UNIVERSITY OF NAIROBI - Latest News in Mechanical …mechanical.uonbi.ac.ke/sites/default/files/cae...i UNIVERSITY OF NAIROBI THE IMPACT OF ADVANCED MANUFACTURING TECHNOLOGIES (AMTs)

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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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4.4.10. 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


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


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


.................................................................................................................................................. 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.

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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.


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


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


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


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.



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.


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


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:

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


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



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


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


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.


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


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


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.


RESPONS

E



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


0

5

10

15

20

25

30

35


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


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:


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.


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


0

5

10

15

20

25

30

35

40

45

50


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


0

10

20

30

40

50

60


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

REFERENCES

1. Accenture. (2014). Out of Inventory: Skills Shortage Threatens Growth of US

Manufactruing. Washington, D.C.: US Manufacturing Institute.

2. Agrawal, T. (2013). Vocational education and training programs (VET): An Asian

perspective. Asia-Pacific Journal of Cooperative Education, 14(1), 15-26.

3. Bigsten, A., Kimuyu, P., & Söderbom, M. (2010). Chapter 10: The Manufacturing

Sector,” forthcoming in (ed.) C. Adam, P. Collier and N. Ndung’u, Kenya: Policies for

Prosperity. Oxford University Press and Central Bank of Kenya.

4. Das, K., & G., K. (2009). Are Labour-Intensive Industries Generating Employment in

India? Evidence From Firm Level Survey. The Indian Journal of Labour Economics,

52(3), 1-22.

5. Deloitte. (2013). Global Manufacturing Competitiveness Index. New York, NY.

6. Engineers Australia. (2014). Engineers Australia Policy Note. Barton ACT 2600.

Sydney.

7. Gunawardana, K. (2006). Introduction of Advanced Manufacturing Technology: A

Literature Review. Sabaragamuwa University Journal, 6(1), 116-134.

8. Haron, M., & Chellakumar, J. (2012). Efficiency Performance of Manufacturing

Companies in Kenya: Evaluation and Policies. International Journal of Managemnet

and Business Research (IJMBR), 2(3), 235-242.

9. Ikinya, S. (2013). Technician Engineering Training and Employability in Kenya: Focus

on Thika and Meru Technical Training Institutes. PHD thesis published: The Catholic

University of East Africa, Nairobi.

10. Institute For Defense Analyses. (2012). Emerging Global Trends in Advanced

Manufacturing. Alexandria.

11. Jelke et al. (2008). Indicators for the Representativeness of Survey Response.

Proceedings of Statistics Canada Symposium 11-522-X, (pp. 1-10).

12. Kenya National Bureau of Statistics. (2015). Economic Survey 2015. Nairobi: KNBS.

13. Mate, A., & Kabiru, B. (2014). Automation and Changing Technologies: Issues and

Concerns for Manufacturing Industries in Kenya. Nairobi: University of Nairobi.

56

14. Mathew, A., & Sharma, A. D. (2015). Advanced Manufacturing Technology

Implementation in Small Scale Manuafcturing Industries. International Journal of

Advanced Technology in Engineering and Science, 3(1), 1-10.

15. National Association of Manufacturers. (2013). AMT Techniques Among US Middle

Market Manufacturers. Washington, DC.

16. Nyori, G., & KObonyo, P. (2015). Advanced Manufacturing Technology Adoption

And Organizational Structure. International Journal of Innovative Research and

Advanced Studies (IJIRAS), 2(9), 1-13.

17. Nyori, G., & KObonyo, P. (2015). Advanced Manufacturing Technology Adoption in

Manufacturing Companies in Kenya. International Journal of Social, Behavioral,

Educational, Economic, Business and Industrial Engineering, 9(10), 3452-3464.

18. Nyori, G., & Ogutu, M. (Oct 2015). Human Factors as Determinants of AMT Adoptions

in Structure of a Manufacturing Company. The Indian Journal of Labour Economics,

2(10), 286-298.

19. Rotman, D. (2013, June 12). How Technology is Destroying Jobs. Retrieved from MIT

Technology Review: https://www.technologyreview.com/s/515926/how-technology-

is-destroying-jobs/

20. Rutkin, A. H. (2013, September 12). Report Suggests Nearly Half of U.S. Jobs Are

Vulnerable to Computerization. Retrieved from MIT Technology Review:

https://www.technologyreview.com/s/519241/report-suggests-nearly-half-of-us-jobs-

are-vulnerable-to-computerization/

21. Series, S. P. (2014). Engineering In Asia – A Labour Market Perspective 2014.

Retrieved from Spring Asia: www.springasia.com/a-labour-market-perspective-2014

22. Sivo et al. (2006). How Low Should You Go? Low Response Rates and the Validity of

Inference IS Questionnaire Research. Journal of the Association for Information

Systems, 6(7), 351-414.

23. Training Magazine. (2014). 2014 Training Industry Report. Minneapolis, MN.

24. UK Commission for Employment and Skills. (2015). Evidence Report 93: Sector

insights: Skills and Performance Challenges in the advanced manufacturing sector.

57

25. Viswanadham, N. (2002). The Past, present and Future of Supply-Chain Automation.

IEEE Robotics & Automation Magazine, 1-9.

26. Yussuf, R.M.; Chek, L.W.; Hashmi, M.S.J. (2005). Advanced Manufacturing

Technologies in SMEs. CACCI Journal, Vol 1, 1-11.

27. Hall, J., & Polaroid, U. K. (1996). MRP2 Developments. Manufacturing and Business

Excellence: Strategies, Techniques, and Technology, 333.

28. Wu, G., Shao, F., & Hu, B. (1992). Hierarchical structure of a computer-integrated

quality management system in a CIM environment. Computers in industry, 20(2), 177-

185.

29. Zhou, Y., & Chuah, K. B. (2002). Computer-integrated manufacturing in China: a

report of industrial field surveys. International Journal of Operations & Production

Management, 22(3), 271-288.

30. Svobodová, L. (2011, July). Advanced manufacturing technology utilization and

realized benefits. In International CSCC Multiconference, Corfu July (pp. 14-17).

31. Li, B. H., Wu, C., Deng, J. T., Gao, Y. C., Xue, J. S., Gu, G. Q., & Zhang, S. S. (1995).

Computer integrated manufacturing in China. In Computer Applications in Production

Engineering (pp. 425-440). Springer US.

32. Singh, R. K., Garg, S. K., Deshmukh, S. G., & Kumar, M. (2007). Modelling of critical

success factors for implementation of AMTs. Journal of Modelling in Management,

2(3), 232-250.

33. Komninos, I. (2002). Product life cycle management. Thessaloniki: Aristotle University

of Thessaloniki.

34. Rink, D. R., & Swan, J. E. (1979). Product life cycle research: A literature review.

Journal of business Research, 7(3), 219-242.

35. Anderson, C. R., & Zeithaml, C. P. (1984). Stage of the product life cycle, business

strategy, and business performance. Academy of Management journal, 27(1), 5-24.

36. Shannon, D. M., & Bradshaw, C. C. (2002). A comparison of response rate, response

time, and costs of mail and electronic surveys. The Journal of Experimental Education,

70(2), 179-192.

37. Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in

organizational research. Human Relations, 61(8), 1139-1160.

58

38. Terziovski, M. (2010). Innovation practice and its performance implications in small

and medium enterprises (SMEs) in the manufacturing sector: a resource‐based view.

Strategic Management Journal, 31(8), 892-902.

39. Novak, G. M., Patterson, E. T., Gavrin, A. D., & Christian, W. (1999). Just-In-Time

Teaching Blending Active Learning with Web Technology.

40. Karolak, D. W., & Karolak, N. (1995). Software engineering risk management: A just-

in-time approach. IEEE Computer Society Press.

41. Koff, G. A., & Boldrin, B. (2008). Automated guided vehicles. Materials Handling

Handbook, 2nd Edition, 273-314.

42. Ding, Y., & Newman, K. (2004). U.S. Patent No. 6,691,067. Washington, DC: U.S.

Patent and Trademark Office.

43. Mkrkdtth, J.R., & Surksh, N.C. (1986). Justification techniques for Advanced

Manufacturing Technologies. International Journal of Production Research, 24(5),

1043-1057.

44. Kusiak, A. (1987). The generalized group technology concept. International journal of

production research, 25(4), 561-569.

45. Selim, H. M., Askin, R. G., & Vakharia, A. J. (1998). Cell formation in group

technology: review, evaluation and directions for future research. Computers &

Industrial Engineering, 34(1), 3-20.

46. Viswanadham, N. (2002). The Past, present and Future of Supply-Chain Automation.

IEEE Robotics & Automation Magazine, 1-9.

47. Yussuf, R., Chek, L., & Hashmi, M. (2005). Advanced Manufacturing Technologies in

SMEs. CACCI Journal, 1-11.

59

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

60

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

61

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

62

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.

63

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)

64

☐ 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)



☐ Neutral



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?

66

☐ 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)



☐ Neutral



9. Has the adoption of AMTs necessitated a change in hierarchical structure for your

firm? (where possible, please provide relevant data)



☐ Neutral



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?


☐ Moderate effect

☐ Neutral

☐ Small effect


iii. Qualifications for skilled labour?


☐ Moderate effect

☐ Neutral

☐ Small effect


lxviii

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