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

KENYA

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A) 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 organization’s 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.

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

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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.

B) 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

organization’s 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 enterprise’s 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

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.

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

contribution to the country’s economic development. It has the potential to generate

foreign exchange earnings through exports and diversify the country’s economy.

This sector has grown over time both in terms of its contribution to the country’s

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). Kenya’s 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

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-

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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

government’s 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 focus

on their customers more efficiently. With such exposition, managers will

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

SCM orientation.

C) 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.

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

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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

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.

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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 Cronbach’s 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.

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