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Drivers of Procurement Performance in the Motor
Industry: A Case of CMC Motors Group, Kenya
Juma Veronica Ayieko1 & Dr. Justus Kinoti
2
1M.Sc Scholar, Jomo Kenyatta University of Agriculture & Technology, Kenya
2Lecturer, Jomo Kenyatta University of Agriculture & Technology, Kenya
ABSTRACT
The procurement function is becoming more and more dynamic and complex. This study therefore
sought to examine the drivers of procurement performance in the motors industry with a focus on
CMC Motors Group,Kenya .The study adopted a descriptive research design with a focus on 158
respondents obtained through stratified random sampling of 528 employees from CMC Motors
Group operations in Kenya. The study analyzed the relationship between procurement perfomance in
the motor industry and procurement policy,electronic procurement,supplier relationship management
and outsourcing using regression analysis as was expressed through the multiple linear regression
model .The study results showed that the correlation coefficient was 0.799. This indicates a very
strong postive relationship between the independent variable and depedent variable. The data showed
that the high R square is 0.638. It shows that the independent variables in the study were able to
explain 63.80% variation in the procurement perfomance in the organization while the remaining
36.20% is explained by the variables or other aspects outside the model. This implies that these
variables are very significant and they therefore need to be considered in any effort to boost
procurement perfomance in the organization. The study therefore identifies variables as critical
drivers of procurement perfomance in the organization. The study recommend that organization
should come up with procurement policy and provide the assurance that the policy process (policy
formulation, implementation, monitoring and evaluation) is operating optimally with effectiveness,
efficiency, and economy, and is underpinned by broader principles such as rule of law, transparency,
accountability and integrity. The study also recommends that the distribution organization should
invest on electronic procurement like ERP which will help them coordinate the organization activities.
The study recommends for the improvement of supplier management to enhance procurement
performance in the motor industry.
Keywords: procurement performance, supplier management, motor industry
INTRODUCTION
Procurement performance has become a conventional standard in the contemporary world. Kakwezi
and Nyeko (2014), state that procurement performance is associated with effectiveness and efficiency
of procurement operations. Most organizations are incorporating good procurement practices in a bid
to retain competitive market leadership in the international corporate environment. There are
numerous enticements for organizations to engage procurement as a business success strategy.
Notably effective procurement performance is heightened by various drivers as presented in previous
scholarly literature. Vagadia (2012) established that organizations are now re-examining their
business models and structures and outsourcing is being a tool for business transformation. Hong and
Kwon (2012), postulates that in today‘s dynamic market environment, procurement is positioned as a
critical integrative business process and its focus has been extended from short term cost
minimization to long term value creation and delivery.
Procurement guidelines and procedures play an unexplainable role in the overall performance. Victor
(2012), noted that procurement expenditure could be minimized through implementation of effective
procurement practices. Burt et al. (2010), affirms that every organization develops procedures to
enable its personnel implement policies and designed to meet her objectives. The current Supply chain
trends shows that the global dynamic nature of procurement cannot escape technology as it is a
International Journal of Business & Law Research 6(1):8-26, Jan.-Mar., 2018
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leading driver in this field, (Hong & Kwon, 2012). Competence in procurement functions culminate to
key benefits including improved efficiency, reduced transaction costs, increased productivity and
visible controls. In reference to causal motives for procurement outsourcing, firms strive to search for
the best price or seek efficient ways of acquisition from outside sources. According to Epiq
Technologies (2010) report, IT enables a firm to organize its interactions with its most crucial
suppliers, a set of built-in monitoring tools to help control costs, assure maximum supplier
performance and keeping an open line of communication with potential suppliers during a business
process. According to Shaw (2010), every organization should put in place effective systems of
procurement to protect shareholder‘s funds while (Lewis & Roehrich ,2009) agitate that procurement
is a key activity in the supply chain.
Procurement pointedly impacts on the overall accomplishment of an emergency response depending
on how it is managed. Oyuke and Shale (2014) focused on role of strategic procurement practices on
organizational performance and resonate that organizations must maximize the use of procurement in
every aspect of the business, linking across all members of the supply chain, increasing the speed of
information transfer, and reducing non-value adding tasks. Procurement trends are changing
simultaneously across both the public and private sector platforms. (Prajogo et al., 2008) reported that
public procurement as a theme is consistent with research in the private sector that has shown how
value chain activities affect innovation. Vagadia (2012), examined the role of strategic sourcing in a
changing world, and established that organizations were now re-examining their business models and
structures and outsourcing is being a tool for business transformation. The restrictive literature
available authenticating causal relationship between procurement performance and drivers such as
procurement policy, electronic procurement, supplier relationship management and outsourcing in
essence is has given the researcher the determination to delve into the drivers of procurement
performance in the motor industry more specifically at CMC Motors Group, Kenya
Global Perspective on Procurement Performance
Globalization has given rise to a new era of international competition that is reshaping global
production and trade and altering the organization of industries (Gereffi, 2011). From its inception
during World War I & II, when the procurement function seemed important for the prime purpose of
obtaining raw materials, supplies, and services needed to keep the factories and mines operating, the
procurement function has grown in leaps and bounds over the decades. Mahmood (2010), posed an
estimation that 18.42% of the world‘s gross domestic product (GDP) was spent through procurement.
It was further estimated that procurement accounts for 9%–13% of the GDP of the economies of
developing countries. In the wake of the 2008–09 global economic crisis, the rapid growth of
productive capabilities in China, India and other large emerging economies has created a profound
shift in global demand for both finished goods and intermediates from north to south, with both
positive and negative implications for developing country exporters, (Kaplinsky & Farooki, 2011).
Regional Perspective of Procurement Performance
Procurement performance is fast becoming an economic driver in many developing countries in
Africa. According to Agaba and Shipman (2008) Sub-Sahara Africa channels between U$ 30-43
billion to the procurement market in the region. This demand for a well-functioning procurement
system particularly for the developing countries where procurement usually accounts for high
proportion of total expenditure at 40% against the global average of 12-20%. This perhaps is anchored
on the lack of financial muscle and technical expertise required to produce the substances on demand
locally in Africa.
Kenyan Perspective on Procurement Performance
Effective procurement performance is desired by most firms primarily for efficiency and cost
reduction. This is vital for the survival and growth of firms because most customers view the
performance of firms based on the supply chain practices and habits adopted by the firm, and this
form the basis of their ultimate future buy and relationship decisions. Awino (2011), postulates that
measurement of performance of firms is based on both quantitative and qualitative performance
indicators. Juma (2010), observes that procurement performance is the backbone of an organization
success since it contributes to competitive purchase and acquisition of quality goods that puts the
organization products or services in the competitive edge in the market. Juma further holds that poor
procurement performance in the private sector has been a problem due to incompetent staff,
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traditional procurement procedures, and inability to embrace e-procurement, poor coordination of
procurement activities, lack of quality assurance policies and lack of proper regulations.
Adoption of best procurement practices has been a challenging journey to most Kenyan firms. Juma
(2010), affirms in his study that lack of quality systems in the private sector contributes to increase of
defect and wastes. The study further links the increase of loss due to lead time and inability of the
private sector in Kenya to compete with other players globally as based on poor investment in
information communication technology. This significantly reduces procurement performance in the
private sector in Kenya. Nasra (2014), notes that effective and efficient procurement systems and
collaborative relationships are essential to the achievement of organizational goals, cost reduction and
supply chain performance. Banda (2009), asserts that private sectors in Africa are grappling with
setting up the operational framework of procurement processes while developed countries as United
States and European countries main problem is how to make it efficient. Larson (2009), established
major fundamental differences between the public and private sector procurement as the reporting
structure, regulating bodies, funding sources and operating motives. The study further reports that the
public sector is governed by legislative bodies, laws, and untold numbers of state and federal
regulations. The private sector is guided by boards of directors, business plans and the organization‘s
purchasing policies. While the private sector has been extensively covered in study, there exists little
literature on the possible drivers of procurement performance in the motor industry in Kenya.
The Motor Industry in Kenya
Kenya‘s GDP per capita is growing very fast. Expenditure on the purchase of cars, motorcycles and
other vehicles accounted for 1.5% of total consumer expenditure in 2015 and is expected to remain
relatively stable to 2025 as incomes rise. The volume of imported cars and motorcycles has been on
the increase due to the availability of attractive credit from financial institutions and the rise of the
middle-class, (Schiller & Pillay, 2016). According to the Kenya National Bureau of Statistics (KNBS)
the volume of imported vehicles between 2003 and 2012 have grown at over 300% from 33 000 units
to 110,474 units (KNBS, 2015). Passenger vehicles were Kenya‘s fourth largest import overall in
2014, valued at US$420 million and making up 2.3% of total imports (by value) while commercial
vehicles ranked seventh, valued at US$370 million. To date, Kenya is still highly dependent on
imports to meet domestic demand, with imports making up 94% of bilateral automotive trade and
second-hand vehicles accounting for over 80% of those imports, (Delloitte, 2016).
Because of the regional gateway at the port of Mombasa, 99.9% of Kenya‘s automotive exports target
other African countries, with Uganda and Tanzania being the biggest markets. Kenya‘s automotive
market is largely focused on retail and distribution of vehicles, and after-sales support in servicing and
spare parts sales (Delloitte, 2016). The main motor vehicle dealers operating within the country are
Toyota (East Africa), Cooper Motor Corporation (CMC), General Motors (GM), Simba Colt and DT
Dobie, Honda Motors, and other small upcoming local dealerships. (KPMG, 2015). Small scale
assembly of motor vehicles is done at three assembly plants, the General Motors East Africa (GMEA)
plant in Nairobi, the Associated Vehicle Assemblers (AVA) plant in Mombasa and the Kenya Vehicle
Manufacturers (KVM) plant in Thika. All three of the plants are operating below their capacity.
However, the country‘s good infrastructure, relative to other countries in the region, as well as it is
physical and strong economic position within the East Africa Community (EAC), make it a potential
hub for automotive assembly and production in the region, (Schiller & Pillay, 2016).
Profile of CMC Motors Group
The CMC website reveals a detailed history of the Motor firm, (CMC, 2017). CMC Motors Group
was incorporated as a private limited in July 1948 to sell Land Rovers. In 1956 Cooper Motor
Corporation was converted from a private company to a public company which provided vehicle
sales, parts, service and administration under one roof. CMC Motors Group Ltd was a public
company until April 2014, when shareholders, unable to agree and in consistent boardroom wrangles,
opted to be paid off by a Dubai firm Alffutaim Group KSh7.5 billion in a buyout that has effectively
seen the auto dealer delist from the Nairobi Securities Exchange (CMC, 2017).
CMC Motors Group has seven branches countrywide namely Mombasa, Nakuru, Meru, Nanyuki,
Kisumu, Eldoret and Kitale, and six divisions at the headquarters in Nairobi. Other trading
subsidiaries owned by CMC Holdings Ltd include Kenya Vehicle Manufactures Ltd (KVM), Cooper
Motor Corporation (Uganda) Ltd, Hughes Motors (Tanzania) Ltd and Hughes Agriculture (Tanzania)
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Ltd. CMC Motors Group holds exclusive distribution of Ford, Mazda, Suzuki, Maruti, Nissan Diesel,
Eicher and MAN range of trucks (medium and heavy commercial) buses, New Holland, and an
extensive range of farming implements from ploughs to irrigation equipment. (CMC, 2017).
The Kenyan government identified the automotive and auto parts industry as a major economic driver
in the Kenya national industrialisation Policy Framework released in 2010, (Delloitte, 2016).In view
of the aggressiveness in growth of the automobile sector In Kenya, and having reviewed in detail the
organization layout and operations from the information as fore cited, the researcher feels that CMC is
the most suitable place for study because of the diverse brands and activities that the organization
deals with coupled with the regional coverage muscle flex that CMC enjoys in East Africa.
Statement of the Problem
The procurement function is evolving every day, the urge to keep up with the upcoming market trends
has given many organizations sleepless nights figuring out the best practices to adopt to improve
efficiency, effectiveness, to remain competitive and to achieve the overall organization‘s Objectives.
According to Agaba and Shipman (2008) Sub-Sahara Africa channels between U$ 30-43 billion to the
procurement market in the region. This demands for a well-functioning procurement system
particularly for the developing countries where procurement usually accounts for high proportion of
total expenditure at 40% against the global average of 18.42%. The motor industry in Kenya is
increasingly becoming dynamic and complex. According to the Kenya National Bureau of Statistics
(KNBS) the volume of imported vehicles between 2003 and 2012 grew over 300% from 33 000 units
to 110,474 units (KNBS, 2015). To date, Kenya is still highly dependent on imports to meet domestic
demand, with imports making up 94% of bilateral automotive trade and second-hand vehicles
accounting for over 80% of those imports, (Delloitte, 2016).
The Kenyan government identified the automotive and auto parts industry as a major economic driver
in the Kenya national industrialisation Policy Framework released in 2010, (Delloitte, 2016).Despite
the Motor industry having expanded the job market and enabled Kenya‘s GDP to grow, it still faces
stiff encounters arising from competing second-hand motor vehicle industry, general business
slowdown, tighter credit markets following the capping of interest rates and reduced government
leasing. Based on in-market research, (Deloitte, 2016) estimates that in three African countries,
Ethiopia, Kenya, Nigeria at least 8 out of 10 imported vehicles are used vehicles. According to KMI
(2017), the new vehicle market dropped 30.5% to 13,869 units in 2016.This open market and stiff
competition have led to most of the motor companies in Kenya trying to re strategize in a bid to
survive, with procurement process changes and massive layoffs taking effect in a bid by the firms to
protect their margins, with payroll expenses ranking among the biggest cost items.
Several studies have been carried out vastly on procurement performance but little attention has been
given to the vibrant motor industry. For example, Larson (2009) established major fundamental
differences between the public and private sector procurement as the reporting structure, regulating
bodies, funding sources and operating motives. Kakwezi and Nyeko (2010) studied efficiency and
effectiveness of the procurement function and concludes that procurement efficiency and procurement
effectiveness of the purchasing function are measures of procurement performance. According to
(Seroney and Mwangangi, 2014) contend that procurement policies and information flow does affect
the competitiveness of the motor industry in Kenya. In view of the detailed assessment of the motor
industry in Kenya, the researcher views procurement performance as one of the strategies that ought
to be intensely considered in this industry. CMC Motors Group Kenya is suitably placed for this study
because of the diverse activities that the organization deals with coupled with the regional coverage
muscle flex. Therefore, the study sought to imperatively determine the drivers of procurement
performance in the motor industry in Kenya, a case of CMC Motors Group Kenya.
Objectives of the Study
The general objective of the study was to examine the drivers of procurement performance in the
motor industry in Kenya.
The specific objectives of this study were:
i. To establish the influence of procurement policy on procurement performance in the motor
industry in Kenya.
ii. To determine the influence of electronic procurement on procurement performance in the
motor industry in Kenya.
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iii. To examine the effect of supplier relationship management on procurement performance in
the motor industry in Kenya.
iv. To assess the influence of outsourcing on procurement performance in the motor industry in
Kenya.
Research Questions
The following questions guided the study;
i. How does procurement policy influence procurement performance in the motor industry in
Kenya?
ii. What is the influence of electronic procurement on procurement performance in the motor
industry in Kenya?
iii. To what extent does supplier relationship management affect procurement performance in the
motor industry in Kenya?
iv. Does outsourcing influence procurement performance in the motor industry in Kenya?
LITERATURE REVIEW
Theoretical Review
In this study, theories will be used to provide a deeper understanding of the drivers of procurement
performance in CMC Motors Group,Kenya.
Resource Dependency Theory
Resource dependency theory was first experienced by (Pfeffer & Salanick, 1978). This theory states
that firms don‘t have all the resources they need, therefore, to some degree, they depend on their
external environment for some resources. Captivatingly, the initial drive of the resource dependence
was originally developed to provide an alternative perspective to economic theories of mergers and
board interlocks, and to understand precisely the type of IORs that had played such a large role in
market failures then, (Pfeiffer, 2003). This theory holds three core concepts namely; social context
matters; secondly that organizations have strategies to enhance their autonomy and pursue interests;
and thirdly power is important for understanding internal and external actions of organizations.
Social-Technical Systems Approach Theory
This theory will guide the study on the second variable. Passmore (1988), holds that the socio-
technical systems approach is based on the premise that every organization consists of the people, the
technical system and the environment. People (the social system) use tools, techniques and knowledge
(the technical system) to produce goods or services valued by consumers or users (who are part of the
organization‘s external environment). Passmore (1988) further emphasises that an equilibrium among
the social system, the technical system and the environment is necessary to make the organization
more effective. Embracing electronic procurement as a meaningful tool for use is equally important as
it propels better results.
Supply-chain operations reference (SCOR) model
This theory re-counts the third variable in the study namely Supplier relationship management.
Supply chain operations reference model (SCOR) was first developed by the management consulting
firm PRTM, now part of price water house coopers and was endorsed by supply chain council as a
supply chain management diagnostic tool as observed by (Achieng & Rotich, 2013). PRTM
management outlines SCOR as a tool that enable the users to address, improve and communicate the
activities within a supply chain and all the other parties involved. It spans from supplier to the
customer, (Simchi-levi, 2008). The SCOR model ―provides a unique framework that links business
processes, metrics, best practices and technology features into a unified structure to support
communication among supply chain partners and to improve the effectiveness of supply chain
management and related supply chain improvement activities, (Supply Chain Council, 2009). SCOR
is based on several distinct management processes which include planning, sourcing, make decisions,
deliver and return (Rolf, 2007).
Transaction Cost Theory
This theory underpins the fourth variable namely outsourcing which is directly reflected through the
transaction cost theory‘s pillars of what to buy since the process of buying is ultimately intertwined.
Firms need to consider both production costs and transactions costs for any outsourcing transaction.
This way the firm can justify the need and eventually realize the cost benefits achieved through the
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outsourcing process. Transaction Cost theory focuses on the reasons to firm‘s existence, most
effective strategies for maximizing profits, the core function of firms and what should be acquired by
firms in relation to the firm‘s goals. Simon (1957) carried out a study and postulates that the main
hypothetical argument of this theory is concerned with the conditions under which certain
characteristics of the transaction or the object of the transaction would lead to its internal hybrid, or
external governance. The two-important fundamental behavioral assumptions are bounded rationality
and opportunism. (Simon, 1957). Bounded rationality argues that human beings are limited.
Therefore, it is only possible for both parties in a transaction to sign an incomplete contract, while the
opportunism assumption holds that people cunningly behave opportunistically at the expense of
others. The danger of opportunism is assumed to be less likely within a firm than in market
coordination since it can be prevented within a firm by means of the authority principle namely,
hierarchy, (Simon, 1957).
Conceptual Framework
The conceptual framework is a plane of interlinked concepts that together provide a comprehensive
understanding of a phenomenon or phenomena. The concepts that constitute a conceptual framework
support one another, articulate their respective phenomena, and establish a framework-specific
philosophy. (Jabareen, 2009). The concepts on focus in this study as tabulated below are procurement
policy, electronic procurement, supplier relationship management and outsourcing.
Independent Variables
Figure 2.1: Conceptual Framework
Independent Variables Dependent Variable
Figure 2.1: Conceptual Framework
Procurement Policy
Procurement structure
Communication
Distribution layout
Rules and regulations
Supplier Relationship Management
Supplier commitment
Supplier development
Supplier selection
Supplier negotiation
Outsourcing
Capital
Focus on core competencies
Technical capacity
Risk transfer
Procurement Performance
Cost reduction
Achieved lead-times
Quality of goods purchased
Electronic Procurement
Enterprise resource planning
Training
Electronic data integration
Internet connectivity
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RESEARCH METHODOLOGY
Research Design
Descriptive research design was used in the study. Descriptive research seeks to establish factors
associated with certain occurrences, outcomes, conditions or types of behavior. Descriptive research
seeks to obtain information that describes existing phenomena (Kothari, 2012). The design is
considered appropriate as it permits an exhaustive study of the problem under investigation.
Target Population
The target population is the total number of subjects targeted by the study (Oso & Onen, 2011).
Mugenda and Mugenda, (2009) postulates that target population should have some observable
characteristics, to which the research intends to generalize the results of the study. Data by the
consulting company Price water house coopers (PWC, 2011) lists the players in the Kenyan motor
industry as Toyota East Africa, Cooper Motor Corporation (CMC), General motors, DT Dobie, Simba
colt, Subaru, Foton, Tata, Nissan, Mahindra and most recent entrants south Korean car manufacturer
Hyundai, Honda. For purpose of this study, the study population contained a total of 528 members of
staff from CMC Motors Group Kenya drawn from procurement and related departments who were
engaged or benefited from procurement activities as tabulated below;
Table 3. 1 Target Population
Category Population Percentage (%)
Top Level Management 45 8.52
Middle Level Management 148 28.03
Operation Level
Total
335
528
63.4
100
Source: CMC Motor Group (2017)
Sampling Frame
A sample is a small proportion of targeted population selected using some systematic form. Kothari
(2012) describes a sample frame as the list that includes all members of the population from which a
sample is to be taken; it is the complete list containing all the sampling units of the population. For
purpose of this study, the study population contained a total of 528 members of staff from CMC
Motors Group Kenya distributed across top level management, middle level managers and operational
level staff as obtained from the list of employees as per CMC Motors Group Kenya register (CMC,
2017).
Sampling Size and Sampling Technique
Sample size is the number of items involved in the study as the respondents in the study. Sampling
technique on the other hand is the scientific process through which the sample elements are selected
(Mugenda & Mugenda, 2009). The study applied the stratified random sampling technique to select a
sample size of 158 respondents obtained from the subgroups using simple random sampling. This
ensured that the sampling units have an equal chance in the study. The study sample population was
30% thus constituting 158 respondents who formed the sample size. Respondents from within each
group in the % proportions bearing to the indicated population as whole were taken using
proportionate sampling. The use of stratification focuses on reducing standard error by providing
some control over the possible variance. Table 3.2 shows the sampling frame that was 30% of the
target population that was taken as the sample.
The formula that mainly applies in most baseline stratified samples is also given as:
n= Z2PQ
m2
P = Likely value of the parameter
m= Permissible margin of error
Z = Value of the standard normal deviate corresponding to a level of significance
n= desired sample size
Q = 1-P
P = 0.2
m = 0.05
Z = 1.96
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n= {1.962 x 0.2 (1-0.2)} ÷ 0.05
2
n = 157.8 thus 158 respondents
To determine the sample size of each category of employees working in procurement and operations
according to the levels of management, proportionate stratified sampling was used as follows;
For Top Level Management
TLM = 45×158 = 15 employees
528
For Middle Level Management
MLM = 148×158 = 45 employees
528
For Operation Level Management
OLM = 335×158 = 100 employees
528
The respondent from every subgroup was then selected for inclusion in the sample size using simple
random sampling. This ensured that the sampling units had equal chance in the study. The sample
distribution is given as shown in Table 3.2;
Table 3.2: Sample Size Distribution
Category Target population(N) Percentage Sample size(n)
Top Level
Management
45 8.52 13
Middle Level
Management
148 28.3 45
Operation Level
Total
335
528
63.44
100
100
158
3.6 Data Collection Instruments Sukaran (2010), notes that for any research to reach its objective, the identification of an appropriate
means of data collection is obligatory. This study used primary data for statistical analysis. Kothari
(2012) outlines Primary data as data which is collected afresh and for the first time, and thus happens
to be original in character. This study used questionnaires as data collection tools. According to
(Mugenda & Mugenda, 2009), questionnaires are commonly used to obtain important information
about the population and each item in a questionnaire addresses a specific objective and research
question of the study. This tool was used because it enables the researcher to collect data from many
respondents within a short period of time, are easier to administer and analyze, are economical in
terms of time and money, there is low cost, it is free from the bias of the interviewer answers are in
respondent‘s own words, respondents have adequate time to give well thought answers, respondents
who are easily approachable can also be reached conveniently and large sample can be made use of
and thus the results can be made more dependable and reliable (Kothari , 2012). The questionnaires
consisted of mostly closed-ended and a few open-ended items.
Data Collection Procedure
The researcher adopted the drop and pick method to deliver the questionnaire to the selected
employees of CMC Motors Group Kenya and sent personalized emails with a return email address to
employees not within the regional coverage, Nairobi. The researcher monitored closely to ensure all
questionnaires issued to the respondents are received by maintaining a register for the questionnaires
issued and returned, (Mugenda & Mugenda, 2009). A total of 158 questionnaires were distributed to
the targeted respondents of the identified firms. Out of the population covered, 126 were responsive
representing a response rate of 79.75%. This was above the 50% which is considered adequate in
descriptive statistics according to Mugenda & Mugenda (2012)
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Pilot Testing
Questionnaires were first administered to 10% of the respondents from the sample populated prior to
the main study to test validity and reliability of the research instrument. Mugenda & Mugenda (2009),
contends that 1 to 10% of the sample is sufficient for pilot test; and further points out that, the
accuracy of data to be collected is largely dependent on the data collection instruments in terms of
validity and reliability. The Cronbach‘s alpha was used to test reliability of the data collection
instrument and an alpha of between 0.7 and 0.8 was considered enough to confirm and reflect the
internal consistency of the instrument, as guided by a study carried out by (Kothari, 2012). The
Cronbach‘s alpha results were Procurement Policy (.873), Electronic Procurement (.791), Supplier
Relationship Management (.723) and Outsourcing (.822), ranging between 0.723 and 0.873 and
therefore the construct was acceptable as shown by Table 4.2.
Data Analysis
To enable the researcher, understand the data collected and assign meaning to the resulting statistics,
an analysis of data was done to summarize the essential features and relationships of data to
generalize and determine patterns of behavior and outcomes. The completed questionnaires were
reviewed for completeness and consistency before responses can be processed. Qualitative and
quantitative techniques were used in the data analysis. Content analysis was done, while descriptive
analysis such as mean, frequencies and percentages were used to analyze the data. Data representation
was done through tables, graphs, figures and charts. Regression analysis using Multiple Linear
Regression model was employed to establish the significance of the independent variables on the
dependent variable. Data was organized and interpreted on account of concurrence to objectives using
assistance of the computer package, statistical package for social scientists (SPSS) version 22, to
communicate research findings. Tables and charts were used for data presentation. After analysis and
interpretation of data, a final report was presented summarizing the findings and conclusions as well
as the research recommendations. Regression Analysis was run to examine the relationship among the
independent and the dependent study variables which are set out in the objectives of the study. The
regression model is as below;
Y=βo+β1X1+β2X2+β3X3+β4X4+ε
Where;
Y= Procurement performance of CMC Motors Group Kenya
βo =Constant
X1= Procurement Policy
X2=Electronic Procurement
X3= Supplier Relationship Management
X4= Outsourcing
β1, β2, β3, β4 = Regression co-efficient
ε =Error
β0 represents the constant
β1,2,3,4 are regression coefficients
RESULTS AND DISCUSSION
The study sought to establish the drivers of performance in the motor industry in Kenya with specific
reference to CMC Motors Kenya. The study presents the research findings for all the study objectives
which include Procurement policy, Electronic procurement, Supplier relationship management,
Information management and procurement performance in motor industry in Kenya.
Procurement Policy and Procurement Performance
The first objective of the study was to determine the effects of procurement policy on procurement
performance in motor industry in Kenya. This study was interested in evaluating the impact of
procurement policy on procurement performance in CMC Motors Group, Kenya. This section
presents findings to statements posed in this regard with responses given on a five-point Likert scale
(where 5 = Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1= Strongly Disagree). Table 4.1
presents the findings. The scores of ‗strongly disagree‘ and ‗disagree‘ have been taken to represent a
statement not agreed upon, equivalent to mean score of 0 to 2.5. The score of ‗Neutral‘ has been taken
to represent a statement agreed upon moderately, equivalent to a mean score of 2.6 to 3.4. The score
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of ‗agree‘ and ‗strongly agree‘ have been taken to represent a statement highly agreed upon equivalent
to a mean score of 3.5 to 5.0.
Table 4.1 below presents the findings. With a grand mean of 3.250, a majority of respondents were
neutral with most statements posed as regards influence of procurement policy on procurement
performance in the motor industry in Kenya. The respondents were neutral that the staff has a good
knowledge and procurement ethics all through the procurement structure. (M=3.36578); The
implemented communication policy can be a hindrance to achieving efficient procurement
performance. (M=3.3572); Distribution layout require a centralized procurement which maximizes on
the overall costs saved but can affect efficiency. (M=3.5290); The distribution layout policy impacts
on the overall procurement performance (M=3.7862); Procurement structure is contributory to the
overall procurement performance. (M=3.4456); rules and regulations provide guidance to the
procurement process(M=3.2567). The rules and regulations have helped to reduce corruption and
enhanced transparency (M=3.2358). The study results imply that procurement policy does play a very
important role on the procurement performance in the motor industry in Kenya. The study findings
are in line with literature review by Wisegeek (2013) who stated that procurement policies entail a set
of rules and regulations put in place to govern the process of acquiring goods and services needed by
an organization to function efficiently procurement policies determine the overall progress and
outcome regarding the organization set goals. Victor (2012) also established that procurement
expenditure could be minimized through implementation of effective procurement practices.
Procurement functions in a centralized procurement setup must be carried out with efficiency and
cascaded through the procurement cycle for satisfaction to be felt in the entire organization. Bandiera
et al. (2009) established that a central agency can produce considerable cost savings. Any deviation
from the norm will be felt in the procurement operations efficiency.
The study agrees with study by OECD (2014) who observed that good governance provides the
assurance that the policy process (policy formulation, implementation, monitoring and evaluation) is
operating optimally with effectiveness, efficiency, and economy, and is underpinned by broader
principles such as rule of law, transparency, accountability and integrity. Similarly, (Bandiera et al.,
2009) provide an important finding regarding the comparison between centralized and decentralized
procurement, holding that a central agency can produce considerable cost savings. Policies set
regarding channels of communication in the procurement function should be sensible, controllable but
fair to the functioning of the department. Table 4.1: Influence of Procurement Policy on Procurement Performance
Procurement Policy Mean Std. Dev
The staff has a good knowledge and procurement ethics all through the
procurement structure.
3.6578 .4321
The implemented communication policy can be a hindrance to achieving efficient
procurement performance.
3.3572 .5672
Distribution layout requires a centralized procurement which maximizes on the
overall costs saved but can affect efficiency.
3.5290 .9812
The distribution layout policy impacts on the overall procurement performance. 3.7862 .6218
Procurement structure is contributory to the overall procurement performance. 3.4456 .3843
Rules and regulations provide guidance to the procurement process 3.2567 1.5324
The rules and regulations have helped to reduce corruption and enhanced
transparency with the public sector
3.2358 .6543
Composite Mean 3.2451
Electronic Procurement and Procurement Performance
The second objective of the study was to determine the effects of electronic procurement on
procurement performance in motor industry in Kenya. This study was interested in evaluating the
impact of electronic procurement on procurement performance in CMC Motors Group, Kenya. This
section presents findings to statements posed in this regard with responses given on a five-point Likert
scale (where 5 = Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1= Strongly Disagree). Table
4.2 presents the findings. The scores of ‗strongly disagree‘ and ‗disagree‘ have been taken to represent
a statement not agreed upon, equivalent to mean score of 0 to 2.5. The score of ‗Neutral‘ has been
taken to represent a statement agreed upon moderately, equivalent to a mean score of 2.6 to 3.4. The
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score of ‗agree‘ and ‗strongly agree‘ have been taken to represent a statement highly agreed upon
equivalent to a mean score of 3.5 to 5.0. Table 4.5 below presents the findings. With a grand mean of
3.1126, a majority of respondents were neutral with most statements posed as regards influence of
electronic procurement on procurement performance in the motor industry in Kenya. The respondents
were neutral that the use of the enterprise resource planning software is a major contributor to
procurement efficiency at CMC Motors Group (M=3.3335); Consistent training of stakeholders
improves the level of electronic procurement performance at CMC Motors Group (M=3.7654);
Electronic procurement has led to the electronic data integration leads to cost reduction (M=3.3245);
Electronic data integration enables the organization to streamline processes (M=3.5643); E-
procurement Facilitates real time response to customers. (M=3.2223); Enterprise resource planning
facilitates real time response to customers (M=3.2210). Internet connectivity influence the day to day
procurement activities (M=3.5424).The study results imply that electronic procurement is very
important on the procurement performance in the motor industry in Kenya.
The study findings agree with the study by Singh & Sanders (2007) who implied that it is imperative
to appreciate that internet connectivity is the driving force behind electronic procurement. The use of
e-purchasing tools to integrate systems within and between organizations is a potentially powerful
source of supply chain improvement. These e-procurement technologies can be clustered into three
main groups – e-sourcing tools, e-process tools, and e-transaction tools. According to (Khanapuri,
Nayak, Sharma &Soni, 2011), E-procurement results to profitability, control and simplicity in the
process of corporate procurement. Further, e-procurement leads to reduction in lead time & cost of
procurement and enhanced transparency (Bof & Previtali, 2010). Technology is advancing every day
and therefore employees require continuous effective training that will automate to efficiency. In a
related study, Iqbal et al (2014) maintains that an employee will become more efficient and
productive if he trained well. Table 4.2: Influence of Electronic Procurement on Procurement Performance
Electronic Procurement Mean Std. Dev
The use of the enterprise resource planning software is a major contributor to
procurement efficiency at CMC Motors Group
3.3335 .4321
Consistent training of stakeholders improves the level of electronic procurement
performance at CMC Motors Group
3.7654 1.6890
Electronic procurement has led to the electronic data integration leads to cost
reduction.
3.3245 .5480
Electronic data integration enables the organization to streamline processes. 3.5643 1.5320
Enterprise resource planning facilitates real time response to customers. 3.2223 .5623
Electronic data integration improves transparency in the procurement process. 3.2210 1.2609
Internet connectivity influence the day to day procurement activities 3.5424
Composite Mean 3.1126
Supplier Relationship Management and Procurement Performance
The third objective of the study was to determine the effects of supplier relationship management on
procurement performance in motor industry in Kenya. This study was interested in evaluating the
impact of supplier relationship management on procurement performance in CMC Motors Group,
Kenya. This section presents findings to statements posed in this regard with responses given on a
five-point likert scale (where 5 = Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1= Strongly
Disagree). Table 4.6 presents the findings. The scores of ‗strongly disagree‘ and ‗disagree‘ have been
taken to represent a statement not agreed upon, equivalent to mean score of 0 to 2.5. The score of
‗Neutral‘ has been taken to represent a statement agreed upon moderately, equivalent to a mean score
of 2.6 to 3.4. The score of ‗agree‘ and ‗strongly agree‘ have been taken to represent a statement highly
agreed upon equivalent to a mean score of 3.5 to 5.0. Table 4.3 presents the findings.
Table 4.3 below presents the findings. With a grand mean of 3.2252, a majority of respondents were
neutral with most statements posed as regards influence of supplier relationship management on
procurement performance in the motor industry in Kenya. The respondents were neutral that supplier
commitment is of great essence to the overall procurement cycle at CMC Motors Group (M=3.2235);
Supplier development impacts on the concurrent procurement activities at CMC Motors Group
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(M=3.5690); The organization does the supplier selection for honouring the agreed vendor terms of
payment develops trust and propels future transactions smoothly (M=3.5214); Sharing information in
advance of changing needs is vital to facilitate fore planning by both the vendors and CMC
(M=3.6320); Supplier negotiations enhance reliable problem-solving mechanisms which are in place
between CMC and suppliers (M=3.4521).The study findings indicate that supplier relationship
management influence procurement performance in the motor industry. For successful partnership,
both vendors and customers must build their trust and always strive to perform better than agreed, as
this form the basic part of the relational investments that arise which are vital for future prosperity.
Trust is a combination of performance, relationship and commitment capability. Yang et al. (2009)
resonates that failing to establish supplier commitment, a buyer firm can suffer from shortage of
continuous and reliable inputs ranging from office supplies and equipment, to facility maintenance
and repair services in support of their operations. As an organizational resource, a committed supplier
is willing to resist short-term alternatives, make investments, and allocate resources to satisfy the
requirements of buyer firms.
Table 4.3: Influence of Supplier Relationship Management on Procurement Performance
Supplier Relationship Management Mean Std. Dev
Supplier commitment is of great essence to the overall procurement cycle at CMC
Motors Group.
3.2235 .5690
Supplier development impacts on the concurrent procurement activities at CMC
Motors Group.
3.5690 .2368
The organization does the supplier selection for honouring the agreed vendor terms
of payment develops trust and propels future transactions smoothly.
3.5214 .5690
Sharing information helps in negotiations in advance of changing needs is vital to
facilitate fore planning by both the vendors and CMC.
3.6320 .2236
Supplier negotiations enhance reliable problem-solving mechanisms which are in
place between CMC and suppliers.
3.4521 .3452
Composite Mean 3.2252
Outsourcing and Procurement Performance
The fourth objective of the study was to determine the influence of outsourcing on procurement
performance in motor industry in Kenya. This study was interested in evaluating the impact of
outsourcing on procurement performance in CMC Motors Group, Kenya. This section presents
findings to statements posed in this regard with responses given on a five-point likert scale (where 5 =
Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; 1= Strongly Disagree). Table 4.4 presents the
findings. The scores of ‗strongly disagree‘ and ‗disagree‘ have been taken to represent a statement not
agreed upon, equivalent to mean score of 0 to 2.5. The score of ‗Neutral‘ has been taken to represent a
statement agreed upon moderately, equivalent to a mean score of 2.6 to 3.4. The score of ‗agree‘ and
‗strongly agree‘ have been taken to represent a statement highly agreed upon equivalent to a mean
score of 3.5 to 5.0.
With a grand mean of 3.2252, a majority of respondents were neutral with most statements posed as
regards influence of outsourcing on procurement performance in the motor industry in Kenya. The
respondents were neutral that outsourcing of services at CMC Motors Group relieves the company
from worrying about the massive capital that would have invested (M=3.6583); The outsourced
personnel at CMC Motors Group have a remarkable technical savvy (focus on core competencies)
because it is their line of specialty (M=3.8850); The technical capacity is embraced in outsourcing at
CMC Motors Group results to staff reduction resulting to reduced spend (M=3.4560); Technical
capacity is important because of outsourcing, CMC Motors has developed business relationships and
become competitive (M=3.5632); CMC Motors Group serves as a bench mark to other players in the
market through risk transfer (M=3.8803). Outsourced services transfer risks at CMC Motors
Group(M=3.5680). The study findings indicate that outsourcing influence procurement performance
in the motor industry. Gupta et al., (2009) state that in creating competitive advantage, a firm needs
the ability to make good use of resources. The concept of core competence has been developed to
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support more efficient identification and utilization of an organization's strength. A related study by
(Dorasamy et al., 2010) shows a significant relationship between outsourcing of functions and risks.
Risk has been well-defined as a measure of the likelihood and severity of adverse effects (Haimes,
2009). Mwikali and Kavale (2012) reveal that cost factors, technical capability, quality assessment,
organizational profile, service levels and risk factors, are key factors affecting supplier selection in
procurement management. Pamela (2013) echoes by citing supplier financial capacity expertise as one
of the key factors which determine the ultimate performance of both the supplier and procurement
performance, depicting a high correlation between the financial capacity of supplier and ability of
supplier to deliver. Table 4.4: Influence of Outsourcing on Procurement Performance
Outsourcing Mean Std. Dev
Outsourcing of services at CMC Motors Group relieves the company from
worrying about the massive capital that would have invested.
3.6583 .4320
The outsourced personnel at CMC Motors Group have a remarkable technical
savvy (focus on core competencies) because it is their line of specialty
3.8850 1.4680
The technical capacity is embraced in outsourcing at CMC Motors Group results to
staff reduction resulting to reduced spend.
3.4560 .5358
Technical capacity is important because of outsourcing, CMC Motors has
developed business relationships and become competitive
3.5632 .5908
CMC Motors Group serves as a bench mark to other players in the market through
risk transfer
3.8803 .3265
Outsourced services transfer risks at CMC Motors Group 3.5680 .3256
Composite Mean 3.4568
Procurement Performance
The study sought to examine the drivers of procurement performance in the motor industry in Kenya, attributed
to the influence of procurement policy, electronic procurement, supplier relationship management and
outsourcing. The study was particularly interested in three key indicators, namely order fulfillment, cost
reduction and lead time reduction, with all the three studied over a 5 year period, running from 2012 to 2016.
Findings in Table 4.5 reveal improved procurement performance across the 5 year period running from the year
2012 to 2016. Order fulfillment recorded positive growth with a majority affirming to less than 10% in 2012
(42.3%) and 2013 (37.7%), to 10% in 2014 (36.1%) then more than 10% in 2015 (41.1%) and 2016 (37.5%).
A similar trend was recorded in cost reduction, growing from less than 10% (44.1%) in 2012, to more than 10%
in 2013 (36.4%), 2014 (40.4%) and 2015 (37.3%). Lead time reduction further recorded positive growth with a
majority affirming to less than 10% in 2012 (37.9%) and 2013 (35.9%), to 10% in 2014 (35.9%) and 2015
(35.3%) then by more than 10% in 2016 (36.2%). It can be deduced from the findings that key procurement
performance indicators have considerably improved as influenced by among other procurement management
attributes, the influence of procurement policy, electronic procurement, supplier relationship management and
outsourcing. Order fulfillment, cost reduction and lead time reduction have particularly improved by at least 10
percent in the organization pointing to the significance of procurement policy, electronic procurement, supplier
relationship management and outsourcing in the supply chain process.
Table 4.5: Procurement Performance
Quality of goods purchased 2012 2013 2014 2015 2016
Improved by less than 10% 42.3 37.7 31.6 30.7 29.5
Improved by 10% 31.8 32.9 36.1 28.2 33
Improved by more than 10% 25.9 29.4 32.3 41.1 37.5
Cost reduction 2012 2013 2014 2015 2016
Improved by less than 10% 44.1 35.2 33.4 25.7 27.1
Improved by 10% 31.7 32.6 30.2 33.9 35.6
Improved by more than 10% 23.5 32.2 36.4 40.4 37.3
Timely Purchases-Stock out Reduction
(Achieved lead times)
2012 2013 2014 2015 2016
Improved by less than 10% 37.9 35.9 31.2 25.7 33.1
Improved by 10% 36.2 31.3 35.9 35.3 30.7
Improved by more than 10% 25.9 32.8 32.9 39 36.2
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Multiple Regression Analysis
The study adopted a multiple regression analysis so as to establish the relationship of independent
variables and dependent variables. The study applied SPSS to compute the measurements of the
multiple regression analysis. According to the model summary Table 4.6, the coefficient of
determination (R2) is used to measure how far the regression model‘s ability to explain the variation
of the independent variabies. The correlation coefficient was 0.799. This indicates a very strong
postive relationship between the independent variable and depedent variable. The data showed that
the high R square is 0.638. It shows that the independent variables in the study were able to explain
63.80% variation in the procurement perfomance in the organization while the remaining 36.20% is
explained by the variables or other aspects outside the model. This implies that these variables are
very significant and they therefore need to be considered in any effort to boost procurement
perfomance in the organization. The study therefore identifies variables as critical drivers of
procurement perfomance in the organization.
Table 4.6: Model Summary
Model R R2 Adjusted R
2 Std. Error of the Estimate
.799 .638 .599 .001
ANOVA Results
F-test is done to test the effect of independent variables on the dependent variable simultaneously.
The F-statistic test basically shows whether all the independent variables included in the model
jointly influence on the dependent variable. Based on the study results of the ANOVA Test or F-test
in Table 4.7, obtained F-calculated was 6.7960 greater the F-Table (4.8095) with significance of
0.000. Since the significance level of 0.000<0.05 we conclude that the set of independent variables
affect the procurement performance in the motor industry (Y-dependent variable) and this shows that
the overall model was significant.
Table 4.7: ANOVA
Model Sum of
Squares
d.f Mean Square F Sig.
Regression 12.888 4 3.222 6.7960 .000a
Residual 56.890 120 .4741
Total 69.778 125
NB: F-critical Value = 4.8095;
Regression Coefficients
The results of multiple regression analysis obtained regression coefficients t value and significance
level as indicated in Table 4.9. The study conducted a multiple regression analysis so as to determine
the relationship between the dependent variable and independent variables. The general form of the
equation was to predict procurement perfomance mpanies from procurement policy, electronic
procurement, supplier relationship management and outsourcing is: (Y = β0 + β1X1 + β2X2 + β3X3 +
β4X4 +ε) becomes: Y= 12.344+ 0.766X1+ 0.569X2+ 0.710X3 + 0.621X4. This indicates that
procurement perfomance = 12.344 + 0.766* Procurement Policy + 0.569*Electronic Procurement +
0.710*Supplier Relationship Management + 0.621*Outsourcing + 2.902. From the study findings on
the regression equation established, taking all factors into account (independent variables) constant at
zero procurement perfomance was 12.344. The data findings analyzed also shows that taking all
other independent variables at zero, a unit increase in procurement policy would lead to a 0.766
increase in procurement perfomance; a unit increase in electronic procurement would lead to a 0.569
increase in procurement perfomance, a unit increase in supplier relationship management would lead
to 0.710 increase in procurement perfomance and a unit increase in outsourcing would lead to 0.621
increase in procurement perfomance. This infers that procurement policy contributed most to
procurement perfomance. Based at 5% level of significance, procurement policy had a t-value
(5.665>1.96) with a .000 level of significance; electronic procurement had a t-value (3.654 > 1.96)
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with a .011 level of significance, supplier relationship management had a t-value (4.876>1.96) with a
.004 level of significance and ICT Integration had a t-value (2.771>1.96) with a .035 level of
significance hence the most significant factor was procurement policy.
Table 4.9: Coefficient Results
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
β Std. Error β
(Constant) 12.344 2.902 7.615 .000
X1_Procurement Policy .766 .110 .765 5.665 .000
X2_Electronic Procurement .569 .130 .654 3.654 .011
X3_Supplier Relationship
Management
.710 .145 .555 4.876 .004
X4_Outsourcing .621 .174 .532 2.771 .035
CONCLUSIONS
From the study findings, the study settles that procurement performance in motor industry is affected
by procurement policy, electronic procurement; supplier relationship management and outsourcing as
the major factors that mostly affect procurement performance in motor industry in Kenya.
The study findings similarly conclude that procurement policy is the first important factor that affects
procurement performance in the motor industry in Kenya. The regression coefficients of the study
show that procurement policy has a significant influence on procurement performance in the motor
industry in Kenya. This implies that increasing levels of procurement policy would increase the levels
of procurement performance in the motor industry in Kenya. This shows that procurement policy has
a positive influence on procurement performance in the motor industry in Kenya.
Additionally, the study concludes that electronic procurement is the second important factor that
affects procurement performance in the motor industry in Kenya. The regression coefficients of the
study show that electronic procurement has a significant influence on procurement performance in the
motor industry in Kenya. This implies that increasing levels of electronic procurement would increase
the levels of procurement performance in the motor industry in Kenya. This shows that electronic
procurement has a positive influence on procurement performance in the motor industry in Kenya.
Further, the study concludes that supplier relationship management is the third important factor that
affects procurement performance in the motor industry in Kenya. The regression coefficients of the
study show that supplier relationship management has a significant influence on procurement
performance in the motor industry in Kenya. This implies that increasing levels of supplier
relationship management would increase the levels of procurement performance in the motor industry
in Kenya. This shows that supplier relationship management has a positive influence on procurement
performance in the motor industry in Kenya.
Finally, the study concludes that outsourcing is the fourth important factor that affects procurement
performance in the motor industry in Kenya. The regression coefficients of the study show that
outsourcing has a significant influence on procurement performance in the motor industry in Kenya.
This implies that increasing levels of outsourcing would increase the levels of procurement
performance in the motor industry in Kenya. This shows that outsourcing has a positive influence on
procurement performance in the motor industry in Kenya.
RECOMMENDATIONS
The organization should provide the assurance that the procurement policy and subsequent policy
process (policy formulation, implementation, monitoring and evaluation) is operating optimally with
effectiveness, efficiency, and economy, and is underpinned by broader principles such as rule of law,
transparency, accountability and integrity. The procurement Policies set regarding channels of
communication in the procurement function should be sensible, controllable but fair to the functioning
of the department.
The study also recommends that the organization should invest on electronic procurement like ERP as
full automation of the procurement process will help them coordinate the organization activities.
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Correspondingly, Electronic procurement will help on transit, improved billing systems hence
reducing paper work on supply chain activities.
The study recommends for the improvement of supplier management to enhance procurement
performance in the motor industry. The companies require to be recognizant with the importance of
supplier reliability. The low inventory levels would mean that the supplier would begin their own
procurement process which prolong the lead time. Long lead time has the impression that the specific
supplier is less efficient, or he just has more customers than he can serve thus delaying deliveries.
Further, e-procurement leads to reduction in lead time & cost of procurement and enhanced
transparency. Technology is advancing every day and therefore employees require continuous
effective training that will automate to efficiency and to maintain it, the employee can only become
more efficient and productive if they are well trained. The study also recommends that the firms
should become more and more dependent on their suppliers and the suppliers should be seen as a as
key resources in the development of the buyer's own capabilities and performance.
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