IMPACT OF INDUSTRIAL AUTOMATION & ROBOTICS ON HUMAN RESOURCES

A STUDY ON THE IMPACT OF
INDUSTRIAL AUTOMATION AND ROBOTICS
ON HUMAN RESOURCES
AT TAFE
By
DINESH KUMAR. G
Reg. No : 311614631010
A Project Report Submitted To The Faculty Of
DEPARTMENT OF MANAGEMENT STUDIES
In The Partial Fulfilment Of The Requirements
For The Award Of The Degree
Of
MASTER OF BUSINESS ADMINISTRATION
MISRIMAL NAVAJEE MUNOTH JAIN ENGINEERING COLLEGE
(Affiliated to Anna University)
THORAIPAKKAM, Chennai 600 097
JULY 2016
ANNA UNIVERSITY
CHENNAI – 25

BONAFIDE CERTIFICATE
Certified that this project report “A STUDY ON THE IMPACT OF INDUSTRIAL
AUTOMATION AND ROBOTICS ON HUMAN RESOURCES AT TAFE ”, is the bonafide
work of DINESH KUMAR. G (Reg. No: 311614631010) who carried out the research
under my supervision.
HEAD OF THE DEPARTMENT RESEARCH SUPERVISOR
Dr. Shanthi Nachiappan Mrs. P. Tamilselvi
Professor & HOD Assistant Professor
Department of Management Department of Management
M.N.M Jain Engineering College M.N.M Jain Engineering College
Jothi Nagar, Thoraipakkam, Jothi Nagar, Thoraipakkam,
Chennai – 600 097. Chennai – 600 097.
Submitted for Project and Viva Examination held on: ………………….
INTERNAL EXAMINER EXTERNAL EXAMINER

DECLARATION
I DINESH KUMAR. G, Student of Department of Management Studies, Misrimal
Navajee Munoth Jain Engineering College, Thoraipkkam, hereby declare that the
project entitle “A STUDY ON THE IMPACT OF INDUSTRIAL AUTOMATION AND
ROBOTICS ON HUMAN RESOURCES AT TAFE” Written and submitted by me under
the supervision of Mrs.P.Tamilselvi, Asst. Professor, Department of Management
Studies is my original work.
I also declare that this report has not been submitted to any other university or
institute for the award of any fellowship, degree or diploma.
Place:
Date : (DINESH KUMAR. G)

ABSTRACT
This research is conducted to study about the impact of industrial automation
and robotics on human resources. There are some facts prevailing generally that the
Industrial automation increases the productivity to a larger extent and reduces the
labour cost for the companies. This will be very much beneficial for the companies but
there is a problematic situation of technological unemployment arising in world-wide
due to technological changes.
Other countries can withstand that situation of technological unemployment but
country like India where the total population has an illiterate level of around 40%. So
this technological change of Industrial Automation and Robotics will create a huge
negative impact on our country.
This research should be dealt with the low level employees in a manufacturing
unit, so the research is conducted at TAFE –Perambur, Chennai, which has employees
of around 300. This research was surveyed using a questionnaire which helped us get
the relevant information about the topic. Then the data was recorded, classified,
summarised and analysed using percentage analysis and SPSS statistical tools.
The results were obtained with the motive of finding a balanced solution for the
problem of technological unemployment. I hope this research will be beneficial for all
of us.

ACKNOWLEDGEMENT
I am greatly indebted to many personalities for their kind help, encouragement
and guidance for me to prepare and finish this project successfully.
I also take this opportunity to thank Shri. HARISH L METHA (Secretary-
Administration) and Sri. L. JASWANTH MUNOTH (Secretary-Academic) and Dr. C.
CHANDRASEKAR CHRISTOPHER (Principal) and Dr. M. D. K. KUMARASWAMY (Vice
– Principal) MNM Jain Engineering College, Thoraipakkam, Chennai.
I thank Dr. SHANTHI NACHIAPPAN, Professor and Head, Department of Management
Studies, MNM Jain Engineering College, Thoraipakkam, Chennai for providing me
an opportunity to do the project work.
My sincere thanks to my project guide Mrs. P. TAMILSELVI, Assistant Professor,
Department of Management Studies, MNM Jain Engineering College, Thoraipakkam,
Chennai for her valuable guidance and advice over the days which made interest on my
project work.
I express my sincere gratitude to Tractors And Farm Equipments Ltd, Perambur,
Chennai for giving me a great opportunity to do my project in their esteemed
organization.
My greatest thanks are to my well-wishers my parents and friends for their
encouragement throughout this project work.

TABLE OF CONTENTS
Abstract
Acknowledgement
List of Tables
List of Charts
List of Figures
Chapters:
Chapter No. Title Page No.
1 Introduction 1
1.1. Background Of Topic 1
1.2. Background Of Study 7
1.3. Industry Profile 10
1.4. Company Profile 11
1.5. Statement Of Problem 23
1.6. Objectives Of The Study 24
1.7. Need & Importance Of Study 25
2 Literature Survey 26
2.1. Literature Reviews 27
2.2. Research Gap & Conclusion 31
3 Research Methodology 32
3.1. Research Design 32
3.2. Limitations Of Study 33
3.3. Sampling Technique 34
3.4. Data Collection 35
3.5. Tools For Analysis 38
4 Data Analysis & Interpretation 42
4.1. Percentage Analysis & Interpretation 43
4.2. Statistical Tools Analysis & Interpretation 62
5 Conclusions 80
5.1. Summary Of Findings 81
5.2. Suggestions & Recommendations 83
5.3. Conclusions 84

Appendix 85
References & Bibliography 91

LIST OF TABLES
S.No Title Of Table Page No.
1 Table Showing Age Of Respondents. 43
2 Table Showing Qualification Of Respondents. 44
3 Table Showing Experience Of Respondents. 45
4 Table Showing Automation Experience Of Respondents. 46
5 Table Showing Respondents Opinion On Productivity. 49
6
Table Showing Respondents Opinion On Increase Of
Productivity. 48
7 Table Showing Respondents Opinion On Labour Cost. 49
8
Table Showing Respondents Opinion On Decrease Of Labour
Cost. 50
9 Table Showing Respondents Opinion On Health Issues. 51
10
Table Showing Respondents Opinion On Health Issue Level
Changes. 52
11
Table Showing Comfortability Level On Technology
Adaptation Of Respondents. 53
12
Table Showing The Method Adapted By Respondents For
Learning Technology. 54
13
Table Showing The Opinion Of Respondents On Technology
Adaptation. 55
14 Table Showing The Respondents Opinion On Unemployment. 56
15
Table Showing The Respondents Own Unemployment
Experience. 57
16
Table Showing Respondents Opinion On Others Getting
Unemployed. 58
17
Table Showing Respondents Choice On Others Getting
Unemployed. 59
18
Table Showing The Respondents Opinion On Industrial
Automation & Robotics. 60
19
Table Showing Chi-Square Test Summary On Productivity
And Experience. 62
20
Table Showing Symmetric Measures Test On Productivity
And Experience. 62

21
Table Showing Cross Tabulation On Productivity And
Experience. 63
22
Table Showing Chi-Square Test On Productivity And
Experience. 64
23
Table Showing Chi-Square Test Summary On Labour Cost
And Experience. 66
24
Table Showing Symmetric Measures Test On Labour Cost And
Experience. 66
25
Table Showing Cross Tabulation On Labour Cost And
Experience. 67
26
Table Showing Chi-Square Test On Labour Cost And
Experience. 68
27
Table Showing Kruskal-Wallis H Test Description On Age
with Health Issues. 70
28
Table Showing Kruskal-Wallis H Test Ranks On Age with
Health Issues. 70
29
Table Showing Kruskal-Wallis H Test Statistics On Age with
Health Issues. 71
30
Table Showing Chi-Square Test Summary On Qualification
And Adaptation Method. 72
31
Table Showing Symmetric Measures Test On Qualification
And Adaptation Method. 72
32
Table Showing Cross Tabulation On Qualification And
Adaptation Method. 73
33
Table Showing Chi-Square Test On Qualification And
Adaptation Method. 74
34
Table Showing Chi-Square Test Summary On Age And Impact
On Automation. 76
35
Table Showing Symmetric Measures Test On Age And Impact
On Automation. 76
36
Table Showing Cross Tabulation On Age And Impact On
Automation. 77
37
Table Showing Chi-Square Test On Age And Impact On
Automation. 78

LIST OF CHARTS
S.No Title Of Chart Page No.
1 Chart Showing Age Of Respondents. 43
2 Chart Showing Qualification Of Respondents. 44
3 Chart Showing Experience Of Respondents. 45
4 Chart Showing Automation Experience Of Respondents. 46
5 Chart Showing Respondents Opinion On Productivity. 47
6
Chart Showing Respondents Opinion On Increase Of
Productivity.
48
7 Chart Showing Respondents Opinion On Labour Cost. 49
8
Chart Showing Respondents Opinion On Decrease Of Labour
Cost.
50
9 Chart Showing Respondents Opinion On Health Issues. 51
10
Chart Showing Respondents Opinion On Health Issue Level
Changes.
52
11
Chart Showing Comfortability Level On Technology
Adaptation Of Respondents.
53
12
Chart Showing The Method Adapted By Respondents For
Learning Technology.
54
13
Chart Showing The Opinion Of Respondents On Technology
Adaptation.
55
14 Chart Showing The Respondents Opinion On Unemployment. 56
15
Chart Showing The Respondents Own Unemployment
Experience.
57
16
Chart Showing Respondents Opinion On Others Getting
Unemployed.
58
17
Chart Showing Respondents Choice On Others Getting
Unemployed.
59
18
Chart Showing The Respondents Opinion On Industrial
Automation & Robotics.
61
19
Chart Showing Chi-Square Test On Productivity And
Experience.
64
20
Chart Showing Chi-Square Test On Labour Cost And
Experience.
68
21
Chart Showing Chi-Square Test On Qualification And
Adaptation Method.
74
22
Chart Showing Chi-Square Test On Age And Impact On
Automation.
78

LIST OF FIGURES
S.No Title Of Figures Page No.
1
Labour Cost Savings From Adoptation Of Advanced
Industrial Robots
7
2
Estimated World Wide Annual Supply Of Industrial
Robots
8

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CHAPTER 1 :INTRODUCTION
1.1.Background Of Topic:
Industrial robot as defined by ISO 8373:
An automatically controlled, reprogrammable, multipurpose manipulator
programmable in three or more axes, which may be either, fixed in place or mobile for use in
industrial automation applications.
The evolution of Industrial Automation and Robotics started in the year 1954, when
the first programmable Robot is designed by George Devol. He coins the term Universal
Automation. The company which produced the first Industrial Robot was Unimation.
Then later the company was purchased by Condec Corporation and after that the
Company changed its name as American Machine and Foundary Corporation (AMF
Corporation).
The first Industrial Robot was purchased by General Motors Automobile Factory in
New Jersey, in the year 1962. It performed only welding operations. But now the technological
changes and upgradations has taken Industrial Automation to an extraordinary level where
Robots are used even on Space.
Evolution Of Industrial Automation And Robotics:
1972
Robot production lines installed in Europe
1973
First robot to have six electromechanically driven axes
1973
Scheinemann started production of Vicarm/Stanford arm at Vicarm Inc, USA

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1973
Hitachi (Japan) developed the automatic bolting robot for concrete pile and pole industry.
1974
The first minicomputer-controlled industrial robot comes to market.
1974
The first arc welding robots go to work in Japan.
1974
The first fully electric, microprocessor-controlled industrial robot, IRB 6 from ASEA
1974
Hitachi (Japan) developed the first precision insertion control robot "HI-T-HAND Expert".
1975
The Olivetti "SIGMA" a cartesian-coordinate robot, is one of the first used in assembly
applications
1975
ABB developed an industrial robot with a payload up to 60 kg.
1975
Hitachi (Japan) developed the first sensor based arc welding robot "Mr. AROS".
1976
Robots in space
1977
Hitachi (Japan) developed an assembly cell to assemble vacuum cleaners with 8 TV cameras
and two robot arms
1978
First six-axis robot with own control system RE 15 by Reis, Obernburg, Germany
1978
Programmable Universal Machine for Assembly (PUMA) was developed by
Unimation/Vicarm; USA, with support from General Motors

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1978
Hiroshi Makino, University of Yamanashi, Japan, developed the SCARA-Robot (Selective
Compliance Assembly Robot Arm)
1979
Nachi, Japan, developed the first motor-driven robots
1980
First use of machine vision. At the University of Rhode Island, USA, a bin-picking robotics
system demonstrated the picking of parts in random orientation and positions out of a bin.
1980
Hitachi (Japan) developed the first commercially available all-rotary-type motor-driven
articulated "Process Robot (PW-10)".
1981
GM installed "CONSIGHT", a machine vision system
1981
PaR Systems, USA, introduced its first industrial gantry robot
1982
IBM develops a programming language for robotics, AML.
1983
Flexible Automated Assembly Lines
1984
Adept, USA, introduced the AdeptOne, first direct-drive SCARA robot
1984
ABB, Sweden produced the fastest assembly robot (IRB 1000)
1985
Wittmann, Austria developed CNC robot
1985
KUKA introduces a new Z-shaped robot arm whose design ignores the traditional
parallelogram.

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1985
FANUC developed assembly robots to assemble robots
1990
In the early 1990s, several manufacturers implement network capabilities and protocols.
1992
Wittmann, Austria introduced the CAN-Bus control for robots
1992
ABB, Sweden, launched an open control system (S4).
1992
Demaurex, Switzerland, sold its first Delta robot packaging application to Roland.
1994
Motoman introduced the first robot control system (MRC) which provided the synchronized
control of two robots.
1996
KUKA, Germany, launched the first PC-based robot control system.
1998
Reis Robotics launches the 5 robot control generator
1998
ABB, Sweden, developed the FlexPicker, the world s fastest picking robot based on the delta
robot developed by ReymondClavel, Federal Institute of Technology of Lausanne (EPFL).
1998
New features include collision detection to avoid damage and load identification to optimize
performance.
1998
Güdel, Switzerland, launched the RoboLoop system, the only curved-track gantry and transfer
system.
1999
First remote diagnosis for robots via Internet by KUKA, Germany

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1999
Reis introduces integrated laser beam guiding within the robot arm
2002
Reis Robotics enables direct interaction between human workers and robots
2003
Robots go to Mars
2003
Robocoaster, the first entertainment robot based on an articulated robot by KUKA, Germany
2004
Motoman, Japan, introduced the improved robot control system (NX100) which provided the
synchronized control of four robots, up to 38 axis.
2006
Comau, Italy, introduced the first Wireless Teach Pendant (WiTP)
2006
KUKA, Germany presents the first Light Weight Robot
2006
Motoman, Japan, launched human sized single armed (7 axis) and dual armed robot (13 axis)
with all of the supply cables hidden in the robot arm.
2007
Motoman, Japan, launched super speed arc welding robots which reduces cycle times by 15%,
the fastest welding robots in existence in 2007.
2007
KUKA, Germany, launched the first long range robot and heavy duty robot with a payload of
1,000 kg
2007
With the first systems realized in 2006, Reis Robotics became market leader for photovoltaic
module production lines

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2008
FANUC, Japan, launched a new heavy duty robot with a payload of almost 1,200kg
2009
YaskawaMotoman, Japan, introduces control system to sync up to 8 robots
2009
ABB, Sweden, launched the smallest multipurpose industrial robot, IRB120
2010
KUKA (Germany) launched a new series of shelf-mounted robots (Quantec) with a new
controller KR C4
2011
First Humanoid Robot in Space
This is how the Automation and Robotics came into manufacturing industries and
replaced Human work and gave them some relaxation. But on the other hand it grabs the
employment opportunities of illiterate people and compels them to improve their skills and
knowledge.

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1.2.Background Of Study:
Earlier Industrial Robots had limited intelligence, autonomy and operational degrees
of freedom. They were mostly designed to perform one or two sets of repetitive tasks in a
highly controlled environment.
But now Industrial Automation and Robotics are an indispensable part of today s large
manufacturing Industries. These Intelligent machines have taken over many of the tasks
requiring high precision, speed and endurance. They are becoming increasingly smarter, more
flexible and more autonomous, with the capability to make decisions and work independently
of Humans.
Figure 1:

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Figure 2:
So these Industrial Automations and Robotics are believed by most developed
countries like America, Japan, China, Germany, Korea, etc. that they reduced the labour cost
very much and increases the Productivity. And so even Indian Manufacturing companies are
installing these Industrial Automation and Robots trying to adopt the AMT – Advanced
Manufacturing Technology, having the same belief of reducing labour cast and increasing
productivity.
But on the other hand, there is some evidence that they have reduced the employment
of low-skilled workers, and to a lesser extent, middle-skilled or semi-skilled workers. That too
in India, a highly populated country with 25% illiterate people, so approximately 40% of the
working population is considered to be low-skilled and semi-skilled. If the Indian
manufacturing industry is implementing the Industrial Automation and Robotics, these low-
skilled and semi-skilled worker population is in a very threatening situation of losing their
employment.

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This research is conducted at Tractors And Farm Equipments Ltd which is the largest
manufacturer of Tractors in the world after Mahindra & Mahindra. The survey has been taken
from the low level labours at the machine shop to study about the Impact Of Industrial
Automation And Robotics on Human Resources. And also to know about the Employment
Opportunities, Labour Costs, Productivity, Safety and Welfare of Employees, and the
advantages and disadvantages of Industrial Automation and Robotics, and to find a solution
for the problem on Employment.

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1.3.Industry Profile:
The Automotive industry is the key driver of any growing economy. A sound
transportation system plays a pivotal role in a country s rapid economic and industrial
development. The well-developed Indian automotive industry ably fulfils this catalytic role by
producing a wide variety of vehicles. The automobile industry comprises automobile and auto
component sectors. It includes passenger cars; light, medium and heavy commercial vehicles;
multi-utility vehicles such as jeeps, scooters, motorcycles, three-wheelers and tractors; and
auto components like engine parts, drive and transmission parts, suspension and braking
parts, and electrical, body and chassis parts.
)ndia s automotive industry is now worth $ billion and expected to grow $ billion
in another ten years. The Indian automotive industry is growing at a very high rate with sales
of more than one million passenger vehicles per annum. The overall growth rate is 10-15 per
cent annually. )ndia is the world s second largest manufacturer of two-wheelers, fifth largest
manufacturers of commercial vehicles as well as largest manufacturer of tractors. It is the
fourth largest passenger car market in Asia and home to the largest motorcycle manufacturer.
Major players in this sector include Tata, Mahindra, Daewoo Motor India, Hyundai Motors
India and General Motors India, Maruti, Ashok Leyland, Bajaj, Hero Honda, Ford, Fiat and few
other players.
The Indian auto components industry is worth $10 billion. Indigenous firms like
Bharat Forge, Sundaram Fasteners, Minda Industries and Gabrial India Ltd. are in the
limelight. There is a boom in the auto components segment because of strong demand and
robust economy. Also, the industry has strong forward and backward linkages with almost
every other engineering segment. The component production range includes engine parts
31%, drive transmission and steering parts 19%, suspension and braking parts 12%,
electrical parts 10%, equipments 12%, body and chassis 9% and others 7%.
Indian companies are very optimistic. The Auto Components Manufacturers
Association (ACMA) along with McKinsey has pegged domestic demand for components at
$20-25 billion in 2015 from $1.4 billion in 2004-05. This would take the overall industry size
to $40-45 billion by 2015 in India. The Indian automotive industry has made rapid strides
since delicensing witnessing the entry of several new manufacturers with state-of-the-art
technology.

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1.4.Company Profile:
About TAFE
Tractors and Farm Equipment Limited (TAFE), is an Indian tractor major
incorporated in 1960 at Chennai, with an annual turnover of INR 96 billion (2014-15).
The third-largest tractor manufacturer in the world and the second largest in India by
volumes, TAFE wields 25% market share of the Indian tractor industry with a sale of
over 170,000 tractors (domestic and international) annually.
TAFE's partnership with AGCO Corporation and the Massey Ferguson brand for 53
years is a stellar example of its commitment to building long-term relationships with its
stakeholders, through fair and ethical business practices.
TAFE has earned the trust of customers through its range of products that are
widely acclaimed for its quality and low cost of operation. A strong distribution network
of over 1000 dealers effectively backs TAFE's three iconic tractor brands of Massey
Ferguson, TAFE and Eicher. TAFE exports tractors, both in partnership with AGCO and
independently, powering farms in over 75 countries which includes developed countries
in Europe and the Americas.
Besides tractors, TAFE and its subsidiaries have diverse business interests in
areas such as farm-machinery, diesel engines and gensets, engineering plastics, gears
and transmission components, batteries, hydraulic pumps and cylinders, passenger
vehicle franchises and plantations.
From a humble beginning with just one tractor model in 1961, TAFE today is
recognized as a high quality mass-manufacturer with an extensive product range to meet
the expectations of every farmer and every farm mechanization need. TAFE's R&D
facilities are centers of excellence renowned for their innovative design and engineering
expertise and have been recognized by the Department of Scientific and Industrial
Research, Government of India. Extensive research and testing ensures that TAFE's
products meet its exacting performance standards.

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TAFE's plant at Turkey manufactures a range of tractors for distribution in Turkey
through AGCO's dealer network, while another new facility has been setup at China to
cater to TAFE's ever growing global sourcing needs and value addition to its Indian and
worldwide operations. TAFE acquired Eicher's tractors, gears and transmission
components and engines business in 2005 through a wholly owned subsidiary, TAFE
Motors and Tractors Limited (TMTL).
With six tractor plants, an engine s plant, two gears and transmission components
plants, two engineering plastics units, two facilities for hydraulic pumps and cylinders
and one batteries plant besides other facilities, TAFE employs over 2500 engineers apart
from a number of specialists in other disciplines.
TAFE believes in sound corporate governance and is reputed for being a
consistent profit-making company and ethical business practices. TAFE's commitment to
CSR involves contribution to the environment and society while facilitating growth of all
stakeholders with equal fervor, embodying the role of a responsible corporate citizen.
TAFE's social focus has been significant since inception and it contributes towards
education, healthcare, agriculture, community development and supporting traditional
art forms.
TAFE is committed to the Total Quality Movement (TQM). In the recent past
various plants of TAFE have garnered, three 'TPM Excellence Awards' from the Japan
Institute of Plant Management, the 'Frost & Sullivan - IMEA Award' for significant
progress towards reliable processes, the 'Regional Contributor Award' for quality
supplies from Toyota Motor Company, Japan, and the 'Manufacturing Supply Chain
Operational Excellence - Automobiles Award' at the second Asia Manufacturing Supply
Chain Summit for its supply chain transformation, as well as a number of other regional
awards for TPM excellence. Its tractor plants are certified under ISO 9001 and under ISO
14001 for their environment friendly operations. In 2008, Business Standard awarded
TAFE the 'Star Award for Unlisted Companies' and in 2013the Public Relations Council of
India conferred TAFE with the 'Corporate Citizen of the Year'.

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TAFE has been recently named the 'Best Employer in India 2013' by Aon Hewitt
and has the distinction of receiving commendation for 'Significant Achievement on the
journey towards Business Excellence' by the CII-EXIM Bank Business Excellence Award
jury in 2012.
TAFE is a part of the Amalgamations Group based at Chennai, one of India's
largest light engineering groups, comprising of 41 companies, involved in the design,
development and manufacture of diesel engines, automobile components, light
engineering goods, plantations and services.
Vision
The philosophy that drives TAFE was first enunciated by its CMD in 1992. "To us
in TAFE, Excellence is not something that we engineer, inspect and input into our
tractors. It is an innate desire to attain the best that comes from within each of us. It
defines our lives at work and at home and ripples out into the world around us" (Source:
Corporate Film-1992).
This inspired TAFE to evolve its Vision & Values in 1999, through a company-wide
participative exercise with external facilitation from IIM, Bangalore. The Vision & Values
statement captures key elements of TAFE's culture, strengths and aspirations.
Subsequently, Warwick Manufacturing Group (WMG), UK, facilitated each element of the
V&V being linked to CSFs of the company to translate 'Vision' to 'Reality' through a
Vision to Reality Plan (VTRP) that forms basis of the company's Business Plan.
To achieve the distinction of being the first choice among the farming community
of India and ensuring a growing presence in international markets through setting
leadership standards of performance and customer care in the agricultural machinery
business.

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Core Values
TAFE's core values define our beliefs, principles and practices. It outlines the
conduct of business in our everday lives, dictates our overarching vision and corporate
strategy.
The new core values logo is a symbolic depiction of progress, energy and integrity.
It vividly portrays the value sets practised within the organization.
The outer wheel indicates enduring progress and the inner chakra infuses whorls
of energy that permeate the entire organization. The inner white space attributes justice
and fairness, the perfect canvas for inscribing our values.
People
MallikaSrinivasan, TAFE’s Chairman and CEO, is a thought leader, recognized
for entrepreneurship, commitment to excellence and contribution to Indian agriculture
machinery business and academia. In January 2014, the Government of India conferred
her with the prestigious Padma Shri award for her contribution to Trade and Industry.
In a span of 25 years, she has established TAFE as a quality mass manufacturer of
tractors, a lean and resilient organization that can effectively weather the cyclicality of
the tractor business. With her special emphasis on product development, she has
ensured significant expansion of TAFE s product range.
Padma Shri A. Sivasailam, TAFE’s former chairman was a visionary, a pioneer
and exemplar of intergrity. He ensured that excellence was institutionalized across the
organization and it is reflected in his quote Excellence is not something that we
engineer, inspect or input into our tractors. It is an innate desire to obtain the best that
comes from within each of us. It defines out lives at work and at home and ripples out
into the world around us . He was elected chairman of The Amalgamations Group, in
August 1968 and held that position till his death in 2011. He was conferred with the
prestigious Padma Shri award for his contribution to Trade and )ndustry in .

15
Shri. S. Anantharamakrishnan, TAFE’s founding chairman was a man who
nurtured a dream of a self-sufficient, prosperous and industrialized India. The
Amalgamations Group, a brainchild of Shri. S. Anantharamakrishnan, is today at the
forefront as one of )ndia s largest light engineering conglomerates. With his vision for
mechanisation of Indian farming, The Amalgamations Group ventured into the
manufacture of tractors in 1960, which marked the birth of Tractors and Farm
Equipment Ltd (TAFE).
Manufacturing Units
 Chennai
TAFE's first plant which now houses its R&D and the total machining operations of key
tractor components.
 Madurai
This modern tractor assembly plant at Kalladipatti near Madurai is set among verdant
fields and orchards.
 Doddaballapur
In a serene place called Doddaballapur, close to the city of Bangalore, is housed another
TAFE tractor plant.
 Bhopal
The Eicher Tractor plant at Mandidheep houses Eicher R&D facilities apart from a new
line to manufacture the Massey Ferguson range.
 Alwar
Alwar is in Rajasthan where Eicher diesel engines are made for captive consumption at
TAFE Motors and Tractors, as well as for supply to other original equipment
manufacturers in the industrial sector.

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 Parwanoo
Parwanoo is in the picturesque state of Himachal Pradesh where transmission
components, camshafts etc. are made for captive consumption by TMTL.
 Turkey
Located at Manisa, the plant was inaugurated on the 10th of September 2010 and is
expected to ultimately produce 15000 tractors a year. This new TAFE subsidiary - TAFE
VeTarimEkipmaniSanayiVeTicaret Limited, Sirketi, is set against an imposing
background of hills making it an apt location for manufacturing.
 China
Research & Development (R&D)
TAFE and TMTL have a well-established R&D function operating out of their
Sembium (Chennai) and Mandideep (Bhopal) plants, staffed with over 300 specialist
engineers. The main thrust of the R&D function is sustainability and economic benefit to
society and to launch products that meet evolving customer needs continually,
leveraging the most relevant technologies in both products and processes.
TAFE's R&D facility at Chennai is a centre of excellence in farm machinery design
and development since its inception in 1961. It has also been recognized by DSIR,
Ministry of Science and Technology, Government of India, for many years now. It has
developed a range of products that have helped TAFE expand in both the domestic and
exports markets and empowered it to become one of the top exporters of tractors from
India and being the preferred choice of customers in over 82 countries. Through IT
enabled processes, new product launch cycle time has been significantly reduced.
TMTL's R&D centre at Bhopal is a well-equipped farm machinery and application
engineering centre that has successfully brought out a range of cost effective, robust and
user-friendly tractors and equipment over the last fifty years. Developing a range of both
air-cooled and water-cooled tractors, the centre has a strong focus on cost, customer and
innovation, with an extensive testing and validation facilities.

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Capabilities
Our products are widely recognized for durability, fuel economy, productivity,
ergonomics, safety and easy maneuverability. The R&D function has been proactively
instrumental in enabling us design and develop technically advanced products and
processes to satisfy evolving customer needs and aspirations. The R&D function is
equipped with state of the art facilities adhering to our commitment of delivering
products that exceed customer expectations.
Some of our capabilities are listed below:
 Fully Networked CAD/ CAE Environment
 Virtual Design Development Process
 Accelerated Testing setup for various aggregates
 Field Load Data Acquisition System
 Strain measurement, vibration and noise measurement system
 Kinematics Analysis
The R&D function at TAFE has specialized facilities like:
 Computerized, climate controlled state of art vehicle dynamometer test facilities.
 Four Square and other durability test facilities.
 4WD Axle Test setup for testing endurance.
 Torture Track test setup.
 Every product that rolls out of our factories comes with a promise of trust, quality
and reliability.
Quality Focus
The Amalgamations group is one of India's largest light engineering groups with
interests in businesses that are predominantly in automobile components but also
extend to diverse areas such as plantations, batteries, printing, book selling, pesticides
and plantation products, engineering tools and paints. With a heritage of over 150 years
of serving customers from farmers to tycoons the group comprises of over 40 companies
with manufacturing units spread across India and a workforce of over 15000 employees.

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Originally comprising of a few European owned and managed companies, the group is
now wholly owned and managed by Indian promoters. The group is well known for its
strict adherence to quality in products and services and the highest standards of
corporate governance and business ethics. Financial prudence is a hall mark of the
group, leading to it being one of the least leveraged of groups with expansions being
mostly funded from internal accruals.
ISO 9001, ISO 14001 and ISO 27001 Certified Company
The focus on Total Quality Management, upgrading Vendor Quality and
productivity and strategic tie-ups with quality management specialists from Japan and
the change management exercise facilitated by the Warwick Management Group has paid
rich dividends in terms of more cost effective operations as well as being acknowledged
by customers as manufacturers of quality products.
Distribution Reach
TAFE's tractors are delivered to customers in India through a domestic network of
over 1000 dealers supported by over 2000 sales outlets. The distribution and sale of its
tractors is facilitated by the company's own fully trained sales staff of over 450 people,
and close to 3000 dealer sales staff. The tractor group has a continually expanding reach
through the setting of a large number of sales outlets and the group aims at reaching a
state when any customer in India should be able to reach a sale point within 50
kilometres of his farm. The sales effort is supported by strategically located area offices
for every major market which helps co-ordinate all sales efforts. The sales team also
facilitates financing of tractors purchases through networking with banks and other
financial institutions.
To ensure product support availability at major markets, customers are serviced
through the nationwide network of dealers apart from specialist parts distributors. TAFE
has an exclusive service team distributed among its various area offices to provide
additional service support to dealers when and where needed. In the international
markets where TAFE directly sells its tractors, distribution is through large distributors
who have their own distribution and product support network.

19
All dealer service personnel, both domestic and international, are trained at
TAFE's Product Training Centre near Chennai on usage, servicing and trouble shooting of
products through extensive and structured training programs that use a judicious mix of
class room training, on the field and in work shop training as well as through the aid of
digital media.
Collaborators & Associates
TAFE has a number of associations with industry and technology leaders such as AGCO
Corp of USA, AVL of Austria, Warwick Manufacturing Group of the UK, Carraro from Italy to
name a few, in its pursuance of product quality and overall excellence.
TAFE has an on-going collaboration with AGCO Corporation, Duluth, Georgia.
AGCO is one of the world's largest manufacturer and distributor of Agricultural
Equipment, selling its products in over 140 countries. The collaboration has lasted for 50
years and is built on mutual trust and respect for each other's competencies. These
competencies are individually and collectively leveraged for mutual benefit across
geographies through innovative arrangements for specific markets.
At TAFE we have been working with the Warwick Manufacturing Group, now
known as WMG, since the late nineties in managing change at TAFE through
companywide initiatives. With their wide exposure to manufacturing technologies,
materials and sustainability, operations and business management, TAFE has managed
the changing environment in the industry through collaboratively devised approaches
towards business that have helped ensure sustainable success despite its cyclic nature.
Markets
TAFE looks at the world as its market place. Its acquired knowledge-base of diverse
agro climatic zones, crops and cropping practices of over forty years has empowered it to
design and develop tractors and aggregates to suit any climatic zone or application in the
world. Its product development and styling strengths have been validated by worldwide

20
acceptance of its products and styling. TAFE has a presence in over 85 countries, including
markets such as the Americas, Eastern Europe, Africa and South Asia.
TAFE has leveraged its long association with its collaborator AGCO Corp to address
emerging needs in the markets by offering a range of tractors designed, engineered and
manufactured in India. Further it works jointly with AGCO in providing components and
aggregates for the manufacture of tractors by AGCO and its subsidiaries in various markets.
TAFE s presence in Europe is through a network of distributors. TAFE s European
office at Vienna oversees the European operations.
African continent is the emerging giant in terms of market requirements for farm
machinery. Recognising the importance of this market, TAFE has been making inroads into
this market through a number of distributors. Initiatives are in place to further strengthen the
distribution channel in association with our collaborators. At present the Emerging Export
Markets at TAFE oversees market initiatives in Africa.
The Asian markets are covered through distributors established by our Exports &
Emerging Markets division and as TAFE' s range expands, a greater presence in the Asian
market is expected. In its present range, TAFE boasts of a predominant presence in South East
Asia with the key markets being Sri Lanka, Bangladesh, Myanmar and Nepal.
International Business Unit
An exclusive International Business Unit handles export of tractor components
and aggregates to discerning buyers. Based out of Chennai, the unit also handles the
marketing of tractors through AGCO in key markets such as the Americas and in other
specific territories. The International Business Unit is poised to be a significant revenue
earner, especially of components and aggregates to manufacturing units in the AGCO
group.
An exclusively designed range of sub 100 HP tractors are sold under the Massey
Ferguson brand through our collaborators AGCO Corp, USA, in the American markets
through AGCO's distribution channel.

21
Exports & Emerging Markets (EEM)
TAFE sells a range of tractors in the sub 100 HP segment, under its own brand
"TAFE" with its distinctive livery of coral orange and grey in Europe and other emerging
markets through select distributors in the various countries. The distributors in turn
have their own channels for reaching the tractors to customers and providing necessary
pre and post-sale support.
Starting from neighbouring countries in South Asia, TAFE' s footprint in the EEM
area spans over 75 countries including nations in Europe, the Middle East and Africa.
TAFE, empowered by over 50 years of experience in meeting the needs of customers
from the vast range of crops and agro climatic zones in India, is uniquely positioned to
offer robust, fuel efficient and cost effective products. The products have been very well
received in the various countries and are in fact predominant in several south Asian
countries against stiff international competition.
Sustainability
Sustainability at TAFE encompasses the economic, environmental and societal
aspects of the world we live in. We believe in working with stakeholders to evolve a
business that:
Aligns with our values to ensure long term, consistent and reasonable returns on
investments to our investors.
Respects the environment we live in and takes all steps to minimize/ eliminate any
adverse impact on it while conserving natural resources, through utilization of renewable
energy sources where possible and limiting consumption of non-renewable energy sources.
Recognize our responsibility towards the society at large, and more specifically, the
people we serve as a business.
Sustainability is at the heart of TAFE s manufacturing processes. Even before
formal regulatory guidelines were in place, TAFE has been continually improving its
policies and procedures to enable sustainability in its operations.

22
As a builder of diesel-fuelled machines, we understand that going green and
minimizing the effects on the environment is not just a matter of meeting regulatory
requirements or guidelines, but a serious responsibility.
TAFE has always been driving its employees to think green at every level and has
implemented a combination of techniques to reduce the impact of manufacturing on the
environment.
While TAFE is persistently engaged in improving its processes and products, it
consciously aims at reducing its carbon footprint. We strive to minimize the use of
precious natural resources of energy and water in the manufacturing process at every
stage by employing innovative ideas.
Every parameter of environmental sustainability is linked to an ambitious target,
stricter than government-proposed norms. This stands testimony to the solemnity that
TAFE attaches to sustainability.

23
1.5. Statement Of Problem:
1. Industrial Automation And Robotics Increases The Productivity With Reduced
Labour Cost.
2. Industrial Automation And Robotics Results In Technological Unemployment For
Low And Semi-Skilled Labours.
3. Industrial Automation And Robotics Prevents Health Issues Like Mental Stress And
Physical Injuries. They Also Provide The Employees With New Technical Jobs For
Which They Have To Be Educated And Trained.

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1.6. Objectives Of The Study:
Primary Objective:
1. To study about the impact of industrial automation and robotics on human
resources.
Secondary Objectives:
2. To know whether industrial automation and robotics reduces labour cost and
increases productivity.
3. To check whether industrial automation and robotics reduces Physical Injuries and
Mental Stress of Employees.
4. To study whether the low and semi-skilled employees can adapt to the technological
upgradations.
5. To check whether industrial automation and robotics results in unemployment of
low and semi-skilled labours.

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1.7. Need & Importance Of The Study:
This study is mainly concerned about the Industrial Automation and Robotics that
creates Technological Unemployment which might affect the Economic growth of a nation,
even though they increase the productivity of firms. That too when India is considered for that
matter, the main constraint is the literacy rate of the population.
The study also covers some parts like welfare of the employees, the adaptation of
Technological Upgradation by the population, and mainly the educational reforms which will
help them to adapt the Technology.
However, Technology must be upgraded and it must be adapted by everyone to lead a
smarter life style and standard of living. But that technological upgradation must not affect
the Economic growth or destroy the employment opportunities of low-skilled workforce.
The importance of the study is to find a balanced solution for the problem of
technological unemployment and to find if there is any need for educational reforms to make
the adaptation of technology easier for the population.

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CHAPTER 2 : LITERATURE SURVEY
A literature review is a text of a scholarly paper, which includes the current
knowledge including substantive findings, as well as theoretical and methodological
contributions to a particular topic. Literature reviews are secondary sources, and do not
report new or original experimental work. Most often associated with academic-oriented
literature, such reviews are found in academic journals, and are not to be confused with book
reviews that may also appear in the same publication. Literature reviews are a basis for
research in nearly every academic field. A narrow-scope literature review may be included as
part of a peer-reviewed journal article presenting new research, serving to situate the current
study within the body of the relevant literature and to provide context for the reader. In such
a case, the review usually precedes the methodology and results sections of the work. A
literature review is an evaluative report of information found in the literature related to your
selected area of study. The review should describe, summarise, evaluate and clarify this
literature. It should give a theoretical base for the research and help you (the author)
determine the nature of your research. Works which are irrelevant should be discarded and
those which are peripheral should be looked at critically.
A literature review is more than the search for information, and goes beyond being a
descriptive annotated bibliography. All works included in the review must be read, evaluated
and analysed (which you would do for an annotated bibliography), but relationships between
the literature must also be identified and articulated, in relation to your field of research.
"In writing the literature review, the purpose is to convey to the reader what
knowledge and ideas have been established on a topic, and what their strengths and
weaknesses are. The literature review must be defined by a guiding concept (eg. your
research objective, the problem or issue you are discussing, or your argumentative thesis). It
is not just a descriptive list of the material available, or a set of summaries.
Literatures, Researches, and Articles, from the last two decades were taken into
consideration as the intervention of Automation and Robotics started since that.

27
2.1. Literature Reviews:
1.Automation And Organisational Performance: The Case Of Electronics
Manufacturing Firms In Singapore.
- Poh-Kam Wong, PhyllisisM.Ngin (1997)
This research was conducted to study the impact of Industrial Automation on
productivity of the Organisation and Labours safety and well-being. The study concluded
that improvement in Operational performance was not at the expense of labours well-
being and it showed a significant positive correlations.
This is because the labours were emphasized and insisted in skills development
and training to learn and get through withthe Industrial Automation and Technology
Upgradation.
2.Automation Effect On Jobs.
- David Greenfield (Sep1, 2011)
This article tells that automation creates more jobs that are technical in nature
instead of manual work.
People think that Automation has been as largely responsible for the reduction in
the American manufacturing work force. But many Chinese would disagree, as they too
have seen how factories can now produce more goods with fewer workers.
No industry remains static forever, change in the make-up of workforce in
inevitable. In 1900 38% of US workforces are farmers, but now only 1% is farmers. That
doesn t mean that the remaining 37% are unemployed. This is mainly due to workforce
shift. The same logic applies to manufacturing industry and that is why education is
becoming more crucial nowadays. Over the time there will be lesser manual jobs
available.

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3. The Impact Of Automation And Robotics On The Global Labour Market.
(April 20, 2013)
This articles main concern was the loss of jobs due to Automation and Robotics. In
this case Africa has been considered in a problematic condition.
Africa s Robotics use is set to remain the lowest by far of any region at just
units by 2015, meaning its productivity and competitiveness could be hindered in
comparison to other regions that are increasing their use of Automation.
The forecast of increased use of Automation could contribute to an increase in
unemployment rates globally, as the working age population aged 15-64 is set to grow
from 4.6 billion in 2012 to 5.0 billion by 2020. Many of the increased numbers of the
economically active global population may be competing for a reduced number of
available employment opportunities as a result of Automation.
Africa is a developing nation like India, and the article says that the
unemployment level will increase as there is an increase in the population.
4. Impacts Of Robotics On Employment, Safety, Quality, Productivity, And
Efficiency.
- Seegrid (Oct 15, 2013)
This article tells that Automation and Robotics will not replace or substitute all
the work done by labours and professionals manually. It also takes care of safety and
welfare and prevents health issues like mental stress & physical illness. And it also
creates more technical job opportunities for which the workforce has to be trained.
Robots are designed to be a helping hand or a high-tech tool. They help people
with tasks that would be difficult, unsafe, boring, and repetitive for a human to perform.

29
5. An Article By William H.Davidow and Michael S.Malone.
(Dec 10, 2014)
This article says that the simplistic policy answer is better training. But at this
pace of change, improving the educational system will be perpetually too little and too
late. Evan if this program is implemented it might keep up with 40% rate of progress for
only a little while.
Ultimately we need a new and better educational approach. But the lost
employment opportunities are lost.
6. Robots At Work.
- Graetz G. & Michaels G. (2015)
This article concludes that over 17 countries are using Automation and Robotics
in their manufacturing. The author finds that there is no such significant increase in the
productivity of the Organisation. Even if there is an increase in Productivity, it also
resulted in the increase of Investment on Automation.
Ultimately it is just a loss of employment and Capital Investment, but no
significant increase in productivity.
7. Good Jobs In The Age Of Automation.
- BSR (June 2015)
This study tells that there is an increase in productivity and decrease in labour
costs after the implementation of Robotics and Automation, which is really a good thing
for the Organisation.
But the pace, nature and ubiquity of technological change will have significant
impacts on job availability and quality, as well as on Human capital needs and
expectations of employees.

30
We need to find a balanced solution which increases the Productivity as well as
employees welfare which includes creation and preservation of good jobs. Or else that
will affect the growth of economy.
8. Estimating The Impact Of Robots On Productivity And Employment.
- George Graetz& Guy Michaels (July 14, 2015)
The study summarises that Industrial Robots made significant contributions to
labour productivity and aggregate growth, and also increased wages and total factor
Productivity. While fears that Robots and Automation destroy jobs on a large scale have
not materialized, we find some evidence that Robots reduced low and middle skilled
workers employment. The growing trends indicate that Robots will continue to play and
important role in improving Productivity.
9. Automation Technology And Its Impact On Jobs.
- TrevirNath (Oct 5, 2015)
This article tells that the emergence of Automation and Robotics in Industries
cannot be left ignored although they destroy jobs of blue collar and manual work.
Instead we must learn and try to adapt the technological advancements and find
new jobs.

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2.2. Research Gap &Conclusion:
We can extract some facts from the above articles and researches which will be
helping us to form our Hypotheses to proceed with the Research. There are some
information given by the Middle level employees of TAFE about the same problem of
Technological unemployment and welfare of employees. The important thing to be noted
down from the interaction is that, TAFE has undergone a major technological
upgradation named CNC (Computerised Numerical Control) Machines, which were
introduced in the year 2008 with the obvious plan of increasing the productivity and
decreasing the labour cost. They have succeeded in their strategy with the proof that the
number of employees came down from around 1500 in the year 2008 to around 300 at
present and the productivity has increased above 75%. This decrease in the number of
employees is due to voluntary retirement by most of them and the others are due to
normal age retirement. But the strategy used to reduce the number of employees is that
there was no proportionate intake of employees in the place of the retiring employees.
The Research gap is that the previous authors have not found a solution for this
problem of technological unemployment, which this research concentrates on doing.
Considering these facts as a base the Hypotheses were framed for this Research.

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CHAPTER 3 :RESEARCH METHODOLOGY
3.1.Research Design: Descriptive Research:
Descriptive research is used to describe characteristics of a population or phenomenon
being studied. It does not answer questions about how/when/why the characteristics
occurred. Rather it addresses the "what" question (what are the characteristics of the
population or situation being studied?) The characteristics used to describe the situation or
populations are usually some kind of categorical scheme also known as descriptive categories.
For example, the periodic table categorizes the elements. Scientists use knowledge about the
nature of electrons, protons and neutrons to devise this categorical scheme. We now take for
granted the periodic table, yet it took descriptive research to devise it. Descriptive research
generally precedes explanatory research. For example, over time the periodic table s
description of the elements allowed scientists to explain chemical reaction and make sound
prediction when elements were combined.
Hence, descriptive research cannot describe what caused a situation. Thus, descriptive
research cannot be used to as the basis of a causal relationship, where one variable affects
another. In other words, descriptive research can be said to have a low requirement
for internal validity.
The description is used for frequencies, averages and other statistical calculations.
Often the best approach, prior to writing descriptive research, is to conduct a survey
investigation. Qualitative research often has the aim of description and researchers may
follow-up with examinations of why the observations exist and what the implications of the
findings are.
In the same way this Research is to study about the impact of automation and robotics
on the employees. So Descriptive Research Design will be the suitable one for this research.

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3.2. Limitations Of the Study:
 This study is conducted at TAFE manufacturing plant in Perambur, Chennai only. So the
results might not be suitable for the other companies.
 The employees who left the company could not be interviewed, but they might give more
appropriate answers, as they are on the affected side.
 The employees were giving sarcastic and lethargic answers as a way of showing their
anger towards the management.
 Some employees were even acted unwilling to answer the questions asked.
 The total population of 300(approx.) could not be interviewed as there is time constraint.
So only 200 employees were randomly interviewed according to their availability.

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3.3.Sampling Technique: Probability Sampling:
Probability sampling is a sampling technique wherein the samples are gathered in a
process that gives all the individuals in the population equal chances of being selected.
In this sampling technique, the researcher must guarantee that every individual has an
equal opportunity for selection and this can be achieved if the researcher
utilizes randomization.
The advantage of using a random sample is the absence of
both systematic and sampling bias. If random selection was done properly, the sample is
therefore representative of the entire population.
The effect of this is a minimal or absent systematic bias which is the difference
between the results from the sample and the results from the population. Sampling bias is
also eliminated since the subjects are randomly chosen.
Simple Random Sampling is the easiest form of probability sampling. All the researcher
needs to do is assure that all the members of the population are included in the list and then
randomly select the desired number of subjects.
There are a lot of methods to do this. It can be as mechanical as picking strips of paper
with names written on it from a hat while the researcher is blindfolded or it can be as easy as
using a computer software to do the random selection for you.
The total targeted Population is around 300 and the Sample Size is 200 taken from
that. The employees could not be interviewed in a systematic or pre-defined manner due to
their work time constraints. So they were interviewed randomly whenever they came out for
breaks between the working hours.

35
3.4.Data Collection:
Data collection is the process of gathering and measuring information on targeted
variables in an established systematic fashion, which then enables one to answer relevant
questions and evaluate outcomes. The data collection component of research is common to all
fields of study including physical and social sciences, humanities and business. While
methods vary by discipline, the emphasis on ensuring accurate and honest collection remains
the same. The goal for all data collection is to capture quality evidence that then translates to
rich data analysis and allows the building of a convincing and credible answer to questions
that have been posed.
Data Collection Method : Primary data
Primary data may be collected either through observation or through direct
communication with respondents in one form or another through personal interviews. There
are several ways of collecting primary data.
Regardless of the field of study or preference for defining data
(quantitative or qualitative), accurate data collection is essential to maintaining the integrity
of research. Both the selection of appropriate data collection instruments (existing, modified,
or newly developed) and clearly delineated instructions for their correct use reduce
the likelihood of errorsoccurring.
A formal data collection process is necessary as it ensures that data gathered are both
defined and accurate and that subsequent decisions based on arguments embodied in the
findings are valid. The process provides both a baseline from which to measure and in certain
cases a target on what to improve.
Primary data may be collected through Personal Interview.In the personal interviews
the interviewer asks questions generally in a face to face contact. Through interview method
more and reliable information may be obtained. Personal information can be obtained easily
under this method. It is, however, a very expensive and time consuming method, especially
when large and widely spread geographical sample is taken. Certain types of respondents,
such as officials, executives or people of high income groups, may not be easily accessible.

36
In this method, the respondent may give wrong and imaginary information. For
effective interview there should be a good rapport with respondents which is often very diffi-
cult to develop. For a good result the interviewer s approach should be friendly, courteous,
conversational and unbiased for which a proper training is required.
Data Collection Instrument: Questionnaire
A Questionnaire is a research instrument consisting of a series of questions and other
prompts for the purpose of gathering information from respondents. Although they are often
designed for statistical analysis of the responses, this is not always the case. The
questionnaire was invented by the Statistical Society of London in 1838. A copy of the
instrument is published in the Journal of the Statistical Society, Volume 1, Issue 1, 1838, pages
5–13.
Questionnaires have advantages over some other types of surveys in that they are
cheap, do not require as much effort from the questioner as verbal or telephone surveys, and
often have standardized answers that make it simple to compile data. However, such
standardized answers may frustrate users. Questionnaires are also sharply limited by the fact
that respondents must be able to read the questions and respond to them. Thus, for some
demographic groups conducting a survey by questionnaire may not be concrete.
As a type of survey, questionnaires also have many of the same problems relating to
question construction and wording that exist in other types of opinion polls.
Basic rules for questionnaire item construction:
 Use statements which are interpreted in the same way by members of different
subpopulations of the population of interest.
 Use statements where persons that have different opinions or traits will give different
answers.
 Think of having an "open" answer category after a list of possible answers.
 Use only one aspect of the construct you are interested in per item.
 Use positive statements and avoid negatives or double negatives.

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 Do not make assumptions about the respondent.
 Use clear and comprehensible wording, easily understandable for all educational levels
 Use correct spelling, grammar and punctuation.
 Avoid items that contain more than one question per item (e.g. Do you like
strawberries and potatoes?).
 Question should not be biased or even leading the participant towards an answer.
Based on these rules and procedures the questionnaire for this research has been
framed in an unbiased manner with an intension to get the clean reflection about the mind set
of employees on Industrial Automation and Robotics.

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3.5. Tools For Analysis:
1. Percentage Analysis:
A per cent is a number expressed with a % sign that represents a fraction with a
denominator of 100 (which is the same as a decimal in which the decimal point is moved two
places to the left). Many people use per cent and percentage interchangeably. However,
sometimes they are differentiated by per cent meaning "per 100" and percentage meaning the
actual quantity (i.e., the per cent multiplied by the number of which a per cent is being
taken). Percentage analysis is the method to represent raw streams of data as a percentage (a
part in 100 - per cent) for better understanding of collected data.
2. Chi-Square Test:
The chi-square test for independence, also called Pearson's chi-square test or the chi-
square test of association, is used to discover if there is a relationship between two categorical
variables.
Assumptions:
When you choose to analyse your data using a chi-square test for independence, you need to
make sure that the data you want to analyse "passes" two assumptions. You need to do this
because it is only appropriate to use a chi-square test for independence if your data passes
these two assumptions. If it does not, you cannot use a chi-square test for independence.
These two assumptions are:
 Assumption #1: Your two variables should be measured at an ordinal or nominal
level (i.e., categorical data).
 Assumption #2: Your two variable should consist of two or more
categorical, independent groups. Example independent variables that meet this
criterion include gender (2 groups: Males and Females), ethnicity (e.g., 3 groups:
Caucasian, African American and Hispanic), physical activity level (e.g., 4 groups:

39
sedentary, low, moderate and high), profession (e.g., 5 groups: surgeon, doctor, nurse,
dentist, therapist), and so forth.
3. Kruskal-Wallis H Test:
The Kruskal-Wallis H test (sometimes also called the "one-way ANOVA on ranks") is a
rank-based nonparametric test that can be used to determine if there are statistically
significant differences between two or more groups of an independent variable on a
continuous or ordinal dependent variable. It is considered the nonparametric alternative to
the one-way ANOVA, and an extension of the Mann-Whitney U test to allow the comparison of
more than two independent groups.
For example, you could use a Kruskal-Wallis H test to understand whether exam
performance, measured on a continuous scale from 0-100, differed based on test anxiety
levels (i.e., your dependent variable would be "exam performance" and your independent
variable would be "test anxiety level", which has three independent groups: students with
"low", "medium" and "high" test anxiety levels). Alternately, you could use the Kruskal-Wallis
H test to understand whether attitudes towards pay discrimination, where attitudes are
measured on an ordinal scale, differed based on job position (i.e., your dependent variable
would be "attitudes towards pay discrimination", measured on a 5-point scale from "strongly
agree" to "strongly disagree", and your independent variable would be "job description",
which has three independent groups: "shop floor", "middle management" and "boardroom").
It is important to realize that the Kruskal-Wallis H test is an omnibus test statistic and
cannot tell you which specific groups of your independent variable are statistically
significantly different from each other; it only tells you that at least two groups were different.
Since you may have three, four, five or more groups in your study design, determining which
of these groups differ from each other is important. You can do this using a post hoc test (N.B.,
we discuss post hoc tests later in this guide).
This "quick start" guide shows you how to carry out a Kruskal-Wallis H test using SPSS
Statistics, as well as interpret and report the results from this test. However, before we
introduce you to this procedure, you need to understand the different assumptions that your

40
data must meet in order for a Kruskal-Wallis H test to give you a valid result. We discuss these
assumptions next.
Assumptions
When you choose to analyse your data using a Kruskal-Wallis H test, part of the process
involves checking to make sure that the data you want to analyse can actually be analysed
using a Kruskal-Wallis H test. You need to do this because it is only appropriate to use a
Kruskal-Wallis H test if your data "passes" four assumptions that are required for a Kruskal-
Wallis H test to give you a valid result. In practice, checking for these four assumptions just
adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS
Statistics when performing your analysis, as well as think a little bit more about your data, but
it is not a difficult task.
Before we introduce you to these four assumptions, do not be surprised if, when analysing
your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., is not
met). This is not uncommon when working with real-world data rather than textbook
examples, which often only show you how to carry out a Kruskal-Wallis H test when
everything goes well! (owever, don t worry. Even when your data fails certain assumptions,
there is often a solution to overcome this. First, let s take a look at these four assumptions:
 Assumption #1: Your dependent variable should be measured at
the ordinal or continuous level (i.e., interval or ratio). Examples ofordinal
variables include Likert scales (e.g., a 7-point scale from "strongly agree" through to
"strongly disagree"), amongst other ways of ranking categories (e.g., a 3-pont scale
explaining how much a customer liked a product, ranging from "Not very much", to "It
is OK", to "Yes, a lot"). Examples of continuous variables include revision time
(measured in hours), intelligence (measured using IQ score), exam performance
(measured from 0 to 100), weight (measured in kg), and so forth.
 Assumption #2: Your independent variable should consist of two or more
categorical, independent groups. Typically, a Kruskal-Wallis H test is used when you
have three or more categorical, independent groups, but it can be used for just two
groups (i.e., aMann-Whitney U test is more commonly used for two groups). Example
independent variables that meet this criterion include ethnicity (e.g., three groups:

41
Caucasian, African American and Hispanic), physical activity level (e.g., four groups:
sedentary, low, moderate and high), profession (e.g., five groups: surgeon, doctor, nurse,
dentist, therapist), and so forth.
 Assumption #3: You should have independence of observations, which means that
there is no relationship between the observations in each group or between the groups
themselves. For example, there must be different participants in each group with no
participant being in more than one group. This is more of a study design issue than
something you can test for, but it is an important assumption of the Kruskal-Wallis H
test. If your study fails this assumption, you will need to use another statistical test
instead of the Kruskal-Wallis H test (e.g., a Friedman test).
As the Kruskal-Wallis H test does not assume normality in the data and is much less
sensitive to outliers, it can be used when these assumptions have been violated and the use of
a one-way ANOVA is inappropriate. In addition, if your data is ordinal, a one-way ANOVA is
inappropriate, but the Kruskal-Wallis H test is not. However, the Kruskal-Wallis H test does
come with an additional data consideration,Assumption #4, which is discussed below:
 Assumption #4: In order to know how to interpret the results from a Kruskal-Wallis H
test, you have to determine whether thedistributions in each group (i.e., the
distribution of scores for each group of the independent variable) have the same
shape (which also means the same variability).

42
CHAPTER 4 : DATA ANALYSIS & INTERPRETATION
The process by which sense and meaning are made of the data gathered in qualitative
research, and by which the emergent knowledge is applied to clients' problems. This data
often takes the form of records of group discussions and interviews, but is not limited to this.
Through processes of revisiting and immersion in the data, and through complex activities of
structuring, re-framing or otherwise exploring it, the researcher looks for patterns and
insights relevant to the key research issues and uses these to address the client's brief.
Analysis of data is a process of inspecting, cleaning, transforming, and
modelling data with the goal of discovering useful information, suggesting conclusions, and
supporting decision-making. Data analysis has multiple facets and approaches, encompassing
diverse techniques under a variety of names, in different business, science, and social science
domains.
Data interpretation is part of daily life for most people. Interpretation is the process
of making sense of numerical data that has been collected, analyzed, and presented. People
interpret data when they turn on the television and hear the news anchor reporting on a poll,
when they read advertisements claiming that one product is better than another, or when
they choose grocery store items that claim they are more effective than other leading brands.
A common method of assessing numerical data is known as statistical analysis , and
the activity of analyzing and interpreting data in order to make predictions is known
as inferential statistics . Informed consumers recognize the importance of judging the
reasonableness of data interpretations and predictions by considering sources of bias such as
sampling procedures or misleading questions, margins of error, confidence intervals, and
incomplete interpretations.

43
18%
14%
39%
29%
Age
21-30
31-40
41-50
Above 50
4.1. Percentage Analysis & Interpretation:
Age:
Table 1: Table Showing Age Of Respondents:
Age No.of Respondents % of Respondents
21-30 36 18
31-40 28 14
41-50 78 39
Above 50 58 29
Total 200 100
Chart 1: Chart Showing Age Of Respondents:
Interpretation:
The age category consists of 39% of people from 41 to 50 years of age and 29%
of people above 50 years of age. These two age categories constitute the biggest size of
total sample size.

44
27%
11%
50%
12%
0%
Qualification
10th
12th
Diploma
UG
PG
Qualification:
Table 2: Table Showing Qualification Of Respondents:
Qualification No.of Respondents % Of Respondents
10th 54 27
12th 22 11
Diploma 101 50.5
UG 23 11.5
PG 0 0
Total 200 100
Chart 2: Chart Showing Qualification Of Respondents:
Interpretation:
50% of the sample size consists of Diploma holders basically ITI graduates. And
38% of the sample size consists of employees who have finished only school education.
Under Graduates are very less in number (12%).

45
5%
13%
14%
68%
Experience
<5Years
<10Years
<15Years
>15Years
Experience:
Table 3: Table Showing Experience Of Respondents:
Experience No.of Respondents % Of Respondents
<5 years 10 5
<10years 26 13
<15years 28 14
>15years 136 68
Total 200 100
Chart 3: Chart Showing Experience Of Respondents:
Interpretation:
This is the total work experience that an employee is having. The total sample
size consists of 68% of employees who are having more than 15 years of experience.

46
6%
29%
62%
3%
Experience
<5Years
<10Years
<15Years
>15Years
1. How long you’ve dealing with Automation &Robotics ?
Table 4: Table Showing Automation Experience Of Respondents:
Chart 4: Chart Showing Automation Experience Of Respondents:
Interpretation:
This is the experience of the employees with handling of Automation and
Robotics. 62% of them are experiencing Automation and Robotics for around 15 years.
Experience No.of Respondents % Of Respondents
<5 years 13 6.5
<10years 58 29
<15years 124 62
>15years 5 2.5
Total 200 100

47
96%
4%
Increased Productivity
Yes
No
2. Do you think that Automation & Robotics has increased the Productivity ?
Table 5: Table Showing Respondents Opinion On Productivity
Chart 5: Chart Showing Respondents Opinion On Productivity:
Interpretation:
96% of the employees are accepting that there is an increase in the Productivity
level after employing CNC Machines.
Increased Productivity ? No.of Respondents % Of Respondents
Yes 193 96.5
No 7 3.5
Total 200 100

48
13%
57%
29%
1%
Increased Productivity
< 25 %
< 50 %
< 75 %
> 75 %
3. If Yes, to what extent it has increased the Productivity ?
Table 6: Table Showing Respondents Opinion On Increase Of Productivity:
Chart 6: Chart Showing Respondents Opinion On Increase Of Productivity:
Interpretation:
57% of the employees are telling that the productivity has increased around
50% and 27% are accepting that the productivity has increased around 75% after
employing CNC Machines.
Increased Productivity ? No.of Respondents % Of Respondents
< 25 % 26 13.5
< 50 % 109 56.5
< 75 % 56 29
> 75 % 2 1
Total 193 100

49
96%
4%
Reduced Labour Cost
Yes
No
4. Do you think that Automation & Robotics has reduced the Labour Cost ?
Table 7: Table Showing Respondents Opinion OnLabour Cost:
Chart 7: Chart Showing Respondents Opinion OnLabour Cost:
Interpretation:
Again 96% of the employees are telling that the Labour Cost has decreased a lot
after employing the CNC Machines.
Reduced LabourCost ? No.of Respondents % Of Respondents
Yes 193 96.5
No 7 3.5
Total 200 100

50
14%
59%
27%
0%
Reduced Labour Cost ?
< 25 %
< 50 %
< 75 %
> 75 %
5. If Yes, to what extent it has reduced the Labour Cost ?
Table 8: Table Showing Respondents Opinion On Decrease Of Labour Cost:
Chart 8: Chart Showing Respondents Opinion On Decrease Of Labour Cost:
Interpretation:
86 % of the employees are telling that the labour cost has decreased by 75%, in
that 59% are telling it has decreased by 50%.
Reduced LabourCost ? No.of Respondents % Of Respondents
< 25 % 27 14
< 50 % 113 58.5
< 75 % 53 27.5
> 75 % 0 0
Total 193 100

51
99%
1%
Health Issues ?
Yes
No
6. Have you ever experienced Physical Injuries and Mental Stress before
employing Automation &Robotics ?
Table 9: Table Showing Respondents Opinion On Health Issues:
Chart 9: Chart Showing Respondents Opinion On Health Issues:
Interpretation:
99% of the employees are accepting that they had health issues like mental
stress and physical injuries before employing Automated Machines.
Health Issues ? No.of Respondents % Of Respondents
Yes 198 99
No 2 1
Total 200 100

52
27%
64%
7%
2% 0%
Health Issues
Decreased Much
Decreased
No Change
Increased
Increased Much
7. Whether those Physical Injuries and Mental Stress have been increased or
decreased after employing Automation& Robotics ?
Table 10: Table Showing Respondents Opinion On Health Issue Level Changes:
Chart 10: Chart Showing Respondents Opinion On Health Issue Level Changes:
Interpretation:
91% of the employees are accepting the fact that the health issues have
decreased after employing CNC Machines, in that 27% are telling the Health issues have
decreased very much when compared with manual working.
Health Issues No.of respondents % Of Respondents
Decreased Much 55 27.5
Decreased 129 64.5
No Change 13 6.5
Increased 3 1.5
Increased Much 0 0
Total 200 100

53
21%
68%
8%
3%
0%
Technology Adaptation
Very Easily
Easily
Not That Easy
Tough
Very Tough
8. How easily you adapted to the Technological Upgradation of Industrial
Automation and Robotics ?
Table 11: Showing Comfortability Level On Technology Adaptation Of Respondents:
Chart 11: Chart Showing Comfortability Level On Technology Adaptation Of
Respondents:
Interpretation:
89% of the employees are telling that they felt the adaptation of technology was
easy, in that 21% of employees are telling that it was very much easier for them.
Technology Adaptation No.of Respondents % Of Respondents
Very Easily 42 21
Easily 136 68
Not That Easy 16 8
Tough 6 3
Very Tough 0 0
Total 200 100

54
1%0%0%
99%
Adapted Method
Educational Qualification
Separate Course
Off The Job Training by TAFE
On The Job Training by TAFE
9. How did you learn to adapt to those Technological Upgradation of Industrial
Automation &Robotics ?
Table 12: Table Showing The Method Adapted By Respondents For LearningTechnology
Chart 12:Chart Showing The Method Adapted By Respondents For Learning
Technology:
Interpretation:
99% of the employees are accepting with pride that TAFE has given them On The
Job Training which made them to adapt the technological change.
Adapted Method No.of Respondents % Of Respondents
Educational Qualification 3 1.5
Separate Courses 0 0
Off The Job Training by TAFE 0 0
On The Job Training by TAFE 197 98.5
Total 200 100

55
56%
35%
0%
9%
Adaptation Method
Educational Qualification
Separate Course
Off The Job Training
On The Job Training
10. According to you, which measure can be taken to get adapted to those
Technological Upgradation of Industrial Automation &Robotics ?
Table 13: Table Showing The Opinion Of Respondents On Technology Adaptation:
Chart 13: Chart Showing The Opinion Of Respondents On Technology Adaptation:
Interpretation:
91% of the employees are recommending that education is the effective way to
impart complete knowledge about the technological changes. Inthat, 35% are
suggesting that a separate course would be much better.
Adaptation Method No.of Respondents % Of Respondents
Educational Qualification 113 56.5
Separate Courses 69 34.5
Off The Job Training 0 0
On The Job Training 18 9
Total 200 100

56
30%
70%
Creating Unemployment
Yes
No
11. Do you think Industrial Automation & Robotics create Unemployment ?
Table 14: Table Showing The Respondents Opinion On Unemployment:
Chart 14: Chart Showing The Respondents Opinion On Unemployment:
Interpretation:
70% of the employees are giving the surprising answer that no unemployment is
created due to technological changes in their company. Only 30% of employees are
telling that unemployment is created due to technological upgradation.
Creating Unemployment ? No.of Respondents % Of Respondents
Yes 61 30.5
No 139 69.5
Total 200 100

57
12%
88%
Suffered Unemployment
Yes
No
12. Have you ever suffered unemployment due to Industrial Automation and
Robotics ?
Table 15: Table Showing The Respondents Own Unemployment Experience:
Chart 15: Chart Showing The Respondents Own Unemployment Experience:
Interpretation:
88% of the employees have not suffered the situation of unemployment but the
important thing to be noted is that 12% of the employees are feeling that they are not
having job security.
Suffered Unemployment ? No.of Respondents % Of Respondents
Yes 25 12.5
No 175 87.5
Total 200 100

58
30%
70%
Creating Unemployment
Yes
No
13. Have you ever heard of anyone suffereing from Unemployment due to
Industrial Automation &Robotics ?
Table 16:Table Showing Respondents Opinion On Others Getting Unemployed:
Chart 16: Chart Showing Respondents Opinion On Others Getting Unemployed:
Interpretation:
70% of the sample size is refusing that they have not even heard of
unemployment situation due to technological changes. But the remaining 30% have
seen others suffering the situation of unemployment.
Suffering Unemployment ? No.of Respondents % Of Respondents
Yes 61 30.5
No 139 69.5
Total 200 100

59
44%
38%
18%
Who Is Unemployed ?
Colleague
Friend
Family Friend
14. If Yes, whom have you heard of suffering Unemployment due to Industrial
Automation &Robotics ?
Table 17: Table Showing Respondents Opinion On Others Getting Unemployed:
Chart 17: Chart Showing Respondents Opinion On Others Getting Unemployed:
Interpretation:
From the previous interpretation 44% of them are telling that they have seen
their own colleagues leaving the job for a reason that they cannot adapt the
technological changes.
Who Is Unemployed ? No.of Respondents % Of Respondents
Colleague 27 44.3
Friend 23 37.7
Family Member 11 18
Total 61 100

60
15. On the whole Industrial Automation & Robotics is an Advantage or
Disadvantage to Employees ?
Table 18: Table Showing ThePespondents Opinion On Industrial Automation &
Robotics:
IA & R No.of Respondents % Of Respondents
Much Advantage 31 15.5
Advantage 73 36.5
Slight Advantage 64 32
Nothing To Say 3 1.5
Slight Disadvantage 25 12.5
Disadvantage 3 1.5
Much Disadvantage 1 0.5
Total 200 100

61
15%
36%
32%
1%
13%
2%
1%
IA & R
Much Advantage
Advantage
Slight Advantage
Nothing To Say
Slight Disadvantage
Disadvantage
Much Disadvantage
Chart 18:Chart Showing TheRespondents Opinion On Industrial Automation &
Robotics:
Interpretation:
When the employees were asked whether they feel the Automation and Robotics
as advantage or disadvantage for them, 68% of them has answered above the mark (i.e)
36% are telling that it is really an advantage for them, while 32% are telling that it is
slight disadvantage for them due to the fact that there is no increase in their pay levels.

62
4.2. Statistical Tools Analysis & Interpretation:
1. This Chi-Square test is performed to know whether the experience of the employees
has any influence on their opinion about the increase in Productivity.
The option of >75% was selected by only 2 employees, so they have been clubbed
together to make the calculation effective.
Null Hypothesis (H0): The experience of employees does not affect their opinion on the
increase of Productivity.
Alternate Hypothesis (H1): The experience of employees affects their opinion on the
increase of Productivity.
Table 19: Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Experience *
Productivity
193 96.5% 7 3.5% 200 100.0%
Table 20: Symmetric Measures
Value
Approx.
Sig.
Nominal by
Nominal
Phi .828 .000
Cramer's V .585 .000
N of Valid Cases 193

63
Table 21: Experience * Productivity Cross tabulation
Productivity
Total
< 25 % < 50 % > 50 %
Experience
< 5
Years
Count 0 6 4 10
% within
Experience
0.00% 60.00% 40.00% 100.00%
% within
Productivity
0.00% 5.50% 6.90% 5.20%
% of Total 0.00% 3.10% 2.10% 5.20%
< 10
Years
Count 0 1 25 26
% within
Experience
0.00% 3.80% 96.20% 100.00%
% within
Productivity
0.00% 0.90% 43.10% 13.50%
% of Total 0.00% 0.50% 13.00% 13.50%
< 15
Years
Count 0 5 23 28
% within
Experience
0.00% 17.90% 82.10% 100.00%
% within
Productivity
0.00% 4.60% 39.70% 14.50%
% of Total 0.00% 2.60% 11.90% 14.50%
> 15
Years
Count 26 97 6 129
% within
Experience
20.20% 75.20% 4.70% 100.00%
% within
Productivity
100.00% 89.00% 10.30% 66.80%
% of Total 13.50% 50.30% 3.10% 66.80%
Total
Count 26 109 58 193
% within
Experience
13.50% 56.50% 30.10% 100.00%
% within
Productivity
100.00% 100.00% 100.00% 100.00%
% of Total 13.50% 56.50% 30.10% 100.00%

64
Chart 19: Showing Chi-Square Test Summary On Productivity And Experience:
Table 22: Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 132.180a 6 .000
Likelihood Ratio 144.623 6 .000
Linear-by-Linear
Association
68.262 1 .000
a. 4 cells (33.3%) have expected count less than 5. The minimum
expected count is 1.35.

65
Interpretation:
When reading this table we are interested in the results of the "Pearson Chi-Square"
row. We can see here that χ = . , p = .000. This tells us that there is statistically
significant association between Experience and Opinion of increase on productivity. Phi and
Cramer's V are both tests of the strength of association. We can see that the strength of
association between the variables is very strong.Therefore H1 is accepted.

66
2. This Chi-Square test is performed to know whether the experience of the employees
has any influence on their opinion about the decrease in Labour Cost.
The option of >75% was not selected by anyone, so it has not been considered for
calculation.
Null Hypothesis (H0): The experience of employees does not affect their opinion on the
decrease of Labour Cost.
Alternate Hypothesis (H1): The experience of employees affects their opinion on the
decrease of Labour Cost.
Cases
Valid Missing Total
Experience * Labour
Cost
193 96.5% 7 3.5% 200 100.0%
Value
Approx.
Sig.
Nominal by
Nominal
Phi .872 .000

67
Table 25: Experience * Labour Cost Crosstabulation
Labour Cost
Total
< 25 % < 50 % < 75 %
Experience
< 5
Years
Count 0 8 1 9
% within
Experience
0.00% 88.90% 11.10% 100.00%
% within
Labour
Cost
0.00% 7.10% 1.90% 4.70%
% of Total 0.00% 4.10% 0.50% 4.70%
< 10
Years
Count 0 1 25 26
% within
Experience
0.00% 3.80% 96.20% 100.00%
% within
Labour
Cost
0.00% 0.90% 47.20% 13.50%
% of Total 0.00% 0.50% 13.00% 13.50%
< 15
Years
Count 0 5 23 28
% within
Experience
0.00% 17.90% 82.10% 100.00%
% within
Labour
Cost
0.00% 4.40% 43.40% 14.50%
% of Total 0.00% 2.60% 11.90% 14.50%
> 15
Years
Count 27 99 4 130
% within
Experience
20.80% 76.20% 3.10% 100.00%
% within
Labour
Cost
100.00% 87.60% 7.50% 67.40%
% of Total 14.00% 51.30% 2.10% 67.40%
Total
Count 27 113 53 193
% within
Experience
14.00% 58.50% 27.50% 100.00%
% within
Labour
Cost
100.00% 100.00% 100.00% 100.00%
% of Total 14.00% 58.50% 27.50% 100.00%

68
Linear-by-Linear
Association
61.944 1 .000
a. 4 cells (33.3%) have expected count less than 5. The minimum
expected count is 1.26.
Chart 20: Chart Showing Symmetric Measures Test On Productivity And Experience:

69
Interpretation:
significant association between Experience and Opinion of decrease on labour cost.
Phi and Cramer's V are both tests of the strength of association. We can see that the
strength of association between the variables is very strong. Therefore H1 is accepted.

70
3. This Kruskal-Wallis H Test is performed to study the health issues experienced by
the employees according to their age differences.
The option of Increased Much was not selected by anyone, so it has not been
considered for calculation.
Null Hypothesis (H0): The age of employees does not affect their opinion on Health
Issues.
Alternate Hypothesis (H1): The age of employees affects their opinion on Health
Issues.
Table 28: Kruskal-Wallis H Test
Ranks
Age N
Mean
Rank
Health Issues 21-30 36 55.57
31-40 28 37.86
41-50 78 126.01
Above
50
58 124.33
Total 200
Table 27: Descriptive Statistics
N Mean
Std.
Deviation Minimum Maximum
Health Issues 200 1.81 .564 1 3
Age 200 2.79 1.054 1 4

71
Table 29: Test
Statisticsa,b
Health Issues
Chi-Square 111.871
df 3
Asymp. Sig. .000
a. Kruskal Wallis Test
b. Grouping Variable: Age
Interpretation:
The mean rank (i.e., the "Mean Rank" column in the Ranks table) of the Health Issues
for each Age Category can be used to compare the opinion of the different age category
employees. Whether these age categories have different opinion on health issues can be
assessed using the Test Statistics table which presents the result of the Kruskal-Wallis H test.
That is, the chi-squared statistic (the "Chi-Square" row), the degrees of freedom (the "df"
row) of the test and the statistical significance of the test (the "Asymp. Sig." row).
A Kruskal-Wallis H test showed that there was a statistically significant difference in opinion
on health issues between the different age categories, χ2(2) = 111.871, p = 0.000, with a mean
rank pain score of 55.57 for 21-30 age category, 37.86 for 31-40 age category, 126.01 for 41-
50 age category and 124.33 for Above 50 age category.Therefore H1 is accepted.

72
4. This Chi-Square test is performed to know whether the Qualification of the
employees has any influence on their opinion about the Method of adaptation of
technological changes.
There were no employees with PG Qualification in the sample size, so it has not been
considered for calculation.
Null Hypothesis (H0): The Qualification of employees does not affect their opinion on
the adaptation method of Technological changes.
Alternate Hypothesis (H1): The Qualification of employees affects their opinion on the
adaptation method of Technological changes.
Value
Approx.
Sig.
Nominal by
Nominal
Phi .174 .413
Cases
Valid Missing Total
Qualification *
Adaptation Method
200 100.0% 0 .0% 200 100.0%

73
Table 32: Qualification * Adaptation Method Crosstabulation
Adaptation Method
Total
Education
Separate
Courses
On The
Job
Training
Qualification
10th
Count 35 12 7 54
% within
Qualification
64.80% 22.20% 13.00% 100.00%
% within
Adaptation
Method
31.00% 17.40% 38.90% 27.00%
% of Total 17.50% 6.00% 3.50% 27.00%
12th
Count 11 10 1 22
% within
Qualification
50.00% 45.50% 4.50% 100.00%
% within
Adaptation
Method
9.70% 14.50% 5.60% 11.00%
% of Total 5.50% 5.00% 0.50% 11.00%
Diploma
Count 55 38 8 101
% within
Qualification
54.50% 37.60% 7.90% 100.00%
% within
Adaptation
Method
48.70% 55.10% 44.40% 50.50%
% of Total 27.50% 19.00% 4.00% 50.50%
UG
Count 12 9 2 23
% within
Qualification
52.20% 39.10% 8.70% 100.00%
% within
Adaptation
Method
10.60% 13.00% 11.10% 11.50%
% of Total 6.00% 4.50% 1.00% 11.50%
Total
Count 113 69 18 200
% within
Qualification
56.50% 34.50% 9.00% 100.00%
% within
Adaptation
Method
100.00% 100.00% 100.00% 100.00%
% of Total 56.50% 34.50% 9.00% 100.00%

74
Chart 21: Chart Showing Chi-Square Test On Qualification And Adaptation Method:
Value df
Asymp. Sig.
(2-sided)
Linear-by-Linear
Association
.020 1 .886
a. 3 cells (25.0%) have expected count less than 5. The
minimum expected count is 1.98.

75
Interpretation:
row. We can see here that χ = . , p = .413. This tells us that there is no statistically
significant association between Qualification and Opinion on method of adapting
technological changes. Phi and Cramer's V are both tests of the strength of association. We can
see that the strength of association between the variables is very weak. Therefore H0 is
accepted.

76
5. This Chi-Square test is performed to know whether the Age of the employees has any
influence on their opinion about the Impact Of Industrial Automation & Robotics.
Null Hypothesis (H0): The Age of employees does not affect their opinion about the
Impact Of Industrial Automation & Robotics.
Alternate Hypothesis (H1): The Qualification of employees affects their opinion about
the Impact Of Industrial Automation & Robotics.
Cases
Valid Missing Total
Age *
Impact
200 100.0% 0 .0% 200 100.0%
Value
Approx.
Sig.
Nominal by
Nominal
Phi .803 .000

77
Age
Much
Advantage
Advantage
Slight
Advantage
Nothing
To Say
Slight
Dis-
advantag
e
Dis-
advantage
Much Dis-
advantage
Count 17 12 2 2 2 1 0 36
% within
Age
47.2% 33.3% 5.6% 5.6% 5.6% 2.8% .0% 100.0%
% within
Impact
54.8% 16.4% 3.1% 66.7% 8.0% 33.3% .0% 18.0%
% of
Total
8.5% 6.0% 1.0% 1.0% 1.0% .5% .0% 18.0%
Count 14 13 1 0 0 0 0 28
% within
Age
50.0% 46.4% 3.6% .0% .0% .0% .0% 100.0%
% within
Impact
45.2% 17.8% 1.6% .0% .0% .0% .0% 14.0%
% of
Total
7.0% 6.5% .5% .0% .0% .0% .0% 14.0%
Count 0 31 42 0 5 0 0 78
% within
Age
.0% 39.7% 53.8% .0% 6.4% .0% .0% 100.0%
% within
Impact
.0% 42.5% 65.6% .0% 20.0% .0% .0% 39.0%
% of
Total
.0% 15.5% 21.0% .0% 2.5% .0% .0% 39.0%
Count 0 17 19 1 18 2 1 58
% within
Age
.0% 29.3% 32.8% 1.7% 31.0% 3.4% 1.7% 100.0%
% within
Impact
.0% 23.3% 29.7% 33.3% 72.0% 66.7% 100.0% 29.0%
% of
Total
.0% 8.5% 9.5% .5% 9.0% 1.0% .5% 29.0%
Count 31 73 64 3 25 3 1 200
Total
% within
Age
15.5% 36.5% 32.0% 1.5% 12.5% 1.5% .5% 100.0%
% within
Impact
100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
% of
Total
15.5% 36.5% 32.0% 1.5% 12.5% 1.5% .5% 100.0%
Age * Impact Crosstabulation
Impact
Total
21-30
31-40
31-40
Above-
50
Table 36:

78
Pearson Chi-Square 129.034a 18 0
Likelihood Ratio 141.15 18 0
Linear-by-Linear Association 48.011 1 0
a. 15 cells (53.6%) have expected count less than 5. The minimum expected count is .14.
Chart 22: Chart Showing Chi-Square Test On Age And Impact On Automation

79
Interpretation:
significant association between Age and Impact of Industrial Automation and Robotics. Phi
and Cramer's V are both tests of the strength of association. We can see that the strength of
association between the variables is very strong. Therefore H1is accepted.

80
CHAPTER 5 : CONCLUSIONS
A conclusion is the last part of something, its end or result. When you write a paper,
you always end by summing up your arguments and drawing a conclusion about what you've
been writing about.
The phrase in conclusion means "finally, to sum up," and is used to introduce some
final comments at the end of a speech or piece of writing. The phrase jump to
conclusions means "to come to a judgment without enough evidence." A foregone
conclusion is an outcome that seems certain.
As we have finished all the required analysis, now they need to be concluded and
summarized so that the research will be fulfilled. The first thing will be Findings of the study,
then the suggestions for the facts acquired from the research, then a proper conclusion for the
research.

81
5.1. Summary Of Findings:
 The age category consists of 39% of people from 41 to 50 years of age and 29% of
people above 50 years of age. These two age categories constitute the biggest size of
total sample size.
 50% of the sample size consists of Diploma holders basically ITI graduates. And 38% of
the sample size consists of employees who have finished only school education. Under
Graduates are very less in number (12%).
 The total sample size consists of 68% of employees who are having more than 15 years
of experience.
 62% of them are experiencing Automation and Robotics for around 15 years.
 96% of the employees are accepting that there is increase in the Productivity level after
employing CNC Machines.
 57% of the employees are telling that the productivity has increased around 50% and
27% are accepting that the productivity has increased around 75% after employing CNC
Machines.
 Again 96% of the employees are telling that the Labour Cost has decreased a lot after
employing the CNC Machines.
 86 % of the employees are telling that the labour cost has decreased by 75%, in that
59% are telling it has decreased by 50%.
 99% of the employees are accepting that they had health issues like mental stress and
physical injuries before employing Automated Machines.
 91% of the employees are accepting the fact that the health issues have decreased after
employing CNC Machines, in that 27% are telling the Health issues have decreased very
much when compared with manual working.

IMPACT OF INDUSTRIAL AUTOMATION & ROBOTICS ON HUMAN RESOURCES

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