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UNIVERSITY OF NAIROBI
THE IMPACT OF ADVANCED MANUFACTURING TECHNOLOGIES (AMTs) ON
TECHNICAL LABOUR IN MANUFACTURING COMPANIES IN KENYA
PROJECT CODE: GNM/01/2016
BY:
ABUNGU, NIGEL TAWO
MAINGI, SOLOMON MUTUNGA
And
OMBARA, RYAN JOEL
This project is submitted to the University of Nairobi as a requirement for the award of the
degree of BSc. Mechanical and Manufacturing Engineering.
Supervisor: Eng. Dr George M. Nyori
Department of Mechanical & Manufacturing Engineering, School of Engineering,
University of Nairobi.
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DEDICATION
It is with genuine gratefulness and warmest regard that we dedicate this project to our families,
close friends and colleagues. Your support throughout our five-year course will be forever
cherished.
In Loving Memory of Kihara Michael Macharia (1992-2013).
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DECLARATION
We declare that we have developed and written the enclosed final year project completely by
ourselves, and have not used sources or means without declaration in the text. Any thoughts
from others or literal quotations are clearly marked. This final year project was not used in the
same or in a similar version to achieve an academic grading nor is it being published elsewhere.
Abungu, Nigel Tawo F18/1512/2011 ______________________________
Maingi, Solomon Mutunga F18/1487/2011 ______________________________
Ombara, Ryan Joel F18/1467/2011 ______________________________
This research project has been submitted for University examination with my approval as the
University Supervisor.
Name: ___________________________ Signature: ________________________
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ACKNOWLEDGEMENTS
Our sincere gratitude and thanks to Almighty God, for His goodness and grace that have
enabled us complete our undergraduate degree.
We would also like to express our sincere gratitude to Eng. Dr George M. Nyori, our
Supervisor, for his constant encouragement, thorough supervision, valuable suggestions and
advice throughout the period of this study.
We would also like to convey our sincere thanks to the Department of Mechanical &
Manufacturing Engineering for their financial assistance, support and encouragement.
Special thanks to: Tessa Oraro, Eng. Ndirangu (ABM Kenya), Eng. Thubi (NMC) and Eng.
James Mwangi (HACO Tiger) for their input and support in the compilation of this project.
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TABLE OF CONTENTS
DEDICATION........................................................................................................................... i
DECLARATION...................................................................................................................... ii
ACKNOWLEDGEMENTS .................................................................................................. iii
LIST OF TABLES .................................................................................................................. vi
LIST OF FIGURES ............................................................................................................. viii
LIST OF ABBREVIATIONS ................................................................................................. x
ABSTRACT ............................................................................................................................ xii
CHAPTER ONE: INTRODUCTION .................................................................................... 1
1.1. Historical Developments in Advanced Manufacturing Technologies .................. 1
1.2. Uptake and Integration of Advanced Manufacturing Technologies in Kenya ... 2
1.2.1. Historical Context of Manufacturing in Kenya .............................................. 2
1.2.2. Advanced Manufacturing Technologies in Kenya .......................................... 3
1.3. Problem Statement and Objectives ......................................................................... 3
CHAPTER TWO: LITERATURE REVIEW ....................................................................... 5
2.1. Introduction ............................................................................................................... 5
2.2. Advanced Manufacturing Technologies Implementation ..................................... 5
2.3. Impact of Advanced Manufacturing Technologies ................................................ 6
2.4. Impact of Advanced Manufacturing Technologies on Jobs, Education and Skills
..................................................................................................................................... 7
2.4.1. Impact on Training ............................................................................................ 8
2.4.2. Impact on Innovation and Research & Development .................................... 8
CHAPTER THREE: RESEARCH METHODOLOGY .................................................... 10
3.1. Objectives ................................................................................................................. 10
3.2. Research Design....................................................................................................... 10
3.3. Population, Sampling Technique and Sample Size .............................................. 10
3.4. Reliability and Viability .......................................................................................... 11
3.5. Questionnaire Design .............................................................................................. 12
3.6. Data Analysis ........................................................................................................... 13
CHAPTER FOUR: RESULTS AND DATA ANALYSIS .................................................. 14
4.1. Introduction ............................................................................................................. 14
4.2. Response Rate and Time......................................................................................... 14
4.3. Data Quality and Cost ............................................................................................ 14
4.4. Research Questions ................................................................................................. 14
4.4.1. Research Question 1 ........................................................................................ 14
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4.4.2. Research Question 2 ........................................................................................ 16
4.4.3. Research Question 3 ........................................................................................ 19
4.4.4. Research Question 4 ........................................................................................ 21
4.4.5. Research Question 5 ........................................................................................ 23
4.4.6. Research Question 6 ........................................................................................ 28
4.4.7. Research Question 7 ........................................................................................ 32
4.4.8. Research Question 8 ........................................................................................ 37
4.4.9. Research Question 9 ........................................................................................ 39
4.4.10. Research Question 10 ...................................................................................... 41
CHAPTER FIVE: DISCUSSION, CONCLUSION AND RECOMMENDATIONS ...... 48
5.1. DISCUSSION .......................................................................................................... 48
5.1.1. Interaction of Advanced Manufacturing Technologies by the Engineer .... 48
5.1.2. Effect of Advanced Manufacturing Technologies on Existing Labour, Skills
and Training in the Manufacturing Sector .................................................................. 49
5.1.3. Effect of Advanced Manufacturing Technologies Adoption on Firms’
Organizational Structure ............................................................................................... 50
5.1.4. Trends Analysis Based on Further AMT Adoption by Firms in the Sector ..
............................................................................................................................ 51
5.2. CONCLUSIONS ..................................................................................................... 52
5.3. RECOMMENDATIONS ........................................................................................ 53
REFERENCES ....................................................................................................................... 55
APPENDICES ........................................................................................................................ 59
APPENDIX A: List of Manufacturing and Processing Companies in Kenya .............. 59
APPENDIX B: List of Shortlisted Companies for Project Study .................................. 62
APPENDIX C: Questionnaire ........................................................................................... 63
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LIST OF TABLES
Table 4.1: Table of the AMTs used by companies. ................................................................. 15
Table 4.2: Table of how long companies have used AMTs. ................................................... 17
Table 4.3: Table of General statistical data of how long companies have used AMTs. .......... 17
Table 4.4: Table of extent of integration of AMTs in firms’ operations. ................................ 19
Table 4.5: Table of how AMTs have influenced productivity over the past 10 years. ............ 21
Table 4.6: Table of General Data of how AMTs have influenced productivity over the past 10
years. ........................................................................................................................................ 22
Table 4.7: Table of size of workforce in terms of qualified Engineers 10 years ago. ............. 24
Table 4.8: Table of General Statistical Data size of workforce in terms of qualified Engineers
10 years ago. ............................................................................................................................ 24
Table 4.9: Table of the size of workforce in terms of blue collar technicians 10 years ago. .. 26
Table 4.10: Table of general statistical of data the size of workforce in terms of blue collar
technicians 10 years ago. ......................................................................................................... 26
Table 4.11: Table of the size of workforce in terms of qualified Engineers at the present. .... 28
Table 4.12: Table of general statistical data of the size of workforce in terms of qualified
Engineers at present. ................................................................................................................ 28
Table 4.13: Table of the size of workforce in terms of blue collar technicians at the present. 30
Table 4.14: Table of general statistical data of the size of workforce in terms of blue collar
technicians at the present. ........................................................................................................ 30
Table 4.15: Table of general statistical data of the personnel that have been trained locally. 32
Table 4.16: Table of the personnel that have been trained locally. ......................................... 33
Table 4.17: Table of the personnel that have been trained abroad. ......................................... 35
Table 4.18: Table of the general statistical data of the personnel that have been trained abroad.
.................................................................................................................................................. 35
Table 4.20: Table of how Engineering departments have been affected by assimilation of
AMTs. ...................................................................................................................................... 37
Table 4.21: General statistical data of how Engineering departments have been affected by
assimilation of AMTs. ............................................................................................................. 37
Table 4.22: Table of how adoption of AMTs have necessitated a change in hierarchical
structure of organisations. ........................................................................................................ 39
Table 4.23: Table of general statistical data of how adoption of AMTs have necessitated a
change in hierarchical structure of organisations. .................................................................... 39
Table 4.24: Table of firms’ projection on staff size due to assimilation of AMTs.................. 41
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Table 4.25: Table of general statistical data of firms’ projection on staff size due to assimilation
of AMTs. .................................................................................................................................. 42
Figure 4.25: A pie-chart of frequency of number of firms with regard to staff size projection
.................................................................................................................................................. 42
Table 4.26: Table of firms’ projections on training costs due to assimilation of AMTs. ........ 44
Table 4.27: Table of general statistical data of firms’ projections on training costs due to
assimilation of AMTs. ............................................................................................................. 44
Table 4.28: Table of firms’ projections on requirement for qualifications for skilled labour due
to assimilation of AMTs. ......................................................................................................... 46
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LIST OF FIGURES
Figure 4.1: A pie-chart of frequency of the number of firms in percentage with regard to AMT
usage ........................................................................................................................................ 15
Figure 4.2: Bar graph of the type of AMTs used by companies. ............................................. 16
Figure 4.3: A pie-chart of frequency of number of firms in percentage with regard to number
of years of AMT usage ............................................................................................................ 18
Figure 4.4: Bar graph of how long companies have used AMTs. ........................................... 18
Figure 4.5: A pie-chart of frequency in percentage of number of firms with regard to extent 20
Figure 4.6: Bar graph of extent of integration of AMTs in firms’ operations. ........................ 20
Figure 4.7: A pie-chart of frequency of number of firms in percentage with regard to extent of
productivity .............................................................................................................................. 22
Figure 4.8: Bar graph of how AMTs have influenced productivity over the past 10 years. .... 23
Figure 4.9: A pie-chart of frequency of number of firms with regard to number of qualified
engineers .................................................................................................................................. 25
Figure 4.10: Bar graph of size of workforce in terms of qualified Engineers 10 years ago. ... 25
Figure 4.11: A pie-chart of frequency of number of firms in percentage with regard to number
of blue-collar technicians ......................................................................................................... 27
Figure 4.12: Bar graph of the size of workforce in terms of blue collar technicians 10 years
ago. ........................................................................................................................................... 27
Figure 4.13: A pie-chart of frequency of number of firms in percentage with regard to number
of qualified engineers at present .............................................................................................. 29
Figure 4.14: Bar graph of the size of workforce in terms of qualified Engineers at the present.
.................................................................................................................................................. 29
Figure 4.15: A pie-chart of frequency of number of firms in percentage with regards to number
of blue-collar technicians ......................................................................................................... 31
Figure 4.16: Bar graph of the size of workforce in terms of blue collar technicians at the present.
.................................................................................................................................................. 31
Figure 4.17: A pie-chart of frequency of number of firms with regard to number of personnel
trained locally........................................................................................................................... 33
Figure 4.18: Bar graph of the personnel that have been trained locally. ................................. 34
Figure 4.19: A pie-chart of frequency of number of firms with regard to personnel trained
abroad ....................................................................................................................................... 36
Figure 4.20: Bar graph of the personnel that have been trained abroad. ................................. 36
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Figure 4.21: A pie-chart of frequency of number of firms with regard to effect on engineering
departments .............................................................................................................................. 38
Figure 4.22: Bar graph of how Engineering departments have been affected by assimilation of
AMTs. ...................................................................................................................................... 38
Figure 4.23: A pie-chart of frequency of number of firms with regard to effect on hierarchical
structure.................................................................................................................................... 40
Figure 4.24: Bar graph of how adoption of AMTs have necessitated a change in hierarchical
structure of organisations. ........................................................................................................ 40
Figure 4.25: A pie-chart of frequency of number of firms with regard to staff size projection
.................................................................................................................................................. 42
Figure 4.26: Bar graph of firms’ projections on staff size due to assimilation of AMTs. ....... 43
Figure 4.27: A pie-chart of frequency of number of firms with regard to projected training costs
.................................................................................................................................................. 45
Figure 4.28: Bar graph of firms’ projections on training costs due to assimilation of AMTs. 45
Figure 4.29: A pie-chart of frequency of number of firms with regard to projected effect on
skilled labour ............................................................................................................................ 47
Figure 4.30: Bar graph of firms’ projections on requirement for qualifications for skilled labour
due to assimilation of AMTs.................................................................................................... 47
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LIST OF ABBREVIATIONS
AGV- Automated Guided Vehicles
AMT-Advanced Manufacturing Technologies
AMT-Assembly and Machining Technologies
APM - Automated Process Monitoring
API - Automated Process Inspection
ASRS- Automated Storage and Retrieval Systems
BARCODE - Bar Code Inventory Tracking
CAD - Computer Aided Design
CAM - Computer Aided Manufacturing
CAQC - Computer Aided Quality Control
CIM - Computer Integrated Manufacturing
CNC – Computer Numerical Control
DNC – Direct Numerical Control
ERP-Enterprise Resources Planning
FMS - Flexible Manufacturing System
FMC - Flexible Manufacturing Cells
GT-Group Technology
IMT-Integrated Manufacturing Technologies
JIT - Just-in-Time Manufacturing
LOOP - Closed Loop Process Control
MHT-Material Handling Technologies
MRM- Mabati Rolling Mills
MRP1- Material Requirement Planning
MRP2 - Manufacturing Resources Planning
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NMC- Numerical Machining Complex
NC – Numerical Control
PDET- Product Design and Engineering Technology
PPT-Production Planning Technologies
SMT-Surface Mounting Technology
SPC - Statistical Process Control
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ABSTRACT
This study seeks to investigate the impact of Automated Manufacturing Technologies (AMTs)
on technical labour within the manufacturing industry in Kenya. It sought to determine the
effect that AMT adoption has had on absorption, deployment and retention of technical labour
and how the organizational structure is affected by the same. The analysis framework involved
a detailed look into the level of integration of AMTs by firms; the effect of AMTs on firm
productivity; an in-depth analysis of the engineering and technical labour workforce of firms
(10 years ago and currently); an assessment of training and deployment of technical labour in
these firms and finally a projection of future trends anticipated in terms of staff size, training
costs and qualifications for skilled labour. The study population consisted of lead production
engineers or their equivalents in the said manufacturing companies. The study was
concentrated within the Greater Nairobi Area Athi River and Thika. The study involved
formulating a Likert Survey Questionnaire which we physically presented to our sample firm
population consisting of 30 manufacturing entities. Of these, 17 (57%) responded positively to
the survey. The results of the study showed that manufacturing firms continue to assimilate
AMTs into their production lines and systems, with consequent significant effects being
witnessed with regard to productivity and staff retention and deployment in these firms.
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CHAPTER ONE: INTRODUCTION
1.1. Historical Developments in Advanced Manufacturing Technologies
From 1900, industries highly adopted Advanced Manufacturing Technologies (henceforth,
AMTs) that facilitated mass production of goods and efficient service delivery. In the
manufacturing sector, the new technological advances have revolved around machine tools and
equipment. Advances in automation of machine tools began about 30 years ago with the first
generation of numerically controlled machines (NCs) which did not become widely used until
1970s. The second generation, computerized numerical controlled machine (CNC) were
introduced in the 1970s: these facilitated capacity to produce high volume standardized parts
and products necessary for competitive success in undifferentiated markets. The first AMT
technologies were introduced in the 1950s but it was not until the 1970s that the adoption of
AMTs took off and the 1980s that their use became widespread. Today, almost all currently
produced manufacturing equipment incorporates some electronics element and thus fits the
definition for AMTs. (Gunawardana, 2010)
AMTs can be defined as the use of innovative technologies to improve production processes
products. They are divided into 5 main groups (Nyori and Ogutu, 2015), namely:
a) Product Design and Engineering Technology (PDET)
Within this group are such applications as Computer Aided Design (CAD), Computer
Aided Manufacture (CAM), Computer Aided Engineering (CAE) and Group
Technology (GT).
b) Production Planning Technologies (PPT)
These technologies include Material Requirement Planning (MRP), Manufacturing
Requirement Planning (MRP II) and Enterprise Resources Planning (ERP)
c) Material Handling Technologies (MHT)
These include Automated Storage and Retrieval Systems (ASRS) and Automated
Guided Vehicles (AGV)
d) Assembly and Machining Technologies (AsMT)
These include Computer-aided Quality Control (CAQC), Robotics and Numerical
Controlled Machines (NC/CNC/DNC)
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e) Integrated Manufacturing Technologies (IMT)
These include Flexible Manufacturing Cells/Systems (FMC/FMS) and Computer-
Integrated Manufacturing (CIM)
There are a total of 26 AMTs applied in manufacturing processes that traverse a wide range of
engineering domains (Baldwin, 1995). In addition to these technologies, modern industries rely
on sensors and actuators in the running of their production processes.
1.2. Uptake and Integration of Advanced Manufacturing Technologies in Kenya
1.2.1. Historical Context of Manufacturing in Kenya
Kenya’s early post-independence years saw an industrial strategy reliant on import substitution,
effectively leading to promotion of the manufacturing sector over the agricultural one. At first,
this seemed to bear fruits with real income doubling in 9 years (1963-1972) amounting to an
average annual growth rate of about 8% well above the population growth rate of 3.4%. In the
1970s, the government intensified the degree of import substitution and as a result the share of
manufacturing in the modern sector of the economy rose from 8% to 13% between 1970 to
1980 (Gerdin,1997). Towards the end of the 1970s, the economy nonetheless faced some
challenges, most notably oil prices and volatile commodity prices (Kenya Manufacturing
Enterprise Survey 2000; pg. 5)
By the early 1980s, the country had witnessed economic and political instability. At the end of
1984, per capita income fell for 4 successive years enough to wipe out a good portion of
economic growth witnessed in the years prior. By 1988, the economy gained a foothold due to
structural adjustments; the years 1986 through 1990 saw stable per capita growth (at an average
of 3% per year). In the early 1990s, the economy went into decline partly due to international
events and partly due to slippage in macroeconomic management (Bigsten, 2001).
By 1999, the manufacturing sector employed approximately 219000 people, amounting to 13%
of the total wage employment in the modern sector; nonetheless growth of manufacturing
during the 1990s was slow. The average annual growth rate of employment during 1991
through 1999 was 1.9%, well below the population growth rate. While the formal
manufacturing sector has been relatively static during the 1990s, the informal Jua Kali sector
has expanded rapidly according to the official statistics (Kenya Manufacturing Enterprise
Survey, 2000; pg. 7)
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In 2011, the industrial sector in Kenya contributed 16% to the country’s GDP; the contribution
to GDP from industry has been more or less constant throughout the 2000s. Kenya’s industrial
production growth rate for 2011 was 3.1% (CIA World Fact-book 2011). In 2011, the
manufacturing sector in Kenya employed 254000 people equivalent to 13% of total
employment. In addition, the informal Jua Kali sector contributes a further 1.4 million workers
(Mars Group Kenya, Manufacturing and Industry Sector Report, 2011)
There is thus no debate on the importance of this sector to the economy. Nonetheless, in the
2012-2013 World Economic Forum Global Competitiveness Report, Kenya ranks 68th out of
144 countries in the report, a performance that has been blamed on some factors, among them
underdeveloped institutional frameworks and skills (Mars Group Kenya, Manufacturing and
Industry Sector Report, 2011).
It is a result of this that the government included as one of its guiding principles of its
industrialisation policy: Technology and Innovation which it recognises as central to rapidly
changing consumer tastes and preferences whilst also boosting productivity and
competitiveness of the industrial sector (Kenya National Industrialisation Policy
Framework,2010)
1.2.2. Advanced Manufacturing Technologies in Kenya
As part of a strategy to integrate technology in production/manufacturing firms in Kenya have
begun to adopt AMT in their production processes at different scales of individual operation
(Nyori and Ogutu, 2015)
Some of the documented advantages of AMTs include: achieving higher quality levels in
manufacturing, reduction of manufacturing cycle times and lowering costs as it permits
integration of full functionality of production and manufacturing processes with computer
technologies (Sun et al, 2007)
1.3. Problem Statement and Objectives
With the increased industrialisation of Kenya (and much of the world’s underdeveloped
democracies) and the nation’s emphasis on achieving the developmental goals set out in the
Vision 2030 project, the uptake and integration of AMTs in the manufacturing sector is bound
to proliferate over the coming years.
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This trend is not dissimilar to that witnessed in some of the world’s foremost industrial nations,
such as Germany, Japan, the United States and much of the South-East Asian block. The
teething problems and difficulties experienced by companies in these countries are likely to be
replicated by companies operating in Kenya.
The overarching objective of this research study is to assess the impact that the increased uptake
and integration of AMTs is having on the absorption, deployment and retention of
engineers/technical labour. We aim to determine if any parallels can be drawn between the
present situation in Kenya and that experienced in the more-developed countries beforehand.
To this end, the specific objectives of this project are:
1. To establish how the engineer interacts with AMTs with respect to his/her job and the
effect of automation on these engineers in both managerial and technical capacities.
2. To establish the effect that increased integration of AMTs is having on the uptake of
graduate engineers and blue-collar workers into the workforce.
3. To establish the change in roles and responsibilities of engineers as a consequence of
automation.
4. To establish the expected consequences to the workforce due to changes in the
automation of production processes in the manufacturing sector.
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CHAPTER TWO: LITERATURE REVIEW
2.1. Introduction
AMTs, as earlier referenced, refer to a group of integrated hardware and software packages
based mostly on technologies that if properly enforced and controlled can improve the potency
and effectiveness of the firm. The most important strategic advantages that their technologies
supply are the hyperbolic flexibility and responsiveness enabling a company boost
considerably its aggressiveness within the marketplace. It has been viewed as a strategic
weapon to realise competitive advantage, boost productivity and performance, boost quality
and quality of production and even reduce lead time (Mathew and Sharma, 2015, pg. 4)
The wide range of sophisticated computer based technologies and information based processes
is widely considered to be the future backbone of future production technology (Nyori and
Ogutu, 2015). A study by The American Institute for Defence Analyses (Institute for Defence
Analyses 2012, pg. 8) gave the following reasons for the stated assumption: the ubiquitous role
of information technology; the reliance on modelling and simulation in the manufacturing
process; the acceleration of innovation in global supply management; the move toward rapid
changeability of manufacturing in response to customer needs and external impediments and
the acceptance of support of sustainable engineering. Nonetheless, all this is pegged on the
seamless adoption of the technologies by these firms and their ability to take into account:
managerial capabilities, workers and skill requirements, organizational structure and
technological capabilities (Yussuf et al, 2005).
2.2. Advanced Manufacturing Technologies Implementation
Small and Medium Enterprises (SMEs) are the backbone of the industrialisation process of
many developed countries and play a vital role in increasing a country’s economy. To survive
and grow, they must adopt strategic technologies and innovative management practices. It has
been shown through various studies that AMTs can be integrated into small firms and that these
small firms have considerable advantages over large firms in AMT implementation. It has
actually been posited that utilisation of AMTs by small manufacturers may improve their
competitive position and financial performance. AMT implementation is nonetheless a
strategic decision that requires both operational and organizational changes where human
factors, skills and managing change play as important a role as the technology itself with
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majority of the benefits accruing not from the technology itself but the organization and
methodological changes required to be made to support it. (Yussuf et al 2005, pg. 2)
This has nonetheless hindered by factors classified as follows (Efstathiades et al, 2002):
i. Company-related (lack of knowledge and skills in the workforce, general lack of
skilled staff)
ii. Supplier-related (distance from the supplier to the technology)
iii. Government-related (taxation, policies)
Successful implementation of AMTs enhances embraces the structure, culture and strategy of
any organization; these together with human resources and management practices in terms of
qualities, attitudes and behaviour bestows upon the firm a competitive edge over its rivals.
Education and coaching of staff to handle the technologies is also crucial to the winning
implementation of AMTs. (Mathew and Sharma, 2015) Nyori and Ogutu (2015) further
suggested that human factors are just as integral in AMT adoption in firms as the cadre of
technology being employed by the firm. They further went to state that AMT technology
requires workers to be equipped with new skills, attitudes, system procedures and even social
structures in order to perform in their new role as the overall competitive advantage of AMTs
is hinged on the creation of a flexible, multi-skilled, knowledgeable work-force.
2.3. Impact of Advanced Manufacturing Technologies
AMTs are documented to have far reaching effects on the firm as we know it to be. For instance,
in production, AMTs are known to reduce waste, reduce labour costs, gain a competitive
advantage and ultimately improve profit margins. (US National Association of Manufacturing,
2013). It has also been credited with potential to bestow earlier entrance to the market, faster
responses to market needs and even higher quality products with improved consistency and
reliability. On the other hand, AMT has been known to bestow cost-related problems associated
with adoption to users (Gunawardana, 2010)
AMT has also impacted on organizational structure and hierarchy of firms. Nyori and Ogutu
(2015) propose that adoption of new manufacturing technologies by a manufacturing firm
warrants a review of its organizational structure, although this is not the case for decentralized
organizational structures that allow for the flexible use of AMTs thereof: centralization of
decision making has been found to be a hindrance to firms enjoying the flexible benefits of
AMTs. (Yussuf et al, 2005, pg. 7) For proper adoption of AMTs, behaviours, attitudes and
organizational culture has thus to be factored in.
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These technologies have also had an impact on managers who basically have to evaluate the
capabilities of the organization in the context of the chosen AMT. Managers have to factor in
the company vision and how the AMT chosen will seek to promote it. Any decision with
regards to physical, psychological, financial and even cultural impact of the technology on the
firm begins from management hence they must be on the vanguard of assessing the impact of
the technology on the firm. (Mathew and Sharma, 2015)
2.4. Impact of Advanced Manufacturing Technologies on Jobs, Education and Skills
Adoption of AMTs leads to changes in composition of labour force in favour of workers with
higher skill levels. Further, employee development and empowerment strategies are enacted to
promote the said changes; skill upgrading of the workforce occurs after new technologies are
implemented on the factory floor (Siegel and Walman, 1997).
A report by Accenture entitled ‘Manufacturing Skills and Training Study 2014’ on US
manufacturing firms revealed that more than 75% manufacturers had a severe shortage of
skilled resources while over 80% of manufacturers reported a moderate to severe shortage of
the same skilled labour in their firms. Another study by The UK Commission for Employment
and Skills (June 2015) revealed that that there is an urgent need for employers, universities and
vocational training institutions to liaise and ensure that technology and skills are integrated in
the various programmes available to give workforce the ground and adaptability to function
with respect to their changing roles.
A report ‘Engineering in Asia, A Labour Market Perspective 2014’ also revealed a skills gap
in the manufacturing sector as a result of AMT adoption, with firms readjusting accordingly.
For instance, a case study in Japan, where more than two-thirds of CNC machines were utilised
by SME companies, revealed that more than 40% of the workforce is made up of college-
educated engineers and all had been trained on the use of CNC machines. (Yusuff et al, 2005)
Closer home, Kenya faces the same quandary of lack of a skilled labour force: in fact, the
statistics speak for themselves. In the wider Engineering profession, the country has a total of
close to 7328 registered engineers against the international benchmark of 68000 engineers for
a population of 40 million citizens; the total number of technician engineers stood at 306341
against a recommended national inventory of close to 1 million (Ikinya, 2010): the figures
provide a grim outlook. A similar study revealed that 23% of engineering students change their
career to non-related courses (Waithanji, 2002). The dearth in skilled labour is apparent.
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2.4.1. Impact on Training
As a result of the aforementioned effects on labour, training has become an endeavour that
competitive firms just cannot wish away. In deed statistics from the US manufacturing sector
reveals that manufacturing firms spent an average of USD 1100(~ KES 110000) on training
each of their employees per annum (Training Industry Report, 2014). A related report revealed
that an overwhelming 95% of AMT users have shifted their recruitment and training strategies
to assess the skills gap in the market, with 51% of them creating new internal programs for the
same and a further 42% collaborating with technical skills and community colleges (US
National Association of Manufacturers, 2013).
The situation in Kenya is no different; a report on assessment of training in workplaces in
Nairobi conducted by MOHEST in 2012 revealed that several pioneers in the manufacturing
sector such as MRM, Nairobi Bottlers and Toyota Kenya have set up in house training institutes
to train their own graduates since they did not trust graduates’ skills acquired from TVET
institutions. (Ikinya, 2010)
The essence of training of labour is best captured by the previously alluded to paper entitled
‘Manufacturing Skills and Training Study 2014’ which revealed from the industry players that
increased production costs and revenue losses as a result of skills shortages cost manufacturers
up to 11% of earnings annually. In deed it has been cited that training time and expense is one
of the key challenges surrounding implementation of AMT amongst manufacturers. (US
National Association of Manufacturing, 2013)
2.4.2. Impact on Innovation and Research & Development
Findings from ‘The US National Association of Manufacturing report of 2013’ revealed that
92% of current middle market users of AMTs and 78% of non-users in the US had and would
start, respectively, to implement AMT techniques over the next 3-5 years with key motivators
being improved production output and improved profitability. This, The US National
Association of Manufacturers says, would compel manufacturing firms that want to stay ahead
of the pack, to competitively invest in innovations and expand on their research and
development efforts.
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A study on manufacturing firms in Nairobi and Athi River (Kinyanjui, 2015, pg. 127), revealed
a direct correlation between adoption of technology and global competitiveness in order for
firms not to be left behind. Furthermore, a report by General Electric on the Kenyan state of
innovation (The Economist, 2014), reveals that the government plans to increase its Research
and Development allocation kitty from 1% of GDP in 2010 to 2%; plans were also underway
that would lead to the creation of an industry led partnership with the government, that would
see partnerships between universities and companies in industry.
Another report on manufacturing in Africa (KPMG, 2014), ranked Kenya as one of the 3 best
performing countries on its manufacturing Environment index, with the country being ranked
34th globally on capacity for innovation and 53rd globally on the overall pillar of innovation-in
fact the country has been complemented as a result of its companies’ capacity to innovate and
spend on Research and Development; nonetheless, there is a lot of room for improvement in
terms of reform and policy in this area.
10
CHAPTER THREE: RESEARCH METHODOLOGY
3.1. Objectives
This study aims to investigate the effect that AMTs are having on the absorption,
deployment and retention of technical labour within the manufacturing industry in
Kenya. As earlier defined in the introduction, the core objectives of this study include
the following:
i) To determine how the engineer interacts with the AMTs with respect to
their job and to assess the effect of advanced manufacturing technology
on engineers in both managerial and technical capacities.
ii) To assess the effect that increased integration of AMTs is having on the
uptake of graduate engineers into the workforce.
iii) To assess the change in roles and responsibilities of engineers as a
consequence of advanced manufacturing technology.
iv) To predict the expected consequences to the workforce due to
advancements in the manufacturing industry.
3.2. Research Design
We settled on a questionnaire survey as our research design method as it conferred upon us as
a team several merits such as its cost efficiency, ability to reach out to a wider population and
also the ease of obtaining a wide array of opinions from respondents.
3.3. Population, Sampling Technique and Sample Size
The study set its boundaries around AMT integration of manufacturing companies in the
Greater Nairobi Area, as an indicator to the general situation in Kenya. Samples were taken
from six manufacturing sub-sectors, covering the whole range of the industry. The eight sub-
sectors include Food & Beverage, Construction/Material industry, Chemical and
Pharmaceutical, Plastics and Packaging, Automobile and Parts and Textiles & Apparel. A total
of 30 companies were shortlisted as integral to our survey, as they met our primary criteria of
being manufacturing/processing companies and operating in the Greater Nairobi Area.
(Appendix B) We further grouped these companies according to their area of specialisation.
We restricted ourselves to physical administration of the said questionnaires due to reliability
in terms of one-on-one interaction with our audience, quicker data collection and also ability
11
to access many firms at a go. We also sought to administer e-mail based questionnaires, but
this option was not exercised by our respondents.
3.4. Reliability and Viability
The primary objective of our survey, and hence the questionnaire, was to ensure standardisation
and comparability of the data collected across the interviews, where all interviewees were
asked the same questions. Of the two forms of questionnaires (open-ended and close-ended)
we opted for a predominantly close-ended questionnaire. This is because we aimed to also
obtain individual responses from our interviewees.
For the scaling of our questionnaires, we were presented with the following options: Likert
Scale, Multiple choice, Ordinal, Categorical Numerical and Comparative Scale. For the
purposes of this questionnaire, we opted to predominantly use a Likert Scale. The rationale
behind this choice was that a Likert scale is user friendly, offers superior comparability of data
and also ease of data processing. However, we also employed multiple choice questions in our
questionnaire, especially where we sought to determine which specific types of AMTs our
sample companies utilised.
We encountered a challenge in terms of response rates since in fact only 171 out of the 30
contacted manufacturing entities got back to us, translating to a 57% response rate. This could
be attributed to factors such as: sensitivity of the nature of the survey and also a difficult sample
group: for example, firms such as Bhachu Industries and Kapa Ltd actually refused to
cooperate, with others hoarding our questionnaires and eventually not giving us back any
feedback despite our attempts to re-contact them. We believe that the information presented
and analysed is representative of the entire population of manufacturing companies in Kenya
as a whole. This is because the companies we profiled are market-leaders and companies used
to benchmark in the manufacturing sector.
1 One of the respondent companies, DPL, provided their response questionnaire some time
later than the allocated response time, hence their data was not factored into our previous
analysis of the 16 other respondent firms.
12
3.5. Questionnaire Design
The questionnaire consisted of 3 pages with a cover letter from the Chairman of the
Department of Mechanical and Manufacturing Engineering also attached. (See
appendix C)
It cut across 5 major themes, as explained below:
Level of integration (interaction with AMTs)
Here, we aimed at establishing a basic understanding of how much our sample
companies had interacted with AMTs in their operations. It included questions
on which AMTs companies use in their operations and for how long these
AMTs had been used.
Effect on existing technical labour (& managers)
In this section, we aimed at determining, from the companies, what general
effects the uptake of AMTs had on the existing staff. We looked to corroborate
our research findings as to whether the uptake of AMTs had improved
productivity, cooperation and communication and also whether it had
necessitated further training or even resulted in layings-off.
Uptake of graduate engineers
This section primarily concerned itself with determining whether the integration
of AMTs had affected the uptake of graduate engineers within industry. The
questions aimed at assessing how the process of employing engineers fresh from
school had been changed, i.e. the frequency of employment, the need for
training, etc.
Roles and responsibilities
In this section, we aim at determining the scale to which roles of responsibilities
of technical workers had been affected by the uptake and integration of AMTs.
This involved a rather general assessment, ideally to be given by the foreman
on the workshop floor.
13
Expected consequences
For the final section of our questionnaire, we aimed at determining what
expected consequences engineers were to face as a result of increased
integration of AMTs in industry. The purpose of this section was to contrast the
responses with what has already been researched, particularly in the West and
the Far East.
3.6. Data Analysis
The data collected during our study was collated and analysed using Microsoft Excel. Our
decision to employ Excel stems from the software’s simple user interface, flexibility and its
ability to handle complex statistical calculations as demanded by the scale of our study. Further
details are provided in Chapter 4.
14
CHAPTER FOUR: RESULTS AND DATA ANALYSIS
4.1. Introduction
This chapter involves a summary of the results obtained from the undertaken survey; the use
of Excel Spreadsheet for analysis was due to its various data analysis tools such as descriptive
statistics in form of histograms, bar charts, pie charts as well as its ability to do measures of
central tendency such as mode, mean, median and variance. The sub-sectors that were
researched included are: Food and beverage, automotive, fabricated metals, construction and
material, chemical and pharmaceutical, textile and apparel, plastics and packaging.
4.2. Response Rate and Time
As initially mentioned we employed physical presentation of the questionnaire as our principal
means of data collection with the response rate from our contacts standing at 57% of the overall
in light of the aforementioned constraints. We also employed web-based surveys, although this
option was not exercised by any of our respondents. Responses from the various contacted
companies varied mainly depending on factors such as company bureaucracy and availability
of respondents (production managers or their equivalents), with some taking longer times and
others not responding at all.
4.3. Data Quality and Cost
Jelke Bethlehem et al (2008) warn against use of response rate as the only indicator of quality
of survey data. As such the quality of data is also affected by the manner of filling in of the
variables in the questionnaire. In our case, the respondents did attempt to give all the necessary
required answers. Much of the cost involved printing of the questionnaire material as well as
transport logistics to the field.
4.4. Research Questions
4.4.1. Research Question 1
The question read: Which of the following Advanced Manufacturing Technologies (AMTs)
does your firm utilise for operations?
This question sought to establish a basic understanding of the extent to which our sample
companies had interacted with AMTs in their operations. We sought to determine the most
prevalent AMTs used in manufacturing companies across several manufacturing sectors. Some
of the AMTs represented include CAD, CAE, JIT, CIM, FMS, LOOP, MRP 1, MRP 2, CAQM,
SPC, CNC, SMT, FMC, AMH, APM, API, BARCODE and CAM which we asked our
respondents to specify if need be.
The analysis was done with the statistics represented in frequency tables as shown below,
together with pie charts and a bar graph for the same.
15
ACROSS ALL
SUB-
SECTORS AMTs USED FREQUENCY
FREQUENCY
OF USE IN %
OF USE ACROSS
ALL SUB-
SECTORS
CAD 6 19.35
MRP 1 8 25.81
BARCODE 4 12.90
MRP 2 5 16.13
JIT 4 12.90
APM 3 9.68
OTHERS 1 3.20
CUMULATIVE
FREQUENCY 31 100
Table 4.1: Table of the AMTs used by companies.
Figure 4.1: A pie-chart of
frequency of the number of
firms in percentage with
regard to AMT usage
19%
26%
13%
16%
13%
10%3%
PIE CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE
WITH REGARDS TO AMT USAGE BY COMPANIES
CAD MRP 1 BARCODE MRP 2 JIT APM OTHERS
16
Figure 4.2: Bar graph of the type of AMTs used by companies.
4.4.2. Research Question 2
This question read: For how long has your Company used the above mentioned AMTs?
This question sought to find out the time period for which the respective manufacturing entities
had put the aforementioned technologies to use in their various manufacturing processes: this
would in turn provide a sneak peek into how long it had taken for integration of AMTs into the
company manufacturing processes.
The various responses were as shown in the tables below, with representation being done in
numbers as follows: [1]: 1-2 yrs., [2]:3-4 yrs., [3]:5-6 yrs., [4]:7-8 yrs., [5]:9+yrs.
This representation was then shown statistically via frequency tables as well as pie-charts and
a bar graph for the same.
0
5
10
15
20
25
30
CAD MRP 1 BARCODE MRP 2 JIT APM OTHERS
FREQ
UEN
CY
IN P
ERC
ENTA
GE
AMT USED
BAR GRAPH OF FREQUENCY OF USE IN PERCENTAGE VERSUS AMT USED
Across sub-all sectors it was noted that MRP 1 leads the way at 25.81%, followed by CAD at
19.35% MRP 2 at 16.13%, BARCODE and JIT at 12.90%.
17
Table 4.2: Table of how long companies have used AMTs.
Table 4.3: Table of General statistical data of
how long companies have used AMTs.
ACROSS ALL SUB-
SECTORS
RESPONSE
FREQUENCY IN
TERMS OF NO. OF
FIRMS FREQUENCY IN (%)
NEVER 1 6.25
1 TO 2 1 6.25
3 TO 4 2 12.50
5 TO 6 4 25
7 TO 8 2 12.50
9+ 6 37.50
CUMULATIVE
FREQUENCY 16 100
GENERAL
STATISTICAL
DATA
ACROSS ALL
SUB-
SECTORS
MEAN 3.44
MODE 5
VARIANCE 2.37
STANDARD
DEVN 1.54
MAXIMUM 5
MINIMUM 0
RANGE 5
18
Figure 4.3: A pie-chart of frequency of number of firms in percentage with regard to number
of years of AMT usage
Figure 4.4: Bar graph of how long companies have used AMTs.
6%6%
12%
25%
13%
38%
PIE CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH REGARD
TO NO. OF YEARS OF AMT USAGE
NEVER 1 TO 2 3 TO 4 5 TO 6 7 TO 8 9+
0
5
10
15
20
25
30
35
40
NEVER 1 TO 2 3 TO 4 5 TO 6 7 TO 8 9+
FREQ
UEN
CY
OF
NO
.OF
FIR
MS
IN
PER
CEN
TAG
E
NO. OF YEARS
BAR GRAPH OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE VERSUS NO. OF YEARS OF AMT
USAGE
Across all sub-sectors, it was found out that most companies actually had an interaction with AMTS
for 9+ years (37.5%), followed by an interaction time of 5-6 years (25%).
19
4.4.3. Research Question 3
This question read: To what extent have these AMTs been integrated into the firm’s
operations?
In this case we simply sought to know the level of integration of AMTs to firm operations.
Once again numbers were used to represent the various responses as shown in the tables below:
[1] - To a great extent, [2]-To a moderate extent, [3]-Neutral, [4]-To a small extent, [5]-
Negligible extents. Frequency variables were also represented in tables with pie-charts and a
bar graph used to show the visual representation.
Table 4.4: Table of extent of integration of AMTs in firms’ operations.
ACROSS ALL
SUB-SECTORS
RESPONSE
FREQUENCY IN
TERMS OF NO.
OF FIRMS FREQUENCY (%)
GREAT EXTENT 11 68.75
MODERATE
EXTENT 3 18.75
NEUTRAL
SMALL EXTENT 1 6.25
NEGLIGIBLE
EXTENT 1 6.25
CUMULATIVE
FREQUENCY 16 100
20
Figure 4.5: A pie-chart of frequency in percentage of number of firms with regard to extent
Figure 4.6: Bar graph of extent of integration of AMTs in firms’ operations.
69%
19%
6%6%
PIE CHART OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS WITH
REGARD TO EXTENT OF RESPONSE
GREAT EXTENT MODERATE EXTENT NEUTRAL
SMALL EXTENT NEGLIGIBLE EXTENT
01020304050607080
GREAT EXTENT MODERATEEXTENT
NEUTRAL SMALL EXTENT NEGLIGIBLEEXTENT
FREQ
UEN
CY
OF
NO
. OF
FIR
MS
IN
PER
CEN
TAG
E
EXTENT OF RESPONSE
BAR GRAPH OF FREQUENCY OF NUMBER OF FIRMS IN PERCENTAGE VERSUS EXTENT OF
RESPONSE
Across all sectors it was determined that most respondents had integrated AMTs into their
operations to a great extent (68.75%), followed by those who had done so to a moderate extent
(18.75%), with the others following.
21
4.4.4. Research Question 4
This question read: How have AMTs influenced the productivity of your firm over the past
10 years? (Where possible please provide relevant data)
The question sought to know the extent to which the respondent firms’ productivity had been
influenced for the past decade. None of the firms was able to furnish us with relevant data to
back up their responses. Once more numbers were used to represent the various responses as
delineated in the tables below as follows: [1] - To a great extent, [2]-To a moderate extent, [3]-
Neutral, [4]-To a small extent, [5]-Negligible extent. Frequency tables were also created with
pie-charts and a bar-graph used to do the visualization of the same.
Table 4.5: Table of how AMTs have influenced productivity over the past 10 years.
ACROSS
ALL SUB-
SECTORS
RESPONSE
FREQUENCY
IN TERMS
OF NO. OF
FIRMS FREQUENCY (%)
GREAT
EXTENT 12 75
MODERATE
EXTENT 2 12.5
NEUTRAL
SMALL
EXTENT 1 6.25
NEGLIGIBLE
EXTENT 1 6.25
CUMULATIVE
FREQUENCY 16 100
22
GENERAL DATA ON ALL SUB-SECTORS
MEAN 1.56
MODE 1
VARIANCE 1.37
STANDARD
DEVN 1.17
RANGE 4
MINIMUM 1
MAXIMUM 5
Table 4.6: Table of General Data of how AMTs have influenced productivity over the past 10
years.
Figure 4.7: A pie-chart of frequency of number of firms in percentage with regard to extent of
productivity
75%
13%
6%6%
PIE CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH
REGARD TO EXTENT OF PRODUCTIVITY
GREAT EXTENT MODERATE EXTENT NEUTRAL
SMALL EXTENT NEGLIGIBLE EXTENT
23
Figure 4.8: Bar graph of how AMTs have influenced productivity over the past 10 years.
4.4.5. Research Question 5
4.4.5. Research Question 5
This question read: 10 years ago, what was the size of your manufacturing workforce in
terms of:
i. Qualified Engineers
In this case we simply sought to know the number of employed Engineers our respondents had
in their firms 10 years ago.
The responses were statistically represented by numbers as follows: [1]0-2, [2]3-4, [3]5-6, [4]7-
8, [5]9+. Frequency tables were subsequently drawn up with pie-charts and a bar-graph
following thereafter to show the representation, as shown below:
0
10
20
30
40
50
60
70
80
GREAT EXTENT MODERATEEXTENT
NEUTRAL SMALL EXTENT NEGLIGIBLEEXTENTFR
EQU
ENC
Y O
F N
O.O
F FI
RM
S IN
P
ERC
ENTA
GE
EXTENT OF PRODUCTIVITY
BAR GRAPH OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS VERSUS EXTENT OF PRODUCTIVITY
Across all sub-sectors, a majority 75% of the respondents attributed to AMTs influencing their
productivity to a great extent followed by the moderates at 12.5%, with the others following in
order.
24
Table 4.7: Table of size of workforce in terms of qualified Engineers 10 years ago.
Table 4.8: Table of General Statistical Data size of workforce in terms of qualified Engineers
10 years ago.
ACROSS ALL SUB-
SECTORS
RESPONSE
FREQUENCY IN
TERMS OF NO. OF
FIRMS FREQUENCY (%)
0 TO 2 5 31.25
3 TO 4 6 37.50
5 TO 6 2 12.50
7 TO 8 2 12.50
9+ 1 6.25
CUMULATIVE
FREQUENCY 16 100
GENERAL
STATISTICAL
DATA
MEAN 2.25
MODE 2
VARIANCE 1.44
STANDARD DEVN 1.20
RANGE 4
MINIMUM 1
MAXIMUM 5
25
Figure 4.9: A pie-chart of frequency of number of firms with regard to number of qualified
engineers
Figure 4.10: Bar graph of size of workforce in terms of qualified Engineers 10 years ago.
31%
37%
13%
13%
6%
PIE CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH REGARD TO RANGE OF QUALIFIED ENGINEERS
10 YEARS AGO
0 TO 2 3 TO 4 5 TO 6 7 TO 8 9+
0
5
10
15
20
25
30
35
40
0 TO 2 3 TO 4 5 TO 6 7 TO 8 9+
FREQ
UEN
CY
IN P
ERC
ENTA
GE
OF
NO
. OF
FIR
MS
RANGE OF QUALIFIED ENGINEERS 10 YEARS AGO
BAR GRAPH OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS VERSUS RANGE OF QUALIFIED
ENGINEERS 10 YEARS AGO
Across all sub-sectors it was determined that majority of the respondents had 3-4 qualified
engineers (37.50%), followed by another 31.25% who indicated to have had between 0-2 engineers
during that period. This was rounded off by 12.5% of the respondents who had between 5-6 and 7-8
engineers. Only 6.25% of the respondents were said to have 9+ engineers during that time.
26
ii. Blue-collar technicians
The question sought to know the number of blue collar technicians employed in the respondent
firms a decade ago. Numbers were assigned to represent the responses as follows: [1]: 0-10
[2]:11-20 [3]:21-30 [4]:31-40 [5]:40+. Statistical tables were then used to represent the
variables and the visual impression represented with pie-charts and bar graphs.
Table 4.9: Table of the size of workforce in terms of blue collar technicians 10 years ago.
Table 4.10: Table of general statistical of data the size of workforce in terms of blue collar
technicians 10 years ago.
ACROSS ALL SUB- SECTORS
RESPONSE
FREQUENCY IN TERMS OF NO.
OF FIRMS FREQUENCY (%)
0 TO 10 6 37.50
11 TO 20 4 25
21 TO 30 3 18.75
31 TO 40 2 12.50
40+ 1 6.25
TOTAL 16 100
GENERAL
STATISTIC
AL DATA
ACROSS
ALL SUB-
SECTORS
MEAN 2.25
MODE 1
VARIANCE 1.56
STANDARD
DEVN 1.25
RANGE 4
MINIMUM 1
MAXIMUM 5
27
Figure 4.11: A pie-chart of frequency of number of firms in percentage with regard to number
of blue-collar technicians
Figure 4.12: Bar graph of the size of workforce in terms of blue collar technicians 10 years
ago.
37%
25%
19%
13%
6%
PIE CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH REGARD
TO RANGE OF BLUE-COLLAR TECHNICIANS 10 YEARS AGO
0 TO 10 11 TO 20 21 TO 30 31 TO 40 40+
0
5
10
15
20
25
30
35
40
0 TO 10 11 TO 20 21 TO 30 31 TO 40 40+
FREQ
UEN
CY
IN P
ERC
ENTA
GE
OF
NO
. OF
FIR
MS
RANGE OF BLUE-COLLAR TECHNICIANS 10 YEARS AGO
BAR GRAPH OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS VERSUS RANGE OF BLUE-COLLAR
TECHNICIANS 10 YEARS AGO
Across all the sub-sectors, most respondents (37.5%) of the total, disclosed that they had between
0-10 technicians, followed by another 25% who indicated a team of between 11-20, 18.75% positing
between 21-30,12.50% indicating between 31-40 and lastly 6.25% having a team of 40+ technicians.
28
4.4.6. Research Question 6
4.4.6 Research Question 6:
This question read: At present, how much of your workforce are:
i. Qualified engineers
We sought to know the present workforce in the respondent firms in terms of qualified
Engineers.
The responses were statistically represented by numbers as follows: [1]0-2, [2]3-4, [3]5-6, [4]7-
8, [5]9+. Frequency tables were subsequently drawn up with a bar-graph to show the
representation, as shown below:
Table 4.11: Table of the size of workforce in terms of qualified Engineers at the present.
Table 4.12: Table of general statistical data of the size of workforce in terms of qualified
Engineers at present.
ACROSS ALL SUB- SECTORS
RESPONSE
FREQUENCY IN TERMS OF NO. OF
FIRMS FREQUENCY (%)
0 TO 2 2 12.50
3 TO 4 6 37.50
5 TO 6 4 25
7 TO 8 3 18.75
9+ 1 6.25
TOTAL 16 100
GENERAL
STATISTIC
AL DATA
ACROSS
ALL SUB-
SECTORS
MEAN 2.69
MODE 2
VARIANCE 1.21
STANDARD DEVN 1.1
RANGE 4
MINIMUM 1
MAXIMUM 5
29
Figure 4.13: A pie-chart of frequency of number of firms in percentage with regard to number
of qualified engineers at present
Figure 4.14: Bar graph of the size of workforce in terms of qualified Engineers at the present.
12%
38%
25%
19%
6%
PIE-CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH REGARD TO RANGE OF QUALIFIED ENGINEERS
CURRENTLY
0 TO 2 3 TO 4 5 TO 6 7 TO 8 9+
0
5
10
15
20
25
30
35
40
0 TO 2 3 TO 4 5 TO 6 7 TO 8 9+
FREQ
UEN
CY
OF
NO
. OF
FIR
MS
IN
PER
CEN
TAG
E
RANGE OF QUALIFIED ENGINEERS AT PRESENT
BAR GRAPH OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS VERSUS RANGE OF QUALIFIED
ENGINEERS AT PRESENT
For all respondents, it was determined that most (37.5%) had a current work-force of between 3-4
engineers, followed by 25% who indicated to have one of between 5-6 engineers, 18.75% who
indicated one of between 7-8 engineers, 12.50% who had one of between 0-2 engineers and finally
6.25% who had a work-force of 9+ engineers.
30
ii. Blue-collar technicians
We sought to know the current workforce in the respondent firms in terms of blue collar
technicians. Numbers were again chosen to represent the responses as follows: [1]: 0-10 [2]:11-
20 [3]:21-30 [4]:31-40 [5]:40+ with statistical tables being used to represent the variables and
the visual impression coming from bar graphs.
ACROSS ALL SUB-SECTORS
RESPONSE
FREQUENCY IN TERMS OF NO.
OF FIRMS FREQUENCY (%)
0 TO 10 3 18.75
10 TO 20 5 31.25
20 TO 30 4 25
30 TO 40
OVER 40 4 25
TOTAL 16 100
Table 4.13: Table of the size of workforce in terms of blue collar technicians at the present.
Table 4.14: Table of general statistical data of the size of workforce in terms of blue collar
technicians at the present.
GENERAL
STATISTICAL
DATA
ACROSS ALL
SUB-
SECTORS
MEAN 2.81
MODE 2
VARIANCE 2.03
STANDARD DEVN 1.42
RANGE 4
MINIMUM 1
MAXIMUM 5
31
Figure 4.15: A pie-chart of frequency of number of firms in percentage with regards to number
of blue-collar technicians
Figure 4.16: Bar graph of the size of workforce in terms of blue collar technicians at the present.
19%
31%
25%
25%
PIE-CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH
REGARDS TO RANGE OF BLUE COLLAR TECHNICIANS CURRENTLY
0 TO 10 10 TO 20 20 TO 30 30 TO 40 OVER 40
0
5
10
15
20
25
30
35
0 TO 10 10 TO 20 20 TO 30 30 TO 40 OVER 40
FREQ
UEN
CY
OF
NO
. OF
FIR
MS
IN
PER
CEN
TAG
E
RANGE OF NO. OF BLUE COLLAR TECHNICIANS CURRENTLY
BAR GRAPH OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE VERSUS RANGE OF BLUE COLLAR
TECHNICIANS CURRENTLY
Across all firms that were respondent, 31.25% who were the majority indicated to have a current
work-force of blue collar technicians of between 10-20, with another 25% indicating one of 40+ and
20-30 each and 18.75% indicating one of between 0-10.
32
4.4.7. Research Question 7
This question read: Of the present workforce, how many have been trained:
i. Locally:
In this case we sought to know what representation of the current workforce in the
aforementioned firms had attained their professional qualification locally.
The data was represented with variable responses in terms of numbers as follows: [1]0-5, [2]6-
10, [3]11-15, [4]16-20, [5] 20+. Frequency tables were then drawn up with pie-charts and a
bar-graph following thereafter to show the representation, as shown below:
Table 4.15: Table of general statistical data of the personnel that have been trained locally.
GENERAL
STATISTIC
AL DATA
ACROSS
ALL SUB-
SECTORS
MEAN 3.62
MODE 5
VARIANCE 2.11
STANDARD DEVN 1.45
RANGE 4
MINIMUM 1
MAXIMUM 5
33
Table 4.16: Table of the personnel that have been trained locally.
Figure 4.17: A pie-chart of frequency of number of firms with regard to number of personnel
trained locally
12%
12%
19%
13%
44%
PIE-CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH REGARD
TO RANGE OF PERSONNEL TRAINED LOCALLY
0 TO 5 6 TO 10 11 TO 15 16 TO 20 OVER 20
ACROSS ALL SUB-SECTORS
RESPONS
E
FREQUENCY IN TERMS OF
NUMBER OF FIRMS FREQUENCY (%)
0 TO 5 2 12.50
6 TO 10 2 12.50
11 TO 15 3 18.75
16 TO 20 2 12.5
OVER 20 7 43.75
TOTAL 16 100
34
Figure 4.18: Bar graph of the personnel that have been trained locally.
ii. Abroad:
In this question, we sought to find out from our respondents the share of their current workforce
that had attained their professional training abroad.
Numbers were again chosen to represent the responses as follows: [1]0-5, [2]6-10, [3]11-15,
[4]16-20, [5] 20+, with frequency tables being drawn up and pie-charts and a bar-graph
following thereafter to show the representation, as shown below:
0
5
10
15
20
25
30
35
40
45
50
0 TO 5 6 TO 10 11 TO 15 16 TO 20 OVER 20FREQ
UEN
CY
OF
NO
. OF
FIR
MS
IN
PER
CEN
TAG
E
RANGE OF PERSONNEL TRAINED LOCALLY
BAR GRAPH OF FREQUENCY OF NO. OF FIRMS VERSUS RANGE OF PERSONNEL TRAINED
LOCALLY
Across all sub- sectors, a 43.75% majority indicated to have a locally trained work-force of 20+
employees, with 18.75% indicating one of between 11 to 15, and a tie of 12.50% emerging between
firms that indicated to have between 0-5, 6-10 and 16-20 employees.
35
Table 4.17: Table of the personnel that have been trained abroad.
GENERAL
STATISTICAL
DATA
ACROSS ALL
SUB-
SECTORS
MEAN 1.31
MODE 1
VARIANCE 0.21
STANDARD DEVN 0.46
RANGE 1
MINIMUM 1
MAXIMUM 2
Table 4.18: Table of the general statistical data of the personnel that have been trained abroad.
ACROSS ALL SUB-SECTORS
RESPONSE
FREQUENCY IN TERMS OF NO.OF
FIRMS FREQUENCY (%)
0 TO 5 11 68.75
6 TO 10 5 31.25
11 TO 15
16 TO 20
OVER 20
TOTAL 16 100
36
Figure 4.19: A pie-chart of frequency of number of firms with regard to personnel trained
abroad
Figure 4.20: Bar graph of the personnel that have been trained abroad.
69%
31%
PIE CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH
REGARDS TO PERSONNEL TRAINED ABROAD
0 TO 5 6 TO 10 11 TO 15 16 TO 20 OVER 20
0
10
20
30
40
50
60
70
80
0 TO 5 6 TO 10 11 TO 15 16 TO 20 OVER 20FREQ
UEN
CY
OF
NO
. OF
FIR
MS
IN P
ERC
ENTA
GE
RANGE OF PERSONNEL TRAINED ABROAD
BAR GRAPH OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE VERSUS RANGE OF
PERSONNEL TRAINED ABROAD
Across all sub-sectors, a 68.75% majority indicated to have between 0-5 employees trained abroad
with 31.25% indicating to have 6-10 of theirs trained abroad.
37
4.4.8. Research Question 8
This question read: How have your engineering departments been affected due to the
assimilation of AMTs? (Where possible, please provide relevant data)
We sought to determine the impact that assimilation of AMTs had on the respondent
manufacturing firms. None of the firms was able to provide us with relevant data to back up
their responses.
We used numbers to represent the variable responses as follows: [1] - To a great extent, [2]-To
a moderate extent, [3]-Neutral, [4]-To a small extent, [5]-Negligible extent. We then drew up
statistical tables together with pie-charts and a bar-graph to show the visual representation of
the same as shown below:
Table 4.20: Table of how Engineering departments have been affected by assimilation of
AMTs.
GENERAL
STATISTICAL
DATA
MEAN 2.31
MODE 1
VARIANCE 1.71
STANDARD DEVN 1.31
RANGE 4
MINIMUM 1
MAXIMUM 5
Table 4.21: General statistical data of how Engineering departments have been affected by
assimilation of AMTs.
ACROSS ALL SUB-
SECTORS
RESPONSE
FREQUENCY IN TERMS
OF NO. OF FIRMS FREQUENCY (%)
Great 6 37.50
Moderate 4 25
Neutral 2 12.50
Small 3 18.75
Negligible 1 6.25
TOTAL 16 100
38
Figure 4.21: A pie-chart of frequency of number of firms with regard to effect on engineering
departments
Figure 4.22: Bar graph of how Engineering departments have been affected by assimilation of
AMTs.
37%
25%
13%
19%
6%
PIE CHART OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS WITH REGARDS TO EFFECT ON
ENGINEERING DEPARTMENTS BY AMT ADOPTION
Great Moderate Neutral Small Negligible
0
5
10
15
20
25
30
35
40
Great Moderate Neutral Small
FREQ
UEN
CY
IN T
ERM
S O
F N
O. O
F FI
RM
S IN
P
ERC
ENTA
GE
RESPONSE
BAR GRAPH OF FREQUENCY IN TERMS OF NO. OF FIRMS IN PERCENTAGE VERSUS RESPONSE
Across all sub-sectors, a majority of 37.50% indicated to their engineering departments having had a
great effect upon their operations with the said assimilation, with another 25% indicating a
moderate effect upon their operations, 18.75% indicating a small effect upon their departments,
12.50% indicating a neutral effect and 6.25% indicating a negligible change in theirs.
39
4.4.9. Research Question 9
This question read: Has the adoption of AMTs necessitated a change in hierarchical
structure for your firm? (Where possible, please provide relevant data)
We sought to know from the respondents to what extent integration of AMTs in firms had
necessitated a change in the hierarchical structure of their firms. None of the firms was able to
provide us with relevant data to back up their responses.
Once more numbers were used to represent the various responses as delineated in the tables
below as follows: [1] - To a great extent, [2]-To a moderate extent, [3]-Neutral, [4]-To a small
extent, [5]-Negligible extent. Frequency tables were also created with pie-charts and a bar-
graph also drawn to represent the data visually.
ACROSS ALL SUB-SECTORS
RESPONS
E
FREQUENCY IN TERMS OF NO.
OF FIRMS FREQUENCY (%)
Great 4 25
Moderate 4 25
Neutral 2 12.50
Small 1 6.25
Negligible 5 31.25
TOTAL 16 100
Table 4.22: Table of how adoption of AMTs have necessitated a change in hierarchical
structure of organisations.
GENERAL
STATISTIC
AL DATA
ACROSS
ALL SUB-
SECTORS
MEAN 2.88
MODE 5
VARIANCE 2.48
STANDARD DEVN 1.58
RANGE 4
MINIMUM 1
MAXIMUM 5
Table 4.23: Table of general statistical data of how adoption of AMTs have necessitated a
change in hierarchical structure of organisations.
40
Figure 4.23: A pie-chart of frequency of number of firms with regard to effect on hierarchical
structure
Figure 4.24: Bar graph of how adoption of AMTs have necessitated a change in hierarchical
structure of organisations.
25%
25%
13%
6%
31%
PIE CHART OF FREQUENCY IN TERMS OF NO.OF FIRMS IN PERCENTAGE
WITH REGARDS TO EFFECT OF AMTs ON FIRM HIERARCHICAL STRUCTURE
Great Moderate Neutral Small Negligible
0
5
10
15
20
25
30
35
Great Moderate Neutral Small Negligible
FREQ
UEN
CY
IN P
ERC
ENTA
GE
IN T
ERM
S O
F N
O. O
F FI
RM
S
RESPONSE
BARGRAPH OF FREQUENCY IN TERMS OF NO. OF FIRMS IN PERCENTAGE VERSUS RESPONSE
Across all sub-sectors, a majority of 31.25% indicated a negligible change in their company hierarchy
as a result of AMT assimilation, 25% indicating a great and moderate change in their hierarchical
structure, 12.50% indicating a neutral change and 6.25% indicating a small change in their
hierarchical structure as a result of AMT adoption.
41
4.4.10. Research Question 10
This question read: With the increased assimilation of AMTs into the production process,
what effects has your firm projected on the following where possible, please provide
relevant data):
i. Staff size
Here, we sought to find out from the firms what their projected staff sizes would look like in
future as a result of increased uptake of AMTs into their operations. None of our respondents
exercised the option of providing relevant quantitative data.
We again used numbers to represent the variable responses as follows: [1]-Significant effect,
[2]-Moderate effect, [3]-Neutral, [4]-Small effect, [5]-Negligible effect. We followed up by
drawing up statistical tables together with pie-charts and a bar-graph to show the visual
representation of the same as shown below:
ACROSS ALL SUB-SECTORS
RESPONS
E
FREQUENCY IN TERMS OF
NO. OF FIRMS FREQUENCY (%)
Significant 2 12.50
Moderate 11 68.75
Neutral
Small 1 6.25
Negligible 2 12.50
TOTAL 16 100
Table 4.24: Table of firms’ projection on staff size due to assimilation of AMTs.
42
GENERAL
STATISTICAL
DATA
ACROSS ALL
SUB-
SECTORS
MEAN 2.38
MODE 2
VARIANCE 1.36
STANDARD DEVN 1.17
RANGE 4
MINIMUM 1
MAXIMUM 5
Table 4.25: Table of general statistical data of firms’ projection on staff size due to assimilation
of AMTs.
Figure 4.25: A pie-chart of frequency of number of firms with regard to staff size projection
12%
69%
6%
13%
PIE-CHART OF FREQUENCY IN PERCENTAGE OF NO.OF FIRMS WITH REGARD TO PROJECTION ON STAFF
SIZE AS A RESULT OF AMT ADOPTION
Significant Moderate Neutral Small Negligible
43
Figure 4.26: Bar graph of firms’ projections on staff size due to assimilation of AMTs.
0
10
20
30
40
50
60
70
80
Significant Moderate Neutral Small NegligibleFREQ
UEN
CY
IN P
ERC
ENTA
GE
OF
NO
. OF
FIR
MS
RESPONSE
BARGRAPH OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS VERSUS RESPONSE
A majority 68.75% of the total respondents across the sectors proposed a moderate future projected
increase in staff size as a result of continued AMT adoption, with 12.50% proposing a significant and
negligible future effect respectively whilst 6.25% suggested a small effect on staff size as a result of
increased AMT adoption.
44
ii. Training costs
In this case, we sought to find out the projected future effect on training costs of contacted
firms as a result of continued assimilation of AMTs in firm operations. In this case, we also
lacked supporting documents from the firms with regard to the given responses.
Numbers to represent the variable responses as follows: [1]-Significant effect, [2]-Moderate
effect, [3]-Neutral, [4]-Small effect, [5]-Negligible effect. Statistical tables were then drawn
up to represent the frequencies, followed by pie-charts and a bar-graph for visual representation
as shown below:
Table 4.26: Table of firms’ projections on training costs due to assimilation of AMTs.
Table 4.27: Table of general statistical data of firms’ projections on training costs due to
assimilation of AMTs.
ACROSS ALL SUB-
SECTORS
RESPONSE
FREQUENCY OF
NUMBER OF FIRMS IN
PERCENTAGE FREQUENCY (%)
Significant 6 37.50
Moderate 7 43.75
Neutral 1 6.25
Small 1 6.25
Negligible 1 6.25
TOTAL 16 100
GENERAL
STATISTICAL
DATA
ACROSS
SUB-
SECTORS
MEAN 2
MODE 2
VARIANCE 1.25
STANDARD DEVN 1.12
RANGE 4
MINIMUM 1
MAXIMUM 5
45
Figure 4.27: A pie-chart of frequency of number of firms with regard to projected training costs
Figure 4.28: Bar graph of firms’ projections on training costs due to assimilation of AMTs.
38%
44%
6%
6%6%
PIE CHART OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS WITH REGARD TO ANTICIPATED
EFFECT ON TRAINING COSTS AS A RESULT OF AMT ADOPTION
Significant Moderate Neutral Small Negligible
0
5
10
15
20
25
30
35
40
45
50
Significant Moderate Neutral Small Negligible
FREQ
UEN
CY
IN P
ERC
ENTA
GE
OF
NO
. OF
FIR
MS
RESPONSE
BAR GRAPH OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE VERSUS RESPONSE
Across all company sub-sectors, a majority of 43.75% indicated a projected moderate effect on
future firm training costs, followed by 37.50% who indicated a significant effect on future firm
training costs as a result of the same, with 6.25% indicating a neutral, small and negligible future
effect on training costs as a result of the same.
46
iii. Qualifications for skilled labour:
We sought to know how continued integration of AMTs in firm operations would affect the
required qualifications needed from potential employees: would they require to be more
skilled?
We assigned numbers to represent the variable responses as follows: [1]-Significant effect, [2]-
Moderate effect, [3]-Neutral, [4]-Small effect, [5]-Negligible effect. We then followed up our
analysis by drawing up statistical tables, and concluded by coming up with pie-charts and a
bar-graph for visual representation as shown:
Table 4.28: Table of firms’ projections on requirement for qualifications for skilled labour due
to assimilation of AMTs.
ACROSS ALL SUB-
SECTORS
RESPONSE
FREQUENCY IN
PERCENTAGE IN TERMS
OF NO. OF FIRMS FREQUENCY (%)
Significant 8 50
Moderate 4 25
Neutral 3 18.75
Small
Negligible 1 6.25
TOTAL 16 100
47
Figure 4.29: A pie-chart of frequency of number of firms with regard to projected effect on
skilled labour
Figure 4.30: Bar graph of firms’ projections on requirement for qualifications for skilled labour
due to assimilation of AMTs.
50%
25%
19%
6%
PIE CHART OF FREQUENCY OF NO. OF FIRMS IN PERCENTAGE WITH REGARD TO
PROJECTED EFFECT OF AMT ADOPTION ON QUALIFICATIONS FOR SKILLED LABOUR
Significant Moderate Neutral Small Negligible
0
10
20
30
40
50
60
Significant Moderate Neutral Small Negligible
FREQ
UEN
CY
IN P
ERC
ENTA
GE
OF
NO
. OF
FIR
MS
RESPONSE
BARGRAPH OF FREQUENCY IN PERCENTAGE OF NO. OF FIRMS VERSUS RESPONSE
Across all sub-sectors, half of the respondents projected a significant change in their skill
labour requirements, with a further 25% projecting a moderate change in the same,
followed by 18.75% who were ambivalent on a future change on the same, with 6.25%
projecting a negligible change in future skill requirements as a result of continued AMT
adoption by firms.
48
CHAPTER FIVE: DISCUSSION, CONCLUSION AND RECOMMENDATIONS
5.1. DISCUSSION
The focal point of our study was on AMT manufacturing companies. Due to the total number
of respondents to our survey (17), we opted to aggregate the collected data. This would help
provide a broad picture on the impact that AMTs have had on the manufacturing landscape in
the Greater Nairobi Area. However, in our analysis, clear trends emerged from two major sub-
sectors, i.e. Food & Beverage and Construction/Material. These are the largest manufacturing
sub-sectors in the Kenyan economy. We hence opted to expound further on these sub-sectors.
This allowed for a better understanding of these sub-sectors in terms of their structure and AMT
usage. The collected data hence serves the purpose of presenting a general understanding of
AMT usage and its effect on manufacturing companies, while showing how its effect on
Kenya’s two biggest sub-sectors deviates from the norm.
5.1.1. Interaction of Advanced Manufacturing Technologies by the Engineer
Our analysis of the manufacturing companies within the Greater Nairobi Area revealed a high
uptake of Automated Manufacturing Technologies, with 94.12% of our respondents utilising
some sort of automated technology in their operations. When aggregated, we determined that
the most common AMTs in industry were CAD, MRP1 and MRP2, which were used by
61.29% of our respondents. Within the Construction/Materials sub-sector, the most prevalent
automated technologies were Computer Aided Design (CAD) and Material Requirement
Planning (MRP1). CAD is classified as a Production Design and Engineering Technology
(PDET) and enables designers to view objects under a variety of representations and test these
objects by simulating real-world conditions. It is mainly used at the beginning of the production
process. MRP1 is classified as a Production Planning Technology (PPT) and is concerned with
production scheduling and inventory control. It helps engineers to keep adequate inventory
levels to assure required materials are available when needed.
In the Food & Beverage sector, the most prevalent AMTs were BARCODE, MRP1, MRP2 and
CAQC. MRP2 is similarly classed with MRP1 as a PPT, and is used to coordinate the resources
needed for production and synchronisation of supply chains. Conversely, CAQC and
BARCODE are classified as AMTs. They are broadly used for repetitive functions and work
without permanent alteration of equipment. They are chiefly used at the tail-end of the
production process to assess finished products.
49
The majority of respondents had interacted with these AMTs for a minimum of 5 years (75%).
However, no clear trend emerged from our analysis of the Construction/Material sub-sector.
The respondents’ responses ranged from ‘Never’ to ‘9+ years’ at equal percentages (25%). We
attributed this disparity in interaction to the age and life cycle stage of the respective
respondents. The more-established companies were more likely to have interacted with AMTs.
Conversely, for the Food & Beverage sub-sector, there was a clearer trend to the same, with
66.67% of respondents having used AMTs in their operations for at least 7 years. This indicated
that the uptake and integration of AMTs was higher in the Food & Beverage industry than in
the other sub-sectors.
Put into context of our first objective, our analysis agrees with existing studies, for instance
Nyori and K’Obonyo (2015) that determined that Kenyan companies had begun adopting
AMTs, albeit at low levels. Material Handling Technologies were found to be the most
dominant, as most companies profiled were in the Food & Beverage sub-sector. Nonetheless,
comparisons with other markets revealed that the country is still lagging behind more
developed economies. For instance, 47% of middle-market manufacturing firms in the US
indicated current AMT usage (National Association of Manufacturing, 2015)
5.1.2. Effect of Advanced Manufacturing Technologies on Existing Labour, Skills and
Training in the Manufacturing Sector
When asked what the size of their workforces were 10 years ago, most of our respondents
indicated that they employed 3 to 4 qualified engineers (37.5%) and a maximum of 10 blue-
collar technicians (37.5%). From the outset it was clear that the companies had greater numbers
of blue-collar technicians as compared to qualified engineers. The sizes of the workforces 10
years ago can be attributed to the stage of the companies on the business cycle. We found that
most of our respondents were in the nascent stages of the business cycle (equivalent to the
introduction stage of the product life cycle) which are characterised by trying to obtain a
customer base for their products. For the companies, at this stage, the priority would be to
generate just enough income to breakeven and cover basic expenditure, including replacement
of capital assets, remunerations, etc. Hence, as smaller workforce would have helped minimise
recurrent expenditure.
On asking about the size of the workforces at present, we noted a general increase across all
sectors. The majority of our respondents currently have between 3 to 4 qualified engineers
(37.5%) and a blue-collar workforce numbering between 10 and 40 (81.25%). When broken
50
down by sector, the food and beverage sector exhibited an increase in both numbers of
engineers and blue-collar technicians. On average, the number of engineers increased from 0-
6 to about 5-8 whereas the number of technicians increased from about 0-10 to around 11-20.
For the fabricated metals industry, there was a no increase in the number of engineers (this
remained at around 3-4) while the blue-collar technicians’ numbers increased from 11-20 to
20-30 on average.
From our analysis, it was determined that most manufacturing firms had an average of 3-4
engineers in the preceding 10-year period. This number did not significantly change over the
10 years. Furthermore, there was a negligible increase in the numbers of both engineers and
blue-collar technicians. This is indicative of a low absorption rate of graduate engineers and
qualified engineers into the manufacturing sector. This is a trend that implies that graduates
and professionals may be altering career paths to other fields (Waithanji, 2002). Our findings
appear in line with the study done by Nyori and Ogutu (2015) that indicated only about 5% of
the workforce being trained engineers.
A comparison with more-developed economies indicates the situation is similar. For instance
the Asian engineering market, though experiencing a boon at present, is expected to suffer a
shortage of skilled engineers (Springasia, 2015). This is also the case in the US where the
growth in the sector is being hampered by a dearth in skilled labour force (Accenture, 2014).
This is due to a shift in the engineering sector where most employers are looking to replace
their workforce with skilled college or degree holders who are easily trained (Yussuf et al,
2005).
5.1.3. Effect of Advanced Manufacturing Technologies Adoption on Firms’
Organizational Structure
From our analysis, 37.50% of our respondents indicated that their uptake of AMTs had a great
effect on their engineering departments’ operations. 25% indicated a moderate effect upon their
operations, 18.75% indicating a small effect upon their departments, 12.50% indicating a
neutral effect and 6.25% indicating a negligible change in theirs. This effect can be linked to
the maturity stage of the companies we profiled. We determined that for the less-established
manufacturing companies, the uptake of AMTs proved integral to the departmental
organisation structure. For the bigger and more established firms, AMTs had no major effect
as they were primarily used to facilitate the production process. We sought to obtain
51
quantitative data to further corroborate our findings. However, our respondents were reluctant
to provide documented relevant data, as it was deemed confidential.
In our analysis of hierarchical change, a majority of 31.25% indicated a negligible change in
their company hierarchy as a result of AMT assimilation, 25% indicating a great and moderate
change in their hierarchical structure, 12.50% indicating a neutral change and 6.25% indicating
a small change in their hierarchical structure as a result of AMT adoption. This ties in to our
aforementioned findings. AMTs were found to only affect the engineering departments but had
very little effect on companies as a whole. Our respondents, however, were reluctant to provide
any relevant documented data to further prove our findings, as such data were deemed
confidential.
In the context of our objectives, our analysis is congruent with the work posited by Yussuf et
al (2005) who stated that AMT adoption generally does not affect decentralised organisational
structures. Most of our respondents exhibited hybrid organisational structure. However, their
use of AMTs appeared not to affect their structure either at present or in future. With regard to
changing of roles and responsibilities, it was noted that the adoption of AMTs necessitated
knowledge of bespoke software by engineers. Furthermore, some level of practical or
mechanical skills is critical for the engineer to survive in the new production environment.
5.1.4. Trends Analysis Based on Further AMT Adoption by Firms in the Sector
When asked about their projected trends regarding staff size, the majority of our respondents
(68.75%) expected a moderate increase in staff size as a result of increased AMT adoption.
12.50% expected either a significant or negligible effect whilst 6.25% suggested a small effect
on staff size as a result of increased AMT adoption. This indicated to us that the majority of
companies had stabilised their workforces, but recognised the need to possibly alter the size of
their workforces in future. This tied in with our earlier question regarding the change in
engineering departments. Where most companies have experienced changes in their
engineering departmental structure as a result of AMT adoption, they would prefer to retain
existing labour with a good understanding of the operations while supplementing this with new
workers to assist as the companies grow in scale.
Regarding training, firms were willing to train the existing workforce on any new production
methods introduced instead of employing new personnel. They would only employ one person
52
to train their current workforce. As a result of this, their projection for future staff increase was
moderate.
Most of companies were not planning to expand their operations by searching new areas to
invest in either within the country or abroad. This meant that the firms would not have a
massive recruitment in the near future.
Across all company sectors a majority of 43.75% indicated a projected moderate effect on
future firm training costs, followed by 37.50% who indicated a significant effect on future firm
training costs as a result of the same, with 6.25% indicating a neutral, small and negligible
future effect on training costs as a result of the same. Assimilation of AMTs in the companies
was happening at a slow rate and therefore firms didn’t project to spend much resources in
training. Failure to adopt high technology at a fast rate was evident in their inability to produce
quality goods and services compared to their counterparts in the Europe and west Asia.
In all the companies that were researched, 50% of all respondents projected a significant future
effect on qualifications for skilled labour as a result of increased AMT adoption in firms, 25%
indicated a moderate future effect on labour qualifications on firms, and 18.75% indicated a
neutral future effect whilst 6.25% projected a negligible future effect on qualifications of
skilled labour. Firms however showed interest in the benefits of assimilation AMTs in their
operations and were targeting to employ highly qualified personnel who can fit easily in current
technological in future.
5.2. CONCLUSIONS
From our study, the following conclusions were drawn:
1. The uptake of automated technologies in the Greater Nairobi Area, and in the country, is
fairly robust and is steadily growing. The most-utilised AMTs in industry are CAD, MRP1
and MRP2. This was attributed to their flexibility across different sectors and the ease with
which they could be grasped by trainees, which reduced the need for excessive training
costs.
2. With regard to the current workforces and skills challenges, we noted that employers in
the manufacturing sector generally tended towards having fewer engineers and more blue-
collar technicians. The rate of uptake of engineers to blue-collar technicians stood at 1:3
for the purposes of our study. This means that for every one engineer absorbed into
53
industry, three blue-collar technicians were absorbed. This indicated to us that practical or
hands-on skills were deemed more relevant for the manufacturing sector, as compared to
theoretical know-how. This corroborated our literary research, where we found that in
Japan, for instance, small and medium sized entities that employed CNC machines had
40% of their workforce adept in CNC training.
3. AMTs affected small and medium-sized companies greater than the more established firms
with regard to organisational structure and hierarchy. From our research and interviews
conducted with production engineers, we determined that the more established
manufacturing firms used automated processes to facilitate their already-established
operations. For the comparatively smaller firms, AMTs were deemed integral to their
operations, and their organisational structure was formulated to accommodate these
automated technologies.
4. With regard to absorption of freshly-qualified engineers into the manufacturing sector and
their deployment, we determined that the uptake of new engineers into the job market was
relatively low. This was because most of the manufacturing firms we profiled had achieved
maturity and were not considering a major uptake of new engineers. Moreover, with regard
to deployment, we found that qualified engineers were tasked with the design and
maintenance of manufacturing systems, whereas the blue collar personnel conducted most
of the day-to-day operations. This generally agreed with the literature we reviewed.
5. With regard to projected trends, the assimilation of AMTs was not expected to affect the
staff size of the manufacturing firms. Regarding training cost trends, most of the
companies forecasted moderate changes, indicating that they would not be hiring new
personnel who would require intensive training. Existing knowledge would be
supplemented by inexpensive refresher training programmes. The qualifications for skilled
labour were projected to change moderately for the manufacturing firms we profiled.
5.3. RECOMMENDATIONS
1. To counter the chasm in technical skills in the manufacturing sector, we propose that
there should be more investment channelled toward technology training by the National
and County governments, companies, universities & vocational training institutions.
2. We also recommend that more focus be directed to innovation, research and
development of advanced technologies by the relevant stakeholders: government,
organisational management, universities and vocational training institutions. This will
54
potentially drive the uptake of AMTs by local firms, as witnessed in Western
economies.
3. We would also recommend that engineering professionals and graduates take up the
issue of professional development to upgrade and keep abreast with the rapidly
changing technologies in the manufacturing scene.
4. Finally, we recommend that this study be carried out further for each subsector within
the wider manufacturing realm for a more detailed analysis of the same.
55
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APPENDICES
APPENDIX A: List of Manufacturing and Processing Companies in Kenya
Aberdares Beverage Limited
Africa Apparels EPZ Limited
Africa Spirit Co. Ltd
Afro Prime Industries Ltd
Agro Chemical & Food Company Ltd
Alan Dick & Company (EA) Ltd
Alpharama Ltd
Amiran Kenya Limited
Aristocrat Concrete Limited
Associated Vehicle Assemblers Limited
Bamburi Special Products Limited
Bata Shoe Company (Kenya) Limited
Bayer East Africa Limited.
Beta Healthcare International Limited
Bhachu Industries Ltd
Bilflex Industries
Biscept Limited
Bms Industries Ltd
C & P Shoe Industries Limited
Central Glass Industries Limited
Chloride Exide Kenya Ltd
Cooper Motor Corp (K) Limited
Corn Products (Kenya) Limited
Corrugated Sheets Limited
Cosmos Limited
Crywan Enterprises Ltd
D T Dobie & Company (K) Ltd
Decase Chemicals Ltd
Doshi Ironmongers Limited
East African Breweries Ltd
East African Portland Cement Company
Elgon Chemicals Limited
Elle Kenya Ltd
Eveready East Africa Limited
Fai Amarillo Limited
Foam Mattress Ltd
Galaxy Paints & Coatings Ltd
Galsheet Kenya Limited
General Motors East Africa Limited
Geopower Project Company Limited
Gish Holding Ltd
GlaxoSmithKline Ltd
Global Tea and Commodities (Kenya)
Gold Crown Foods EPZ Limited
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Grand Beverages Ltd
Harmony Foods (K) Limited
High Chem Industrials Africa Limited
Honeywell Industry
Iberafrica Power (EA) Limited
Jaydees Knitting Factory Ltd
Julijo Investment Ltd
Kakuzi Limited
Kambu Distillers Ltd
Kedstar Investment
Kefima Suppliers
Kenafric Bakery Limited
Kenpoly Manufacturers Limited
Kenya Clay Products Ltd
Kenya Electricity Generating Company
Kenya Gin Man. Ltd
Kenya Grange Vehicle Industries Limited
Kenya Power & Lighting Company Ltd
Kenya Seed Company Limited
Kenya Wine Argencies Ltd
Keroche Breweries Ltd
Kitale Industries Limited
Krystalline Salt Limited
Lab International Kenya Limited
Laborex Kenya Limited
London Distillers (K) Ltd
Lumat Company Ltd
Lyniber Suppliers Ltd
Mabati Rolling Mills Limited
Mafuko Industries Ltd
Maize Milling Company Limited
Manji Food Industries Limited
Marshalls (EA) Ltd
Mashwa Breweries
Mdi Limited
Metro Plastics (K) Ltd.
Mibbs Ventures
Milly Grain Millers Limited
Mombasa Maize Millers Kisumu Limited
Mombasa Maize Millers Limited
Mombasa Salt Works Ltd
Mzuri Sweet Limited
Nairobi Flour Mills Ltd
National Cereals and Produce Board
Nestle Foods (K) Limited
Omaera Pharmaceuticals Limited
Osho Chemical Industries Limited
Patialla Distillers(K) Ltd
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Pembe Flour Mills Ltd
Pen Bon (K) Ltd
Phillips Pharmaceuticals Limited
Polypipes Ltd
Premier Flour Mills Ltd
Procter & Allan (EA) Ltd
Pwani Oil Products Ltd
Rafiki Millers Ltd
Rhino Beverages Ltd
Rift Valley Textile Ltd
Ryce East Africa Limited
Sadolins Paints (EA) Ltd
Safepak Limited
Sai Pharmaceuticals Limited
Sandstorm (Africa) Limited
Shell Chemicals East Africa Limited
Shelys Africa Limited
Simba Colt Motors Ltd
Somochem (Kenya) Limited
Spin Knit Limited
Spinners & Spinners Ltd
Steel Africa Limited
Sunflag Textile & Knitwear Mills Ltd
Supaflo Flour Mills Limited
Super Foam Ltd
Surgipharm Limited
Swan Industries Ltd
Swan Millers Ltd (Kisumu)
Syngenta East Africa Limited
Texplast Industries Limited.
The Wrigley Co (E A) Ltd
Toyota East Africa Limited
Tsavo Power Company Limited
Twiga Chemical Industries Ltd
Umoja Rubber Products Limited
Unga Farm Care (EA) Limited
Unga Group Ltd
United Aryan (EPZ) Limited
United Chemical Industries Ltd
United Millers Ltd
Uzuri Foods Ltd
Uzuri Manufacturers Limited
Vajas Manufacturers Ltd
Van Rees B V
Wartsila Eastern Africa Limited
Westmont Power (Kenya) Limited
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APPENDIX B: List of Shortlisted Companies for Project Study
A. Construction & Material
1. Savannah Cement
2. Mabati Rolling Mills
3. Aristocrat Concrete Ltd.
4. East African Portland
Cement Company
5. Impala Glass Co. Ltd.
6. Wire Products Ltd.
B. Food & Beverage
1. EABL
2. Bidco
3. Farmer’s Choice
4. Proctor and Allan
5. National Cereals and
Produce Board
6. Kenya Cooperative
Creameries
C. Textiles & Apparel
1. C&P Shoe Industries
2. Africa Apparels EPZ Ltd
D. Chemical & Pharmaceutical
1. Chloride Exide Ltd
2. Beta Healthcare Intl. Ltd.
3. BAT Kenya Ltd
4. Wrigley
E. Automobile/Parts Industry
1. DT Dobie & Co.
2. GM East Africa Ltd
3. Simba Colt Motors Ltd
4. Cooper Motor Corporation
(K) Ltd
5. Ryce (EA) Ltd.
F. Plastics/Packaging
1. Uzuri Manufacturers Ltd
2. Safepak Ltd
3. Kenpoly Manufacturers Ltd
4. Haco Industries
5. DPL
6. Kentainers
7. CG Re-tread Ltd.
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APPENDIX C: Questionnaire
UNIVERSITY OF NAIROBI
This questionnaire is designed to collect data from Advanced Manufacturing Technology (AMT)
Companies in the Greater Nairobi Area to establish the effect that AMTs have had on
organisational structure and technical labour in the Kenyan Manufacturing industry. This
questionnaire is primarily addressed to Engineering/Production Managers (or their equivalent)
within the target companies. We kindly request audience with the managers in order to fill the
questionnaire. The data shall be used for academic purposes only, and will be treated with utmost
confidence. Your participation in facilitating the study is highly appreciated. All information in
this questionnaire will remain absolutely confidential and will be seen only by academic
researchers involved in this study.
A. General
1. Which of the following Automated Manufacturing Technologies (AMTs) does
your firm utilise for operations?
☐ Computer Aided Design (CAD)
☐ Computer Aided Engineering
(CAE)
☐ Just in Time (JIT)
☐ Computer Integrated
Manufacturing (CIM)
☐ FMS (Flexible Manufacturing
Systems)
☐ LOOP (Closed Loop Process
Control)
☐ MRP (Material Requirement
Planning)
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☐ MRP II (Manufacturing
Requirement Planning)
☐ CAQC (Computer Aided Quality
Control)
☐ SPC (Statistical Process Control)
☐ CNC (Numerical Controlled
Machines)
☐ SMT (Surface Mounting
Technology)
☐ FMC (Flexible Manufacturing Cells)
☐ AMH (Automated Material
Handling)
☐ APM (Automated process
Monitoring)
☐ API (Automated Process Inspection)
☐ BARCODE (Barcode Inventory
Tracking)
☐ Computer Aided Manufacturing
(CAM)
☐ Others
If others, please specify AMTs used:
____________________________________________
2. For how long has your company used the above mentioned AMTs?
☐ 1-2 years ☐ 3-4 years ☐ 5-6 years ☐ 7-8 years ☐ 9+ years
3. To what extent have these AMTs been integrated into the firm’s operations?
☐ To a great extent
☐ To a moderate extent
☐ Neutral
☐ To a small extent
☐ Negligible extent
65
B. Productivity
4. How have AMTs influenced the productivity of your firm over the past 10 years?
(where possible, please provide relevant data)
☐ To a great extent
☐ To a moderate extent
☐ Neutral
☐ To a small extent
☐ Negligible extent
C. Personnel/Training
5. 10 years ago, what was the size of your manufacturing workforce in terms of:
i. Qualified engineers?
☐ 0-2 ☐ 3-4 ☐ 5-6 ☐ 7-8 ☐ 9+
ii. Blue-collar technicians?
☐ 0-10 ☐ 10-20 ☐ 20-30 ☐ 30-40 ☐ 40+
6. At present, how much of your workforce are:
i. Qualified engineers?
☐ 0-2 ☐ 3-4 ☐ 5-6 ☐ 7-8 ☐ 9+
ii. Blue-collar technicians?
☐ 0-10 ☐ 10-20 ☐ 20-30 ☐ 30-40 ☐ 40+
7. Of the present workforce, how many have been trained:
i. Locally?
☐ 0-5 ☐ 5-10 ☐ 10-15 ☐ 15-20 ☐ 20+
ii. Abroad?
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☐ 0-5 ☐ 5-10 ☐ 10-15 ☐ 15-20 ☐ 20+
D. Deployment
8. How have your engineering departments been affected due to the assimilation of
AMTs? (where possible, please provide relevant data)
☐ To a great extent
☐ To a moderate extent
☐ Neutral
☐ To a small extent
☐ Negligible extent
9. Has the adoption of AMTs necessitated a change in hierarchical structure for your
firm? (where possible, please provide relevant data)
☐ To a great extent
☐ To a moderate extent
☐ Neutral
☐ To a small extent
☐ Negligible extent
E. Trends analysis (future projections)
10. With the increased assimilation of AMTs into the production process, what effects
has your firm projected on the following? (where possible, please provide relevant
data)
i. Staff size?
☐ Significant effect
☐ Moderate effect
☐ Neutral
☐ Small effect
☐ Negligible effect
67
ii. Training costs?
☐ Significant effect
☐ Moderate effect
☐ Neutral
☐ Small effect
☐ Negligible effect
iii. Qualifications for skilled labour?
☐ Significant effect
☐ Moderate effect
☐ Neutral
☐ Small effect
☐ Negligible effect
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