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AN EMPIRICAL INVESTIGATION OF SUPPLY CHAIN MANAGEMENTBEST PRACTICES IN LARGE PRIVATE MANUFACTURING FIRMS IN

KENYA

ByAwino Zachary Bolo, PhD.

Department of Business AdministrationSchool of Business - University of Nairobi

Nairobi-KenyaEmail: [email protected]

Gituro Wainaina, PhD.Department of Management Science

University of NairobiNairobi-Kenya

Email: [email protected] and/or [email protected]

____________________________________________________________________

Abstract

Today, large companies are mainly focusing on becoming efficient and flexible in

their manufacturing methods in order to handle uncertainty in the business

environment. To do this, they need different strategies to manage the flow of goods

from the point of production to the consumer. However, most firms have not been

able to formulate the right strategies required to achieve this objective in Supply

Chain Management (SCM), this calls for a strategic fit of an organizations core

competencies, strategy and core capability. The paper focuses on SCM best practices

in large private manufacturing firms in Kenya. The preliminary tests employed the

use of Kaiser Mayer-Olkin (KMO) and Bartletts Test. In this case, KMO measures

the sampling adequacy which should be greater than 0.5 for a satisfactory analysis to

proceed. The outcome revealed a measure of 0.583, an indication that the Bartletts

Test of spericity is significant implying that the correlation matrix is non-singular

and therefore, the factor analysis model is satisfactory.A sample of 52 large private

manufacturing companies, which are members of Kenya Association of

Manufacturers (KAM) was used. To establish SCM best practices, 39 variables

were used to measure the level of application among these firms. The variables

were analyzed using factor analysis procedure to achieve a simple and meaningful

structure, that is, have a nonzero loading of the explained variance for each

individual factor, varimax rotation was done. As a result, 11 critical factors were

established as the best practices: operating policies, linkages within supply chain

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firms, improved performance, information technology systems, strategic alliance,

performance measures, goal orientation, customer relationships, guidelines and

procedures, supplier selection and supplier evaluation. When benchmarked, these

practices were found to be universal and compares with the best practices globally.

The implications of the findings are also discussed.

Key words: competitive advantage, core capabilities, core competencies, Bestpractices , value chain

Introduction

Large companies today mainly focus on becoming efficient and flexible in their

manufacturing methods in order to handle uncertainty in the business environment,

they need different strategies to manage the flow of goods from the point of

production to the end user. However, they have not been able to formulate the right

strategies required to achieve this noble task in SCM. This call for a strategic fit of

an organizations core competencies, strategy and core capability, which is an

emerging paradigm in the study of strategic management and specifically in SCM.

Corporations have increasingly turned to global markets for their supplies. The

globalization of supply chains has forced companies to look for better and more

inter-linked systems between SCM competencies, multiple SCM strategies and the

implementation processes and SCM capabilities to coordinate the flow of materials

into and out of the company as opposed to the fragmented systems, which have

characterized many organizations. Companies and distribution channels today

compete more on the basis of time and quality, having defect-free products to

customers faster and more reliably than the competitor is no longer seen as a

competitive advantage but simply as a market place requirement. Customers

consistently demand that products are delivered faster, on time, and with no damage.

This can only be achieved with proper coordination of efforts by linking systems and

processes to create synergy. Each of these necessitates better coordination with

suppliers and distributors, and constitutes the linkage between SCM core

competencies, strategy and SCM core capabilities, which are not easy to match.

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This combination creates a competitive edge within the system that cannot be copied

by the competitor in the market place; hence becomes core capability of the firm.

The global orientation and increased performance-based competition, combined with

rapidly changing technology and economic conditions, all contribute to market place

uncertainty. This uncertainty requires greater flexibility on the part of the individual

companies and distribution channels, which in turn, demands for more flexibility in

channel relationships. For this to be achieved, a firm must have a fit between SCM

competencies, implementation of strategy and SCM capability with its suppliers and

distributors. This will enhance competitive advantage of the business and improve

corporate performance.

Literature Review

It is, therefore, important to reflect on the views of various strategic management

scholars on the concept of strategic management as it relates to this paper and how it

affects the micro-and macro-economic environment. Strategic management is the

organizations pre-selected means or approach to achieving its goals or objectives,

while coping with current and future external conditions (Digman, 1990). Strategic

management aims at achieving an enterprises mission and objectives by reconciling

its resources with opportunities and threats in the business environment (Smit et al.,

1993). It is concerned with policy decisions affecting the entire organization the

overall objective being to position the organization to deal effectively with its

environment. These explanations give clarity on the relationships and linkages

between and amongst the variables of the study. However, understanding SCM

philosophy is required in order to appreciate the linkage with strategic management.

Previous studies in SCM have considered the measurement of competencies,

strategy, capabilities and the effect of each on performance. For example, Caeldries

and van Dierdonck (1988) used strategy and performance as key variables in a study

between strategy and performance of large firms in Belgium; they used survey

methodology. Johnson and Scholes (1999) did a similar study in USA and used the

same methodology and variables. Day (1994) used core capabilities as independent

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variable and performance as the dependent variable, using a baseline survey

methodology. Stanley and Gregory (2001) used strategy implementation as the

independent variable and performance as the dependent variable applying a

triangulation methodology consisting of literature review, survey and case studies.

Manufacturing is an important sector in Kenya and it makes a substantial

contribution to the countrys economic development. It has the potential to generate

foreign exchange earnings through exports and diversify the countrys economy.

This sector has grown over time both in terms of its contribution to the countrys

gross domestic product and employment. The average size of this sector for tropical

Africa is 8 per cent. Despite the importance and size of this sector in Kenya, it is

still very small when compared to that of the industrialized nations United Nations

Industrial Development Organization ((UNIDO) 1987). Kenyas manufacturing

sector is going through a major transition period largely due to the structural reform

process, which the Kenya Government has been implementing since the mid-

eighties with a view to improving the economic and social environment of the

country.

Kenya Association of Manufacturers (2002) posits that removal of price controls,

foreign exchange controls and introduction of investment incentives have, however,

not resulted in major changes in the overall economy. In particular, they have not

improved the manufacturing performance. Therefore, to build a self-sustaining

industrial sector, it is necessary to establish strategic linkages within the domestic

economy. Some efforts have to be made to promote strategic options among supply

chains so as to enhance spread effects of industrial growth and to facilitate transfer

of technology, skills and growth of small and medium scale sub-contractors. The

linkages of the study variables in SCM in Kenya are weak and because of this, there

exists little inter-industry integration in the country. This has resulted in consistently

low manufacturing value added in the sector (KAM 1989).

Growth in the sector was, however, impeded by depressed domestic demand,

increased oil prices and transport costs. Rising operating costs mainly as a result of

high power costs coupled with deteriorating road and rail networks further

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dampened growth in the sector. The growth in manufacturing sector was mainly

attributed to rise in output of the agro-processing industries. These included sugar,

milk, grain milling, fish, tea, oils and fats processing sub-sectors. Other key sub-

sectors of manufacturing that performed well were: manufacture of cigarettes,

cement production, batteries (both motor vehicles and dry cells), motor vehicle

assembly and production of galvanized sheets.

The Kenya Government has always been committed to developing a mixed economy

where both public and private sector companies are present (Kenya Government,

Development Plan 1989-1993). But the public sector participation in manufacturing

is much smaller than the private sector and is still decreasing due to governments

change of policy; the emphasis is now being given to privatization of the industrial

sector.

The main objective of the paper was to determine SCM best practices used by large

private manufacturing firms in Kenya, that is which are the SCM best practices used

by these firms? The findings of this paper will assist the corporate managers to

make sound and informed strategic management decisions and enable them to focuson their customers more efficiently. With such exposition, managers will

understand how firms can perform better and add value to the shareholders under

SCM orientation.

Methodology

The target population was all large private manufacturing entities in Kenya, who are

members of KAM. The main reason for this choice was that these firms were likely

to exhibit an elaborate SCM philosophy and make use of best practices in SCM.

Furthermore, the focus of the research was basically in the manufacturing sector,

other sectors were considered outside the scope of the paper and could not reveal

substantial data for statistical analysis. In total, there are 2,000 companies in the

KAM directory (2004/2005), from which all public sector firms (where the

government holds majority shares) and small companies were eliminated. This left

500 firms, which constituted the sample frame of the target population.

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A survey of 52 large private manufacturing entities was carried out using a stratified

sampling technique. This was necessary to include supply chains with all the

variables of the study for equal chances of selection. At least 10 percent sample of

the population was considered generally acceptable method of selecting samples in

such a study (Stanley and Gregory 2001). In this paper, the sample was stratified

into agro-based industrial sector, engineering and construction industrial sector and

chemical and mineral industrial sector based on the value added by each sector to

the manufacturing industry. For example, agro-based industrial sector added 68

percent, engineering and construction and industrial sector 12 percent, and chemical

and mineral sector 20 percent (KAM 2004). The respondents in the study were

located mainly in Nairobi industrial and Baba Dogo areas respectively, which form

the bulk of manufacturing sector in Kenya and this is where most of the supply chain

firms are found. The sample size is denoted by:

n= n1 + n2 + n3

52 firms = 36 + 6 + 10

where:n is the sample size

n1 is agro-based industrial sector

n2 is engineering and construction industrial sector

n3 is chemical and mineral industrial sector

The paper used primary data obtained through questionnaires with selected team of

managers involved in SCM within the 52 manufacturing entities. The questionnaire

was piloted on 10 firms prior to data collection. This was necessary in order to

identify any ambiguous and unclear questions to the respondents. The

questionnaires were then submitted to the participating firms after the pilot test in

order to get the data and information required. The instrument used for this paper

was adapted from a study by Stanley and Gregory (2001) but modified to suit the

objectives of this paper. This instrument has been used in a previous study of

achieving supply chain alignment for the large private manufacturing firms in the

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United States. A Likert-type scale of seven points (where the lowest value in the

scale was 1 and the highest was 7) was used to collect the data.

To measure the consistency of the scores obtained, and how consistent they were for

each individual from one administration of an instrument to another and from one

set of items to another, the paper used Cronbachs alpha (a measure of the internal

consistency of the questionnaire items) using data from all the respondents.

Separate reliability tests for each of the variables were computed. This included

measuring current supply chain best practices, measuring the effect of supply chain

variables, measuring level of independent effects, measuring level of supply chain

core competencies, measuring the degree of supply chain strategies, measuring the

implementation of supply chain strategies and measuring competitiveness relative to

industry rivals. The key statistic in interpreting the reliability of the scale was the

alpha listed under the reliability co-efficient section at the end of the output. The

value of coefficient alpha ranges from zero (no internal consistency) to one

(complete internal consistency). As to how large the coefficient should be, a value

of no less than 0.70 as a quick rule was used. As shown, all the measurements of the

instrument attained a high degree of reliability since they were above 0.70.

Together with correlation analysis, factor analysis was done to establish the

relationships among the study variables. In particular, factor analysis procedure was

used to measure and establish SCM best practices in the study as applied by various

firms. This method was necessary to reduce a set of several difficult to interpret

correlated variables to few conceptually meaningful relatively independent factors,

which could be easily interpreted. This technique was applied to summarize 39

latent variables or sub-variables representing dominant best practices in SCM. To

make interpretation easier, a linear transformation on the factor solution, varimax

rotation was done, which gave fewer components (factors) that are uncorrelated with

one another.

Results and Discussion

The purpose of this paper was to establish the current SCM best practices in the

large private manufacturing firms in Kenya. This was addressed by using factor

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analysis. The preliminary tests employed the use of Kaiser Mayer-Olkim (KMO)

and Barletts Test. In this case, KMO measures the sampling adequacy which

should be greater than 0.5 for a satisfactory analysis to proceed. From the analysis,

the KMO measure was 0.583, an indication that the Barletts Test of sphericity is

significant. In order to determine the number of factors to retain, the factors with

eigenvalue greater or equal to one were retained. This was further illustrated by

using the scree plot which indicates that the screes started to tee-off after factor 11

showing that only 11 factors explain the characteristics of corporate performance

among Kenyan private firms (Figure 1). The factor loadings were then used to put

together the factors into 11 groups constituting the SCM best practices.

Figure 1: Scree Plot for the Supply Chain Management Variables

The items were grouped based on the magnitude of their factor loadings in all the

corresponding factor components in this case, there are 11 factor components

implying that the 38 variables (see table 1) could be reduced into 11 factors

constituting the current SCM best practices as shown in Table 2 below. An item is

considered to belong to a factor component if its factor loading corresponds to that

particular component and is relatively higher than its factor loadings in the other

factor components. For example, variable 38 belongs to component five because its

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S c re e

-2 .0 0 0

0 .0 0 0

2 .0 0 0

4 .0 0 0

6 .0 0 0

8 .0 0 0

1 0 .0 0 0

1 2 .0 0 0

1 4 .0 0 0

1 2 3 4 5 6 7 8 9 1 01 11 21 31 41 51 61 71 81 92 02 12 22 32 42 52 62 72 82 93 03 13 23 33 43 53 63 73 83 9

C o m p o n e n t N

Eigenvalues


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factor loading of 0.732 is relatively higher than any other loadings within the

components and so on.

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Table 1: Variables which constitute SCM best practices in large private manufacturing firms in Kenya

Sr.No.

Variables Components

1 2 3 4 5 6 7 8 9 10 11

1. A common set of operating policies areshared by member of the SC

.706 .402 .077 .021 .176 -.074 .222 .057 -.01 .097 .072

2. A written agreement or contract is anintegral part of all alliances

.862 -.079 .058 -.019 -.021 .161 .220 -.017 .126 .216 .074

3. Adequate information systems linkagesexist with customers

.126 .482 -.01 .377 .036 .244 .062 -.172 .469 .434 -.2

4. Adequate information systems linkagesexist with suppliers

.302 .482 .107 .600 .068 .018 .027 -.180 .027 .151 -.3

5. Clear guidelines and procedures used forcreating alliances .041 .121 .074 .099 -.102 .136 .125 -.098 .855 -.164 -.2

6. Clear guidelines and procedures usedfor monitoring alliances

-0.197 0.102 0.161-

0.1340.114

-0.080

-0.067

-0.056

0.186-

0.8870.005

7. Consistent performance measures areused across different dept/functions

.366 .798 .002 -.115 .055 .109 .092 .049 .125 .037 .151

8. Current information systems satisfy SCcommunication requirements

.883 .254 .069 .063 .075 .133 .029 -.035 .049 -.087 -.1

9. Customer al liances operate underprinciples of shared rewards and risks

.389 .069 .107 .017 .851 .020 .055 .109 .053 -.179 .032

10. Customer relationships are evaluated onthe basis of their profitability

.531 -.169 .019 -.079 -.032 -.238 -.149 .581 .030 .116 .268

11. Efforts of increase inter-functionalcoordination over the past 3 years

.196 .045 -.06 .190 .659 .130 .391 -.044 -.22 .010 .084

12. Employers are more loyal to ourorganization today than 3 year ago

.196 .238 -.11 .406 .038 .357 .129 -.140 -.44 -.384 -.3

13. Our firm is more loyal to its employeesthan 3 years ago

-.024 .404 .062 .581 -.120 .389 .139 -.014 .303 .210 -.1

14. High level of trust have been establishedwith important customers

.467 .303 .035 .325 .089 .307 .532 .060 .163 .270 .0

15. Information applications are integratedwithin the firm

.198 .332 .026 .248 .446 .018 .635 .240 .121 .041 .0

16. Information systems are highlyintegrated through out the SC

.504 .611 -.09 .337 .169 .245 .164 -051 .073 .042 -.1

17. Middle managers are empowered tomake operation decision than 3 yearsago

.148 .646 .234 .187 .028 .344 .294 .050 -.01 -.383 .0

18. More process-oriented performancemeasures tracked today than 3 yearsago

.187 .114 .167 .098 .031 .866 -.033 -.003 .064 .078 -.1

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19. More SC performance measures trackedtoday than 3 years ago

-.038 .405 .197 .027 .180 .696 .158 -.181 .091 .030 .013

20. My firms aggressively seeks tounderstand customers requirements

.811 .012 .064 .049 .239 .042 -.021 .187 .152 -.166 .349

21. My firm customizes products and/orservices for important customers

.481 .021 -.11 .156 .110 -.356 -.181 -.004 -.03 -.040 .647

Sr.No.

VariablesComponents

1 2 3 4 5 6 7 8 9 10 11

22. My firm has adopted a key accountapproach for managing its bestcustomers

.149 -.051 .001 -.204 -.224 -.154 -.183 .549 -.10 -.075 .641

23. My firm is flexible in terms of

accommodating customers specialrequests

-.246 .094 .156 .803 .160 -.120 -076 -.88 .00 .103 .184

24. My firm regularly solicits customer input .021 .114 -.19 .240 .94 .081 .249 .746 -.27 .055 .227

25. My firm understands the competitivecomparatives throughout the SC

-.003 .679 .417 .394 .170. .192 .165 .085 -.06 -.183 .076

26. Non-management employees are moreempowered to make operating decisions

.085 -.816 -.12 -.163 -.238 -.087 -.112 .139 .311 -.044 .084

27. Operating goals are consistent amongSC members

.186 .234 .571 .015 .091 .238 .582 -.294 -.12 -.072 .0

28. Overall strategies in SCM have improvedover past 3 years

.115 .100 .865 .158 -.009 .141 .308 -.017 -.03 .020 -.2

29. Overall SC core capabilities haveimproved over past 3 years

-.134 .144 .906 .207 .182 .068 .103 .045 .088 -.011 .095

30. Overall SC core competencies haveimproved over past 3 years

.184 -.003 .933 .094 .054 .126 -.094 .028 .110 -.053 .063

31. Significant investments are made inapplication-specific information systems

-234 -.010 .393 .675 .320 .227 .067 .152 -.02 -.220 .163

32. Significant investments are being madein enterprise-wide information systems

.239 .065 .276 .768 .176 .123 .327 .110 .020 .099 -.2

33. Strategic objectives are closely alignedamong members of the SC .557 .117 .320 .092 .278 .532 .069 .184 .000 .252 .016

34. Supplier alliances operate underprinciples of shared rewards and risks

.312 -.196 .052 -.006 .094 .020 .045 .-.072 .855 -.060 .238

35. Supplier performance is closelymonitored and is the basis for futurebusiness

.382 .174 .061 .051 .462 .355 .142 .082 .103 .228 .573

36. Suppliers are carefully screened andassessed before they are selected

-.099 .186 .163 .074 .563 .199 .073 .036 -.03 .624 .016

37. The internet is emerging as key tool tomanage customer and supply linkages

-.056 .356 .255 .367 .732 .025 -.112 .053 .063 .108 -.1

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38. Value-added resources are sharedamong SC members

.019 -.066 .137 -.101 .305 -.019 -.098 .879 .015 .003 -.1

Extraction Method: Principal Component AnalysisRotation Method: Varimax with Kaiser Normalization.a- Rotation converged in 17 interactions: Total Variance Explained

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With this kind of classification, all the items were put in their respective componentsto come up with the summary in Table 2. Column 1 shows the number of factorsgenerated, column 2 shows the items within a particular factor component, column 3indicates the highest factor loading for each item, and column 4 provides anappropriate reduction interpretation description to each component.

Table 2: Factor Reduction for Supply Chain Management Best Practices of LargePrivate Manufacturing Firms in Kenya

Factor Item Description Factor Loadings Interpretation

1

(i) Common set of operating policies areshared by members of the supply chain

(ii) Written agreement or contract is anintegral part of all alliances

(iii) Current information systems satisfysupply chain communicationrequirements

(iv) My firm aggressively seeks to understandcustomers requirements

(v) Strategic objectives closely alignedamong members of supply chain

0.706

0.862

0.883

0.811

0.557

Operatingpolicies

2

(i) Consistent performance measures areused across differentdepartments/functions

(ii) Information systems are highly integratedthroughout the supply chain

(iii) Middle managers are empowered to makeoperation decisions than 3 years ago

(iv) My firm understands the competitivecomparatives throughout the supply chain

(v) Non-management employees are moreempowered to make operating decisions

0.798

0.611

0.646

0.679

-0.816

Linkages withinsupply chainfirms

3

(i) Overall strategies in SCM have improvedover the past 3 years

(ii) Overall SC core capabilities have

improved over past 3 years(iii) Overall supply chain core competencies

have improved over past three years

0.865

0.906

0.933

Improved

performance

4

(i) Adequate information systems linkagesexist with supply

(ii) Our firm is more loyal to its employeesthan 3 years ago

(iii) My firm is flexible in terms ofaccommodating customers specialrequests

(iv) Significant investments are made in

0.600

0.581

0.803

0.675

Informationtechnologysystems

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application specific information systems(v) Significant investments are being made in

enterprise wide information systems0.768

5

(i) Customer alliances operate underprinciples of shared rewards and risks

(ii) Efforts of increase inter-functionalcoordination over the past 3 years

(iii) My firm regularly solicits customer input(iv) The internet is emerging as a key tool to

manage customer and supply interaction

0.851

0.6590.940

0.732

Strategicalliance

6

(i) More process-oriented performancemeasures tracked today than 3 years ago

(ii) More supply chain performance measuresare tracked today than 3 years ago

0.866

0.696

Performancemeasures

7

(i) High level of trust has been establishedwith important customers

(ii) Information applications are integratedwithin the firm

(iii) Operating goals are consistent acrossdepartments within my firm

(iv) Operating goals are consistent amongsupply chain members

0.532

0.635

0.732

0.582

Goal orientation

8

(i) Customer relationships are evaluated onthe basis of their profitability

(ii) Value-added resources are shared amongsupply chain member

0.581

0.879

Customer

relationships

9(i) Clear guidelines and procedures are used

for creating alliances(ii) Supplier alliances operate under

principles of shared rewards and risks

0.855

0.855

Guidelines andprocedures

10(i) Suppliers are carefully screened and

assessed before they are selected(ii) Clear guidelines and procedures are used

for monitoring alliances

0.624

-0.887

Supplierselection

11

(i) My firm customizes products and/or

services for important customers(ii) My firm has adopted a key account

approach for managing its best customers(iii) Supplier performance is closely

monitored and is the basis for futurebusiness

0.647

0.641

0.573

Supplierevaluation

In conclusion, eleven most critical SCM best practices were established as follows:

operating policies, linkages within supply chain firms, improved performance,

information technology systems, strategic alliance, performance measures, goal

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orientation, customer relationships, guidelines and procedures, supplier selection and

supplier evaluation. The findings show that several factors constitute SCM best

practices in large private manufacturing firms in Kenya. The paper noted that most

of the firms surveyed applies SCM best practices. These practices are universal and

in line with the findings of other studies (Porter 1985, Caldries and van Diedonck

1988, Mentzer and Konrad 1991, Rich and Hines 1997, Cox 1999, Kilpatrick et al.,

2000 and Stanley and Gregory 2001).

Conclusion

The results of this paper indicate that operating policies, linkages within supply

chain firms, improved performance, information technology systems, strategic

alliance, performance measures, goal orientation, customer relationships, guidelines

and procedures, supplier selection and supplier evaluation are the most important

SCM practices of large private manufacturing firms in Kenya. After benchmarking,

the paper established that SCM best practices used in the large private

manufacturing sector in Kenya are universal, since they compare well with other

studies of SCM best practices globally. The universality of these practices has been

attributed to the reforms undertaken by the Kenya government in the past years aswell as the emergence of multinationals in the manufacturing sector.

The joint effect of core competencies, core capabilities, strategy and implementation

has influenced corporate performance in most of the large manufacturing

organizations surveyed in the private sector in Kenya. As SCM variables, they

support other findings in strategic management where the concept has been used to

achieve an enterprises mission and objectives by reconciling its resources with

opportunities and threats in the business environment. However, to succeed, these

variables need to be applied jointly as revealed in the paper, so that synergy can be

achieved to enhance corporate performance and that Information Technology (IT)

(as shown by factor 4 in table 1) should be developed within the large private

manufacturing firms. The usage of IT systems in these firms has not been fully

explored and the application of this resource is still limited. For IT to be fully

developed, firms should formulate policy framework and guidelines, which will

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facilitate the linkage of the joint SCM variables to ensure efficient and effective

utilization of resources within the supply chain.

To make the economy more vibrant and to improve productivity, proper corporate

structure and governance need to be put in place where SCM competencies, strategy,

capability, leadership, corporate policies, allocation of resources and management of

change can be used to create synergy. In effect, no single variable can effectively

influence corporate performance. A conducive environment is needed for the

variables to operate jointly in order to improve socio-economic development of

Kenya and spur economic growth.

Though senior managers from the corporate world in the Kenyan manufacturing

sector can benefit from the papers findings, practical implications resulting from

this paper are of particular significance to employers who wish to improve their

corporate efficiency, effectiveness and performance. The following specific

recommendations are made:

The joint effect of SCM variables facilitate strategy implementation since there are

indications that this can create synergy and add value leading to corporate performance; Relevant leadership skills are paramount in the SCM strategy

implementation; and The SCM structures are critical to the implementation process.

The joint effect of SCM variables become even more essential with the rising

importance of business commerce on the Internet, especially for firms in highly

fragmented industries with complex products or services. The rate of change in the

marketplace is increasing as the Internet becomes a more ubiquitous part of the

business market place. The supply chain joint variables is greatly augmented by e-

commerce, e-procurement, e-fulfillment, and other Internet-enabled business

processes.

Acknowledgements

We wish to acknowledge the support of different manufacturing companies, the

University of Nairobi for providing the necessary technical assistance towards this

research. To Professor KObonyo and Dr Martin Ogutu, all from the University of

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Nairobi and for everybody else who gave us both moral and financial support to

complete this work. We recognize your full participation.

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