in supply management strategies (Moand Zhou, 2003). Hence, understanding of organisational

competence is to be raised to embrace supply value chaineffectiveness (Miocevic, 2008). Exploring and measuring the

competence priorities through a new dimension will be

beneficial for the company as well as the value chain.This paper aims to introduce the important competence

management issues of an effective supply value chainmanagement in a four-level strategic analysis framework that

will aid managerial decision-making. The proposed decision

framework is based on literature and practice in the area ofcompetence management, knowledge management, and

supply chain management. A systematic analytical model,

namely the analytic network process (ANP) (Saaty, 1996) willbe used to determine which competence management

attributes are most beneficial in the development of aneffective supply value chain. ANP is a suitable technique to

analyse the dynamic characteristics and complexity of supply

value chain management, which is a necessity for strategicanalysis. Despite the increase in ANP related studies, to our

knowledge there is only one ANP study on knowledge

management related issues in the supply chain (Raisinghaniand Meade, 2005). This provides an opportunity to discuss

further studies based on a new decision framework and theachievements of a case study.The paper is organised as follows: the first section presents

the key constructs used to build the suggested framework andillustrates the theoretical framework. In the following section,

the research methodology based on the analytic networkprocess is outlined. The following sections include initiation

and detailed illustration of the methodology through the case

study and its results. The final section presents some futureresearch directions and concludes the paper.

2. Research constructs and theoretical framework

In our century, knowledge has solidified its position as a key

commodity in the global market; whereas, competencemanagement strategies continue to be a challenge for

achieving goals not only in manufacturing organizations butalso in the value chain. Many competence systems in current

use are well-designed data repositories (Lindgren, 2005),

devoid of the critical and crucial interest driven dynamismrequired. Therefore, most of them cannot respond to the

enhanced goals of the supply value chain (Halley and

Beaulieu, 2009). In the beginning of this century, agilitywas the main goal of any value chain, which is not the case

anymore (Raisinghani and Meade, 2005). Currently, theleading edge goals for holding up the chain are to include

original competitive advantages created by a holistic view of

tangible and intangible components as Kinder (2003)investigates. This is only possible by considering

collaboration and innovation. Establishing the effective

collaboration both in functional operations and strategies

(Cox, 1999; Bowersox et al., 2000) will add value to the

supply chain as the full potential of information flow re-

energizes the business (Bowersox et al., 2005). Participation

in innovation creates a competitive advantage for the supply

chain (Hou and He, 2008; George et al., 2009) if regulatoryand cultural limits of globalisation are removed (Bello et al.,2004).A supply chain consists of different stages and even includes

the customer (Simchi-Levi et al., 2000). That is why

integration gaps are blamed on the competence level of

human resources due to reducing the beneficial influences of

knowledge management in value generation (Madlberger,

2009). Hence, competence created by collaborative

innovation is to be investigated in individual, team or

organisational levels of human interactions. To this end, the

literature survey will be focused on four dimensions as shown

in Figure 1: supply value chain effectiveness goals, knowledge

dynamics, competence levels and success attributes in

competence levels.

2.1 Dimension of effectiveness

The majority of companies in supply chains target correct

order management, demand forecasting, inventory

management, capital efficiency and reduced time-to market

(Tamas, 2000) which are all good responses to market needs.

Chopra and Meindl (2001) extends the basic goal of

responsiveness by focusing on originality of the products

and services provided by the chain. Responsiveness is the

minimum goal of responding to customer requests based on

time, product mix, flexibility, quality and price. It should be

applied by companies in all the stages independent of power

balance or structural differentiation of push or pull (Mentzer

et al., 2007).Perceived differences in difficulty associated with demand

prediction will restrict full responsiveness; hence, competitive

advantages of one supply chain over another are to be sought

using the main factors of trust, consistency and further

uniqueness (Simchi-Levi et al., 2004). Consistency builds

customer trust for the whole chain by sticking to the desired

level of responsiveness over time (Walters and Lancester,

Figure 1 High-level graphical representation of proposed evaluationframework

Effective supply value chain based on competence success

Gulgun Kayakutlu and Gulcin Buyukozkan


Volume 15 · Number 2 · 2010 · 129–138

130

2000). Any small mistake in delivery time, quality and price

may easily cause the customers change the supplier.Originality guarantees competitive uniqueness in the ever-changing global world. Innovation, the main result expected

from knowledge management implementations, should bringup new products, new services, new processes and newsystems by implementing the benefits proposed by advanced

technologies (Mavrotas et al., 2007).

2.2 Dimension of knowledge management dynamics

In order to achieve goals for effectiveness, knowledge

management is to be used as part of a strategy, as anintegration tool and to pertain the relations. Knowledgemanagement dynamics are clustered as strategies,

infrastructure and distribution and sharing as detailed below.

2.2.1 Knowledge management strategiesTo develop process, and share and utilise knowledge in thevalue chain landscape, assets, incentives and creativity must

be the central foci. Knowledge strategies are to be defined forthe entire value chain, which can only be done after havingdefined the organisational strategies. The first major strategy

is to define the “landscape” (Lee et al., 2009). Using thegoverning principles model, the values, goals and expectationsof the value chain should be clarified based on landscapes of

the organisations. This method is concerned with ethical andjudgemental issues (Davenport, 1999; Wiig, 2004).Knowledge assets inventory is expected to be the second

strategy. Each organisation has his own assets but how muchof these assets will be shared and accepted as the value chainasset is the question to be answered (National ResearchCouncil, 2000). Structural, human, relational and social

assets are to be determined as internal to the organisationand/or intra organisations in the chain (Ko et al., 2009). Bestpractices, is a good example of knowledge to be shared. While

defining the knowledge to be shared, trust building should beaccomplished by defining the incentives (Edvinsson, 2002).The last strategy is to define the strength of creativity and

design new ways to improve collaborative creativity.

2.2.2 Knowledge management infrastructureInformation and communication technologies are indeed anintegral part of value chain integration. The decision making

and the technological infrastructure are the two criticalsystems in performance (Stapleton et al., 2006). Value addingnetwork implemented using Internet and the web-based

applications (technological system) will amalgamate thestructural assets, whereas data warehouses and data miningtools (decision systems) will retrieve the knowledge from

combined information inventories (Edvinsson, 2002). As thebenefits of technology are achieved more advanced technologyis requested. A lexicon for the value chain is to be created tocommunicate in the same language independent of culture

and customs, which necessitates the taxonomy work.Knowledge agents, which are developed using artificialintelligence techniques, will help semantic interpretation for

the lexicon to be dynamically generated (Kamruzzaman et al.,2006). Knowledge hubs and knowledge grids will speed upand regulate the knowledge flow (Brown and O’Hare, 2001).

To avoid turning enterprises off because of the expense ofthese technologies, start up should be based on webapplications. A good example of this is the Living Steelproject, which was started by ten steel producers to integrate

the supply chain including construction companies and

architects (Lee et al., 2009). The project tries to achieve the

best return on investment in web applications.

2.2.3 Knowledge distributionReinvention of the values that can be created through an

integrated value chain can only be possible by building trust

(Walter, 2003). Formalising chain learning, fostering

communities of practice and rewarding systems can be

solutions for building trust if power of stages are balanced

(Walters, 2006). Emphasizing natural human processes like

story telling can empower collaboration. It is only possible

through interest groups and idea exchanges that collaborative

creativity is reinforced. Creativity is an individual skill to

retain and workers of a company should be encouraged to

come up with new ideas by accessing chain knowledge

inventory. Dissemination of the knowledge shared in the value

chain in the organisation and rewarding ideas generated are

part of enterprise leadership and would show the flexibility of

company vision (Miocevic, 2008).

2.3 Dimension of competence levels

Current research has strong findings that human relations are

getting more important in supply chain performance (Shub

and Stonebraker, 2009). No longer limited to relations among

managers, opportunities given to teams by evolution of

information and communication technologies are to be

analysed (Shi et al., 2004). Organisations and teams are

made of individuals. The majority of competence studies

conclude that behavioural, motivational, technical and

knowledge based skills (Lindgren, 2005) influence the

degree of achievement of supply value chain goals. It is

obvious that organisational, team and individual competence

levels are interdependent and one cannot be analysed without

the feedback of the other levels.Organisational level in the knowledge-based organisations is

expected to consider knowledge landscape, knowledge assets

as well as information sharing, push/pull balance and synergy

creation. Knowledge landscape defines the coverage of

knowledge clustering, processing and creation that will

structure the business strategies. The power of organisations

is dependent on knowledge assets as well as financial assets

(Walter, 2003). Information sharing policies are the basis of

collaboration in the supply chain. The push/pull power

balance among the stages is important in building trust, and

synergy creation is a necessity for originality.At the team level issues of team success are to be

considered. Cultural integration has become as important

an issue as knowledge sharing in both local and global chains.

Relation among the stages is fragile if the operation teams are

not performing with common integrated goals. Cost can be

reduced also by relying on resource utilisation by either

strategic or operational teams (Madlberger, 2009).

Management and leadership is the crucial attribute of team

success resulting in organisational success. Innovation is the

result of knowledge sharing and can only be measured at the

team level since modifications can neither be decided nor

created by individuals even if it benefits the creativity of the

team members.Individuals build teams and organisations. Expectations at

this level consist of knowledge acquisition, knowledge

understanding, willingness to do something differently and

experiencing different trials (Hoek et al., 2002). These skills

and abilities lead learning and creativity as a necessity of




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knowledge-based organisations and networking for inter-

organisational issues in the supply chain (Lee, 2009). A

purchasing and or supply operator is expected to be as results

oriented as the salesforce. Since turnover will damage

consistency role commitment is inevitable in performance

measures.

3. Research methodology: the analytic networkprocess

Selecting a suitable methodology that can decode the high-

level relationship model presented in Figure 1 is a critical

issue. This methodology should be able to use quantitative,

qualitative, tangible and intangible factors pertaining to the

decision of whether and which success attributes should be

evaluated. With this trend, ANP is found to be capable of

taking the multiple dimensions of information into the

analysis. It is a method that is widely used in decision making

in supplier selection (Chen et al., 2008; Lee, 2009) and

supplier evaluation (Lin, 2008; Liu and Wang, 2009). It is

also used in performance measures (Thakkar et al., 2007; Linet al., 2009).ANP is a general form of the analytical hierarchy process

(AHP) first introduced by Saaty (1980). While the AHP

employs a unidirectional hierarchical relationship among

decision levels, the ANP enables interrelationships among the

decision levels and attributes in a more general form. The

ANP uses ratio scale measurements based on pair wise

comparisons; however, it does not impose a strict hierarchical

structure as in AHP, and models a decision problem using a

systems-with-feedback approach. The ANP refers then to the

systems of which a level may both dominate and be

dominated, directly or indirectly, by other decision

attributes and levels. The ANP approach is capable of

handling interdependence among elements by obtaining the

composite weights through the development of a

“supermatrix”. Saaty (1980) explains the supermatrix

concept similar to the Markov chain process. The

supermatrix development is defined in the next section.There are six major steps in the ANP process:

1 Develop an evaluation network hierarchy showing the

relationships among analysing factors.2 Elicit pair-wise comparisons among the factors

influencing the evaluation.3 Calculate relative-importance-weight vectors of the

factors.4 Form a supermatrix (i.e. a two-dimensional matrix

composed from the relative-importance-weight vectors)

and normalize this supermatrix, so that the numbers in

every column sum to one.5 Calculate converged (“stable”) weights from the

normalized super-matrix.6 Determine overall weightings of evaluation attributes.

These six steps will be discussed in conjunction with the case

study in the following section.

4. Application of the suggested framework

The proposed framework limits the case study to knowledge-

based organisations with a role in the supply chain. Since

different stages in a supply chain show different issues, it is

preferred to work on the average of responses from three

companies representing different stages of a textile chain.

Importance given to competence evaluation and having a

payroll expert also played an important role in the choice ofthe companies studied.Analysis is performed on the average of evaluations by three

assessors from three different companies older than ten years,

all classified as small and medium size, and the all important

textile exporters of Turkey. The first company is a yarnproducer, the second a fabric producer and the third is a

sourcing company.The yarn producer is constructed and run by a mechanical

engineer brought up in cotton fields. Technology andinformation sharing have been the infrastructure of his

strategies since the very first day of establishment. The

youngest brother of the family is trained as a knowledgeengineer in the US to be responsible for organisational

competence management after graduation. Since he is aknowledge engineer, he has implemented knowledge

management processes in the company and has improved

export volume by 30 per cent in the last four years with thetechniques he applied in competence management. He is the

first assessor in the case study.The fabric producer was a classical family company

working in the local market until the mathematical engineerdaughter joined. She finished her master of science in UK

before initiating the export operation and managing the

business process-reengineering project in the company. Sherecently finalised establishing customer knowledge and

intellectual capital management processes. The performancesystem she implemented is based on both intellectual capital

and competence measurements. She is chosen to be the

second assessor.The third assessor is the company owner of a sourcing

company, a textile engineer with a PhD in processengineering. Knowledge management was not a choice but

a natural driver for him when he established his company. His

company operates both traditionally and on a collaborationplatform for e-business. He is one of the early appliers of

reverse price forecasting and runs the competencemanagement himself.The following sub-sections are the result of interviews with

the above-mentioned assessors. The overall objective of the

framework is to determine which competence levels’ attribute

is most influential in the current supply value chainmanagement. Knowing this information enables

management to ascertain if the supply chain is positionedappropriately to meet its strategic objectives.

4.1 The evaluation network hierarchy

The detailed model actually used to evaluate the competence

levels’ success attributes is given in Figure 2.

4.2 Pair-wise comparisons

Eliciting preferences of various components and attributeswill require a series of pair wise comparisons where the

assessor will compare two components at a time with respectto an upper level ’control’ criterion. In ANP, like AHP, pair

wise comparisons of the elements in each level are conducted

with respect to their relative importance towards their controlcriterion (Saaty, 1980).Saaty has suggested a scale of 1 to 9 when comparing the

two components, with a score of 1 representing indifference

between the two components and 9 being overwhelming




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132

dominance of the component under consideration (row

component) over the comparison component (column

component). If a component has a weaker impact on the

control criterion, the range of scores will be from 1 to 1/9,

where 1 represents indifference and 1/9 an overwhelming

dominance by a column element over the row element. When

scoring is conducted for a pair, a reciprocal value is

automatically assigned to the reverse comparison within the

matrix. That is, if aij is a matrix value assigned to the

relationship of component i to component j, then aji is equal

to 1/aij (or aij £ aji ¼ 1). Since many of these values have

strategic importance, we again used the strategic group

decision-making tool, Delphi approach, to assign meaningful

values to the pair wise comparisons. Expertise of the assessors

chosen has avoided the issues caused by difficulty of

evaluation.An example of the pair-wise comparison matrix with respect

to the goal as the controlling factor is shown in Table I. The

weightings have been obtained from our industrial experts by

asking a series of paired comparison questions. For this

matrix in Table I, the experts were asked questions such as:

“In terms of the goal of effective supply value chain what is

the relative importance of the infrastructure dynamic when

compared with the strategies dynamic?” In this example, the

decision maker viewed collection as “slightly more important”

by the score of 3.000 (as shown in the cell at the intersection

of the strategies row and the infrastructure column in Table I).

Reciprocally, the intersection of the strategies row and

infrastructure column shows a score of 0.333. This pairwise

comparison approach is used to populate the matrix.

4.3 Calculation of relative importance weights

Once all the pair wise comparisons are completed, the relative

importance weight for each component is determined. The

priority vector shows that for this study, the knowledge

distribution dynamic was given the highest rating (0.637) (the

weighted priorities are shown as the last column in Table I).

Given that A is the pairwise comparison matrix, the weights

can be determined by expression:

A w ¼ lmax w ð1Þ

where lmax is the largest eigenvalue of A. Saaty provides

several algorithms for approximating w. In this paper a two-

stage algorithm as proposed by Saaty (1996) was used. The

algorithm involves forming a new n £ n matrix by dividing

each element in a column by the sum of the column elements

in the first stage. In the second stage, the elements in each row

of the resultant matrix are summed and divided by the n

elements in the row.This is referred to as the process of averaging over

normalized columns. The procedure may be algebraically

represented as:

wi ¼

PIi¼1

aijPJ

j¼1aij

!

Jð2Þ

where:. wi is the weighted priority for component i.. J is number of columns (components).. I is number of rows (components).

Table I Pair-wise comparison matrix for KM dynamics elements’importance relative to the goal

Goal Strategy Infrastructure Distribution Weights

Strategy 1.000 0.333 0.200 0.105

Infrastructure 3 1.000 0.333 0.258

Distribution 5 3 1.000 0.637

Figure 2 Evaluation network for effective supply value chain management




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This algorithm is one of the better methods for estimation of

eigenvalues, as proposed by Saaty (1996). Sarkis (1998) also

recommended this algorithm because it can be easily used by

practitioners who may only have access to simple

spreadsheets. This algorithm has since been used by many

researchers (such as Meade and Sarkis (1998); Chung et al.

(2005); Wong et al. (2008); and Chen et al. (2009)).In the assessment process there may occur a problem in the

transitivity or consistency of the pair wise comparisons. For an

explanation on inconsistencies in relationships and their

calculations see (Saaty, 1980).The priority vectors for each pair wise comparison matrix

will be needed to complete the various supermatrix

submatrices. We will need a total of 13 priority vectors to

complete our supermatrix. This requirement means that 13

pair wise comparison matrices must be completed. The pair

wise comparison matrix results used below were all tested for

and achieved the consistency goals.

4.4 Supermatrix formation

ANP uses the formation of a supermatrix to allow for the

resolution of the effects of the interdependence that exists

between the clusters within the evaluation network hierarchy.

The supermatrix is a partitioned matrix, where each

submatrix is composed of a set of relationships between

two clusters in the graphical model. A generic supermatrix is

shown in Figure 3, with the notation representing the various

relationships from Figure 2; for instance, “A” is the

submatrix representing the influence relationship between

KM dynamics elements’ and the control factor of the goal of

determining the weights of competence levels’ success

attributes. Table II is the detailed initial supermatrix of the

proposed model.

4.5 Calculation of stable weights from the normalized

super-matrix

The next step with the supermatrix evaluation is to determine

the final relative importance weights of each of the

competence levels. To complete this step and to help

guarantee convergence, the columns of the supermatrix

must be “column stochastic”. That is, the sum of weights of

each column of the supermatrix must be equal to 1. To

complete this task, each of the columns may either be

normalized by dividing the weight in the column by the sum

of that column. For convergence to a final set of weights, we

raise the normalized (column stochastic) supermatrix to the

power 2k þ 1 where k is an arbitrarily large number until

stabilization of the weights occurs (i.e. when values in the

supermatrix do not change when it is multiplied by itself

again, this is also defined as convergence). For our example,

convergence occurred when the supermatrix was raised to the

29th power. The long-term stable weighted values to be used

in the analysis are shown in the converged supermatrix given

in Table III. From the results, it can be observed that the most

important competence level for effective supply value chain is

individual competence (0.487), trailed by team competence

(0.413).

4.6 Final relative importance weight calculation

The sixth and last step in the ANP process is to take the final

results of the converged super-matrix and the eigenvector

values from the earlier pair-wise comparisons and calculate

the relative importance weight for each competence level’s

success attribute. In our model no interdependence between

the competence levels and the attributes level is assumed to

exist.To analyse success attributes of competence levels, a similar

pair wise comparison that was made in step 2 is made for the

attributes level for relative importance weight calculation (or

eigenvector determination). There are three separate pair wise

comparison matrices that have to be developed for this step in

the analysis. Tables IV to VI give the evaluation results.Once all the relative weights have been calculated, a

composite (global) weight for each success attribute is

determined. This is accomplished by aggregating the

weights. The result is a single weight value for competence

levels’ success attribute. The combination of all attributes’

weights is given in Table VII. Based on these results, it

appears innovation (0.150), trailed by continuous learning

(0.143), networking (0.142) and role commitment (0.140)

Figure 3 General submatrix notation for supermatrix

Table II Initial super matrix M for determining the weights of competence levels’ success attributes

SVC targets KM dynamics Competence levels

Goal RESP CONS ORI STRA INF DIST ORG TEAM IND

Goal 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

SVC targets RESP 0.000 0.000 0.000 0.000 0.081 0.188 0.072 0.000 0.000 0.000

CONS 0.000 0.000 0.000 0.000 0.731 0.081 0.649 0.000 0.000 0.000

ORI 0.000 0.000 0.000 0.000 0.188 0.731 0.279 0.000 0.000 0.000

KM dynamics STRA 0.105 0.070 0.188 0.651 0.000 0.000 0.000 0.000 0.000 0.000

INF 0.258 0.178 0.081 0.223 0.000 0.000 0.000 0.000 0.000 0.000

DIST 0.637 0.751 0.731 0.127 0.000 0.000 0.000 0.000 0.000 0.000

Competence levels ORG 0.000 0.000 0.000 0.000 0.055 0.081 0.105 0.223 0.088 0.086

TEAM 0.000 0.000 0.000 0.000 0.173 0.188 0.258 0.127 0.243 0.618

IND 0.000 0.000 0.000 0.000 0.772 0.731 0.637 0.651 0.670 0.297




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respectively are the success attributes that have the biggest

impact on the supply value chain management effectiveness.Role commitment was shown to be one of the most

influential factors by human behaviour analysts (Manthou

et al., 2004: Belkadi et al., 2007). Continuous learning

(Koskivaara, 2004; Hult et al. 2006) and innovation (Thakkar

et al., 2007; Prasad and Martens, 2008) were proven to be

highly effective in performance by knowledge experts. Theinformation technologists emphasize and show importance of

networking in supply chain success since the beginning of this

century (Barson et al., 2000; Walter, 2003; Shi et al., 2004;Ko et al., 2009). Yet, this study is unique in presenting the

important effect of innovation, continuous learning,

networking and role commitment in the integrated analysis.

That is why, the managers of the case companies in a very

classical industry are surprised to see the impressive influence

of those intangible assets.All three companies have decided to revise and extend their

research and development units to include skill development.

5. Concluding remarks and future directions

Effectiveness in supply chain is satisfactory if suppliersidentify and share the company value (Cambra-Fierro andPolo-Redondo, 2008). This paper addresses the need for a

strategic analysis model to assist management in identifyingthe competence levels’ success factors for supply value chainmanagement effectiveness. To this end, an evaluation model is

developed based on a literature survey and refined withindustrial experts. The model considers tangible, intangible,quantitative, qualitative and strategic factors in the evaluation.This model is one of the first efforts to consider competence

levels’ success attributes explicitly within a strategic evaluationprocess.Although it was not a surprise for a knowledge management

expert to see that innovation, continuous learning andnetworking would be the most influential competencefactors; these are the attributes in which companies do not

traditionally invest to develop. In a classical industry liketextiles, competition is forcing supply chain effectiveness andcompanies of any stage are focused on design to achieve

competitive uniqueness. Results of the evaluation supportedthe managers who are in search of innovation.The ANPmethod used in this study offers amore precise and

accurate analysis by integrating interdependent relationships,but requires more time and effort (additional interdependencyrelationships increase geometrically the number of pair wise

comparison matrices). This is why an application of the ANPapproach, as proposed in this study should be targeted at morestrategic decisions, especially for long-term profit and long-term competitiveness considerations.This paper only presented the initial states of the framework

for which we foresee further developments. The most recent

improvements in research of supply chain effectiveness(Soosay et al., 2008; Elmuti et al., 2008; Fawcett et al.,2008) will provide an opportunity to consider a larger span offactors and criteria. Possible extensions include the

consideration of knowledge dynamics to be detailed, andorganizational performance objectives to form a new layer ofsupply operations to be considered. The model just

considered selective processes and did not cover all possibleinteractions. Including additional interactions between andwithin the decision levels would be an enhancement to themodel.Application of the model restricts companies that can

benefit from the framework. Studies are ongoing to improve

the framework to create awareness of knowledge managementdynamics and competence management in more traditionalcompanies since the methodology is transferable.This paper has set the foundation for a systematic

framework in a multidisciplinary approach of investigating afield, which is not yet fully explored in research. We strongly

believe that it will contribute to decision-making processesboth in research and business.

Table III Converged supermatrix at M29

SVC targets KM dynamics Competence levels

GOAL RESP CONS ORI STRA INF DIST ORG TEAM IND

ORG 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100

Competence levels TEAM 0.413 0.413 0.413 0.413 0.413 0.413 0.413 0.413 0.413 0.413

IND 0.487 0.487 0.487 0.487 0.487 0.487 0.487 0.487 0.487 0.487

Table IV The relative importance of the organizational competencesuccess attributes

KL KA IS PPB SCr

Knowledge landscape 1.000 3.000 3.000 5.000 0.333

Knowledge assets 0.333 1.000 5.000 3.000 3.000

Information sharing 0.333 0.200 1.000 5.000 0.333

Push/pull power balance 0.200 0.333 0.200 1.000 3.000

Synergy creation 3.000 0.333 3.000 0.333 1.000

Table V The relative importance of the team competence successattributes

KS CI RU INV ML

Knowledge sharing 1.000 5.000 7.000 0.200 0.200

Cultural integration 0.200 1.000 3.000 0.333 5.000

Resource utilisation 0.143 0.333 1.000 0.200 0.200

Innovation 5.000 3.000 5.000 1.000 3.000

Management/leadership 5.000 0.200 5.000 0.333 1.000

Table VI The relative importance of the individual competence successattributes

RO RC CL NW Cr

Result orientation 1.000 0.200 0.333 0.333 0.333

Role commitment 5.000 1.000 3.000 0.333 3.000

Continuous learning 3.000 0.333 1.000 3.000 5.000

Networking 3.000 3.000 0.333 1.000 5.000

Creativity 3.000 0.333 0.200 0.200 1.000




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Corresponding author

Gulcin Buyukozkan can be contacted at: [email protected]




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