UNIVERSITY OF NAIROBI
AUTOMATION AND CHANGING TECHNOLOGIES: ISSUES AND CONCERNS FOR
ENGINEERS AND INDUSTRIAL RELATIONS FOR MANUFACTURING INDUSTRIES
IN KENYA.
By:
MATE ANTHONY &
LENANA BONAVENTURE KABIRU.
This project is submitted to University of Nairobi as a requirement for the award of the
degree of BSc. in Mechanical and Manufacturing Engineering.
Supervisor:Eng.George.M.Nyori.
School of Engineering, department of mechanical and Manufacturing Engineering,
University of Nairobi
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DEDICATION
This project is dedicated to our beloved parents, colleagues and our siblings who always
wished us to be a successful engineers through the half decade academic duration, thank you
very much.
Mate. Anthony & Lenana.B. Kabiru
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DECLARATION
We hereby certify that the material presented in this research project is entirely our
own
work, except where specific references have been made to the works of other
individuals and scholars
Name.............................................................................ID F18/29648/2009
signature ......................................................................
Name.............................................................................ID F18/2411/2009
signature ......................................................................
This research project has been submitted for university examination with my approval as the
university supervisor.
Name.............................................................................
signature ......................................................................
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Table of Contents
DEDICATION .................................................................................................................................. ii
DECLARATION ............................................................................................................................. iii
ACKNOWLEDGMENTS ................................................................................................................ vi
ABSTRACT .....................................................................................................................................vii
LIST OF TABLES .......................................................................................................................... viii
LIST OF FIGURES ........................................................................................................................... x
LIST OF ABREVIATIONS ............................................................................................................. xii
CHAPTER ONE: INTRODUCTION ................................................................................................ 1
1.1 Historical Developments in Industrial Automation and Manufacturing Control. ....................... 1
1.2 Human Factors in Production Systems ..................................................................................... 1
1.3 Impact Of Automation. ........................................................................................................... 2
1.3.1 On Jobs ............................................................................................................................. 2
1.3.2 On Skills .......................................................................................................................... 3
1.3.3 Occupational Employment of Engineers. ........................................................................... 3
1.3.4 Engineers as Managers. ..................................................................................................... 4
1.4 Objectives of Study. ................................................................................................................. 5
CHAPTER TWO: LITERATURE REVIEW ..................................................................................... 6
2.1 Introduction ............................................................................................................................. 6
2.2 Automation Effects .................................................................................................................. 6
2.2.1 On Global Scale ................................................................................................................ 6
2.2.2 On Employees ................................................................................................................... 7
2.2.3 On Management Processes And Supervisors ..................................................................... 8
2.2.4 On Interpersonal Relations ................................................................................................ 9
2.2.5 Academic Requirements Of Engineers .............................................................................. 9
2.3 Science Technology and Innovation in Kenya ........................................................................ 10
2.4 Future trends in demand and skills for engineering job functions ............................................ 11
CHAPTER 3: METHODOLOGY.................................................................................................... 12
3.1 Objectives of Survey .............................................................................................................. 12
3.2 Sample population ................................................................................................................. 12
3.4 Scaling of Responses and Response Rate ............................................................................... 12
3.5 Questionnaire Design ............................................................................................................ 13
CHAPTER FOUR: DATA ANALYSIS........................................................................................... 13
4.1 Introduction ........................................................................................................................... 14
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4.2 Response ............................................................................................................................... 14
4.2.1 Response Rate................................................................................................................ 14
4.2.2 Response Time................................................................................................................ 14
4.3 Data Quality and Cost ............................................................................................................ 15
4.4 Research Questions ................................................................................................................ 15
4.4.1 Research Question One ................................................................................................... 15
4.4.2 Research Question Two ................................................................................................... 21
4.4.3 Research Question Three................................................................................................. 29
4.4.4 Research Question Four .................................................................................................. 40
4.4.5 Research Question Five ................................................................................................... 43
4.4.6 Research Question Six .................................................................................................... 47
CHAPTER FIVE: DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS....................... 50
5.1 DISCUSSION ....................................................................................................................... 50
5.1.1 Automated Manufactured Technologies .......................................................................... 51
5.1.2 Improvements In Management Practices Due To High Automation Levels ...................... 52
5.2 CONCLUSIONS ................................................................................................................... 53
5.3 RECOMMENDATIONS ....................................................................................................... 54
REFERENCES................................................................................................................................ 55
APPENDICES ................................................................................................................................ xiii
APPENDIX 1: List Of Manufacturing And Processing Companies In Kenya ............................... xiii
APPENDIX 2: Questionnaire........................................................................................................ xv
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ACKNOWLEDGMENTS
We would like to express our sincere thanks and gratitude to Eng.George.M.Nyori, our
supervisor , for his supervision, encouragement, valuable suggestions and friendly advice
throughout the period of this study.
We would like to thank all staff and students in the department of Mechanical
&Manufacturing
Engineering
.Special
thanks
to
Gilbert
Gitonga,Nicholas
Muchui,Eng.M.Ngoroi(Unilever Kenya), Dr Ogolla (chairman of departmant), and all friends
for their encouragements and help.
We would also like to convey our sincere thanks to our parents and the department for their
financialassistance, support and encouragement.
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ABSTRACT
This study is concerned with a research on automation in the manufacturing industry in kenya and
its eventual impact on the role of the engineer as the manager. The analysis framework details the
extent of automation,human and machine interactions and the application of computer based
technologies in the Kenya manufacturing industry.The study population consisted of managing
engineers in the leading manufacturing companies within our sample scope.We concetrated our
space in Nairobi region.The study adresses the managers perception on the eventual structural and
individual adjustments that come with implementation of some Advanced Manufacturing
methods(AMTs).This work involved designing of a likert
survey questionnaire
which was
presented to the respondents by physically visiting 24 manufacturing companies and utilising the
web-based surveyby emailing it to 11 manufacturing companies in the Republic of Kenya.The
results of this study show variable levels of automation.A large number of the manufacturing
plants are applying automation and are trying to increase the automation levels their plants which
comes with significant consequences on the approach to management and the actual role of the
engineer in the manufacturing company.
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LIST OF TABLES
Table 4. 1: Statistical Analysis For Impacts Of Automation On Managers ........................................ 16
Table 4. 2: Statistical Analysis For Impacts Of Automation On Managers ........................................ 16
Table 4. 3: Frequency Table For Reduced Motivation ...................................................................... 17
Table 4. 4: Frequency Table For Need For Retraining ...................................................................... 18
Table 4. 5: Frequency Table For Change Of Hierachial Structure ..................................................... 18
Table 4. 6: Frequency Table For Better Perfomance Output ............................................................. 19
Table 4. 7: Frequency Table For Enhanced Work Relation With Junior Staff ................................... 20
Table 4. 8: Frequency Table For Improved Production Control ........................................................ 20
Table 4. 9: Frequency Table For Change In Mode Of Communicating Instructions .......................... 21
Table 4. 10: General Statistical Data On Considerations While Acquiring An Automation Machine . 21
Table 4. 11: General Statistical Data On Considerations While Acquiring An Automation Machine . 22
Table 4. 12: General Statistical Data On Considerations While Acquiring An Automation Machine . 22
Table 4. 15: Frequency Table For Cost Effectiveness ....................................................................... 24
Table 4. 16: Frequency Table For After Sales Maintanance .............................................................. 24
Table 4. 17: Frequency Table For Currency Of Information ............................................................. 25
Table 4. 18: Frequency Table For Reduction In Labour .................................................................... 26
Table 4. 19: Frequency Table For Distributed Access ....................................................................... 26
Table 4. 20: Frequency Table For Period Of Access......................................................................... 27
Table 4. 21: Frequency Table For Added Value ............................................................................... 27
Table 4. 22: Frequency Table For Ease Of Access ........................................................................... 28
Table 4. 23: Frequency Table For Durability.................................................................................... 29
Table 4. 24: Frequency Table For Reliability ................................................................................... 29
Table 4. 25: General Statistical Data On Amts Adopted In Companies ............................................. 30
Table 4. 26: General Statistical Data On Amts Adopted In Companies ............................................. 30
Table 4. 27: General Statistical Data On Amts Adopted In Companies ............................................. 30
Table 4. 28: General Statistical Data On Amts Adopted In Companies ............................................. 31
Table 4. 29: Frequency Table For ‘Just-In-Time Manufacturing’ ..................................................... 31
Table 4. 30: Frequency Table For ‘MRPII’ ...................................................................................... 32
Table 4. 31: Frequency table for ‘CIM’ ............................................................................................ 33
Table 4. 32: Frequency Table For ‘CAM’ ........................................................................................ 33
Table 4. 33: Frequency table for ‘FMS’ ........................................................................................... 34
Table 4. 34: Frequency table for ‘FMC’ ........................................................................................... 34
Table 4. 35: Frequency table for ‘BARCODE’................................................................................. 35
Table 4. 36: Frequency table for ‘CNC’ ........................................................................................... 36
Table 4. 37: Frequency table for ‘CAD’ ........................................................................................... 36
Table 4. 38: Frequency table for ‘APM’........................................................................................... 37
Table 4. 39: Frequency table for ‘API’ ............................................................................................. 38
Table 4. 40: Frequency table for ‘AMH’ .......................................................................................... 38
Table 4. 41: Frequency table for ‘LOOP’ ......................................................................................... 39
Table 4. 43: Frequency table for ‘SMT’ ........................................................................................... 40
Table 4. 44: General statistics for managers’ decision on aspects of production ................................ 41
Table 4. 45: Frequency table for ‘human labour’ .............................................................................. 41
Table 4. 46: Frequency table for ‘inventory control’ ........................................................................ 42
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Table 4. 47: Frequency table for ‘sequence of production’ ............................................................... 42
Table 4. 48: General statistics on effects of automated office systems a managers work ................... 43
Table 4. 49: Frequency table for responses to ‘rationality’ ............................................................... 44
Table 4. 50: Frequency table for responses to ‘organisation/layout’ .................................................. 44
Table 4. 52: Frequency table for responses to ‘decision design’ ....................................................... 46
Table 4. 53: Frequency table for responses to ‘personal space’ ......................................................... 46
Table 4. 54: Frequency table for responses to ‘social interaction’ ..................................................... 47
Table 4. 55: General Statistics on the engineers’ workload due to 3 levels of automation ................. 47
Table 4. 56: Frequency table for responses to ‘companies not automated’ ........................................ 48
Table 4. 57: Frequency table for responses to ‘semi-automated companies’ ..................................... 49
Table 4. 58: Frequency table for responses to ‘fully -automated companies’ .................................... 49
Table 4. 59: Frequency table for automation technologies applied .................................................... 52
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LIST OF FIGURES
Figure 4.1 pie chart showing the response to ‘engineers motivation’ in percentages
Figure 4.2 pie chart showing the response to ‘need for retraining of engineers’ in percentages
Figure 4.3 is a pie chart showing the response to ‘change in hierarchal structure’ in
percentages
Figure 4.4 is a pie chart showing the response to ‘performance output’ in percentages
Figure 4.5 is a pie chart showing the response to ‘work relations between engineers and their
juniors’ in percentages
Figure 4.6 is a pie chart showing the response to ‘production control’ in percentages
Figure 4.7 is a pie chart showing the response to ‘change in mode of communication
instructions’ in percentages
Figure 4.8 is a pie chart showing the response to ‘quantity of product to meet user need’ in
percentages
Figure 4.9 is a pie chart showing the response to ‘relevance of technology’ in percentages
Figure 4.10 is a pie chart showing the response to ‘cost effectiveness’ in percentages
Figure 4.11 is a pie chart showing the response to ‘after sale maintenance’ in percentages
Figure 4.12 is a pie chart showing the response to ‘currency of information’ in percentages
Figure 4.13 is a pie chart showing the response to ‘reduction in human labour’ in percentages
Figure 4.14 is a pie chart showing the response to ‘distributed access’ in percentages
Figure 4.15 is a pie chart showing the response to ‘period of access’ in percentages
Figure 4.16 is a pie chart showing the response to ‘value addition’ in percentages
Figure 4.17 is a pie chart showing the response to ‘ease of accessibility of the technology’ in
percentages
Figure 4.18 is a pie chart showing the response to ‘durability’ in percentages
Figure 4.19 is a pie chart showing the response to ‘reliability’ in percentages
Figure 4.20 is a pie chart showing the frequencies of response to the question of ‘just-in-time
manufacturing’ as percentages
Figure 4.21 is a pie chart showing the frequencies of response to the question of ‘MRPII’ as
percentages
Figure 4.22 is a pie chart showing the frequencies of response to the question of ‘CIM’ as
percentages
Figure 4.23 is a pie chart showing the frequencies of response to the question of ‘CAM’ as
percentages
Figure 4.24 is a pie chart showing the frequencies of response to the question of ‘FMS’ as
percentages
Figure 4.25 is a pie chart showing the frequencies of response to the question of ‘FMC’ as
percentages
Figure 4.26 is a pie chart showing the frequencies of response to the question of BARCODE’
as percentages
Figure 4.27 is a pie chart showing the frequencies of response to the question of ‘CNC’ as
percentages
Figure 4.28 is a pie chart showing the frequencies of response to the question of ‘CAD’ as
percentages
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Figure 4.29 is a pie chart showing the frequencies of response to the question of ‘APM’ as
percentages
Figure 4.30 is a pie chart showing the frequencies of response to the question of ‘API’ as
percentages
Figure 4.31 is a pie chart showing the frequencies of response to the question of ‘AMH’ as
percentages
Figure 4.32 is a pie chart showing the frequencies of response to the question of ‘LOOP’ as
percentages
Figure 4.33 is a pie chart showing the frequencies of response to the question of ‘SPC’ as
percentages
Figure 4.34 is a pie chart showing the frequencies of response to the question of ‘SMT’ as
percentages
Figure 4.35 is a pie chart showing the response to ‘automation effects on human labour’ in
percentages
Figure 4.36 is a pie chart showing the response to ‘automation effects on inventory control’ in
percentages
Figure 4.37 is a pie chart showing the response to ‘automation effects on sequence of
production’ in percentages
Figure 4.38 is a pie chart showing the response to ‘effects of office automation systems on
engineers’ rationale.’
Figure 4.39 is a pie chart showing the response to ‘effects of office automation systems on
engineers’ organisation/layout.’
Figure 4.40 is a pie chart showing the response to ‘effects of office automation systems on
engineers’ work flexibility.’
Figure 4.41 is a pie chart showing the response to ‘effects of office automation systems on
engineers’ decision design.’
Figure 4.42 is a pie chart showing the response to ‘effects of office automation systems on
engineers’ personal space.’
Figure 4.43 is a pie chart showing the response to ‘effects of office automation systems on
engineers’ quality/ quantity of social interaction.’
Figure 4.44 is a pie chart showing the response to ‘workload of the engineer in nonautomated companies’
Figure 4.45 is a pie chart showing the response to ‘workload of the engineer in semiautomated companies’
Figure 4.46 is a pie chart showing the response to ‘workload of the engineer in fully automated companies’
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LIST OF ABREVIATIONS
AMTs - Advanced Manufacturing methods
AIT – Advanced Information Technology
APM - Automated process monitoring
API - Automated process inspection
AMH - Automated material handling
BARCODE - Bar code inventory tracking
CAD - Computer Aided Design
CAM - Computer Aided Manufacturing
CAPP - Computer Aided Process Planning
CAQC - Computer Aided Quality Control
CIM - Computer integrated manufacturing
CNC – Computer Numerical Control
DNC – Direct Numerical Control
EDI - Electronic Data Interchange
FMS - Flexible Manufacturing System
GM – General motors
FMC - Flexible manufacturing cells
HMI – Human Machine Interaction
ICAM - Integrated Computer Aided Manufacturing
JIT - Just-in-time manufacturing
LOOP - Closed loop process control
MRPII - Manufacturing resources planning
NMC- Numerical machining complex
NC – Numerical Control
OTA - Optimise Task Allocation
PC – Personal Computer
Robot - Robotics
TA – Task Allocation
SPC - Statistical process control
SMT - Surface Mounting Technology
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CHAPTER ONE: INTRODUCTION
1.1 Historical Developments in Industrial Automation and Manufacturing
Control.
Around the year 1900, factory
mechanization facilitated mass production to meet the
consumer's demands for improved products.By the year 1930, transfer lines and fixed
automation were created to facilitate mass production.This resulted in the development of
programmable automation.By the year 1950, numerical control (NC) was developed as an
innovative approach to programmable automation.With the developments in commercially
available computer technology, the application of computers in manufacturing started to
emerge by producing a variety of new technologies By the year 1955, the introduction of
computer aided design (CAD) and developments of NC resulted which led to the evolution of
systems like computer NC (CNC) and direct NC (DNC) By the year 1970,developments in
CAD applications and computer aided manufacturing (CAM) based systems introduced
the concept of CIM, which are collectively named as AMTs (V S Nagalingam and G C
I Lin (1999)), AMTs provide flexibility as well as data driven computer integration
for a manufacturing organization,
in which the manufacturing technology utilized
is
intelligent enough to process the activities with less human intervention.
AMT is used as an umbrella term to describe a variety of technologies which primarily utilize
computers to control, track or monitor manufacturing activities, either directly or indirectly.
Technologies such as Computer Numerical Control (CNC) machine tools, Computer Aided
Design (CAD), Computer Aided Process Planning (CAPP), Electronic Data Interchange
(EDI) and Flexible Manufacturing System (FMS) all involve the use of computer to control
tools and machines, store product information and control the manufacturing process. In
addition, “technologies” or programs which do not directly involve computers are also
considered to be AMTs since they are closely associated with other AMT technologies,
Rahman, I., D. Reynolds, and S. Svaren (2012).
1.2 Human Factors in Production Systems
As noted in the introduction of this project, automation has historically proven to be an
efficient way to achieve cost-effective production in not only discrete parts manufacturing, in
the process industry and in other industrial manufacturing areas (Satchell, 1998). However,
automated production systems also have drawbacks. This insight into the limitations of
automation during the last two decades has resulted in a better understanding of the
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importance of the human operator as a controller and supervisor/manager/engineer
(Chapanis, 1996; Billings, 1997).
A central question in the design of physical automation and information technology has
therefore not only been how to construct the best possible technical systems but also how to
optimise task allocation(OTA) between the technology and its users. The initial contribution
to the field of task allocation was made by Fitts in the early 1950s (Fitts, 1951), who
presented a list of general tasks covering both humans and machines, illustrating where the
performance of one category exceeds that of the other.
1.3 Impact Of Automation.
1.3.1 On Jobs
Change in employment induced by new technology depends on how technology alters the
tasks to be done in manufacturing jobs, on what changes occur in the skills required for
different tasks and jobs, and on how the roles of different occupations change. Total
employment change also depends on changes in how labor is used within and between
industries and changes in labor supply. The effects of automation on work opportunities are
so varied and (at times) so profound that they call into question the basic definition of “skill,”
the identification of where skill fits into the production process, and the relationships between
tasks, skills, occupations and jobs. Changes in task assignments and skill requirements vitiate
traditional occupational descriptions, which form the basis of occupational employment
forecasting.
Managers create jobs by combining sets of tasks and allocating them to individuals (Fayols
implemented principal of division of labour). Jobs with similar descriptions and avenues of
preparation are classified as occupations. Indeed, the design of training programs depends on
the expectation that people in designated occupations or jobs will perform specific tasks .
Unfortunately for the analyst, what is actually done on the job frequently differs from the
formal job description.
Computer-based technologies, including numerical control (NC) technology, have already led
to different staffing patterns within and among countries, varying on the basis of industrial
traditions, labor market conditions,prevailing types of company structure, and national
educational systems. A German analysis of flexible manufacturing systems (FMS), for
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example, concluded that work within an FMS was comprised of a set of tasks that could be
allocated in numerous ways generally not bound by the technology.
1.3.2On Skills
Automation, through its effects on the tasks performed in manufacturing firms, also affects
the types of job skills required for those tasks. In some cases, the creation of new tasks and
the elimination of old ones clearly raises or lowers skill requirements.Often, however, the
effect on skill demand is ambiguous, because the skills associated with individual jobs and
the average skill level of a company’s jobs depend on how work is allocated among/to
individuals. Skill demand also depends on how well employers understand what skills they
really need.
By altering the balance of work between people and machines, automation makes it possible
for managers/engineers to reallocate work in ways that either raise or lower the skill
requirements of jobs. OTA’s appraisal of the effects of automation on the manufacturing
workplace suggests that these technologies will alter both the “depth” and “breadth” of skill
requirements.Intuitively,this could lead to the need for retraining. By defination,skill depth
refers to the input needed to perform an individual task or group of interconnected tasks,
while skill breadth refers to the input needed to perform a set of (nonsimilar) tasks.
1.3.3 Occupational Employment of Engineers.
In the manufacturing industry, engineers are a central factor in the employment changes
expected to occur with automation. Engineers develop automation technologies; they work
with them; yet they are not immune to being displaced by them. Engineers contribute to both
the production and use of automation. The mix of engineers by discipline:- electrical/
electronic and mechanical engineers design equipment and systems, and industrial/
manufacturing engineers as well as electrical and mechanical engineers design applications.
Different industries have different needs for special engineering disciplines, such as
aeronautical/ astronautical, chemical, and metallurgical engineers.
Typically, employers prefer that engineers have at least a bachelor’s degree, although
individuals without such training can be certified by the Assosciation of Manufacturing
engineers & Engineers regestration board to perform certain types of production engineering,
and sometimes individuals attain the title of engineer through promotion from other positions.
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Engineers who perform research usually hold advanced degrees. The employment share of
manufacturing engineers with degrees reflects the fact that employers and schools alike have
historically held this engineering discipline in lower regard than others.
1.3.4 Engineers as Managers.
An engineer is also a manager. Good managers discover how to master five basic functions:
planning, organizing, staffing, leading, and controlling. In his classic book, The Nature of
Managerial Work, Henry Mintzberg describes a set of ten roles that a manager fills. These
roles fall into three categories: Interpersonal, Informational and Decisional
As managers, engineers will plan, organize, direct, and control various functions within
firms. They may also do work similar to that of their subordinates. Programmable automation
will alter the mix and number of managerial personnel. It will probably support growth in
upper management ranks, for three reasons. First, the integration of databases and anticipated
shifts in decision making toward higher staff levels will increase the role of upper
management in the production process. The push for so-called “top down control” facilitated
by computerization and automation, inherently increases the role of upper production,
product variation and competition grows. More managerial input will be required for product
planning and market analysis.
Growth in automation products and markets is itself a source of growth in managerial
employment. Third, change in production technologies may create new operational units
within firms, and associated needs for planning and management. Automation generally
entails new work in database management, software quality assurance and training-activities
which may be undertaken by special staffs and managers. Nevertheless, it is not clear how
much newmanagerial employment the support needs ofmanufacturing automation will
generate, especiallywhere companies already have data processing staffs. Also, more
advanced systems that do not require mastery of special languages or formats, that include
applications generators, or that entail distributed data processing lower the requirement for
special,in-house personnel.
A study evaluating prospects for computer operations managers generally suggested growing
needs for capacity planning, performance monitoring, technical support, security
management, and facilities management . Also, a study of manufacturing firms concluded
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that:The new technology substantially changed the jobs of supervisors and middlemanagement, shifting the focus from watchdog and disciplinarian to planning, training, and
communicating. This is emphasised by manufacturing industry representatives who
frequently point toresistance among lower and middle managers.
1.4Objectives of Study.
The broad objective of this research is to explore how the increase in automation in
manufacturing is affecting the engineer as a production manager. The specific objectivesare
to study
1. How the engineer interacts with human and the ever changing technology with respect
to his/her job as a manager.
2. The extent of automation in manufacturing industry in Kenya.
3. The effect of automation to the engineer as a manager.
4. The change in responsibilities of the engineer as a consequence of automation.
5. The expected consequences due to changes in the automation of production.
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CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction
Competitiveness has made organizations to make a continuing attempt to improve
manufacturing. These improvements include meeting the needs of customer, increasing
volumes of output, improving product quality and reducing product costs. For a
manufacturing company, Advanced Manufacturing Technology (AMT) is the answer. AMT,
according to Myung, E., A. McClaren, and L. Li (2012), as any new technique which, when
adopted is likely to require a change not only in manufacturing practice, but also in
management systems and manufacturer’s approach to the design and production engineering
of the product. These technologies can improve quality through emphasis on quality of design
and can affect cost through emphasis on value engineering.
Displacement of jobs is merely one of the impacts of automation. Substitution of human
labour and skill with computing machinery sways economic stability, increases productivity,
focus shift of highest paying jobs, unemployment. But there are many more effects of
automation on engineers which will be discussed in the following review. An engineer is a
manager in a production process. So we review how much the professional, social, economic
and academic life of an engineer has been transformed (or is expected to change) as a result
of new technologies (automation).
2.2 Automation Effects
2.2.1 On Global Scale
John Diebold candidly points out the dilemma of automation (Diebold J,1959 pg2). He says
that automation is “just more of the kind of stuff which creates more and better jobs all the
time” and is “not essentially different from the process of improving methods of production”
which has been going on throughout human history.
A measure of the global adoption of AMT is reflected in a research project called the
International Manufacturing Strategy Survey, which received responses from 556
manufacturers in 18 countries and found that computer-aided design (CAD), material
requirement planning (MRP), local area networks (LAN), and computer numerical control
(CNC) machines are now the most popular AMTs used in manufacturing (Sun, 2000).
The benefits of AMT have been widely reported and can be classified as tangible and
intangible. The tangible benefits, which are easily quantifiable, include inventory savings,
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less floor space, improved return on investment (ROI), and reduced unit costs. The intangible
benefits, which are difficult to quantify, include an enhanced competitive advantage,
increased flexibility, improved product quality, and quick response to customer demand
(Ariss, Raghunathan, & Kunnathar, 2000). These benefits may still offer many other
improvements with respect to organizational improvements and management/worker
satisfaction. For example, the process of implementing
AMT might lead to better communication, redesigned workflows, or better integration of
work across functional boundaries. Changes in the manufacturing environment and in
technology induce organizations to change the manner in which they operate.
2.2.2 On Employees
In today’s world of manufacturing, unlike other sectors in the economy, the work of wage
employees is becoming increasingly complex as they find themselves having to continuously
upgrade their skills to fit the latest manufacturing technologies (Carnevale, 1991; Dean,
Dean, & Rebalsky, 1996). For example, compared to their day-to-day operations of the past,
employees are now using less manual skills and more intellectual skills as required for
operating automated machinery and processes. Their skills have also become more versatile
in the variety of manufacturing technologies they apply (Markland et al.,1998; Stevenson,
1999). According to Carnevale(1991), Douglas (1997), and Gupta and Ash(1994), employees
are being told less by their supervisors of what to do, as well as when, where, and how to do
it, and are expected to autonomously make more decisions as members of self-directed work
teams.
Researchers agree with two of Deming’s (1994) long-standing opinions regarding trends in
employee performance:Performance outcomes are being greatly influenced in breadth and
depth by increased sophistication of manufacturing and organizational systems, and
employees are being empowered to make less reactive and more proactive job-related
decisions.Thisissue affects engineers and the engineering community as citizens rather than
specifically as engineers. There are issues of competitiveness, international trade, the state of
the economy, the health of given industries, and demographics, among others. Against a
background of modest economic growth, rapid automation mises key issues for employment.
Within states, manufacturing communities particularly those of automotive, agricultural
equipment, and construction equipment have significantly reduced workforces when there
has been large-scale restructuring. There is also a trend of up skilling, meaning that higher
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educated workers replace job functions that were occupied by lower educated workers. Also
the share of IT professionals and service workers has increased, while these functions have
decreased for low educated workers. The number of engineers (engineers is a broad category
of all higher educated technical staff, including chemists, chemical and mechanical
Engineers) has increased overall, due to increases in medium educated engineers, while the
share of low and high educated engineers have decreased (Van der Zee et al, 2009). The
foresight study on skills (Van der Zee et al, 2009) shows that engineering functions in R&D
and production, but also sales and supply change management are functions that are expected
to increase. Overall employment is expected to decrease. Automation will drive competition
for skilled engineers globally, as shown in a 2008 study by PWC:
2.2.3 On Management ProcessesAnd Supervisors
There are a number of ways that automation technology can affect superior-subordinate
relation-ships. Some are direct effects of the technology itself, others are indirect effects
mediated by changes in the physical and temporal nature of work.Automated systems can
affect managers’ perceptions of the degree of rationality, flexibility, and free space of their
work. (Argyris, Feb. 1971) Automated systems have the capability to contribute to increased
rationality.
Automated systems, through their effect on the physical and temporal nature of work, can
affect methods for monitoring and controlling work. This systems can make possible remote
monitoring although many managers do not feel comfortable supervising employees they
cannot see; regardless of the employee's personal preference or the nature of the task.
Automated systems can be utilized to help increase the span of control of managers.
Increasing efficiency of production process and other functions should result in greater free
time for a manager (Canning, Richard G. 1974).
Supervisors must be able to bring out the best from both employee and technology, and learn
to make optimum use of the employee/technology relationship. To do so, supervisors must
understand technology as a concept, be familiar with the latest developments in
manufacturing technology, appreciate the impact of technology on the employee’s work, be
familiar with employee-technology relationship problems and know how to deal with them,
and be prepared to deal with the rapid and continual changes associated with modern
manufacturing technology (Goetsch, 1992; Petersen, 1989). In short, the modern supervisor
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should be a technically oriented team coach (Deeprose, 1995). This specifically refers to
engineer’s role of controlling.
2.2.4 On Interpersonal Relations
Automated systems, especially communications functions and personal applications, can
reduce the quantity and quality of social interaction and social reinforcement in the office.
Social needs play an important part in the motivation of individual workers (Maslow,
Abraham H. 1954: McGregor, D. 1960) however, it is not clearly understood whether that
motivation derives from peer group support, especially for professionals, or is purely social.
Automated office systems, especially communications functions, can affect the number of
"sociometric " links within an organization. The availability of a fast and simple
communications link should increase the amount of communications flowing along existing
paths. This impact can be positive if the communications are satisfactory or negative if
conflict exists or if inappropriate messages are sent. Another danger is that the increase in
upward communication can cause information overload at higher management levels and
lack of ability to differentiate significant information. (Canning, Richard G. 1974).
2.2.5Academic Requirements Of Engineers
There are two broad categories of employees that are of primary relevance when it comes to
materials and production: engineers and assembly line workers. Some companies argue that
there are plainly too few good candidates to hire and engineers tend to have insufficient
understanding for the requirements imposed by new technology and the opportunities they
offer, since their education and training generally have focused on metals. Engineers oversee
activities of assembly-line workers.
Engineering education (BSc, MSc, PhD) is more or less adequate. For all levels, additional
on-the job training is necessary as new BSc and MSc graduates but also PhD’s have
insufficient experience of practicalities and industrial realities and need at least a year on the
job training for industry-production specific knowledge and skills. So there is a need for
engineers that leave university with a more profound understanding of industrial production,
including simulation, automation, logistics etc. as well as of knowledge of design and
manufacturing with anisotropic materials (SRA SusChem, 2005).
Cefic undertook a survey concerning skill needs of the chemical sector. For engineers, project
and innovation management, next to understanding of suppliers and consumers are important.
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Communication skills and team-work skills are seen as important personal skills. Also for
engineers process modelling and simulation, reaction engineering and modern process design
and process intensification are important.
According to Van der Zee et al (2009), IT skills will become increasingly important for
supply chain management and modelling and simulation in R&D and production processes.
Interdisciplinary skills are important for all engineering types (R&D and production). Large
multinational companies expect from the people whom they hire that they keep up to date
with new technological developments
2.3Science Technology and Innovationin Kenya
Kenya Vision 2030 recognizes the role of science, technology and innovation (STI) in a
modern economy, in which new knowledge plays a central role in boosting wealth creation,
social welfare and international competitiveness. Kenya’s prosperity will hinge on integrating
its socio-economic development with sustainable S T & I policy. Technologically and
scientifically, skilled workforce trained to work with modern equipment and production
processes will be the pillar on which Kenya’s aspiration will be anchored.
A study conducted by National Defence College (Volume 1, 2012) entitled “Application of
science and technology in Kenya’s socio-economic development” predicts the following
ramifications of new technology on industries, more specifically, on employment and
industrialization. These are issues of primary importance to a Kenyan engineer as
well.Automation in Kenya will demand the engineer to be well versed with utilization of
modern equipment to produce value added globally competitive goods and services.
Employment in Kenya’s manufacturing sector : Kenya’s manufacturing sector is among the
key productive sectors identified for economic growth and development because of its
immense potential for wealth, employment creation and poverty alleviation.Manufacturing
sector makes an important contribution to the Kenyan economy and Central Bureau of
Statistics (2005) reports that formal employment by the manufacturing sector is 254,000
people, which represents 13 per cent of total employment withan additional 1.4 million
people employed in the informal side of the industry.
Emerging Issues and Challenges facing engineers in manufacturing sector: Low technology,
innovation, research and development uptake: The lack of knowledge, high cost and fear of change
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has led to low technologyuptake. Similarly, there are weak linkages between the technology and
researchproviders and the market.
Limited technical and managerial skills:Strategic management and technical skills are not
developed in a well-structuredand coordinated manner and are virtually absent in MSMIs.
There is also amismatch between available technical skills and market demands due to
poorlinkages between engineering training institutions and the industry(manufacturing and
industry sector report, 2010)
2.4Future trends in demand and skills for engineering job functions
Erick Brynjolfsson (professor at MIT) sloan school of management and his collaborator and
coarthur Andrew Mcfee asserted on how rapid technological change has ben destroying jobs
faster than it is creating them contributing to the stagnation of the median income and growth
of inequality. In the in his demonstration by a chart Brayn show separate lines that represent
productivity and total employment.For instances,4 years after world war 2,the two lines
closely tracked each other,with increases in jobs corresponding to increase in productivity.
The pattern is clear i.e,as businesses generated more values from from their workers the
country as a whole became richer which fueled economic activity and fuelled even more jobs.
In the begining of the year 2000, the lines diverged,productivity continues to rise robustly,but
employmnet suddenly wilts.By 2011,a significant gap appears between the two lines showing
economic with no parrallel increase in job creation.The two called it “the great
decoupling”.Prof says he is confident that technology is behind boththe healty growth in
productivity and the weak growth in jobs.Its a startling assetion beacause it threatens the
faith that many economics place in technological progress in the nearing future.
Computer technologies are changing the types of jobs available and are not always for the
good.High paying jobs requiring creativity and problem solving skills often aided
by
computers have proliferated.The improvements in technology have changed the nature of
work and destroyed some types of jobs in the process.In 1900,41% of americans worked in
agriculture,by 2000 it was only 2%.Likewise the proportion of americans employed in
manufacturing has dropped from 30% in the post world world war2 years to around 10
percent.Todaypartly
because
of
increasing
automation,especially
during
the
1980s.Certainly,the worst consequence would be to have skills which are nolonger needed by
the employer
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CHAPTER 3: METHODOLOGY
3.1 Objectives of Survey
This study attempts to investigate the topic, “ Automation in the manufacturing industry
and how it affects the role of the engineer as a manager” having wider practical perspectives
while specifically concentrating on the impact of the automation implementation on the
engineers performance.A list of the survey objectives were listed under the aim of the
study subtopic
3.2 POPULATION AND SAMPLE SIZE
The target population of this survey is the manufacturing companies in Kenya , narrowing
our scope to the Nairobi area due to proximity reasons. We obtained the
list of
companies(attched apendixe D) in from;(KAM) Kenya Association of Manufacturers,
(KEPSA) Kenya private sector alliance and (KRA) Kenya Revenue Authority.For this study
the companies,which have been selected were precisely manufacturing companies. KAM
groups industrial sectors into twelve categories but for our research situation,we grouped
similar companies together and formulated six categories.Sampling of companies to be
visited was done putting into consideration their location ( the study is biased to companies
based in Nairobi area).An estimated 35% of the manufacturing companies in Nairobi.The
bear minimum percentage threshold used.In this survey two methods have been employed ;physical presentation of the questionairre and the internet(e-mail based) methods.
A
total of 33 companies were identified
and
contacted.This number was largely
influenced by the constraints in financial recourses other than the former reason.for the first
method, the questionnaires were physically presented to 23 manufacturing companies andfor
the second method an e-mail message sent to the
remaining 10 manufacturing
companies.Definatively,the traditional physical survey involves presenting the questionnaires
in person to the selected companies while the online survey involves sending an email with
soft copy version of the same questionarre.
3.4 Scaling of Responses and Response Rate
Scales are ways of ordering possible responses to a question .The primary objective of the
questionnaire is to ensure standardization and comparability of the data across interviews
where everyone is asked the same questions.There are various types of questionnaires
namely:- open-ended and close ended types.we utilised the close ended types.The close ended
types are;-Likert-Scale,multiple-Choice,Ordinal,categorical numerical and the cumulative
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scale types..The type of scale used was a likert intervalscale kind of a questionairre. This is
one of the most common scaled-response format questions in survey design today is the
Likert scale.Our reasons for choosing it were;ability to provide the necessary
information,consideration to the respondent,ability to meet editing, coding and data
processing requirements.Specifically,the one used toaddress the research objective in this
research paper is of the 5-point Likert scale format.The selection options used were very;satisfied ,satisfied ,neither satisfied nor dissatisfied ,dissatisfied and very dissatisfied
Apart from the problem of response errors that have a bearing on the reliability and
validity of the survey and consequently the research study, the problem of low response rate
has always been a major cause of concern to any researcher.During our visits to most
privately-family owed companies there was an obvious reluctancy demonstated. We managed
to get data from physically visiting 21 companies and through email, we got replies from only
3 companies. This resulted in a total of 24 companies to analyse. Several companies were
either uncooperative or the production managers were not available at that moment.The
various types of non-response are grouped and mentioned below; Companies outside the
Nairobi eg;Bata ,located in Limuru; and Companies refusing to co-operate eg; New KCC,
Pipemakers and Change of addresses or wrong addresses eg; Furaha gin manufacturers.
3.5 Questionnaire Design
The physical survey materials werea 4 page printed questionnaire, A cover letter (refer to
Appendixes B & C )printed on the official letter head of the department of Mechanical and
Manufacturing engineering, University of nairobi, stamped and signed by the chairman.
A physical survey had a coverage of 33 of the manufacturing companies in Kenyan.This were
Nairobi-based manufacturing companies.Similarly a procedure was used for the emailbased survey, difference being that no printing was conducted. The draft questionnaire,
which relates to the survey’s objectives had been divided into three sections to look at
several aspects of an engineers job.The details are as per appendix C.
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CHAPTER FOUR: DATA ANALYSIS
4.1 Introduction
This chapter covers the analyses of the results obtained from the survey, together with
the comparison between traditional physical presentation mode of survey and web based
survey specifically delving into the email based mode. The main software used in this
task is SPSS (Statistical Package for the Social Sciences),which is a data management
and analysis product produced by SPSS, Inc in Chicago,Illinois. In SPSS, some
descriptive statistics such as,frequencies, and charts have been used in the analysis of these
results, also correlation and t-test techniques have been used to find out the relationship
between some variables.
4.2Response
4.2.1 Response Rate
The apparent disadvantage of on-line survey is the comparatively low response rate. In this
survey the questionnaire was physically presented to the sample population. The response
rate of the physically presented questionnaire and the emailed questionnaire were 91.03%
and 33.33% respectivelyof the initial questionnares presented.
Conclusively, this resulted into a total of 72.727% response rate. This could have been better
though in this light of massive constraints, its satisfactory. From Figures 4.1, it is clear that
the response rate in physically presented survey is much higher than the response rate in the
web based survey (email-based survey).
This finding is in line with Dommeyer and Moriarty’s argument that online data collection
methods do not result in higher response level. This also supports the work of McDonald and
Adam who found that the response level of online data collection method is far less than half
that of the physical data collection method.
4.2.2 Response Time
Short response time certainly is one of the anticipated advantages in ideal conditions of online surveys. Online surveys allow messages to be instantly delivered to their recipients,
irrespective of their geographic location Ray,et al in their survey of on-line surveys, found
that 34% of the on-line surveys took less than two weeks, 33% between two weeks
and one month and 33% longer than one month.
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In this on-line survey, the first message through the email- based mode of online survey was
sent on the 2nd February 2014. About 6 5% of responses had been received within a
day of the message being sent.The reminder emails were sent on 10th february 2014.
About 22% of the responses within a day of the message being sent.The process of checking
the email account for the remaining responses continued till the
17th of
the month
Therefore the period of time for on-line survey was about 17days.
As for the physical survey, the first responses varied.There was not a definitive approach of
estimating the patterns of response. This highly depended on the individual attitudes of the
managers,the bureaucracy in the companies and the availability of the respondents which
highly depended on their schedule.They took longer times and proved cumbersome due to
transport logistics.
4.3Data Quality and Cost
Schonlau et al have demonstrated that data quality is usually measured by the number of
respondents who have,intentionally or unintentionally,missed at least one survey item or by
the percentage of missed items on the respondents’ questionnaires .
In the online survey no variables was found that have no information in the survey’s database
(respondents
did
give all required answers). Same to the physical presented survey
questionarres.Based on these findings it appears that the respondents had a good
understanding of the variables in the questionarres.
Most of the facilities involved for online survey like the internet for sending and receiving the
emails
were available in the university.Even though the school provided desktop
computers,PCs were preffered for reasons of data security and easy access,so it is possible
to say that there was only marginal cost involved for online survey due to the of internet
access etc.
For the physical survey the costs involved were largely the transport and printing costs,food
and drinks in the field.
4.4 Research Questions
4.4.1 Research Question One
This question read:
The following consequences can be expected to the role of an engineer due to changes in
levels of automation in manufacturing.
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This question sought to find out how much some managerial roles of engineers have been
affected by automation. The engineer, as a manager, is responsible for functions such as
communication, staffing, decision making, controlling and leading. It seeks to find out the
effect of automation on the engineer as regards to motivation, retraining, hierarchal structure,
performance, work relations, production control and modes of communication. All of this
factors influence managerial capacity of the engineer.
In tables 4.1 and 4.2 below, numbers have been used to represent the following responses;
strongly disagree – [1], disagree – [2], neutral – [3], agree – [4], strongly agree – [5]
Tables 4.1 and 4.2 show statistics of the responses given for every question.
Statistics
Reduced
motivation
Number
Respondents
Missing
Mean
Std. Error of Mean
Median
Mode
Std. Deviation
Variance
Range
Minimum
Maximum
24
0
2.75
.183
3.00
3
.897
.804
3
1
4
Need
for Change of
retraining
hierarchal
structure
24
24
0
0
4.71
3.13
.095
.211
5.00
3.50
5
4
.464
1.035
.216
1.071
1
3
4
1
5
4
Better
performance
output
24
0
4.33
.098
4.00
4
.482
.232
1
4
5
Table 4.1:Statistical Analysis For Impacts Of Automation On Managers
Statistics
Number
Enhanced work Improved
relations
with production
junior staff
control
Change in mode of
communication
instructions
Respondents
24
24
24
Missing
0
3.71
.204
4.00
4
.999
.998
4
1
5
0
4.46
.120
4.50
5
.588
.346
2
3
5
0
4.21
.147
4.00
4
.721
.520
3
2
5
Mean
Std. Error of Mean
Median
Mode
Std. Deviation
Variance
Range
Minimum
Maximum
Table 4.2:Statistical Analysis For Impacts Of Automation On Managers
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4.4.1.1 Motivation Among Engineers
When asked whether automation led to reduced
motivation among engineers; the mode is 3
meaning most respondents were indiscriminate,
as show in table 4.1. 41.7% of them ticked on
neutral, 29.2% disagreed, and 8.3 % disagreed
strongly while
only 20.8%
agreed
that
automation caused reduction in motivation of
engineers. Table 4.3 shows this analysis.
Figure 4.1 is a diagrammatic representation of
data given in table 4.3 in percentage form.
figure 4 .1
Figure 4.1
reduced motivation
Valid
strongly disagree
disagree
neutral
agree
Total
Frequency
Percent
Valid Percent
2
7
10
5
24
8.3
29.2
41.7
20.8
100.0
8.3
29.2
41.7
20.8
100.0
Cumulative
Percent
8.3
37.5
79.2
100.0
Table 4.3: Frequency Table For Reduced Motivation
4.4.1.2 Retraining Of Engineers
To answer the question - whether
automation created a need for retraining
of engineers; the study portrays that
most respondents strongly agreed there
is need for retraining (mode is 5), as
shown in table 4.1.
Table 4.4 shows there were only two
types of responses in this question.
70.8% had strong reasons to agree and
29.2% ticked on agree.The figure
4.2,pie chart showing the response to
‘need for retraining of engineers’ in
percentages
figure 4 .2
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need for retraining
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
agree
7
29.2
29.2
29.2
strongly agree
17
70.8
70.8
100.0
Total
24
100.0
100.0
Table 4. 4: Frequency Table For Need For Retraining
4.4.1.3 Change In Hierarchal Structure
To answer the question - whether automation
lead to change in hierarchal structure in a
manufacturing company; research shows that
most respondents agreed this is the case (mode
is 4), as shown in table 4.1
Table 4.5 shows there were four types of
responses in this question. 50% agreed, 20.8%
of respondents disagreed and others were
indiscriminate. 8.3% strongly disagreed there
was need to change hierarchal structure. Figure
figure 4 .3
4.3 is a pie chart showing the response to
‘change in hierarchal structure’ in percentages
change of hierarchal structure
Valid
Frequency
Percent
Valid Percent
Cumulative Percent
strongly disagree
2
8.3
8.3
8.3
disagree
5
20.8
20.8
29.2
neutral
5
20.8
20.8
50.0
agree
12
50.0
50.0
100.0
Total
24
100.0
100.0
Table 4. 5: Frequency Table For Change Of Hierachial Structure
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4.4.1.4 Performance Output
To answer the question - whether automation
lead to better performance output from the
engineer; research shows that most respondents
agreed this is the case (mode is 4), as shown in
table 4.1
figure 4 .4
better performance output
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
agree
16
66.7
66.7
66.7
strongly agree
8
33.3
33.3
100.0
Total
24
100.0
100.0
Table 4.6: Frequency Table For Better Perfomance Output
As shown in table 4.6, there were two types of responses. 66.7% agreed while 33.3% had
strong reasons to agree. Figure 4.4 is a pie chart showing the response to ‘performance
output’ in percentages
4.4.1.5 Work Relations Between Engineers And Their Juniors
To answer the question - whether automation
enhanced work relations between engineers
and their juniors; most respondents agreed
there were enhanced relations (mode is 4), as
shown in table 4.2
Table 4.7 shows there were five types of
responses in this question. 29.2% were
indiscriminate, 4.2% of respondents disagreed
and
figure 4 .5
20.8%
strongly agreed
there
were
enhanced relations. Figure 4.5 is a pie chart
showing the response to ‘work relations
between engineers and their juniors’ in percentages
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enhanced work relations with junior staff
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
strongly disagree
1
4.2
4.2
4.2
disagree
neutral
agree
strongly agree
Total
1
7
10
5
24
4.2
29.2
41.7
20.8
100.0
4.2
29.2
41.7
20.8
100.0
8.3
37.5
79.2
100.0
Table 4.7: Frequency Table For Enhanced Work Relation With Junior Staff
4.4.1.6 Production Control
To answer the question - whether automation
improved
production
control
in
a
manufacturing company; research shows that
most respondents had strong reasons to agree
that in deed it did improve production control
(mode is 5), as shown in table 4.2
Table 4.8 shows there were three types of
responses in this question. 50% strongly
agreed,
45.8%
agreed
and
4.2%
were
indiscriminate.
Figure 4.6 is a pie chart showing the response to
figure 4 .6
‘production control’ in percentages
improved production control
Valid
neutral
agree
strongly agree
Total
Frequency
Percent
Valid Percent
Cumulative Percent
1
11
12
24
4.2
45.8
50.0
100.0
4.2
45.8
50.0
100.0
4.2
50.0
100.0
Table 4. 8: Frequency Table For Improved Production Control
4.4.1.7 Mode Of Communications
To answer the question - whether automation
has changed the mode of communications in
the manufacturing industry; research highlights
that most respondents agreed that there has
been change (mode is 4), as shown in table 4.2
Table 4.9 shows there were four types of
responses in this question. 58.3% agreed,
33.3% strongly agreed. 4.2% of respondents
figure 4 .7
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disagreed and others were indiscriminate. This shows there was change in mode of
communicating instructions.
Figure 4.7 is a pie chart showing the response to ‘change in mode of communication
instructions’ in percentages.
change in mode of communication instructions
Valid
disagree
neutral
agree
strongly agree
Total
Frequency
Percent
Valid Percent
Cumulative
Percent
1
1
14
8
24
4.2
4.2
58.3
33.3
100.0
4.2
4.2
58.3
33.3
100.0
4.2
8.3
66.7
100.0
Table 4. 9: Frequency Table For Change In Mode Of Communicating Instructions
4.4.2 Research Question Two
This question read:
The following criterions were considered to be of importance during selection of the
automation resources to be used.
The engineer is a decision maker in manufacturing and is responsible for resource planning
and control. While selecting the type of technology which should be installed in the company,
the engineer has to balance and choose between the following variables; quantity of products
to be produced, relevance of the technology, after sale service, currency of the technology,
reduction of human labour, distributed access, period of access, value added, ease of
accessibility, durability and reliability.
In tables 4.10, 4.11 and 4.12 below, numbers have been used to represent the following
responses; strongly disagree – [1], disagree – [2], neutral – [3], agree – [4], strongly agree –
[5]
Statistics
Number
quantity to meet Subject relevance
user need
Cost effectiveness
After
maintenance
Respondents
24
24
24
24
Missing
0
4.25
.109
4.00
4
.532
.283
2
3
5
0
3.92
.146
4.00
4
.717
.514
2
3
5
0
4.63
.132
5.00
5
.647
.418
2
3
5
0
4.17
.130
4.00
4
.637
.406
2
3
5
Mean
Std. Error of Mean
Median
Mode
Std. Deviation
Variance
Range
Minimum
Maximum
sale
Table 4. 10: General Statistical Data On Considerations While Acquiring An Automation Machine
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Statistics
Currency of Reduction in man Distributed
information
labour
access
Period of access
Value added
Respondents
24
24
24
24
24
Missing
Mean
Std. Error of Mean
0
3.75
.150
0
4.00
.181
0
3.83
.143
0
4.17
.167
0
4.33
.115
Median
Mode
4.00
3
4.00
4
4.00
4
4.00
4
4.00
4a
Std. Deviation
.737
.885
.702
.816
.565
Variance
.543
.783
.493
.667
.319
Range
Minimum
2
3
3
2
2
3
3
2
2
3
Maximum
5
5
5
5
5
Number
Table 4. 11: General Statistical Data On Considerations While Acquiring An Automation Machine
Statistics
Ease
of Durability of the Reliability
accessibility
machine
Respondents
24
24
24
Missing
Mean
Std. Error of Mean
Median
Mode
0
3.75
.162
4.00
3
0
4.33
.115
4.00
4
0
4.67
.098
5.00
5
Std. Deviation
.794
.565
.482
Variance
Range
Minimum
.630
2
3
.319
2
3
.232
1
4
Maximum
5
5
5
Number
Table 4. 12: General Statistical Data On Considerations While Acquiring An Automation Machine
4.4.2.1 Quantity Of Product To Meet User Need
The question –was the quantity of product
required to meet user needs, an important
consideration while selecting type of
automation resource to be used in the
company; research shows that most
respondents agreed that it was important (mode
is 4), as shown in table 4.10
Table 4.13 shows there were three types of
responses in this question. 66.7% agreed,
figure 4 .8
29.2% strongly agreed. 4.2% of respondents
were indiscriminate. This shows that it quite an
important consideration. Figure 4.8 is a pie chart showing the response to ‘quantity of product
to meet user need’ in percentages.
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quantity to meet user need
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
1
4.2
4.2
4.2
agree
16
66.7
66.7
70.8
strongly agree
7
29.2
29.2
100.0
Total
24
100.0
100.0
Table 4. 13: Frequency Table For Quantity/Ability To Meet User Need
4.4.2.2 Relevance Of Technology
The question –was the relevance of technology,
an important consideration while selecting type
of automation resource to be used in the
company; research shows that most
respondents agreed that it was important (mode
is 4), as shown in table 4.10
Table 4.14 shows there were three types of
responses in this question. 29.2% were
indiscriminate, 50% agreed, 20.8% strongly
agreed that subject relevance was an important
figure 4 .9
consideration. Figure 4.9 is a pie chart showing
the response to ‘relevance of technology’ in percentages
subject relevance
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
7
29.2
29.2
29.2
agree
12
50.0
50.0
79.2
strongly agree
5
20.8
20.8
100.0
Total
24
100.0
100.0
Table 4. 14: Frequency table for subject relevance
4.4.2.3 Cost Effectiveness
The question –was the cost effectiveness of the
technology, an important consideration while
selecting type of automation resource to be
used in the company; research shows that most
respondents strongly agreed that it was
important (mode is 5), as shown in table 4.10
Table 4.15 shows there were three types of
responses
in
this
question.
8.3%
were
indiscriminate, 20.8% agreed, 70.8% strongly
figure 4 .10
23
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agreed that cost effectiveness was an important consideration. Figure 4.10 is a pie chart
showing the response to ‘cost effectiveness’ in percentages
cost effectiveness
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
2
8.3
8.3
8.3
agree
5
20.8
20.8
29.2
strongly agree
17
70.8
70.8
100.0
Total
24
100.0
100.0
Table 4. 15: Frequency Table For Cost Effectiveness
4.4.2.4 After Sale Maintenance
The question –was after sale maintenance of
the machines, an important consideration while
selecting type of automation resource to be
used in the company; research shows that most
respondents agreed that it was important (mode
is 4), as shown in table 4.10
Table 4.16 shows there were three types of
responses in this question. 12.5% were
indiscriminate, 58.3% agreed, 29.2% strongly
agreed that after sale maintenance was an
figure 4 .11
important consideration. Figure 4.11 is a pie
chart showing the response to ‘after sale maintenance’ in percentages
after sale maintenance
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
3
12.5
12.5
12.5
agree
14
58.3
58.3
70.8
strongly agree
7
29.2
29.2
100.0
Total
24
100.0
100.0
Table 4. 16: Frequency Table For After Sales Maintanance
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4.4.2.5 Current Technology
The question –was currency of technology, an
important consideration while selecting type of
automation resource to be used in the company;
research shows that results are tied at the mode
since respondents who choose neutral and those
who agreed are ten each , as shown in table 4.11
figure 4 .12
currency of information
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
10
41.7
41.7
41.7
agree
10
41.7
41.7
83.3
strongly agree
4
16.7
16.7
100.0
Total
24
100.0
100.0
Table 4. 17: Frequency Table For Currency Of Information
Table 4.17 shows there were three types of responses in this question. 41.7% were
indiscriminate, 41.7% agreed, 16.7% strongly agreed that currency of technology was an
important consideration.
Figure 4.12is a pie chart showing the response to ‘currency of information’ in percentages
4.4.2.6 Reduction Of Human Labour
The question –was reduction of human labour in
the company, an important consideration while
selecting type of automation resource to be used
in the company; research shows that most
respondents agreed that it was important (mode
is 4), as shown in table 4.11
Table 4.18 shows there were four types of
responses
figure 4 .13
in
this
question.
4.2%
disagreed,25.0% were indiscriminate, 37.5%
agreed,
33.3% strongly agreed that reduction of human labour was an important consideration.
25
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Figure 4.13 is a pie chart showing the response to ‘reduction in human labour’ in percentages
reduction in man labour
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
1
4.2
4.2
4.2
neutral
agree
strongly agree
Total
6
9
8
24
25.0
37.5
33.3
100.0
25.0
37.5
33.3
100.0
29.2
66.7
100.0
Table 4. 18: Frequency Table For Reduction In Labour
4.4.2.7 Distributed Access
The question –was the machine’s ability to be
used in distributed access , an important
consideration
while
selecting
type
of
automation resource to be used in the
company;
research
shows
that
most
respondents agreed that it was important (mode
is 4), as shown in table 4.11.
Table 4.19 shows there were three types of
responses in this question. 33.3% were
figure 4 .14
indiscriminate, 50% agreed, 16.7% strongly
agreed that distributed access was an important consideration. Figure 4.14 is a pie chart
showing the response to ‘distributed access’ in percentages.
distributed access
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
8
33.3
33.3
33.3
agree
strongly agree
Total
12
4
24
50.0
16.7
100.0
50.0
16.7
100.0
83.3
100.0
Table 4. 19: Frequency Table For Distributed Access
4.4.2.8 Period Of Access
Table 4.20 shows there were four types of responses in this question. 4.2% disagreed, 12.5%
were indiscriminate, 45.8% agreed, 37.5% strongly agreed that period of access was an
important consideration.
Figure 4.15 is a pie chart showing the response to ‘period of access’ in percentages.
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period of access
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
1
4.2
4.2
4.2
neutral
3
12.5
12.5
16.7
agree
11
45.8
45.8
62.5
strongly agree
9
37.5
37.5
100.0
Total
24
100.0
100.0
Table 4. 20: Frequency Table For Period Of Access
4.4.2.9 Value Added
The question –was value addition, an important
consideration while selecting type of
automation resource to be used in the
company; research shows that most
respondents agreed that it was important (mode
is 4), as shown in table 4.11
Table 4.21 shows there were three types of
responses
in
this
question.
4.2%
were
indiscriminate, 58.3% agreed, 37.5% strongly
agreed that value addition was an important
figure 4 .15
consideration. Figure 4.16 is a pie chart
showing the response to ‘value addition’ in percentages
added value
Valid
neutral
agree
strongly agree
Total
Frequency
Percent
Valid Percent
1
14
9
24
4.2
58.3
37.5
100.0
4.2
58.3
37.5
100.0
Cumulative
Percent
4.2
62.5
100.0
Table 4. 21: Frequency Table For Added Value
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4.4.2.10 Ease Of Accessibility
The question –was ease of accessibility of the
technology, an important consideration while
selecting type of automation resource to be
used in the company; research shows that most
respondents were indiscriminate as they choose
‘neutral’ (mode is 3), as shown in table 4.12
Table 4.22 shows there were three types of
responses in this question. 45.8% were
indiscriminate, 33.3% agreed, 20.8% strongly
figure 4 .16
agreed that ease of accessibility of the
technology’ was an important consideration.
Figure 4.17 is a pie chart showing the response to ‘ease of accessibility of the technology’ in
percentages.
ease of accessibility
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
11
45.8
45.8
45.8
agree
8
33.3
33.3
79.2
strongly agree
5
20.8
20.8
100.0
Total
24
100.0
100.0
Table 4. 22: Frequency Table For Ease Of Access
4.4.2.11 Durability
The question –was durability of the machine,
an important consideration while selecting type
of automation resource to be used in the
company; research shows that most
respondents agreed that it was important (mode
is 4), as shown in table 4.12. Table 4.23 shows
there were three types of responses in this
question. 4.2% were indiscriminate, 58.3%
figure 4 .17
agreed, 37.5% strongly agreed that durability
was an important consideration.
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Figure 4.18 is a pie chart showing the response to ‘durability’ in percentages
durability of the machine appliance
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
1
4.2
4.2
4.2
agree
14
58.3
58.3
62.5
strongly agree
9
37.5
37.5
100.0
Total
24
100.0
100.0
Table 4. 23: Frequency Table For Durability
4.4.2.12 Reliability
The question –was reliability of the machine,
an important consideration while selecting type
of automation resource to be used in the
company;
research
shows
that
most
respondents strongly agreed that it was
important (mode is 5), as shown in table 4.12
Table 4.24 shows there were two types of
responses in this question. 33.3% agreed,
66.7% strongly agreed that reliability of the
figure 4 .18
machine was an important consideration.
Figure 4.19is a pie chart showing the response to ‘reliability’ in percentages
reliability
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
agree
8
33.3
33.3
33.3
strongly agree
16
66.7
66.7
100.0
Total
24
100.0
100.0
Table 4. 24: Frequency Table For Reliability
4.4.3 Research Question Three
This question read:
Which automation systems (AMTs) are in use in your production?
The question sought to find out the type of AMTs used in the companies. These were the
technologies that engineers worked with day to day, and thus affected them. The research
outlines, in percentages, the following types of technologies as applied in Kenya’s
manufacturing sector: JIT, MRP, CIM, CAM, FMS, FMC, BARCODE, CNC, CAD, APM,
API, AMH, LOOP, SPC and SMT.
29
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In tables 4.25, 4.26, 4.27 and 4.28 below, numbers have been used to represent the following
responses; no plan to use it in future – [1], never used – [2], slightly used – [3], used – [4],
intensively used – [5]
Frequency variables for JIT, MRPII, CIM, CAM, FMS, FMC, BARCODE, CNC, CAD,
APM, API, AMH, LOOP, SPC and SMT have been tabulated in tables below. Statistics
values such as standarddeviation, variance, range, minimum, maximum, se-mean, mean,
median and mode. Pie charts have been plotted to show pictorial presentation of frequencies
as percentages.
Statistics
Number
Respondents
Missing
Mean
Std. Error of Mean
Median
Mode
Std. Deviation
Variance
Range
Minimum
Maximum
Just in time
Manufacturing
recourse planning
24
0
3.04
.175
3.00
4
.859
.737
2
2
4
24
0
2.67
.167
2.00
2
.816
.667
2
2
4
Computer integrated Computer
aided Flexible
manufacturing
manufacturing
manufacturing
systems
24
24
24
0
0
0
2.67
3.08
2.96
.143
.216
.195
3.00
3.50
3.00
3
4
3
.702
1.060
.955
.493
1.123
.911
3
3
4
1
1
1
4
4
5
Table 4. 25: General Statistical Data On Amts Adopted In Companies
Statistics
Number
Respondents
Missing
Mean
Std. Error of Mean
Median
Mode
Std. Deviation
Variance
Range
Minimum
Maximum
Flexible manufacturing Bar code
cells
tracking
24
24
0
0
3.00
3.42
.217
.300
3.00
3.50
2
5
1.063
1.472
1.130
2.167
4
4
1
1
5
5
inventory Computer numerically
controlled machines
24
0
3.21
.324
3.00
5
1.587
2.520
4
1
5
Computer aided design
24
0
3.33
.177
3.00
3
.868
.754
4
1
5
Table 4. 26: General Statistical Data On Amts Adopted In Companies
Statistics
Automated
monitoring
process Automated
inspection
process Automated
handling
material Closed
control
Respondents
24
24
24
24
Missing
Mean
0
3.08
0
2.92
0
2.75
0
2.67
Std. Error of Mean
.288
.306
.211
.167
Median
Mode
Std. Deviation
Variance
Range
Minimum
2.50
2
1.412
1.993
4
1
2.00
2
1.501
2.254
4
1
3.00
2
1.032
1.065
4
1
3.00
2
.816
.667
3
1
Maximum
5
5
5
4
Number
loop
process
Table 4. 27: General Statistical Data On Amts Adopted In Companies
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Statistics
Statistical process control
Surface
technology
24
24
Mean
0
2.88
0
2.50
Std. Error of Mean
.202
.289
Median
3.00
2.00
Mode
4
2
Std. Deviation
.992
1.414
Variance
.984
2.000
Range
3
4
Minimum
1
1
Maximum
4
5
Number
Respondents
Missing
mounting
Table 4. 28: General Statistical Data On Amts Adopted In Companies
4.4.3.1 Just – In - Time Manufacturing
The question
was about‘just-in-time
manufacturing’ technologyin use.In the
respondents’ production system;research shows
that most respondents used this technology
(mode is 4), as shown in table 4.25
Table 4.29 shows there were three types of
responses in this question. 33.3% never used,
29.2% slightly used and 37.5% of respondents
usedjust-in-time manufacturing technology in
their company. Figure 4.20 is a pie chart
figure 4 .19
showing thefrequencies of response to the
question of ‘just-in-time manufacturing’as percentages
Just in time
Valid
Frequency
Percent
Valid Percent
Cumulative Percent
never used
8
33.3
33.3
33.3
slightly used
7
29.2
29.2
62.5
used
9
37.5
37.5
100.0
Total
24
100.0
100.0
Table 4. 29: Frequency Table For ‘Just-In-Time Manufacturing’
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4.4.3.2 Manufacturing Recourse Planning MRPII
The question – was ‘MRPII’ technologyin use
in the respondents’ production system; research
shows that most respondents neverused this
technology (mode is 2), as shown in table 4.25
Table 4.30 shows there were three types of
responses in this question. 54.2% never used,
25% slightly used and 20.8% of respondents
usedMRPII
technology
in
their
company.Figure 4.21 is a pie chart showing
figure 4. 20
thefrequencies of response to the question of
‘MRPII’as percentages.
manufacturing recourse planning
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never used
13
54.2
54.2
54.2
slightly used
6
25.0
25.0
79.2
used
Total
5
24
20.8
100.0
20.8
100.0
100.0
Table 4. 30: Frequency Table For ‘MRPII’
4.4.3.3 Computer Integrated Manufacturing, CIM
The question – was ‘CIM’ technologyin
use in the respondents’ production system;
research shows that most respondents
slightlyused this technology (mode is 3), as
shown in table 4.25
Table 4.31 shows there were four types of
responses in this question.4.2% never planned to use
this technology, 33.3% never used,54.2% slightly used
and 8.3% of respondentsusedCIM technology in their
figure 4 .21
company.
Figure
4.22
is
a
pie
chart
showing
thefrequencies of response to the question of ‘CIM’as percentages
32
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computer integrated manufacturing
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
1
4.2
4.2
4.2
never used
slightly used
used
Total
8
13
2
24
33.3
54.2
8.3
100.0
33.3
54.2
8.3
100.0
37.5
91.7
100.0
Table 4. 31: Frequency table for ‘CIM’
4.4.3.4 Computer Aided Manufacturing,
CAM
The question – was ‘CAM’ technologyin use in
the respondents’ production system; research
shows that most respondents used this
technology (mode is 4), as shown in table 4.25
Table 4.32 shows there were four types of
responses in this question. 8.3% never planned
to use this technology, 25% never used, 16.7%
slightly
figure 4 .22
used
and
50%
of
respondents
usedCAM technology in their company. Figure
4.23 is a pie chart showing thefrequencies of response to the question of ‘CAM’as
percentages.
computer aided manufacturing
Valid
Frequency
Percent
Valid Percent
Cumulative Percent
never planned
2
8.3
8.3
8.3
never used
6
25.0
25.0
33.3
slightly used
4
16.7
16.7
50.0
used
12
50.0
50.0
100.0
Total
24
100.0
100.0
Table 4. 32: Frequency Table For ‘CAM’
4.4.3.5 Flexible Manufacturing Systems,
FMS
The question – was ‘FMS’ technologyin use in
the respondents’ production system; research
shows that most respondents slightly used this
technology (mode is 3), as shown in table 4.25
Table 4.33 shows there were five types of
responses in this question. 4.2% never planned
figure 4 .23
33
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to use this technology, 25% never used, 50% slightly used, 12.5% used and 8.3% of
respondents intensively usedFMS technologyin their company. Figure 4.24 is a pie chart
showing thefrequencies of response to the question of ‘FMS’as percentages
flexible manufacturing systems
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
1
4.2
4.2
4.2
never used
6
25.0
25.0
29.2
slightly used
12
50.0
50.0
79.2
used
3
12.5
12.5
91.7
intensively
2
8.3
8.3
100.0
Total
24
100.0
100.0
Table 4. 33: Frequency table for ‘FMS’
4.4.3.6 Flexible Manufacturing Cells, FMC
The question – was ‘FMC’ technologyin use in
the respondents’ production system; research
shows that most respondents never used this
technology (mode is 2), as shown in table 4.26
Table 4.34 shows there were five types of
responses in this question. 4.2% never planned
to use this technology, 33.3% never used,
29.2% slightly used, 25% used and 8.3% of
respondents intensively usedFMC technology
figure 4 .24
in their company. Figure 4.25is a pie chart
showing thefrequencies of response to the question of ‘FMC’as percentages.
flexible manufacturing cells
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
1
4.2
4.2
4.2
never used
slightly used
used
intensively
Total
8
7
6
2
24
33.3
29.2
25.0
8.3
100.0
33.3
29.2
25.0
8.3
100.0
37.5
66.7
91.7
100.0
Table 4.34: Frequency table for ‘FMC’
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4.4.3.7 Bar Code Inventory Tracking, BARCODE
The
question
was
about‘BARCODE’ technologyin
use
in
the
respondents’
production system; research
shows that most respondents
intensively used this technology
(mode is 5), as shown in table
4.26
Table 4.35 shows there were
five types of responses in this
question. 8.3% never planned to
use
this
technology,
29.2%
never used, 12.5% slightly used,
figure 4. 25
12.5% used and 37 Figure 4.26
respondents intensively usedBARCODE technology in their company. Figure 4.26 is a pie
chart showing thefrequencies of response to the question of ‘BARCODE’as percentages
bar code inventory tracking
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
2
8.3
8.3
8.3
never used
slightly used
used
intensively
Total
7
3
3
9
24
29.2
12.5
12.5
37.5
100.0
29.2
12.5
12.5
37.5
100.0
37.5
50.0
62.5
100.0
Table 4.35: Frequency table for ‘BARCODE’
4.4.3.8 Computer Numerically Controlled
Machines, CNC
The question – was ‘CNC’ technologyin use in
the respondents’ production system; research
shows that most respondents intensively used
this technology (mode is 5), as shown in table
4.26.Table 4.36 shows there were five types of
responses in this question. 16.7% never
planned to use this technology, 25% never
used, 16.7% slightly used, 4.2% used and
figure 4 .26
37.5% of respondents intensively usedCNC
technology in their company.
35
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Figure 4.27 is a pie chart showing thefrequencies of response to the question of ‘CNC’as
percentages
Computer numerically controlled machines
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
4
16.7
16.7
16.7
never used
6
25.0
25.0
41.7
slightly used
4
16.7
16.7
58.3
used
1
4.2
4.2
62.5
intensively
9
37.5
37.5
100.0
Total
24
100.0
100.0
Table 4.36: Frequency table for ‘CNC’
4.4.3.9 Computer Aided Design, CAD
The question – was ‘CAD’ technologyin use in
the respondents’ production system; research
shows that most respondents used this
technology (mode is 3 and 4), as shown in table
4.26
Table 4.37 shows there were five types of
responses in this question. 4.2% never planned
to use this technology, 8.3% never used, 41.7%
slightly used, 41.7% used and 4.2% of
respondents intensively usedCAD technology
figure 4 .27
in their company. Figure 4.28is a pie chart
showing thefrequencies of response to the question of ‘CAD’as percentages
computer aided design
Valid
Frequency
Percent
Valid Percent
Cumulative Percent
never planned
1
4.2
4.2
4.2
never used
2
8.3
8.3
12.5
slightly used
10
41.7
41.7
54.2
used
10
41.7
41.7
95.8
intensively
1
4.2
4.2
100.0
Total
24
100.0
100.0
Table 4.37: Frequency table for ‘CAD’
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4.4.3.10
Automated
Process
Monitoring,APM
The question – was ‘APM’ technologyin use in
the respondents’ production system; research
shows that most respondents never used this
technology (mode is 2), as shown in table 4.27
Table 4.38 shows there were five types of
responses in this question. 8.3% never planned
to use this technology, 41.7% never used, 8.3%
slightly used, 16.7% used and 25% of
figure 4. 28
respondents intensively usedAPM technology
in their company. Figure 4.29 is a pie chart showing thefrequencies of response to the
question of ‘APM’as percentages.
Automated process monitoring
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
2
8.3
8.3
8.3
never used
slightly used
used
intensively
Total
10
2
4
6
24
41.7
8.3
16.7
25.0
100.0
41.7
8.3
16.7
25.0
100.0
50.0
58.3
75.0
100.0
Table 4.38: Frequency table for ‘APM’
4.4.3.11 Automated Process Inspection, API
The question – was ‘API’ technologyin use in
the respondents’ production system; research
shows that most respondents never used this
technology (mode is 2), as shown in table 4.27
Table 4.39 shows there were five types of
responses in this question. 12.5% never
planned to use this technology, 45.8% never
used, 8.3% slightly used, 4.2% used and 29.2%
figure 4 .29
of respondents intensively usedAPI technology
in their company. Figure 4.30 is a pie chart
showing thefrequencies of response to the question of ‘API’as percentages
37
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automated process inspection
Valid
Frequency
Percent
Valid Percent
Cumulative Percent
never planned
3
12.5
12.5
12.5
never used
slightly used
used
intensively
Total
11
2
1
7
24
45.8
8.3
4.2
29.2
100.0
45.8
8.3
4.2
29.2
100.0
58.3
66.7
70.8
100.0
Table 4.39: Frequency table for ‘API’
4.4.3.12 Automated Material Handling, AMH
The question – was ‘AMH’ technologyin use in
the respondents’ production system; research
shows that most respondents never used this
technology (mode is 2), as shown in table 4.27
Table 4.40 shows there were five types of
responses in this question. 8.3% never planned
to use this technology, 37.5% never used,
29.2% slightly used, 20.8% used and 4.2% of
respondents intensively usedAMH technology
figure 4 .30
in their company. Figure 4.31 is a pie chart
showing thefrequencies of response to the question of ‘AMH’as percentages
Automated material handling
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
2
8.3
8.3
8.3
never used
slightly used
used
intensively
Total
9
7
5
1
24
37.5
29.2
20.8
4.2
100.0
37.5
29.2
20.8
4.2
100.0
45.8
75.0
95.8
100.0
Table 4.40: Frequency table for ‘AMH’
4.4.3.13 Closed Loop Process Control, LOOP
The question – was ‘LOOP’ technologyin use
in the respondents’ production system; research
shows that most respondents never used this
technology (mode is 2), as shown in table 4.27
Table 4.41 shows there were four types of
responses in this question. 4.2% never planned
to
use
this
technology,
41.7%
never
used,37.5% slightly used, and 16.7% of
figure 4 .31
38
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respondents usedLOOP technology in their company.
Figure 4.32 is a pie chart showing thefrequencies of response to the question of ‘LOOP’as
percentages.
closed loop process control
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
1
4.2
4.2
4.2
never used
slightly used
used
Total
10
9
4
24
41.7
37.5
16.7
100.0
41.7
37.5
16.7
100.0
45.8
83.3
100.0
Table 4.41: Frequency table for ‘LOOP’
4.4.3.14 Statistical Process Control, SPC
The question – was ‘SPC’ technologyin use in
the respondents’ production system; research
shows that most respondents used this
technology (mode is 4), as shown in table 4.28
Table 4.42 shows there were four types of
responses in this question. 8.3% never planned
to use this technology, 29.2% never used,
29.2% slightly used, and 33.3% of respondents
usedSPC technology in their company. Figure
figure 4 .32
4.33is a pie chart showing thefrequencies of
response
to
the
question
of
‘SPC’as
percentages.
statistical process control
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
2
8.3
8.3
8.3
never used
slightly used
used
Total
7
7
8
24
29.2
29.2
33.3
100.0
29.2
29.2
33.3
100.0
37.5
66.7
100.0
Table 4. 42: Frequency table for ‘SPC’
4.5.3.15 Surface Mounting Technology, SMT
Figure 4.34
The question – was ‘SMT’ technologyin use in
the
respondents’
production
system;researchshows that most respondents
never used this technology (mode is 2), as
shown in table 4.28.Table 4.43 shows there
39
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were five types of responses in this question. 25% never planned to use this technology,
41.7% never used, 8.3% slightly used, 8.3% used and 16.7% of respondents intensively
usedSMT technology in their company. Figure 4.34 is a pie chart showing thefrequencies of
response to the question of ‘SMT’as percentages.
Surface mounting technology
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
never planned
6
25.0
25.0
25.0
never used
10
41.7
41.7
66.7
slightly used
2
8.3
8.3
75.0
used
2
8.3
8.3
83.3
intensively
4
16.7
16.7
100.0
Total
24
100.0
100.0
Table 4.43: Frequency table for ‘SMT’
4.4.4 Research Question Four
This question read:
Is automation critical in determining the following aspects of production; human labour,
inventory control, sequence of production.
The question sought to find out the extent to which, automation in a company affects the
decisions made by engineers concerning staffing, control and production planning. The above
named issues have been used in the questionnaire, to represent the actual functions of the
manager.
In table 4.44 below, numbers have been used to represent the following responses; strongly
disagree – [1], disagree – [2], neutral – [3], agree – [4], strongly agree – [5].Statistics are
based on all cases with valid data, that is all the 24 companies. Frequency variables involved
are human labour, inventory control and sequence of production. Statistics on the data
involves standard deviation, variance, range, minimum, maximum, mean, median and
mode.Pie charts have been plotted to show pictorial presentation of frequencies as
percentages.
Statistics
Human labour
Inventory control
Sequence
production
Respondents
24
24
24
Missing
Mean
Std. Error of Mean
0
4.00
.190
0
4.33
.155
0
4.50
.159
Median
4.00
4.50
5.00
Mode
Std. Deviation
Variance
Range
Minimum
4
.933
.870
3
2
5
.761
.580
2
3
5
.780
.609
3
2
Number
of
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Maximum
5
5
5
Table 4. 44: General statistics for managers’ decision on aspects of production
4.4.4.1 Human Labour
The question – was automation critical in
determination
of
human
labour
in
the
production process; research shows that most
respondentsagreed
that automation affects
decisions
on
made
human
labour
in
manufacturing (mode is 4), as shown in table
4.44 .Table 4.45 shows there were four types
of responses in this question. 8.3% disagreed,
16.7% were indiscriminate, 41.7% agreed,
figure 4 .34
33.3% strongly agreed that automation affects
decisions made on human labour in manufacturing. Figure 4.35 is a pie chart showing the
response to ‘automation effects on human labour’ in percentages
human labour
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
2
8.3
8.3
8.3
neutral
4
16.7
16.7
25.0
agree
10
41.7
41.7
66.7
strongly agree
8
33.3
33.3
100.0
Total
24
100.0
100.0
Table 4.45: Frequency table for ‘human labour’
4.4.4.2 Inventory Control
The question – was automation critical in
determination of inventory control in the
production process; research shows that most
respondentsstrongly agreed that automation
affects decisions made on inventory control in
manufacturing (mode is 5), as shown in table
4.44.Table 4.46 shows there were three types
of responses in this question. 16.7% were
indiscriminate, 33.3% agreed, 50% strongly
figure 4 .35
agreed that automation affects decisions made
on inventory control in manufacturing.
41
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Figure 4.36is a pie chart showing the response to ‘automation effects on inventory control’ in
percentages.
inventory control
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
neutral
4
16.7
16.7
16.7
agree
8
33.3
33.3
50.0
strongly agree
12
50.0
50.0
100.0
Total
24
100.0
100.0
Table 4.46: Frequency table for ‘inventory control’
4.4.4.3 Sequence of Production
The question – was automation critical in
determination of sequence of production in the
production process; research shows that most
respondentsstrongly agreed that automation
affects decisions made on sequence of
production in manufacturing (mode is 5), as
shown in table 4.44
Table 4.47 shows there were four types of
responses in this question. 4.2% disagreed and
others were indiscriminate, 29.2% agreed,
figure 4. 36
62.5% strongly agreed that automation affects
decisions made on sequence of production in manufacturing. Figure 4.37 is a pie chart
showing the response to ‘automation effects on sequence of production’ in percentages.
Table 4.47 Frequency table for ‘sequence of production’
sequence of production
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
1
4.2
4.2
4.2
neutral
1
4.2
4.2
8.3
agree
7
29.2
29.2
37.5
strongly agree
15
62.5
62.5
100.0
Total
24
100.0
100.0
Table 4.47: Frequency table for ‘sequence of production’
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4.4.5 Research Question Five
This question read:
Automated office systems, especially communication functions and personal applications,
affect the following aspects of a managers/engineers’ work; rationality, managers’ office
layout, flexibility, decision design, free space, quality of social interactions.The question
sought to find out the extent to which, automated office systems in a company affects the
engineers’ rationale, social interactions and personal space.In table 4.48 below, numbers have
been used to represent the following responses; strongly disagree – [1], disagree – [2], neutral
– [3], agree – [4], strongly agree – [5].Statistics are based on all cases with valid data from all
the 24 companies. Frequency variables involved are rationality, managers’ office layout,
flexibility, decision design, free space, quality of social interactions. Statistics on the data
involves standard deviation, variance, range, minimum, maximum, mean, median and
mode.Pie charts have been plotted to show pictorial presentation of frequencies as
percentages.
Statistics
Affected
Managers
rationality
in layout
individual work
perception
Respondents
24
23
24
24
Available
free Reduced
space foroperation quantity/quality of
social
reinforcement
24
24
Missing
Mean
Median
0
3.38
3.50
1
3.96
4.00
0
4.17
4.00
0
3.88
4.00
0
3.13
3.00
0
3.08
3.00
Mode
4
4
4
4
3a
3
Std. Deviation
1.013
.706
.702
.741
.797
.654
Variance
1.027
.498
.493
.549
.636
.428
Range
3
3
3
3
2
3
Minimum
2
2
2
2
2
2
Maximum
5
5
5
5
4
5
Number
work Work flexibilty
Decision design
Table 4. 48: General statistics on effects of automated office systems a managers work
4.4.5.1 Rationality
The question – the presence of automated
office systems in a company has affected the
engineers’
rationale
in
the
course
of
performing his/her duties; research shows that
most respondentsagreed thatautomatedoffice
systems affected the engineers’ rationale
(mode is 4), as shown in table 4.48Table 4.49
figure 4. 37
shows there were four types of responses in
43
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this question. 25% disagreed and others were indiscriminate, 37.5% agreed, 12.5% strongly
agreed that automated office systems affected the engineers’ rationale.Figure 4.38 is a pie
chart showing the response to ‘effects of office automation systems on engineers’ rationale.’
affected rationality in individual work perception
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
6
25.0
25.0
25.0
neutral
6
25.0
25.0
50.0
agree
9
37.5
37.5
87.5
strongly agree
3
12.5
12.5
100.0
Total
24
100.0
100.0
Table 4. 49: Frequency table for responses to ‘rationality’
4.4.5.2 Manager’/Engineers’ Office Layout And Organisation
The question – the presence of automated office
systems in a company has affected the
engineers’ office organisation; research shows
that
most
respondentsagreed
thatautomatedoffice systems affected the
engineers’ organisation(mode is 4), as shown in
table 4.48
Table 4.50 shows there were four types of
responses in this question. 4.2% disagreed,13%
were indiscriminate, 65.2% agreed, 17.4%
figure 4.38
strongly agreed that automated office systems
affected the engineers’ organisation/layout.
Figure 4.39 is a pie chart showing the response to ‘effects of office automation systems on
engineers’ organisation/layout.’
Table 4.50 Frequency table for responses to ‘organisation/layout’
managers work layout
Valid
Missing
Total
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
1
4.2
4.3
4.3
neutral
agree
strongly agree
Total
System
3
15
4
23
1
12.5
62.5
16.7
95.8
4.2
13.0
65.2
17.4
100.0
17.4
82.6
100.0
24
100.0
Table 4. 50: Frequency table for responses to ‘organisation/layout’
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4.4.5.3 Work Flexibility
The question – the presence of automated office
systems in a company has affected the
engineers’ work flexibility; research shows that
most respondentsagreed thatautomatedoffice
systems affected the engineers’ work
flexibility(mode is 4), as shown in table 4.48
Table 4.51 shows there were four types of
responses in this question. 4.2% disagreed,4.2%
were indiscriminate, 62.5% agreed, 29.2%
strongly agreed that automated office systems
figure 4 .39
affected the engineers’ work flexibility.Figure
4.40 is a pie chart showing the response to ‘effects of office automation systems on
engineers’ work flexibility.’
work flexibility
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
1
4.2
4.2
4.2
neutral
1
4.2
4.2
8.3
agree
15
62.5
62.5
70.8
strongly agree
7
29.2
29.2
100.0
Total
24
100.0
100.0
Table 4 51: Frequency table for responses to ‘work flexibility’
4.4.5.4 Decision Design
The question – the presence of automated
office systems in a company has affected the
engineers’ design decision; research shows that
most respondentsagreed thatautomatedoffice
systems affected the engineers’ design decision
(mode is 4), as shown in table 4.48
Table 4.52 shows there were four types of
responses in this question. 4.2% disagreed,
figure 4 .40
20.8% were indiscriminate, 58.3% agreed,
16.7% strongly agreed that automated office
systems affected the engineers’ design decision. Figure 4.41 is a pie chart showing the
response to ‘effects of office automation systems on engineers’ decision design.’
45
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decision design
Valid
disagree
Frequency
1
Percent
4.2
Valid Percent
4.2
Cumulative Percent
4.2
neutral
5
20.8
20.8
25.0
agree
14
58.3
58.3
83.3
strongly agree
Total
4
24
16.7
100.0
16.7
100.0
100.0
Table 4.52: Frequency table for responses to ‘decision design’
4.4.5.5 Available Free Space For Operation
The question – the presence of automated
office systems in a company has affected the
engineers’ personal space; research shows that
most respondentsagreed thatautomatedoffice
systems affected the engineers’ free space
(mode is 4), as shown in table 4.48
Table 4.53 shows there were three types of
responses in this question. 25% disagreed,
37.5% were indiscriminate, 37.5% agreed that
automated office systems affected the
engineers’ free space for operation. Figure 4.42
is a pie chart showing the response to ‘effects
of office automation systems on engineers’
figure 4 .41
personal space.’
available free space for operation
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
6
25.0
25.0
25.0
neutral
9
37.5
37.5
62.5
agree
9
37.5
37.5
100.0
Total
24
100.0
100.0
Table 4.53: Frequency table for responses to ‘personal space’
4.5.5.6 Quality/ Quantity Of Social Interaction
The question – the presence of automated
office systems in a company has affected the
engineers’ quality/ quantity of social
interaction; research shows that most
respondents were indiscriminate with regard to
the ideathatautomatedoffice systems affected
the engineers’ social interactions(mode is 3), as
shown in table 4.48
figure 4. 42
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Table 4.54 shows there were four types of responses in this question. 12.5% disagreed,
70.8% were indiscriminate, 12.5% agreed, 4.2% strongly agreedthat automated office
systems affected the engineers’ social interaction. Figure 4.43 is a pie chart showing the
response to ‘effects of office automation systems on engineers’ quality/ quantity of social
interaction.’
reduced quantity/quality of social reinforcement
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
disagree
3
12.5
12.5
12.5
neutral
17
70.8
70.8
83.3
agree
3
12.5
12.5
95.8
strongly agree
1
4.2
4.2
100.0
Total
24
100.0
100.0
Table 4.54: Frequency table for responses to ‘social interaction’
4.4.6 Research Question Six
This question read:
How has the workload to the engineer changed in the following level of companies?
The variables in question included fully automated, semi-automated, not automated
companies.
The question sought to find out how the amount of work the engineer does, has changed as a
result of automation. This part seeks to find out whether new technology actually does make
the engineers’ job easier. In table 4.55 below, numbers have been used to represent the
following responses; very little – [1], little – [2], normal – [3], much – [4], very much – [5]
Statistics are based on all cases with valid data from all the 24 companies.
Statistics
Not automated
Semi-automated
Fully automated
Respondents
24
24
24
Missing
Mean
0
3.92
0
3.00
0
2.50
Median
4.00
3.00
2.00
Mode
3
3
2
Std. Deviation
.929
.511
.834
Variance
.862
.261
.696
Range
3
2
3
Minimum
2
2
1
Maximum
5
4
4
Number
Table 4.55: GeneralStatistics on the engineers’ workload due to 3 levels of automation
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4.4.6.1 Companies Not Automated
The question – what is the workload of the
engineer in companies with minimum
automation; research shows that most
respondents noted workload was normal and an
equal percentage noted workload was very
much (mode is 2 and 5), as shown in table 4.55
above.
Table 4.56 shows there were four types of
responses in this question. The response was as
follows; 4.2% little, 33.3% normal, 29.2%
figure 4. 43
much and 33.3% noted workload is very much
in manufacturing firm that are not automated.Figure 4.44 is a pie chart showing the response
to ‘workload of the engineer in non-automated companies’
not automated
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
little
1
4.2
4.2
4.2
normal
much
very much
Total
8
7
8
24
33.3
29.2
33.3
100.0
33.3
29.2
33.3
100.0
37.5
66.7
100.0
Table 4.56: Frequency table for responses to ‘companies not automated’
4.4.6.2 Semi-Automated Companies
The question – what is the workload of the
engineer in semi-automated companies;
research shows that most respondents thought
workload will be normal in semi-automated
companies (mode is 3), as shown in table 4.55
above.
Table 4.57 shows there were three types of
responses in this question. The response was as
follows; 12.5% little, 75% normal and 12.5%
figure 4. 44
noted workload is ‘much’ in manufacturing
firms that are semi-automated. Figure 4.45 is a
pie chart showing the response to ‘workload of the engineer in semi-automated companies’
48
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semi-automated
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
little
3
12.5
12.5
12.5
normal
18
75.0
75.0
87.5
much
3
12.5
12.5
100.0
Total
24
100.0
100.0
Table 4. 57: Frequency table for responses to ‘semi-automated companies’
4.4.6.3 Fully-Automated Companies
The question – what is the workload of the
engineer in fully -automated companies;
research shows that most respondents thought
workload will be little in fully -automated
companies (mode is 2), as shown in table 4.55
above.
Table 4.58 shows there were four types of
responses in this question. The response was as
follows; 4.2% very little, 58.3% little, 20.8%
normal and 16.7% noted workload is ‘much’ in
figure 4. 45
manufacturing firms that are fully -automated.
Figure 4.46 is a pie chart showing the response to ‘workload of the engineer in fully automated companies’
fully automated
Valid
Frequency
Percent
Valid Percent
Cumulative
Percent
very little
1
4.2
4.2
4.2
little
14
58.3
58.3
62.5
normal
5
20.8
20.8
83.3
much
4
16.7
16.7
100.0
Total
24
100.0
100.0
Table 4.58: Frequency table for responses to ‘fully -automated companies’
49
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CHAPTER FIVE: DISCUSSION, CONCLUSIONS AND
RECOMMENDATIONS
5.1 DISCUSSION
Analysis of the current automation levels in the kenya manufacturing industry showed that
the plants
were gradually adapting automation technologies.Most companies with an
established
brand
with multinational market reach demonstated
high levels of
automation.Most indeginous companies had reasonable automation levels but lower.
4.2% of the respondents disagreed and strongly disagreed to the assumption that increased
automation levels enhanced the managers work relations with junior staff.On the contrary,a
whooping 41.7% and 20.8% agreed and strongly agreed repectively.29.2% were neutral.
With optimum work relations,the production levels are bound to go high.This will have
positive implications to the management practices of the engineer incharge of manufacturing.
Need for retraining:Most,if not all respondents from the 24 companies had an absolute
conviction that changes in automation levels had a retraining implications.The systems have
to be mastered as the need a different approach in there operation is imminent.This is
reflective on the broader scale as the current engineering training trends are shifting to
specifics eg;- design engineering is becoming a key requirements as the manufacturing
industry starts to evolve into nano-science/precision engineering where efficiency in the most
prioritized element of mufacturing.
Change in hierachial structure of command: The flow of information from policy to
implementaion varies due to restructuring that comes with automation. From basics eg;-the
need for messengers become obsolete with intallation of intercoms. The methods of operation
from books keeeping to innitiation, monitoring of operation and quality control means
systems are more self checking nd balancing. This means a more vertical and direct structure
of management. figure above illustrates the distributions of the respondents opinions.
Reduction of motivation levels: Automation largely does not reduce motivation. Reading
through some of the closing remarks of the respondents, the manager is motivated.
Nonetheless, there were a big number of respondents who were indifferent to the motivation
aspect that comes with automation.That represented 41.7%, considerably big percentage.
Improved production control by the manager: The survey conducted indicated that most
respondents had an increase in product outputs from the inception of automated
manufacturing proceses. This included the high level monitoring to efficient systems that
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handle higher volumes of raw material and reduced processing time this had a close relation
to the the levels of production control.
By relative sense improved production control would be replicated through better perfomance
output.The respective percentages of both responses were above 66% for agree and strongly
agree combined. The research revealed that this was not a shear coincidence but a statistically
consistent trend. The confidence in the positive automation impacts coincided with the
percentage distribution of the adapted systems of automated manufacturing technologies.For
instance,the distributions had the same respondents having a management role on companies
with a technological dstribution that was relative to the effect to the role of engineers.
Reduction of the order processing time: Companies who invested substantially in automation
technologies reported more than 60 percent improvements in reduction of the order
processing time,especially those companies in the food and bevareges sector.
5.1.1 Automated Manufactured Technologies
The analysis of the survey in general,indicates that the use of automated technologies in the
manufacturing industry in Kenya is relatively low and at the same time indicates that the
implementation of automation technologies will increase in the near future.It was only one
type of technology (Computer numerically controlled machines) that indicated no plans of
adaption in a sizeable fraction of companies in the sector at 16.7%.The majority slots varried
around 4.2%.A significantly low percentage.
Most manufacturing companies
have a great enthusiasim and desire to implement
CIM,CAM,FMS, FMC and SPC in future.Reasons may be that the automated manufacturing
(CIM) covers all aspects of design,planning,manufacturing,distribution and management of
products and plants.For the production phase, manufacturing managers are interested in
continued production,minimum stock piling and bottlenecking .
Of all the types of manufacturing CIM was identified to be the type that is intensively
used.This stood at 50%and a cummulative percentage of 91.7%. This pointed to the changing
trends in the utility element a modern manufacturing process demands from the engineer.The
mode of automation rankingthe least consideration is the JIT method hitting a 4.2% low.
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intensively used
Valid
JIT
MRP
CIM
CAM
FMS
Total
Frequency
1
6
12
3
2
24
Percent
4.2
25.0
50.0
12.5
8.3
100.0
Valid Percent
4.2
25.0
50.0
12.5
8.3
100.0
Cumulative Percent
4.2
29.2
79.2
91.7
100.0
Table 4.59: Frequency table for automation technologies applied
5.1.2Improvements In Management Practices Due To High Automation Levels
This section presents the improvement achieved in managers roles through applying high
automation in all categories per sector of plants as per KAMs grouping.Six parameters were
measured,i.e Rationality,Mangers layout,Flexibility,Design decision,Free space and Reduced
quantity /quality of social reinforcement. This is presented with respondents agreement range
of 12.5% to 62.5% whilst those who strongly agreed ranged from 4% to 29% with a
cummulative percentage of both agreed and strongly agreeing ranging from 16.5% to 91.5%.
This were key determinants in reduction of order processing time,
increased delivery
scheduling effectiveness,shorter delivery time,increased procedure and
transparency,increased
product
quality,reduced
inventorylevel,Increased
information
production
flexibility, planning accuracy and reduced administrative expenses.
Reduced inventory level: It is evident that most of companies which invested substantially in
automation technologies incurred improvements in reduced inventory level, especially those
companies which have production volume highly dependant on perishables. The systems
reduced production costs as electricity bills emmanating from refrigeration. This aspect
reduces the risks of possible losses to be incurred as they are passed over to the producer.
Increased production flexibility: The results show that most of companies who invested
substantially in CIM and CAD technologies achieved increased production flexibility ,
especially those companies from the metal sector.The level of management is is more
sophisticated as the tasks and objectives are completely unique from the traditional ones of
abstract verbal order issueing.
The question possed being, “What is the workload to the manager/engineer in the following
levels of companies?”This was an evaluation to determine the intensity of the need of human
rescource requirement in automated manufacturing technology. Most semi automated
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companies highlighted no change in the task levels, this stood at 75%.Respondents indicated
there is very much a work load to the engineer.This stood at at 33.33% of the 24 respondents
interviewed.This was analysis that was carried out in a general sense of the manageral roles
in the three distinct automation environments.
Analysis of the results shows that companies which have a high automation levels had a
less conjested,elaborate and organized pattern of the role of the engineer.For companies with
a level of automation in medium or less, have achieved improvement in reduction of the
order processing time. This information is collected from remarks of the respondents.
5.2CONCLUSIONS
The following conclusions can be drawn;-The response from the industry was more
positive in postal survey than web-based Survey.However,the web-based survey is uses
less time and less costly.This project has elaborated the possible implications of automating
key areas in production systems.The survey results from manufacturing companies in the
country suggest that automating operative decision-making may be questionable, while tactic
and strategic decisions could be supported by automated decision support systems.
The
level
of automation
within
the
manufacturing industry is
extremely
variable.Manufacturing plants within the food and bevarages sector are dominant in terms of
mode.In a sample space of 24 companies they formed the biggest fraction compared to other
individual sectors. Chemicals and phamaceuticals industries are generally highly automated
and are motivated for future development.While, manufacturing plants in the motor vehicle
assembly demonstrated less enthusiasm towards automation.
The current automation application in kenyan manufacturing industry does not depend on
the computer technologies only but rather also on the intergration with the human role
which is a powerful tool in fostering optimized utilization of the automation facilities.The
survey
results
showed
that, Quantity to meet user need,Subject relevance,Cost
effectiveness,After sale maintenance,Currency of information,Reduction in man-labour(blue
collar), Distributed access,Period of Access,Added Value in manufactured product,Ease of
accessibility,Durability
and Utility reliability were key in determining the type of
technology to be adapted.
The advanced role of the engineer means that additional resourse value should be attached in
terms of knowledge to deal withthe new machines.This is achieved through retraining.A
variable that demonstrated a 70.1% fraction of respondents who both agreed and strongly
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agreed.This proved the convinience of autonomy,which in contrast to traditional task
allocations, also considers more open and flexible work settings that involve interpersonal
interaction as well as advanced human-machine interaction and system automation.This was
the established equilibrium in the engineer management functionalities emanating from the
shifting role due to automation.
5.3 RECOMMENDATIONS
Based on the research results , recommendations for further work is appropriate .These would
entail;1. Extensive research should be carried out country wide,
2. Real case studies should be conducted to assess how automated manufacturing has
been used in selected manufacturing sector.
3. Develop a guideline that allows companies to choose the most appropriate
automation level to be implemented to justify the implementation cost and provide
optimum relation with the human resource.
4. Study and develop a guideline for the human-machine transition in quest of adapting
proper automation levels with minimal negative consequenses.
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APPENDICES
APPENDIX 1: List Of Manufacturing And Processing Companies In Kenya
Aberdares Beverage Limited
Africa Spirit Co. Ltd
Afro Prime Industries Ltd
Bilflex Industries
Biscept Limited
Bms Industries Ltd
Crywan Enterprises Ltd
Elle Kenya Ltd
Fai Amarillo Limited
Frm Packers(E.A) Ltd
Gish Holding Ltd
Grand Beverages Ltd
Honeywell Industry
Julijo Investment Ltd
Kambu Distillers Ltd
Kedstar Investment
Kefima Suppliers
East African Breweries Ltd
Kenya Gin Manufact. Ltd
Kenya Wine Argencies Ltd
Keroche Breweries Ltd
London Distillers (K) Ltd
Lumat Company Ltd
Lyniber Suppliers Ltd
Mashwa Breweries
Mdi Limited
Mibbs Ventures
Patialla Distillers(K) Ltd
Pen Bon (K) Ltd
Rhino Beverages Ltd
Sangilia Wine Manuf. Ltd
The Comrade Invest. Co.Ltd
Top Rank Ltd
Udv (K) Ltd
Vine Pack Ltd
Wayne Industries Ltd
Wholesome Beverages Ltd
Zheng Hong (K) Ltd
Arthriver Minning Company
Mumias Sugar Company
Bidco Company
Kappa Oil Ltd
Atta (Kenya) Limited
Bakex Millers Ltd
Bestfoods Kenya Limited
Broadway Bakers Ltd
Cadbury Kenya Ltd
Capwell Industries Ltd
Farmers Choice Ltd
Giloil Company Limited
Kabansora Millers Limited
Kapa Oil Refineries Ltd
Kenafric Industriess Limited
Kenblest Limited
Kitui Flour Mills Ltd
Kitui Millers
Mcneel Millers Ltd
Menengai Oil Refineries Limited
Mibisco Ltd
Mini Bakeries (Nbi) Limited
Mombasa Maize Millers Limited
Mzuri Sweet Limited
Nestle Foods (K) Limited
Pembe Flour Mills Ltd
Premier Flour Mills Ltd
Pwani Oil Products Ltd
Rafiki Millers Ltd
Supaflo Flour Mills Limited
Swan Industries Ltd
Swan Millers Ltd (Mmm Ksm)
The Wrigley Co (E A) Ltd
Unga Group Ltd
Unga Ltd
United Millers Ltd
Uzuri Foods Ltd
Cosmos Limited
Glaxosmithkline Ltd
Phillips Pharmaceuticals Limited
Surgipharm Limited
Laborex Kenya Limited
Procter & Gamble (Ea) Ltd
Twiga Chemical Industries Ltd
Bata Shoe Company (Kenya) Limited
Decase Chemicals Ltd
Lab International Kenya Limited
Rift Valley Textile Ltd
Spin Knit Limited
Spinners & Spinners Ltd
Sunflag Textile & Knitwear Mills Ltd
Umoja Rubber Products Limited
Africa Apparels Epz Limited
Alpharama Ltd
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Beta Healthcare International Limited
C & P Shoe Industries Limited
Corn Products (Kenya) Limited
Ryce East Africa Limited
Toyota East Africa Limited
Simba Colt Motors Ltd
Tsavo Power Company Limited
Wartsila Eastern Africa Limited
Westmont Power (Kenya) Limited
Geopower Project Company Limited
Iberafrica Power (E A) Limited
Kenya Electricity Generating Company
Limited
Kenya Power & Lighting Company Ltd
Kenya Seed Company Limited
Unga Ltd
Van Rees B V
Global Tea And Commodities (Kenya)
Limited
Kakuzi Limited
Central Glass Industries Limited
Kenya Clay Products Ltd
Bamburi Special Products Limited
East African Portland Cement Company
Limited
Corrugated Sheets Limited
Doshi Ironmongers Limited
Galsheet Kenya Limited
Mabati Rolling Mills Limited
Steel Africa Limited
Agro Chemical & Food Company Ltd
Galaxy Paints & Coatings Ltd
Sadolins Paints (Ea) Ltd
Twiga Chemical Industries Ltd
Aristocrat Concrete Limited
Amiran Kenya Limited
Decase Chemicals Ltd
Elgon Chemicals Limited
Shell Chemicals East Africa Limited
Cosmos Limited
Procter & Gamble (Ea) Ltd
United Chemical Industries Ltd
Alan Dick & Company (East Africa)
Limited
Gold Crown Foods Epz Limited
Harmony Foods (K) Limited
High Chem Industrials Africa Limited
Jaydees Knitting Factory Ltd
Kenafric Bakery Limited
Krystalline Salt Limited
Mafuko Industries Ltd
Maize Milling Company Limited
Manji Food Industries Limited
Milly Grain Millers Limited
Mombasa Maize Millers Kisumu Limited
Mombasa Salt Works Ltd
Nairobi Flour Mills Ltd
Omaera Pharmaceuticals Limited
Osho Chemical Industries Limited
Sai Pharmaceuticals Limited
Sandstorm (Africa) Limited
Shelys Africa Limited
Somochem (Kenya) Limited
Vajas Manufacturers Ltd
Unga Farm Care (Ea) Limited
Kitale Industries Limited
National Cereals And Produce Board
Foam Mattress Ltd
Kenpoly Manufacturers Limited
Metro Plastics (K) Ltd.
Polypipes Ltd
Safepak Limited
Super Foam Ltd
Chloride Exide Kenya Ltd
Eveready East Africa Limited
Syngenta East Africa Limited
Texplast Industries Limited.
Bayer East Africa Limited.
Spinners & Spinners Ltd
Uzuri Manufacturers Limited
Spin Knit Limited
United Aryan (Epz) Limited
Bata Shoe Company (Kenya) Limited
Associated Vehicle Assemblers Limited
Bhachu Industries Ltd
Cooper Motor Corp (K) Limited
D T Dobie & Company (K) Ltd
General Motors East Africa Limited
Kenya Grange Vehicle Industries Limited
Marshalls (Ea) Ltd
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APPENDIX 2: Questionnaire
xv
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