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MODELLING OF ENVIRONMENTAL FACTORS TOWARD HUMAN
PRODUCTIVITY AT MANUFACTURING INDUSTRY
MUHAMMAD NAIF HELMI BIN ABDUL MANAP
Faculty of Mechanical Engineering
UNIVERSITI MALAYSIA PAHANG
JUNE 2012
ii
UNIVERSITI MALAYSIA PAHANG
FACULTY OF MECHANICAL ENGINEERING
I certify that the project entitled “MODELLING OF ENVIRONMENTAL
FACTORS TOWARD HUMAN PRODUCTIVITY AT MANUFACTURING
INDUSTRY” is written by Muhammad Naif Helmi Bin Abdul Manap. I have
examined the final copy of this project and in my opinion; it is fully adequate in
terms of scope and quality for the award of the degree of Bachelor of Engineering. I
herewith recommend that it be accepted in partial fulfilments of the requirements for
the degree of Bachelor of Mechanical with Manufacturing Engineering.
Signature :
Name of Panel : DR. ABDUL ADAM BIN ABDULLAH
Position : SENIOR LECTURER
Date :
iii
SUPERVISOR’S DECLARATION
I hereby declare that I have checked this project and in my opinion, this project is
adequate in terms of scope and quality for the award of the degree of Bachelor of
Mechanical Engineering with Manufacturing Engineering.
Signature :
Name of Supervisor : IR. AHMAD RASDAN BIN ISMAIL
Position : HEAD OF PROGRAMME (INDUSTRY)
Date :
iv
STUDENT’S DECLARATION
I hereby declare that the work in this project is my own except for quotations and
summaries which have been duly acknowledged. The project has not been accepted
for any degree and is not concurrently submitted for award of other degree.
Signature :
Name : MUHAMMAD NAIF HELMI BIN ABDUL MANAP
ID Number : ME 08022
Date :
v
ACKNOWLEDGEMENTS
In the name of Allah, the most loving and the most compassionate.
I am grateful and would like to express my sincere gratitude to Defence
Services Sdn. Bhd. for giving me the opportunity to do and completed my final year
project research in the company. I also want to thanks to my project supervisor, Ir.
Ahmad Rasdan Bin Ismail for his invaluable guidance, continuous encouragement
and constant support in order to complete this project. He has always impressed me
with his outstanding professional conduct, his strong conviction for engineering. I
appreciate his consistent support from the first day I started doing the project until
the concluding moments. I am truly grateful for his progressive vision about my
efforts, his tolerance of my naïve mistakes, and his commitment to my future career.
I also would like to express very special thanks to the panels, for their suggestions
and co-operation throughout the project. I would like to dedicate my sincerely thanks
to them for the time spent proofreading and correcting my many mistakes.
My sincere thanks go to all the staff of the Defence Services Sdn. Bhd., who
helped me in many ways and made my stay at the company pleasant and
unforgettable. Many special thanks go to my project‟s partners for their excellent co-
operation, inspirations and supports during this project.
I acknowledge my sincere indebtedness and gratitude to my parents for their
love, dream and sacrifice throughout my life. I am also grateful to all my siblings for
their sacrifice, patience, and understanding that were inevitable to make this work
possible. I cannot find the appropriate words that could properly describe my
appreciation for their devotion, support and faith in my ability to attain my goals.
Special thanks should be given to my friends. I would like to acknowledge their
comments and suggestions, which was crucial for the successful completion of this
project and the report.
vi
ABSTRACT
This study tries to identify the effect of the environmental factors towards the
workers‟ and operators‟ productivity and performance in the manufacturing industry.
The examined parameters were illuminance (lx), noise (dB), air velocity (m/s)
relative humidity (%) and air temperature (°C). One manufacturing parts factory had
been chosen as a location for the study. The subjects were workers at one of the
workstation of the factory. Two sets of representative data including the illuminance
(lx), noise (dB), air velocity (m/s) relative humidity (%) and air temperature (°C)
level and production rate were collected during the study. The production rate data
were collected through observations and survey questionnaires while all the
parameters were measured using Thermal Comfort SERI apparatus which is capable
to measure simultaneously those mentioned environmental factors. The time series
data of fluctuating level of environmental were plotted to identify the significant
changes of factors. Further multiple linear regressions were employed to obtain the
equation model in order to represent the relationship of these environmental factors
towards productivity. The study reveals that the dominant factor contribute to the
productivity at the selected workstation is air velocity and noise whereas the
empirical finding is closely related to the perception study by survey questionnaire
distribution. The productivity prediction equation model obtained is: Productivity =
29.242 – 0.009 illuminance + 6.022 relative humidity – 8.98 air velocity – 0.064
noise – 0.107 air temperature.
vii
ABSTRAK
Tujuan kajian ini adalah untuk mengenalpasti kesan faktor-faktor
persekitaran kepada pencapaian dan pengeluaran para pekerja dan operator di
industri pembuatan. Parameter-parameter yang diukur adalah kadar pencerahan
cahaya (lx), kadar kebisingan (dB), halaju udara (m/s), kadar kelembapan udara (%)
dan suhu udara (°C). Sebuah kilang industri pembuatan dipilih sebagai lokasi untuk
kajian ini. Manakala subjek untuk kajian ini terdiri daripada pekerja-pekerja dari
salah satu stesen kerja yang dipilih di dalam kilang tersebut. Dua set data termasuk
kadar pencerahan cahaya (lx), kadar kebisingan (dB), halaju udara (m/s), kadar
kelembapan udara (%) dan suhu udara (°C) dan kadar pengeluaran telah diambil
semasa kajian. Kadar pengeluaran diambil menerusi pemerhatian dan borang soal
selidik, manakala parameter-parameter lain diukur menggunakan alat pengukur
keselesaan thermal SERI yang mampu mengukur semua factor-faktor persekitaran
tersebut secara serentak. Graf masa melawan setiap factor persekitaran diplot untuk
mengenalpasti setiap perubahan yang berlaku. Kemudian, analisis regresi berganda
digunakan untuk mencari model persamaan untuk mewakili/menunjukkan hubungan
setiap faktor-faktor persekitaran terhadap pengeluaran. Hasil kajian mendapati
bahawa faktor utama yang mempengaruhi pengeluaran di lokasi kajian adalah halaju
udara dan kadar kebisingan dan ia selari dengan hasil kajian daripada borang soal
selidik yang diedarkan. Model ramalan pengeluaran yang diperoleh ialah:
Pengeluaran = 29.242 – 0.009 kadar pencerahan cahaya + 6.022 kadar kelembapan –
8.98 halaju udara – 0.064 kadar kebisingan – 0.107 suhu udara.
viii
TABLES OF CONTENTS
Pages
EXAMINER’S DECLARATION ii
SUPERVISOR’S DECLARATION iii
STUDENT’S DECLARATION iv
ACKNOWLEDGEMENT v
ABSTRACT vi
TABLES OF CONTENTS viii
LIST OF TABLES xii
LIST OF FIGURES xiii
CHAPTER 1 INTRODUCTION
1.0. Introduction 1
1.1. Background Of Study ` 1
1.2. Problem Statement 2
1.3. Objectives of Study 3
1.4. Scope of Study 3
1.5. Significant of Study 3
1.6. Structure of Report 4
ix
CHAPTER 2 LITERATURE REVIEW
2.0 Introduction 5
2.1 Definition of Environmental Ergonomic 5
2.2 Definition of Thermal Comfort 6
2.3 Standard References 7
2.3.1 Summary of Standard References 7
2.3.2 ISO 7730 8
2.3.3 ISO 8996 9
2.3.4 ISO 9920 10
2.4 Environmental Factors 11
2.4.1 Illuminance 11
2.4.2 Thermal Environment (Heat) 12
2.4.3 Air Velocity 13
2.4.4 Noise 14
2.4.5 Relative Humidity 14
2.4.6 Clothing Insulation 15
2.4.7 Metabolic Rate 16
2.5 Thermal Indices 17
2.5.1 Predicted Mean Vote (PMV) 17
2.5.2 Predicted Percentage of Dissatisfied (PPD) 19
2.6 Reviews on Previous Research 21
2.6.1 Effect of environmental factors on workers 21
CHAPTER 3 METHODOLOGY
3.0 Introduction 30
3.1 Description of Workstation 31
3.2 Subject of Study 34
3.3 Procedure of Study 34
x
3.4 Data Collection Method 37
3.4.1 Questionnaire Form 37
3.4.2 Field Work Data Measurement 38
3.4.3 Equipments Used for Data Collection 38
3.5.4 Measurement Parameters 39
3.5 Data Analysis Method 39
3.5.1 PMV and PPD Method 40
3.5.2 Computational Analysis Using SPSS Software 40
CHAPTER 4 RESULT AND DISCUSSIONS
4.0 Introduction 42
4.1 Questionnaire Analysis 42
4.1.1 Respondents Profile Survey 42
4.1.2 Respondents Profile 43
4.1.3 Workers‟ Perception Analysis 44
4.2 Experimental Data Analysis 45
4.2.1. Result for Illuminance 45
4.2.2. Result for Relative Humidity 49
4.2.3. Result for Air Velocity 53
4.2.4. Result for Air Temperature 56
4.2.5. Result for Noise 61
4.3 PMV and PPD Analysis 65
4.4 Comparison of Result to Standard Values 68
CHAPTER 5 CONCLUSION AND RECOMMENDATIONS
5.1 Introduction 69
5.2 Recommendations 69
xi
5.3 Conclusion 69
REFERENCES 71
APPENDICES
A Raw Data 79
B Gantt‟s Chart for Semester 1 80
C Gantt‟s Chart for Semester 2 81
xii
LIST OF TABLES
Table No.
Title Page
2.1 The influence of accuracy of estimate of metabolic rate and
clothing insulation on PMV and PPD values
10
2.2 Thermal insulation for typical combinations of garments 16
2.3 Metabolic rate (human activity level) for different activities 17
2.4 7-point thermal sensation scale 20
2.5 Summary of Previous Research 24
4.0 Illuminance, production rate and time data 46
4.1 Regression and ANOVA analysis of illuminance 48
4.2 Relative humidity, production rate and time data 50
4.3 Regression and ANOVA analysis of relative humidity 52
4.4 Air velocity, production rate and time data 53
4.5 Regression and ANOVA analysis of air velocity 55
4.6 Air temperature, production rate and time data 57
4.7 Regression and ANOVA analysis of air temperature 60
4.8 Noise, production rate and time data 61
4.9 Regression and ANOVA analysis of noise 63
4.10 Metabolic rate value of the workers in the workstation. 65
4.11 Clothing insulation value of the workers in the workstation. 65
4.12 PMV and PPD values at measured locations. 66
4.13 Comparison of results to standard values 68
xiii
LIST OF FIGURES
Figure No.
Title Page
2.1 PMV and thermal sensation 18
2.2 Graph of PPD as a function of PMV 20
3.1 Workflow at the painting workstation 32
3.2 View of the plastic curtains (door) and steel cages 32
3.3 Inside view of the workstation 33
3.4 One of the workers preparing paints 33
3.5 Flow Chart of Study 36
3.6 Thermal comfort instrument 38
4.1 Respondents‟ Gender 43
4.2 Respondents‟ Age 43
4.3 Respondents‟ Working Experiences 44
4.4 Workers‟ Perception Analysis toward environmental factors
of Workplace
45
4.5 Graph of production rate versus illuminance 46
4.6 Time series of illuminance data measured at the workstation 47
4.7 Graph of production rate versus relative humidity 50
4.8 Time series of relative humidity data measured at the
workstation
51
4.9 Graph of production rate versus air velocity 54
4.10 Time series of air velocity data measured at the workstation 54
4.11 Graph of production rate versus air temperature 57
4.12 Time series of air temperature data measured at the
workstation
58
4.13 Graph of production rate versus noise 62
4.14 Time series of noise data measured at the workstation 62
CHAPTER 1
INTRODUCTION
1.0 INTRODUCTION
Environmental ergonomics addresses the problems of maintaining human
comfort, activity and health in stressful environments. Its subject areas include
thermal environments, illumination, noise and hypo- and hyperbaric environments.
Ergonomics can be defined as the application of knowledge of human characteristics
to the design of systems. People in systems operate within an environment and
environmental ergonomics is concerned with how they interact with the environment
from the perspective of ergonomics. Although there have been many studies, over
hundreds of years, of human responses to the environment (light, noise, heat, cold,
etc.) and much is known, it is only with the development of ergonomics as a
discipline that the unique features of environmental ergonomics are beginning to
emerge, (K.C. Parsons, 2000).
1.1 BACKGROUND OF STUDY
In this modern and competitive world of technology, manufacturing industry
is one of the largest and important sectors which can turns under all types of
economic systems such as free market economy and collectivist economy. All the
products generated is competing to gain demand and satisfactory from customers.
Dealing with continuous and challenging competition, company not only needs to
produce quality product but excellence production systems and management also
plays an important roles. In order to achieve that, the human/workers productivity in
the sector needs to be improved and optimize.
2
The aim of the research/study is to improve the human productivity of an
assembly line in manufacturing production industry. The objectives are to asses and
study the effect of some environmental factors condition on the staffs‟ and workers‟
productivity and performance and also to create a better solution to improve them.
Various types of environmental factors will be assessed in this study, which are:
illuminance (lx), noise (dB), air velocity (m/s) relative humidity (%) and air
temperature (°C), (Ismail et al., 2009).
Some environmental factors data such as illuminance (lx), noise (dB), air
velocity (m/s) relative humidity (%) and air temperature (°C) and production rate for
the selected assembly line factory are collected. The time series data of fluctuating
level of environmental were plotted to identify the significant changes of factors.
Then the optimum level for the five factors will be determined for optimum
productivity. Further multiple linear regressions were employed to obtain the
equation model in order to represent the relationship of these environmental factors
towards productivity, (Ismail et al., 2008).
1.2 PROBLEM STATEMENT
Nowadays, for manufacturing company, the most important goals for almost
all manufacturing company is to increase the productivity, which reflect to get a
better production line efficiency. But to achieve this goal, the most important thing to
do is to optimize the human productivity in the industry. The human productivity is
influenced by some environmental factors their workplace. For example, illuminance
(lx), noise (dB), air velocity (m/s) relative humidity (%) and air temperature (°C) of
the workplace will give some effects on the production rate of the workers. This
study tries to identify the effect of the environmental factors that stated above
towards the workers‟ and operators‟ productivity and performance in the
manufacturing industry.
3
1.3 OBJECTIVES OF STUDY
The study is going to be conduct at a selected manufacturing industries
workstation. Basically, the main purposes and objectives in accomplishing this study
are:
a. To determine the dominance effects of environmental factors such as
illuminance (lx), noise (dB), air velocity (m/s) relative humidity (%) and
air temperature (°C) on the operators‟ productivity.
b. To investigated the thermal comfort level experienced by workers by
performing survey approach among the workers in order to collect the
relevant data for thermal comfort assessment and perception on the
comfort level at the working environment at selected industries.
c. Conduct the human perception as well as quantitative measurement of
environmental ergonomics, analysis the data using design of experiment
and optimize the result.
1.4 SCOPES OF STUDY
The scopes of this study are:
a. To conduct the human perception of environmental ergonomics at
selected Manufacturing Industries.
b. Model and optimize the result from quantitative measurement for
ergonomics environment.
1.5 SIGNIFICANT OF THE STUDY
The environmental ergonomics study at manufacturing industries in Malaysia
is considered a good steps to assess thermal environment and implement
environmental ergonomic. From this research, it will help to increase the importance
of environmental ergonomic level awareness among the workers and to be able to
identify the comfortable working environment in industrial sector.
4
1.6 STRUCTURE OF REPORT
This report will be consisting of three chapter which is Chapter
1(Introduction), Chapter 2(Literature Review) and Chapter 3(Methodology). All
these chapters will describe all things and information about the study. Chapter 1 is
focusing about the basic information about the study which is background of study,
problem statement, objective of study, scopes of study, significant of study and the
structure of this report.
While in Chapter 2, it will be focusing about the review of past study that are
related to this study. This chapter includes about environmental ergonomic, thermal
comfort, human productivity and environmental factor. A review of other relevant
research studies is also provided. The review is organized chronologically to offer
insight to how past research efforts have laid the groundwork for subsequent studies,
including the present research effort. The review is detailed so that the present
research effort can be properly tailored to add to the present body of literature as well
as to justly the scope and direction of the present research effort.
Finally, Chapter 3 will be providing a review of the methodology that has
been suggested in conducting the study. It is start with the designing of the study,
where the methodology in performing this study has been review. Framework of the
study in the other hand, will review the planning that have been suggested in
conducting the study. A review of data analysis and modeling software that will be
used also will be discussed in general.
.
CHAPTER 2
LITERATURE REVIEW
2.0 INTRODUCTION
Chapter 2 is explains about the literature review of the project. All the
theories, concepts and others related standard or parameter that included in the study
will be reviewed. It is including about environmental ergonomic, thermal comfort,
human productivity and environmental factor. A review of other relevant research
studies is also provided. The review is organized chronologically to offer insight to
how past research efforts have laid the groundwork for subsequent studies, including
the present research effort. The review is detailed so that the present research effort
can be properly tailored to add to the present body of literature as well as to justly the
scope and direction of the present research effort.
2.1 DEFINITION OF ENVIRONMENTAL ERGONOMIC
Ergonomics can be defined as the application of knowledge of human
characteristics to the design of systems. People in systems operate within an
environment and environmental ergonomics is concerned with how they interact with
the environment from the perspective of ergonomics. Environmental ergonomics will
encompass the social, psychological, cultural and organizational environments of
systems, however to date it has been viewed as concerned with the individual
components of the physical environment (K.C. Parsons, 2000). Some articles state
that, enhanced environmental control improves employee performance and
organizational effectiveness. A growing body of research shows strong links between
degree of environmental control and outcomes such as stress and group and
6
individual performance and speed and cost of business processes between
departments (Carayon and Smith, 2000; Lee and Brand, 2005; O‟Neill, 1998;
O‟Neill and Evans, 2000; O‟Neill, 2007; Robertson, Huang, O‟Neill, & Schleifer,
2008; Sundstrom, Town, Rice, Osborn, and Brill, 1994). The benefits of
environmental control transcend age, generational affiliation, gender, and other
demographic characteristics (O‟Neill, 1998; 2007). Although numerous studies on
the effect of job satisfaction in industries exist, findings were often specific to the
particular investigation, and to date mainly consider individual components of the
physical environment (Clegg et al, 1997). Nonetheless, factors related to job
satisfaction are relevant in the prevention of employee frustration and low job
satisfaction because employees will work harder and perform better if they are
satisfied with their jobs. Many factors affect job satisfaction according to (Bowen et
al, 1994, DeSantis and Durst, 1996 and Gaesser and Whitbourne, 1985).
2.2 DEFINITION OF THERMAL COMFORT
Thermal comfort can be defined as that condition of mind which expresses
satisfaction with the thermal environment (ASHRAE, 2005). Thermal comfort is
very difficult to define. This is because we need to take into account a range of
environmental and personal factors when deciding on the temperatures and
ventilation that will make feel comfortable. The best that we can realistically hope to
achieve is a thermal environment which satisfies the majority of people in the
workplace, or put more simply, „reasonable comfort‟ (HSE, 1999). According to
J.L.M. Hensen (1990) thermal comfort is generally defined as that condition of mind
which expresses satisfaction with the thermal environment (e.g. in ISO 1984).
Dissatisfaction may be caused by the body being too warm or cold as a whole, or by
unwanted heating or cooling of a particular part of the body (local discomfort). From
earlier research (as reported and reviewed in e.g. Fanger, 1972, McIntyre, 1980,
Gagge, 1986) we know that thermal comfort is strongly related to the thermal
balance of the body. This balance is influenced by:
• Environmental parameters like: air temperature (Ta) and mean radiant temperature
(Tr), relative air velocity (v) and relative humidity (rh).
7
• Personal parameters like: activity level or metabolic rate (M) (units: 1 met = 58
W/m2) and clothing thermal resistance (Icl) (units: 1 clo = 0.155 m2.K/W).
2.3 STANDARD REFERENCES
According to K.C. Parsons, 2000, the existing thermal comfort standard (ISO
7730) is considered in terms of these criteria as well as ISO 8996 (metabolic rate)
and ISO 9920 (clothing). The consequences of inaccuracy in estimation of metabolic
rate and clothing insulation show that „reasonable estimates‟ can provide a range of
thermal sensation predictions. The others are ISO/TC 159 SC5, „Ergonomics of the
physical environment, ISO 7726 (instruments), ISO 10551 (subjective measures),
ISO TS 13732 Part 2 (contact with surfaces at moderate temperature), ISO 14505
(vehicles), and ISO 14515 (people with special requirements). Some other standards
are:
ISO 7243: 1995 Hot environments estimation of the heat stress on working man,
based on the WBGT index (wet bulb globe temperature).
ISO 7726: 1998, Thermal environments instruments and methods for measuring
physical quantities.
ISO 7730: 1994, Moderate thermal environments determination of the PMV and
PPD indices and specification of the conditions for thermal comfort.
ISO 9920: 1995, Ergonomics of the thermal environment estimation of the
thermal insulation and evaporative resistance of a clothing ensemble.
ISO 10551: 1995, Ergonomics of the thermal environment assessment of the
influence of the thermal environment using subjective judgement scales.
2.3.1 Summary of Standard References That Were Used
1. Thermal comfort - ASHRAE (American Society of Heating, Refrigeration and
Air-Conditioning Engineers) defined thermal comfort as a condition of mind that
expresses satisfaction with the surrounding environment and the standard thermal
comfort for winter is 68° to 74°F (20° to 23.5°C) and for summer is 73° to 79°F
(22.5° to 26°C)(ASHRAE Standard 55).
8
2. Relative humidity - Relative humidity was influencing employee perception on
the comfortable during working (Attwood et al. 2004). The standard of humidity
is 40% RH (20 to 60% ranges) (ASHRAE Standard 55).
3. Noise - These conditions decrease employee concentration towards tasks which
lead to low employee performance such as low productivity, poor quality,
physicaland emotional stress, which cause high cost (Kahya, E. 2007). The
limitation of noise at industrial, commercial and traffic areas generally is 70 dB
in 24 hours (World Health Organisation (WHO) Guidelines for Community
Noise, 1999).
4. Clothing - winter clothing is 0.8 to 1.2 clo and for winter clothing is 0.8 to 1.2
clo, (ASHRAE Standard 55).
5. Air flow - Air velocity less than 40 fpm (0.2 m/s), (ASHRAE Standard 55).
6. Lighting – Parsons (2000) stated that light can cause both discomfort and positive
sensation. There are have two type of lamps are suitable for factories; high
pressure mercury (HPMV) 50 watt, fair color and has 5000 life hours. High
pressure sodium (HPSV) SON, 90 watt, fair color and has 6000-12000 life hours.
(Bureau of Energy Efficiency). The ISO standard ISO 8995-1:2002 (CIE
2001/ISO 2002) states that in the areas where continuous work is carried out the
maintained work plane illuminance should not be less than 200 lx.
2.3.2 ISO 7730 Moderate Thermal Environments – Determination of the PMV
and PPD Indices and Specification of the Conditions for Thermal
Comfort
This standard describes the PMV (Predicted Mean Vote) and PPD (Predicted
Percentage Dissatisfied) indices and specifies acceptable conditions for thermal
comfort. The PMV predicts the mean value of the votes of a large group of people on
the ISO thermal sensation scale (+3 = hot; +2 = warm; +1 = slightly warm; 0 =
neutral; -1 = slightly cool; -2 = cool; -3 = cold). The PPD predicts the percentage of a
large group of people likely to feel „too warm‟ or „too cool‟. The indices are exactly
as described by Fanger (1970). A draft rating index is provided in the standard as an
equation involving air temperature, air velocity and turbulence intensity. It is
applicable to mainly sedentary people wearing light clothing with a whole-body
9
thermal sensation close to neutral. Recommended thermal comfort requirements are
provided in Annex D of the standard (informative – not a formal part of the
standard). This includes optimum operative temperature; vertical air temperature
gradient; mean air velocity; floor temperature; and relative humidity. ISO 7730 has
been criticised because of its lack of theoretical validity. The PMV/PPD indices were
established in 1970. Since then there have been improvements to the human heat
balance equation. There are also dynamic models of human thermoregulation that
offer more accurate representations of physiological measures such as mean skin
temperature and sweat rate. The prediction of sensation away from neutrality
(towards warm or cool) is based upon the principle of thermal load. This has been
criticised (Humphreys and Nicol, 1996). A more valid approach may be to predict
deviation from neutrality using predictions of body state, such as skin temperature,
sweat rate, or skin wettedness (Gagge et al., 1971).
2.3.3 ISO 8996 Ergonomics – Determination of Metabolic Heat Production
This standard describes six methods for estimating metabolic heat production,
an essential requirement in the use of ISO 7730 and the assessment of thermal
comfort. The methods are divided into three levels according to accuracy. Level 1
provides tables of estimates of metabolic rate (assumed identical to metabolic heat
production) for kinds of activity and occupation. This is „rough information where
the risk of error is great‟. Level II presents tables of estimated metabolic rate based
upon group assessment, specific activities, and measurement of heart rate. This is
„High error risk – accuracy ± 15%‟. The most accurate measure (± 5%) is a method
of estimating metabolic rate by analysis of expired „air‟ from the lungs (indirect
calorimetry). The principle is that energy is produced from burning food in oxygen.
Comparison of the oxygen content of expired air (collected in a Douglas bag or other
method – a typical value will be around 16% to 18%) with that of inspired air (20%)
provides the rate of oxygen used by the body. With adjustments for type of
combustion (from CO2 output) temperature and pressure, the metabolic rate can be
derived from the calorific value of food. The units are presented as Watts per square
meter of the body surface of a standard person (70 Kg, 1.8 m2 male; 60 Kg, 1.6 m2
10
female). For an activity, such as walking up hill, the weight of the person will be
important and adjustments may need to be made.
2.3.4 ISO 9920 Ergonomics of the thermal environment
ISO 9920 provides an extensive database of the thermal properties of clothing
and garments. The properties are based upon measurements on heated manikins
where basic (or intrinsic) thermal insulation is measured as well as vapor permeation
properties of garments and ensembles. It is important to have a view of how
accurately the standard can predict clothing insulation properties. No guidance is
provided on this. If we assume around ± 15% accuracy and combine it with
metabolic rate (± 15% accuracy) the results in Table 2.1 show how the PMV/PPD
indices vary for sitting at rest in a business suit and light activity in a business suit. It
can be seen that predictions of discomfort will vary within the accuracy of metabolic
rate and clothing insulation estimates. Inaccuracies in estimates of environmental
variables will increase this uncertainty.
Table 2.1: The influence of accuracy of estimate of metabolic rate and clothing
insulation on PMV and PPD values (Ogulata, 2001)
11
2.4 THE IMPACT OF ENVIRONMENTAL FACTORS TO HUMAN
PERFORMANCE
In the recent years, several studies have been completed that investigated the
effect of such factors as pollution load and ventilation rate on human productivity.
As a result, it was documented for the first time that the performance of office work
is affected by the indoor air quality. Studies of this nature continue with an extended
scope that includes not only the performance of office employees, but also indoor
environmental effects on the performance of school work by children. Indoor
environment factors to be investigated include air cleaning, temperature control,
ventilation rate, etc. It is also stated that prior literature on the relationship of indoor
environments to productivity has focused primarily on potential direct improvements
in worker‟s cognitive or physical performance from changes in temperatures or
lighting (W.J. Fisk, 2000). Pech and Slade (2006) argued that the employee
disengagement is increasing and it becomes more important to make workplaces that
positively influence workforce. According to Pech and Slade the focus is on
symptoms of disengagement such as distraction, lack of interest, poor decisions and
high absence, rather than the root causes. The working environment is perhaps a key
root causing employee‟s engagement or disengagement. Another research indicates
that improving the working environment reduces complaints and absenteeism while
increasing productivity (Roelofsen, 2002). Wells (2000) stated that workplace
satisfaction has been associated with job satisfaction. In recent years, employees
comfort on the job, determined by workplace conditions and environments, has been
recognized as an important factor for measuring their productivity. Some examples
of the environmental factors are:
2.4.1 Illuminance
Light can cause discomfort to the occupants of an environment as well as
positive sensations such as pleasure and emotional sensations (cold, warm, etc.).
Lighting conditions which produce definite discomfort can generally be identify and
criteria in terms of physical lighting parameters are available for assessing lighting
environments (CIBSE, 1994). The conditions that create emotional responses or
12
pleasant environments are not as well understood and designing for these conditions
remains both an art and a science. Lighting conditions that are satisfactory are
context dependent, depending upon the function of the building, user population, etc.
However, there are a number of measurements of lighting environments that are
related to subjective responses to lighting and recommended limits can be provided
in terms of these parameters. For a detailed discussion the reader is referred to Boyce
(1981) and for practical recommendations to CIBSE (1994). The parameters include
illuminance and illuminance ratios that are related to the acceptable light distribution
arriving on surfaces in a room; Vector/scale ratio and vector direction that affect the
three-dimensional appearance of objects; and measures of surface reflections, color,
glare, and day lighting can all be used to provide guidelines for good lighting
practice. Although light can affect human performance at general tasks, glare can
cause a distraction effect; for example, the main effects of light are on visual
performance (Parson, 2000). At the individual level, research suggests that
environmental control over workstation components has a direct relationship to
performance (O‟Neill, 1994; 2007). Measuring the impact of giving individuals
control over lighting in their environment, Moore, Carter and Slater (2004) found
that the option for control over lighting in individual workspace may account for
higher occupant satisfaction than actual differences in luminance. This study also
reported that workers may be more likely to forgive unsatisfactory features of an
environment if they can control other features related to comfort. The ISO standard
ISO 8995-1:2002 (CIE 2001/ISO 2002) states that in the areas where continuous
work is carried out the maintained work plane illuminance should not be less than
200 lx. In all the reviewed recommendations, the minimum work plane illuminances
in offices were higher. ISO 8995-1:2002 standard does not give any recommendation
for uniformity of illuminance on the work plane, but suggests that the illuminance
in the vicinity of the task should not be too low in comparison to the
illuminance on task area.
2.4.2 Thermal Environment (Heat)
Thermal comfort can be defined as `that condition of mind which expresses
satisfaction with the thermal environment a (ASHRAE, 1966). The reference to &
13
mind' indicates that it is essentially a subjective term; however, there has been
extensive research in this area and a number of indices exist which can be used to
assess environments for thermal comfort. Although simple values of air temperature
or globe temperature can be used to provide conditions for comfort in rooms a more
detailed, practical approach is usually taken. Fanger (1970) suggested three
conditions for comfort; these are that the body is in heat balance and that the mean
skin temperature and sweat rate are within limits required for comfort. Conditions
required for heat balance can be derived from a heat balance equation. Mean skin
temperatures and sweat rates that are acceptable for comfort have been derived from
empirical investigation (Fanger, 1970). A fourth condition for comfort is that there
should be no local discomfort. This could be caused by draughts, radiant asymmetry
or temperature gradients. Wing (1965) and Ramsey (1995) investigated a wide range
of mental tasks and present limits in terms of WBGT values that provide general
guidance on exposure times within which there would be no significant decrement in
mental performance. Decrements in performance occur not only at high
environmental temperatures. Performance at vigilance tasks can be lowest in slightly
warm environments that can have soporific effects. An increase in environmental
stress can then increase performance. In addition, as the rate of chemical reactions in
the body increase with temperature, a person's speed at both physical and mental
tasks can be increased (Poulton, 1976).
2.4.3 Air Velocity
Air speed is the average speed of the air to which the body is exposed. The air
velocity is speed of moving air across the workers and can make the workplace cool
when it is well within the comfort guidelines for dry bulb temperature (Fanger,
1977). The moving air in warm or humid conditions can increase heat loss through
convection without any change in air temperature. Air speed is a rate where the air
moving in the certain distance. The mean air speed should be less than 0.15 m/s
during the winter and 0.25 m/s in the summer (ISO 1984). Air speeds of 0.1 m/s to
0.3 m/s are typical in the comfort zone for sedentary and light work assembly. Often,
fans are brought into work areas as the air temperatures move to warm end of the
comfort zone or above.
14
2.4.4 Noise
Noise is one of the physical environmental factors affecting our health in
today‟s world. Noise is generally defined as the unpleasant sounds which disturb the
human being physically and physiologically and cause environmental pollution by
destroying environmental properties (Melnick, 1979). According to (USEPA, 1974),
Exposure to continuous and extensive noise at a level higher than 85 dBA may lead
to hearing loss. Continuous hearing loss differs from person to person with the level,
frequency and duration of the noise exposed. Hearing losses are the most common
effects among the physiological ones. It is possible to classify the effects of noise on
ears in three groups: acoustic trauma, temporary hearing losses and permanent
hearing loss (Melamed et al, 2001). Blood pressure increases, heart beat
accelerations, appearance of muscle reflexes, sleeping disorders may be considered
among the other physiological effects. The psychological effects of noise are more
common compared to the psychological ones and they can be seen in the forms of
annoyance, stress, anger and concentration disorders as well as difficulties in resting
and perception (Cheung, 2004; Ohrstrom. 1989; Finegold, 1994). For the standards
value, in the U.S., the Occupational Noise Exposure Regulation states that industrial
employers must limit noise exposure of their employees to 90 dB for one 8-h period
(USEPA, 1974; Eleftheriou, 2002). This permitted maximum noise exposure dose is
similar to the Turkey Standard, which is less than 75 dB for one 7.5 h period (Turkey
organization standards, noise exposure regulation, 1986).
2.4.5 Relative humidity
Relative humidity is a term used to describe the water vapor pressure of the
air at a given temperature (Bridger, 1995). If the relative humidity is high, the latent
heat dissipation ability of the body is decreased due to the decrease in vapor pressure
and the increase of sweat remaining on the body (Atmaca and Yigit, 2006).
Workplace environmental conditions, such as humidity, indoor air quality, and
acoustics, have significant correlation with workers‟ satisfaction and performance
(Tarcan et al. 2004; Marshall et al. 2002; Fisk, 2000). Indoor air quality can have a
direct impact on health problems and can lead to uncomfortable workplace
15
environments (Juslen and Tenner, 2005; Fisk and Rosenfeld, 1997; Marshall et al.
2002). On the basis of human exposure studies with clean and humidified air, it was
concluded that low RH (i.e. 10%) had little or no influence on the development of
dry mucous membranes in the eyes and airways in a consistent manner (Andersen et
al., 1974). In their journal, Ho et al. (2008) states that relative humidity can be
calculated based on the procedure recommended by ASHRAE (2005).
2.4.6 Clothing Insulation
The temperature and humidity of the environment may profoundly influence
the body‟s skin and interior temperature (Threlkeld, 1970). The human body is
adapted to function within a narrow temperature range. Generally, the human body
keeps its body temperature constant at 37 ± 0.5 °C under different climatic
conditions. Human thermal comfort depends on combinations of clothing, climate,
and physical activity. The human body converts the chemical energy of its food into
work and heat. The amount of heat generated and lost varies markedly with activity
and clothing levels (Layton, 2001). The heat loss from the body and the feeling of
individual comfort in a given environment is much affected by the clothing worn
(Ogulata, 2001). Clothing slows down the rate of conduction, and the nature of the
clothing influences the rate of conduction loss (Ck). The conduction heat loss is
usually insignificant. Also, the rate of change of heat stored in the body is neglected
in a steady-state heat transfer with its environment (Threlkeld, 1970). The clothing
insulation (Icl) can be estimated directly from the data presented in Table 2.2 for
typical combinations of garments (the values are for static thermal insulation), or
indirectly, by summation of the partial insulation values for each item of clothing,
Iclu.
16
Table 2.2: Thermal insulation for typical combinations of garments (ISO 9920)
2.4.7 Metabolic rates
The metabolic rate is proportional to body weight, and is also dependent upon
the individual‟s activity level, body surface area, health, sex, age, amount of
clothing, and surrounding thermal and atmospheric conditions. Metabolism rises to
peak production at around 10 years of age and drops off to minimum values at old
age. It increases due to a fever, continuous activity, or cold environmental conditions
if the body is not thermally protected. Some information/standards on metabolic rates
are given in ISO 8996. That elderly people often have a lower average activity than
younger people also needs to be taken into account. The metabolic rate (human
activity level) can be estimated directly from the data presented in Table 2.3.
17
Table 2.3: Metabolic rate (human activity level) for different activities
2.5 THERMAL INDICES
2.5.1 Predicted mean vote (PMV)
Predicted mean vote (PMV) is a parameter for assessing thermal comfort in
an occupied zone based on the conditions of metabolic rate, clothing, air speed
besides temperature and humidity. PMV values refer the ASHRAE thermal sensation
scale that ranges from –3 to 3 as follows: 3=hot, 2=warm, 1=slightly warm,
0=neutral, –1=slightly cool, –2=cool, –3=cold. Figure 2.1 summarizes the overall
process of using the six variables associated with thermal comfort sensation to
evaluate the PMV. The general comfort equation developed by Fanger to describe
the conditions under which a large group of people will feel in thermal neutrality is
too complex and cannot be used in real time applications. Individual differences are
&accounted for' by providing a method for predicting the percentage dissatisfied
(PPD) with the environment as a function of PMV values. The PMV index is a
widely used method for assessing thermal comfort. There are a number of other
thermal comfort indices and the standard effective temperature (SET) has been
developed in the USA (Nishi and Gagge, 1977). The SET is a complex index that can
be used in heat and cold stress environments as well as for measuring thermal
comfort. The PMV index has been adopted as the International Standard method for
assessing thermal comfort (ISO 7730, 1994).
18
Figure 2.1: PMV and thermal sensation (Nishi and Gagge, 1977).
Calculation of PMV is as follow:
PMV = [0.303 exp (-0.036M) + 0.028] × {(M-W) – 3.96×10-8
fcl [(Tcl + 273.15)4
– (Trad + 273.15)4] – fclhc (Tcl – Ta) - 3.05 [5.733-0.007 (M-W) – 0.001pw]
– 0.42 [(M-W) -58.15] – 0.0173M (5.867 – 0.001pw) – 0.0014M (34-Ta)}
where,
Tcl = 35.7 – 0.0275(M-W) – Rcl {3.96×10-8
fcl [(Tcl + 273.15)4 – (Tr + 273.15)
4] +
fclhc (Tcl –Ta)}
hc = 2.38 (Tcl –Ta)0.25
2.38 (Tcl –Ta)0.25
> 12.1v0.5
12.1v0.5
2.38 (Tcl –Ta)0.25
≤ 12.1v0.5
fcl = 1.0 + 0.2Icl Icl ≤ 0.5clo
1.05 + 0.1Icl Icl > 0.5clo
Rcl = 0.155Icl
19
The parameters are defined as follows:
• PMV: predicted mean vote.
• M: metabolism (W/m2)
• W: external work, equal to zero for most activity (W/m2)
• Icl: thermal resistance of clothing (Clo)
• fcl: ratio of body‟s surface area when fully clothed to body‟s surface area when
nude.
• Ta: air temperature (ºC)
• Tmrt: mean radiant temperature (ºC)
• Vair: relative air velocity (m/s)
• Pa: partial water vapour pressure (Pa)
• hc: convectional heat transfer coefficient (W/m2 K)
• Tcl: surface temperature of clothing (ºC)
However, The PMV and PPD values also can be calculated using thermal comfort
online calculator which based on ISO7730 (1993). This thermal comfort online
calculator is designed by Dr. Andrew Marsh PhD, B. Arch. (Hon), who is a graduate
architect who specializes in the computer simulation of building performance.
2.5.2 Predicted Percentage of Dissatisfied (PPD)
Predicted Percentage Dissatisfied (PPD) is an index that used as a
quantitative measure of the thermal comfort of a group of people at a particular
thermal environment. The PMV predicts the mean value of the thermal votes of a
large group of people exposed to the same environment. But individual votes are
scattered around this mean value and it is useful to be able to predict the number of
people likely to feel uncomfortably warm or cool. The PPD is an index that
establishes a quantitative prediction of the percentage of thermally dissatisfied
people who feel too cool or too warm. For the purposes of this International
Standard, thermally dissatisfied people are those who will vote hot, warm, cool or
cold on the 7-point thermal sensation scale given in Table 2.4. With the PMV value
determined, calculate the PPD using equation below:
20
PPD = 100 – 95 exp (-0.03353PMV4 – 0.2179PMV
2)
Table 2.4: 7-point thermal sensation scale
+3 Hot
+2 Warm
+1 Slightly warm
0 Neutral
-1 Slightly cool
-2 Cool
-3 Cold
According to Butera F.M. (1998), the relationship of PPD as a function of PMV is
shown as the Figure 2.2 below:
Figure 2.2: Graph of PPD as a function of PMV (Butera F.M. 1998),
21
2.6 REVIEW ON PREVIOUS RESEARCH
There are lots of researches that have been done by others before this about
the effect of environmental factors toward the workers‟ productivity and
performance and also about human comforts. All the past studies are important to be
reviewed in order to do some comparison between the results and methods that will
be used and collected later to make sure that the study can be done correctly.
2.6.1 Effects of Environmental Factors on Workers’ Performances
According to Dawal, et al, (2004) there is significant positive correlations
occurred between job satisfaction and perception of all environmental factors. The
outstanding correlation for Auto1 was perception of light and for Auto2 was
perception of humidity. The results indicated that environment conditions, especially
temperature, humidity, noise and light affect job satisfaction in automotive
industries. The management of both companies should attempt to optimize
temperature, humidity and noise because measurements of these factors are outside
the comfortable boundary and respondents are not satisfied with them. Standard
environmental conditions (including temperature, humidity, noise, and light) for
automotive industries in Malaysia must be revised to maintain workers‟ health
physically and mentally, thereby increasing productivity and job satisfaction as well
as performance. Light, noise, air quality and the thermal environment were
considered factors that would influence the acceptability and performance on the
occupants of premises (Nishi and Gagge, 1977).
Previous research done by Keith (1998) showed that the work environments
were associated with perceived effects of work on health. This research used a
national sample of 2048 workers who were asked to rate the impact of their
respective jobs job on their physical and mental health. Regression analyses proved
that the workers‟ responses were significantly correlated with health outcomes. In
addition to this, Shikdar et al. pointed out that there was high correlation between
performance indicators and health, facilities, and environmental attributes (Nishi and
Gagge, 1977). In other words, companies with higher health, facilities, and
22
environmental problems could face more performance related problems such as low
productivity, and high absenteeism. Employees with complaints of discomfort and
dissatisfaction at work could have their productivity affected, result of their inability
to perform their work properly (Maher et al, 1999). Workplace environmental
conditions, such as humidity, indoor air quality, and acoustics have significant
relationships with workers‟ satisfaction and performance (Keith, 1998; Fisk, 2000;
Chubaj, 2002). Indoors‟ air quality could have a direct impact on health problems
and leads to uncomfortable workplace environments (Shiaw, 2002; Wilson, 2001; Ka
Wing and Wai Tin, 2008).
Most of the past studies that been reviewed are from Ismail and his partners.
However, their studies only focusing in automotive industries which are differ from
this study that is going to be conducted in a manufacturing industry. Furthermore,
their research only about the thermal comfort which is not including environmental
factor that is noise. Their studies have been took place at various stations at
automotive plant industries. They used both physical measurement and questionnaire
survey methods in order to investigate the environmental factors influences and
determine thermal comfort among the workers‟ and their productivity at each
workstation. One of his study that was conducted at tire receiving station has the
lowest PMV value ranges between 1.07 and 1.41 compared at others stations (Ismail
et al., 2009). The average metabolic rate of worker at this station is 116 W/m2 with
the clothing rate of 0.8 clo for short sleeves and light working trousers. Even though,
the tire receiving station still was not comfort with only 54.03% workers satisfied
with thermal condition. The empirical study from PPD and PMV index indicated that
workers working at this were influenced by the heat. It is because nearly half of the
population of subjects satisfied with the thermal comfort while the PMV index
showed the area of work is slightly warm.
Ismail et al. (2009) have studied the dominance effects of environmental
factors such as WGBT (°C), relative humidity (%) and illuminance towards the
workers‟ productivity at Malaysian automotive industry. Two sets of representative
data and production rate collected for this study. Then, Taguchi method was used to
find the sequence of the dominant factor that contributing to the workers‟
23
productivity. The optimum level for the three environmental factors determined for
optimum productivity. The multiple linear regressions were employed to obtain the
equation model in order to represent the relationship of these environmental factors
towards productivity. The study exposed that the dominant factors that contribute to
the productivity at the body assembly production line is WBGT and illuminance.
This experimental finding is almost similar to the perception study through survey
approach. Meanwhile, the thermal comfort assessment at body assembly station
shows that the PMV index was between the range of 1.76 and 2.1. The average
metabolic rate of worker at this station is 116 W/m2 with the clothing rate of 1.1 clo
for long sleeves. As a result, the PPD value higher than tire receiving station with
65% to 81% (Ismail et al., 2009). This shows that the thermal sensation at body
assembly was warm. Furthermore, the paint shop area considered as most discomfort
environment with PMV value was 2.1 and 2.8 with PPD value was 81.1% to 97.8%
(Ismail et al., 2010). The average metabolic rate of worker at this station is 93 W/m2
with the clothing rate of 0.9 clo for long sleeves. This showed that at the paint shop
area the thermal sensation was warm and almost hot. In overall, the findings of the
researches by Ismail and colleagues at Malaysian automotive industry reveals that
the thermal comfort level still poor and required lot of improvisation in order provide
comfort working environment to the worker and it will help to increase the
productivity. Another research from Ismail and colleagues show that the optimum
environmental factors for thermal comfort manage to be predicted through Artificial
Neutral Network‟s (ANN) analysis system which commonly used the method of best
linear relationship. They managed to found the optimum value of production attained
when the WBGT is 24.5°C, relative humidity is 54.86% and lighting value is
146.386 lux from the linear relationship (Ismail et al., 2010). Through these optimum
values, the optimum production rate has been achieved in one manual production line
in the Malaysian automotive company.
One of the study that was conducted by Ismail et. al. in (2009) has almost the
same title as this study which entitled, Modelling Of Environmental Factors Towards
Workers Productivity For Automotive Assembly Line. The only different between
both of the studies is only where the environmental data was collected which one of
them is in automotive industry, while the other one in conducted in manufacturing
24
industry. The paper consists of a study to determine the effects of humidity and of air
temperature on the operator‟s productivity and performance in the Malaysia
automotive industries. The result of the study show that it is indicates a significant
relationship between humidity, wet bulb globe temperature and workers‟
productivity. The finding from Ismail and colleagues shows that the obtained
relationship for relative humidity was Y = 2.79X – 46.1. For WBGT, the obtained
relationship was Y = -13.3X + 425.
Table 2.5: Summary of Previous Research
No. Author(s) and Title Objective Findings
1. Ismail, Rani, Makhbul,
Deros, (2009).
Assessment of
Thermal Comfort and
Optimization of
Environmental Factors
at Automotive
Industry. European
Journal of Scientific
Research. Vol.31.
pp.409-423.
To determine the
dominance effects of
environmental factors such
as Illuminance (lx), relative
humidity (%) and WBGT
(ºC) on the operators‟
productivity at Malaysian
automotive industry.
The study reveals that the dominant
factor contribute to the productivity
at the body assembly production line
is WBGT and Illuminance whereas
the empirical finding is closely
related to the perception study by
survey questionnaire distribution.
The thermal comfort assessments of
this station which is the scale PMV
is 2.1 and PPD is 19% are likely to
be satisfied by the worker.
2. Ismail, Yao, Yunus,
(2009). Relationship
Between Occupational
Stress and Job
Satisfaction: An
Empirical Study in
Malaysia. The
Romanian Economic
Journal.
To measure the effect of
occupational stress on job
satisfaction of academic
employees.
This result demonstrates that level of
physiological stress has increased job
satisfaction, and level of
psychological stress had not
decreased job satisfaction. Further,
the study confirms that occupational
stress does act as a partial
determinant of job satisfaction in the
stress models of the organizational
sector sample.
3. Ismail et. al. (2009).
Optimization of
Environmental
Factors: A Study at
Malaysian Automotive
To determine the effects of
humidity and of air
temperature on the
operator‟s productivity and
performance.
The result of the study show that it is
indicates a significant relationship
between humidity, wet bulb globe
temperature and workers‟
productivity. The obtained
25
Industry. ISSN Vol.27
, pp.500-509.
relationship for relative humidity
was Y = 2.79X – 46.1. For WBGT,
the obtained relationship was Y = -
13.3X + 425.
4. Ismail, Rani, Makhbul,
Nor, Rahman, (2009).
A Study of
Relationship between
WBGT and Relative
Humidity to Worker
Performance, Volume
51.
To show the effect of
temperature and relative
humidity on worker
productivity.
The study shows that there is
extremely strong evidence that the
productivity-humidity model is
significant. The t-value for t1= 4.795
has a p-value of 0.002, which
indicates that the regressor humidity
contributes significantly to the
model. Then, for temperature, it
shows extremely strong evidence
that the productivity-WBGT model
is significant. The t-value for WBGT
t1=-3.620 has a p-value of 0.009,
which indicates that the regressor
WBGT contributes significantly to
the model.
5. Dawal, Ismail, Taha,
(2011). Factors
Affecting Job
Satisfaction in Two
Automotive Industries
in Malaysia. Jurnal
Teknologi. 44 (A): 65-
80.
To investigate how job
satisfaction is affected by
job characteristics, job
environment and job
organization.
This study found that job satisfaction
was significantly correlated with job
characteristics, environments, and
job organization. The environmental
factors did affect job satisfaction and
the strength of the correlation was
influenced by the workers‟
surroundings, depending on the
function of the building.
6. Ismail, Jusoh, Nuawi,
Deros, Makhtar,
Rahman, (2009).
Assessment of
Thermal Comfort at
Manual Car Body
Assembly
Workstation. World
Academy of Science,
Engineering and
Technology. 54 2009.
To determine the thermal
comfort among worker at
Malaysian automotive
industry.
The result of PMV at the related
industry is between 1.8 and 2.3,
where PPD at that building is
between 60% to 84%. The survey
result indicated that the temperature
more influenced comfort to the
occupants.
26
7. Ismail, Jusoh, Sopian,
Usman, Zulkifli,
Rahman, (2009)
To determine the thermal
comfort including the
purpose of air conditioning
systems and natural
ventilation among workers.
The PMV at Stamping Station is
slightly warm. 31% are likely to be
satisfied with thermal comfort at this
station. Door Check Assembly
(Myvi) also slightly warm and PPD
is 54%. For Door Check Assembly
(BLM), the PMV is 1.29 and PPD is
60%. Door Check Assembly for
BLM and Myvi using air
conditioning but there still different
PMV and PPD value.
8. Atmaca, Kaynakli,
Yigit, (2006). Effects
of radiant temperature
on thermal comfort.
Building and
Environment. 42:
3210-3220.
To investigate the local
differences between body
segments caused by high
radiant temperature, and to
analyze the interior surface
temperatures for different
wall and ceiling
constructions with their
effect on thermal comfort.
In this study, PMV index reaches
+1.5 for case (1), +1.1 for case (2)
and +1 for case (3), indicating to be
between slightly warm and warm.
For the insulated walls and ceiling
such as cases of (4), (5) and (6), this
value remain under +0.3. It is shown
that the body segments close the
relatively hot surfaces are more
affected than others and interior
surface temperatures of un-insulated
walls and ceilings exposed to a
strong solar radiation reach high
levels, all of which cause thermal
discomfort for the occupants in
buildings.
9. Wafi, Ismail, (2010).
Occupant‟s Thermal
Satisfaction A Case
Study in Universiti
Sains Malaysia (USM)
Hostels Penang,
Malaysia. ISSN 1450-
216X Vol.46 No.3
(2010), pp.309-319.
To predict the thermal
comfort level of students.
The results obtained showed the
thermal comfort level for male and
female and also that there were
significant differences (P < 0.05)
between all parameters inside and
outside hostels. This study predicted
that climate affects thermal comfort
in hostels located in a warm humid
climate zone and also determined the
actual thermal comfort in the hostel
rooms.
10. Ismail, (2011). To show the effect of For the sound pressure level, the
27
Multiple Linear
Regressions of
Environmental Factors
toward Discrete
Human Performance.
temperature, illuminance
and sound pressure level on
workers‟ productivity.
model equation is given an optimum
value at 88.16 dB which is higher
than the standard value that
permissible via 85 dB for heavy
industry (IFC Environmental
Guidelines for Occupational Health
and Safety). For illuminance, an
optimum illuminance value that
obtained is 417.75 Lux. Compared to
IFC Environmental Guidelines for
Occupational Health and Safety, the
calculated value is located below
than maximum standard value via
500 lx.
11. Ismail, Yusof,
Makhtar, Deros, Rani,
(2010). Optimization
of Temperature Level
to Enhance Worker
Performance in
Automotive Industry.
American Journal of
Applied Sciences 7
(3): 360-365.
To optimize the
temperature level to
enhance worker
performance.
The result shows that, it is apparent
from the linear relationship, the
optimum value of production
(value≈1) attained when temperature
value (WBGT) is 24.5°C. For
comfortable temperature is within
24-27°C (International Organization
for Standard, 2005).
12. Taiwo, (2009). The
influence of work
environment on
workers‟ productivity:
A case of selected oil
and gas industry in
Lagos, Nigeria.
African Journal of
Business Management
Vol. 4 (3), pp. 299-
307.
To analyze the impact of
work environment on
future worker‟s
productivity.
The result of T-test analysis
indicated that employee productivity
problems are within the
environment. All efforts targeted
toward alleviating employee
productivity problems should be
directed at the work environment.
Conducive work environment
stimulates creativity of employees
that may lead to better methods that
would enhance productivity.
13. Ismail et. al. (2008).
Modelling of Workers‟
Productivity Using
Environmental
To determine the effects of
illuminance (lux), relative
humidity (%) and Wet Bulb
Globe Temperature on the
The results from the correlation
analysis revealed that there are a
multiple linear relationships between
the Illuminance, relative humidity
28
Parameters in
Malaysian Electronic
Industry. Journal - The
Institution of
Engineers, Malaysia.
Vol. 70.
operators‟ productivity at
Malaysian electronic
industry.
(%) and Wet Bulb Globe
Temperature and productivity of the
workers. The multiple linear
regression expression obtained was
Productivity = 657.248 – 26.561
relative humidity + 1.343 Wet Bulb
Globe Temperature.
14. Ismail, Haniff, Deros,
Rahman, Nuawi, Rani,
(2010). The Influence
Of Sound Pressure
Level Towards
Workers‟ Production
Rate.
The aim of the study was to
determine the effects of
noise on the operators‟
productivity and
performance at Malaysian
automotive industry.
The results from the correlation
analysis revealed there is a weak
negative relationship between the
sound pressure level (dB) and the
productivity of the workers. The
linear regression analysis further
reveals that there is a linear equation
model with negative slope to
describe the relationship of sound
pressure level (dB and workers
productivity for the assembly section
involved.
15. Ismail, Haniff, Deros,
(2010). Influence Of
Wet-Bulb Globe
Temperature (Wbgt)
Towards Workers‟
Performance: An
Anova Analysis.
ISBN: 978-967-5080-
9501. pp. 435-441.
To determine the effects
Web-Bulb Globe
Temperature (WBGT) on
the operators‟ performance
at Malaysian automotive
industry.
The results from the correlation
analysis revealed there is a weak
negative relationship between the
WBGT and productivity of the
workers. The linear regression
analysis further reveals that there is a
linear equation model with negative
slope to describe the relationship of
WBGT and workers performance for
the assembly section involved. The
linear regression line obtained is Y =
-13.3X + 425.
16. Seppänen et. al.
(2003). Cost Benefit
Analysis Of The
Night-Time
Ventilative Cooling In
Office Building.
To evaluate the potential
productivity benefits of
improved temperature
control, and to apply the
information for a cost-
benefit analyses of night-
time ventilative cooling,
The studies indicate an average 2%
decrement in work performance per
degree o
C temperature rise, when the
temperature is above 25 o
C. When
we use this relationship to evaluate
night-time ventilative cooling, the
resulting benefit to cost ratio varies
29
from 32 to 120.
17. Thwala, Monese,
(2005). Motivation As
A Tool To Improve
Productivity
On The Construction
Site.
To identify the factors that
promotes positive
motivational behaviour
among construction
workers as to improve
production in the
construction site.
There are definite differences
between different cultures as to how
people can be motivated; this also
must be taken into consideration.
Management should play an active
and continuous role in managing on
site motivational processes;
employee‟s desired outcomes should
be tied to performance; and
management should focus on
eliminating performance obstacles.
CHAPTER 3
METHODOLOGY
3.0 INTRODUCTION
This chapter will be providing a review of the methodology that has been
suggested in conducting the study. It is start with the designing of the study, where
the methodology in performing this study has been review. Framework of the study
in the other hand, will review the planning that have been suggested in conducting
the study. A review of data modeling software that will be used also will be
discussed in general.
Nowadays, in Malaysia, the manufacturing industry can be considered as one
of the main source of income for the nation. However, some of the industry cannot
fully optimize the resource and all the workers due to some of the environmental
factors that affected their productivity. This problem not only inconvenience, but also
economic loss due to reduced industrial production. Therefore, this study will be
focusing at one of the manufacturing industry in Malaysia and is chosen as subject.
Because of the factory is consist of not only the machines but also humans, the poor
environmental ergonomic factors such as illuminance, noise, air velocity, air
temperature and relative humidity will reduce the workers‟ job productivity. The
selection of specific location for this at the industry will be done during the initial
plant visit and it is based on few criteria as following:
To choose the study location, some considerations must be taken, such as:
1.The workstation which has many problems with environment factors (temperature,
noise, illuminance and relative humidity); 2.A workstation which produced an
31
amount of products in a range of time and under the effects of temperature and
relative humidity; 3.The location that most workers easily get tired and have less
productivity; 4.The location that frequently received complain from workers
regarding the uncomfortable working environment. This criterion is essential to see
the effect of the temperature, noise, illuminance and relative humidity on the worker
productivity. One critical manual assembly workstation had been chosen as a subject
for the study. The human subjects for the study constitute operators at some of a
station in the factory.
3.1 DESCRIPTION OF WORKSTATION
In order to proceed to data collection, the most crucial step to be taken is
selecting a suitable and fulfilling all the criteria as stated in the above subtopic. A
workshop in Nilai which is one of a sub-company of DRB-HICOM SDN BHD is
selected as the location for the study. For the workstation, the painting room in the
workshop is chosen as the room is considered as the worst case scenario of all the
workstations in the workshop. The selected location producing a product over a
period of time and under the effects of certain environmental factors such as
illuminance, relative humidity, air temperature, noise and air velocity. This criterion
is essential in order to obtain which factors contribute utmost to the worker
productivity based on output of assemblies among operators. The production line
was consists of 5 man operators. The whole station was based on zero level from the
ground in the workshop. In the entrance of the workstation, plastic curtains are used
as door. In addition, the ventilation system of the room is consists of 1 industrial
stand fan, 2 air flow wall fan, and 6 vacuum devices. All these equipments are used
to let the heat and uneasy smell of paints to be flowed out from the room. For
painting job, 4 steel cages and 4 working tables are placed in the room. The part that
needs to be painted will be placed either in the cages or on the tables during painting
session. Other than that, there are also some small cabinets which used to store paints
and a small washroom, just for cleaning purpose. Figure 3.1 below show the
workflow at the painting workstation.
32
Figure 3.1: Workflow at the painting workstation
Figure 3.2: View of the plastic curtains (door) and steel cages
Painting the
part and let it
dry
Preparing
paints and its
spray
Place part on
working table
or inside cage
Put away the
completed
part
33
Figure 3.3: Inside view of the workstation
Figure 3.4: One of the workers preparing paints
34
3.2 SUBJECT OF STUDY
All 5 of the workers in the selected workstation will be the participant in this
study. The number is selected because the workers in the workstation are too little, so
the number of participants to the number of the workers in the workstation be 100%
so that the achieve result will become more accurate. Their anthropometric data such
as sex, age, height, weight will be recorded through questionnaire survey. Their
working experience in particular industry also will be recorded. For field
measurement all employees in this study will consider exposed to a same physical
effect.
3.3 PROCEDURE OF STUDY
To conduct this study, there are steps should be done. First is to do some
review about the previous study that has been done. It is important to get starting
view about how this study will be going to be done. Then few appropriated standards
also required for the study and it obtained through preliminary study. The
methodology and findings of previous researchers reviewed in order to get the better
overview of this study. After that a survey questionnaire also is prepared in order to
obtain the anthropometric data from subject during the experimental study in the
industry. After all the previous steps are completed, it is needed to search for a
manufacturing industry in order to conduct the study there. Once the industry is
found, it is required to request for their permission. Then, there will be a first visit to
the selected industry once after getting their permission to choose and decide a
specific workstation suitable to conduct the study or field work. During the field
work, all the equipment that will be used need to be calibrate before setup them at the
chosen location to start collect the data for every 1 minute of interval. All the
prepared questionnaires will be given to the workers that have been selected as the
participant before or after collecting the environmental data and request them to fill
up the form. The field work is planned to start at 9am until 5pm for two consecutive
days at each location. During the field measurement, the environmental or weather
conditions such as sunny, raining, cloudy will be recorded with the timeline. This is
to correlate the environmental condition to the measured data in discussion part in
35
chapter 4. The measured data that are collected using thermal comfort instrument
will be transferred to laptop and verified at the end of each measurement day to
ensure that data is complete and able to use for thermal verification analysis. The
data obtain through field measurement analyzed using Microsoft Excell software to
obtain the graph of production rate versus all the environmental parameters and PMV
and PPD values of each working environment. The data obtain through questionnaire
survey approach are analyses and correlate with the empirical findings. Finally, all
the measured data is been analyze using SPSS software to find the optimized
environmental factors. Figure 3.5 shows the flow chart of this study.
36
Figure 3.5: Flow Chart of Study
Confirmation
of data
Yes
No
Workers‟ perception using
statistical method
PMV and PPD
method
Model the data using computational analysis
End
Equipment setup and data
measurement
Data collection through survey
approach (Distribute
questionnaire)
Transfer field measurement data
into laptop
A
Result Analysis
Analyze data from questionnaire
ANOVA analysis
Compare results to standard value
Compare results to past study that been reviewed
Conclusion and recommendation
Start
Find and Request
permission from industry to
conduct study
Approval from industry
A
Literature Review
Plant visit to make early
arrangement for study
Problem Identification, Formulate the
Objective of Study and Establish Research
Direction
No
Yes
37
3.4 DATA COLLECTION METHOD
There are two data collection methods that will be used in order to complete
this study. First method is by using survey approach (questionnaire) and the other is
the field work data measurement using an instrument which is thermal comfort
instrument. The questionnaire approach is used to collect the anthropometric data of
the participant.
3.4.1 Questionnaire Form
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. 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 practical. 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. For this study, the
questionnaire form is used to collect the anthropometry data of the subjects are
collected by asking them to fill. Some of the element that needs to be filled by the
subjects is about their personal information, medical problem and about the work
place environment.
3.4.2 Field Work Data Measurement
The measurements data of Illuminance, Relative Humidity (%), Airflow
(m/s), Air Temperature (oC) and Radiant Temperature (
oC), Wet Globe Bulb
Temperature (WBGT) of the surrounding workstation area and an amount of
products were produced. All the parameters are measured using thermal comfort
38
instrument. The production rate is represented the productivity of the workers. The
amount of the products are taken every 30 minutes were compared with the
measurement value of the parameters that been taken. The data measurement is
scheduled to be taken beginning with the start of the dayshift at 9.00 am the daily
measurements continued until the end of the dayshift at 5.00 pm. The time interval
between each data is 1 minute.
3.4.3 Equipments used for data measurement
The field work data measurement will be using thermal comfort instrument
which capable to obtain all parameters that need to be measured.
Figure 3.6: Thermal comfort instrument
3.4.4 Measurement Parameter
All the data or parameters that going to be collected during the field work are
Illuminance, Relative Humidity (%), Airflow (m/s), Air Temperature (oC) and
Radiant Temperature (oC), Wet Globe Bulb Temperature (WBGT). Every one of the
are measured using thermal comfort instrument. Whilst, the activity level of the
39
workers and the clothing will be observed in order to estimate the metabolic rate and
clothing insulations values to calculate the PMV and PPD.
3.5 DATA ANALYSIS METHOD
After securing the all the data, they need to be analyzed using some different
methods and softwares depending on how the data needs to be presented.
The process of evaluating data using analytical and logical reasoning
to examine each component of the data provided. This form of analysis is just one of
the many steps that must be completed when conducting a research experiment. Data
from various sources is gathered, reviewed, and then analyzed to form some sort of
finding or conclusion. There are a variety of specific data analysis method, some of
which include data mining, text analytics, business intelligence, and data
visualizations. Data analytics (DA) is the science of examining raw data with the
purpose of drawing conclusions about that information. Data analytics is used in
many industries to allow companies and organization to make better business
decisions and in the sciences to verify or disprove existing models or theories. Data
analytics is distinguished from data mining by the scope, purpose and focus of the
analysis. Data miners sort through huge data sets using sophisticated software to
identify undiscovered patterns and establish hidden relationships. Data analytics
focuses on inference, the process of deriving a conclusion based solely on what is
already known by the researcher.
3.5.1 Data Analysis Method (PMV and PPD Method)
Predicted mean vote (PMV) is a parameter for assessing thermal comfort in
an occupied zone base on the conditions of metabolic rate, clothing, air speed besides
temperature and humidity. PMV values refer the ASHRAE thermal sensation scale
that ranges from –3 to 3 as follows:
3=hot, 2=warm, 1=slightly warm, 0=neutral, –1=slightly cool, –2=cool, –3=cold.
Predicted percentage dissatisfied (PPD) is used to estimate the thermal comfort
satisfaction of the occupant. It is considered that satisfying 80% of occupant is good;
Air Velocity Sound Level
40
that is, PPD less than 20% is good. All the data that were collected will be described
in PMV and PPD graph of function's form.
3.5.2 Computational Analysis using SPSS software
SPSS (originally, Statistical Package for the Social Sciences) was released in
its first version in 1968 after being developed by Norman H. Nie and C. Hadlai Hull.
SPSS is among the most widely used programs for statistical analysis in social
science. It is used by market researchers, health researchers, survey companies,
government, education researchers, marketing organizations and others. The original
SPSS manual (Nie, Bent & Hull, 1970) has been described as one of "sociology's
most influential books". In addition to statistical analysis, data management (case
selection, file reshaping, creating derived data) and data documentation
(a metadata dictionary is stored in the data file) are features of the base software.
Using this software, it will be used to analyze the data according to bivariate
statistics: ANOVA and correlation of the results. The relationship of all the
parameters between production rates can be correlated using the ANOVA approach
in this software. It will make it easy for analyzing the data as all the calculations are
done itself. The comparison between the actual variation of the group averages and
that expected from the above formula is expressed in terms of the F ratio:
F = (found variation of the group averages) / (expected variation of the group
averages)
Thus if the null hypothesis is correct we expect F to be about 1, whereas
"large" F indicates a location effect. How big should F be before we reject the null
hypothesis? P reports the significance level. In terms of the details of the ANOVA
test, note that the number of degrees of freedom ("d.f.") for the numerator (found
variation of group averages) is one less than the number of groups; the number of
degrees of freedom for the denominator (so called "error" or variation within groups
or expected variation) is the total number of leaves minus the total number of groups.
The F ratio can be computed from the ratio of the mean sum of squared deviations of
each group's mean from the overall mean [weighted by the size of the group] ("Mean
41
Square" for "between") and the mean sum of the squared deviations of each item
from that item's group mean ("Mean Square" for "error"). In the previous
sentence mean means dividing the total "Sum of Squares" by the number of degree of
freedoms.
CHAPTER 4
RESULT AND DISCUSSION
4.0 INTRODUCTION
In this chapter, we will discuss further about the result and analysis for every
data that has been collected. All the data will be displayed in term of graphical and
graph. The graph are including graph of production rate versus illumninance, graph
of production rate versus relative humidity, graph of production rate versus air
velocity, graph of production rate versus air temperature and graph of production rate
versus noise. This is to make them easy to be analyzed. This chapter also contains
PMV and PPD analysis and workers‟ perception study analysis on environmental
factors of work place. In the end of this the chapter, it will discuss the key findings of
this study and comparing with previous research findings. All the data analysis is
done using Statistical Package for Science Socials (SPSS).
4.1 QUESTIONNAIRE (WORKERS’ PERCEPTION STUDY) ANALYSIS
4.1.1 Respondents Profile Survey
The total populations in this company are thirty eight people which work in eleven
different workstations. But, this survey only includes all the workers in the selected
workstation (Painting Room), which are only five people. The working hour of the
workers is from 9a.m. to 6p.m., daily from Monday until Friday and usually only
having one shift. The charts will show the characteristics of the respondents:
43
4.1.2 Respondents’ profiles
Figures 4.1, 4.2, and 4.3 shows all the workers profiles result that were
collected using survey approach. Based on the questionnaires that have been
distributed to all the respondents, the results show that, all the respondents that took
part in this study are 100% male. For the age fraction, most of the respondents are in
the range of 30-39 years old (60%). Another 20% of the respondents are between 20-
29 years old and the rest are between 40-49 years old (20%). The working
experiences of the workers in the workstation mostly in between 1-5 years (39%) and
follows with 11-15 years (23%), <1 year (19%) and between 6-10 years (19%).
Figure 4.1: Respondents‟ Gender
Figure 4.2: Respondents‟ Age
100%
0%
Male
Female
20%
60%
20%20-29
30-39
40-49
44
Figure 4.3: Respondents‟ Working Experiences
4.1.3 Workers’ Perception Analysis toward environmental factors of
Workplace
This section is important part of the questionnaire. In this section, the human
perception will measure based on their experience at the workstation and also about
their job. Moreover, the questionnaire also can be determine the actual condition and
situation that worker has to face every day. The outcome of this section is about the
comfortable of the workstation. Besides that, it‟s about condition of tools and
machinery at the workstation. Furthermore, about improvement or any changes
elements at workstation to increase the level of safety and also human-being. The
scale that has been used easier to respondent categorizing the parameter based on the
level of the condition.
Scale:
1- Strongly disagree
2- Disagree
3- Unsure
4- Agree
5- Strongly agree
According to the Figure 4.4, it is showing about the workers‟ perception towards
environmental factors in the selected workstation.
19%
39%19%
23%< 1 year
1-5 years
6-10 year
11-15 year
>16 year
45
Figure 4.4: Workers‟ Perception Analysis toward environmental factors of
Workplace
4.2 EXPERIMENTAL DATA ANALYSIS
4.2.1 Result for Illuminance
The illuminance levels were taken to identify the effect of relative humidity
on the worker performances. The data of production rate and illuminance (lux) are
taken for every 30 minutes. A graph was plotted to show the relationship between the
production rate and the illuminance levels. The graph in Figure 4.5 describes the
relationship between production rates versus illuminance levels. Based on the graph,
we can note that the production rates were decreased the illuminance increased.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5
Pe
rce
nta
ge\%
Level of condition
Ventilation
Humidity
Noise
Heat
Lighting
46
Table 4.0: Illuminance, production rate and time data
Time Production Rate (units) Illuminance (lx)
10.02-10.32 3 234.87
10.32-10.52 3 153.57
11.02-11.32 5 106.2
11.32-11.52 5 77.63
12.02-12.32 5 76.83
12.32-12.52 5 80.07
14.02-14.32 3 109.1
14.32-14.52 4 73.33
15.02-15.32 5 95.63
15.32-15.52 3 84.9
16.02-16.32 4 86.93
16.32-16.52 4 82.07
17.02-17.32 4 45.63
Figure 4.5: Graph of production rate versus illuminance
y = -0.009x + 4.994R² = 0.252
2
3
4
5
6
40 90 140 190 240
Pro
du
ctio
n R
ate
(un
its)
Illuminance(Lux)
47
Figure 4.6: Time series of illuminance data measured at the workstation
The illuminance has some interesting correlations with the temperatures. It
appears that the value of illuminance were significantly drops as the time goes by.
Starting with values, slightly above 200 lx, the illuminance decreased to values
around of 50 lux during the remaining day as illustrated by Figure 4.6. The values
were not really constant due to the effect from other source of light which is the
sunlight. When any people especially the workers went in or out the workstation,
they will open the plastic curtain that act as the door, which cause the sunlight enters
the room and affect the results. The decrease in illuminance is definitely visible. On
the whole, the environmental characteristics until the end of the day can be regarded
as constant. It is very probable that the measured values are far below the
recommended values of 200 to 500 lux for high contrast performance. There is only
some time when the illuminance exceeds 200 lx. The ISO standard ISO 8995-1:2002
(CIE 2001/ISO 2002) states that in the areas where continuous work is carried out
the maintained work plane illuminance should not be less than 200 lx. So, the
illuminace in this workstation is considered as unsuitable and unsafe for the workers
as most of the times, the illuminance does not exceed 200 lx. The obtained
relationship model between illuminance and production rate was y = -0.009x +
4.994. The results obtained for the illuminance is in-line with the finding from Van
y = -8.337x + 158.8
0
50
100
150
200
250
Illu
min
ance
\lu
x
Time
48
Bommel et al. (2002) and Juslen and Tenner (2005) where the increasing of
illuminance levels lead to an increase in productivity.
Regression and ANOVA analysis
The results for regression and ANOVA analysis were presented in tables below. The
coefficient of determination, R2, of 0.252 indicates that 25.2% of the production rate
variation was due to illuminance variation. The hypothesis was as follows:
Ho: β = 0 (The relationship between illuminance (%) and production rate is not
significant)
Ho: β ≠ 0 (The relationship between illuminance (%) and production rate is
significant)
Table 4.1: Regression and ANOVA analysis of illuminance
Model R R Square Adjusted
R Square
Std. Error
of the
Estimate
1 .502(a) .252 .184 .77883
a. Predictors: (Constant), Illuminance
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression 2.251 1 2.251 3.711 .080(a)
Residual 6.672 11 .607
Total 8.923 12
a. Predictors: (Constant), Illuminance
b. Dependent Variable: Production Rate
In a multiple linear regression model, it is customary to refer to R2 as the
coefficient of the multiple determinations. For the productivity regression model, R2
= 0.252 and the output report R2 x 100% = 25.2%. This can be interpreted using the
equation model obtained, which has approximately 25.2% of the observed variability
in productivity. This means that environmental factors (dependent variable) in this
49
model are able to predict productivity at 25.2% accuracy. In order to understand the
significance of the regression relationship between illuminance level and the
production rate for the area of population, an F-test were conducted. The F value
from the ANOVA is 3.711. The value of the significance level was selected to be
0.05 (α = 0.05). Because the P value is 0.080, we can reject Ho: β ≠ 0 in favor of Ho:
β = 0 at the 0.05 significance level. This strongly suggests that there is no
relationship between the illuminance and the production rate. Thus, there is strong
evidence that the simple linear model relating production rate and illuminance (lx) is
not significant. A study of office workers at the call centre by Boyce (2004) indicated
that illuminace have a statistically significant effect on average handling time that is
greater than 1%. The biggest effect of these variables predicted by the regression is
between 17% to 19% reduction in average handling time. In metal industry, Van
Bommel et al. (2002) conducted a study on the effect of increasing the illuminance
based on increased task performance, reduction of rejects and the decreased number
of accidents. The result of the study revealed that the increasing of illuminance from
the minimum required 300 lx (minimum) to 500 lx could lead to an increase of
productivity from 3% to 11% based realistic assumptions that the increase of
illuminance from 300 lux to 2000 lx would increase the productivity from 15% to
20%.
4.2.2 Result for Relative Humidity
The relative humidity levels were taken to identify the effect of relative
humidity on the worker performances. The data of production rate and relative
humidity (%) are taken for every 30 minutes. A graph was plotted to show the
relationship between the production rate and the relative humidity levels. The graph
in Figure 4.7 describes the relationship between production rates versus relative
humidity levels. Based on the graph, we can note that the production rates were
increased the relative humidity increased.
50
Table 4.2: Relative humidity, production rate and time data
Time Production Rate (units) Relative Humidity (%)
10.02-10.32 3 63
10.32-10.52 3 61
11.02-11.32 5 57
11.32-11.52 5 58
12.02-12.32 5 59
12.32-12.52 5 58
14.02-14.32 3 50
14.32-14.52 4 52
15.02-15.32 5 54
15.32-15.52 3 52
16.02-16.32 4 51
16.32-16.52 4 53
17.02-17.32 4 54
Figure 4.7: Graph of production rate versus relative humidity
y = 0.022x + 2.833R² = 0.011
2
3
4
5
6
45 50 55 60 65
Pro
du
ctio
n R
ate
(un
its)
Relative Humidity(%)
51
Figure 4.8: Time series of relative data measured at the workstation
The relative humidity trend is mostly similar from the illuminance which the
trend is decreasing. However, the trend of the relative humidity values is more
scattered. From Figure 4.8, the relative humidity seems to increase and decrease in
some times. This may have occurs due to the effect of the activities and tools that are
used by the workers. But even so, it still can be conclude that, until the end of the
experiment, the values are decreasing. It seems that the values of 70% relative
humidity are normal in the perceptions of the workers for the tropical climate, but it
is generally agreed that 70% relative humidity value is high. The recommended
values between 50 and 60% relative humidity are overridden. The personal
impression confirms with this fact. The standard of humidity is 40% RH (20 to 60%
ranges) (ASHRAE Standard 55). By comparing the result with the standards, it can
be said that, the relative humidity in the workstation is normal. The finding on the
effect of relative humidity towards productivity is same with the finding by Tsutsumi
et al. (2007) where they had found the subjective performance was at the same level
under four different levels of relative humidity and the relative humidity shows no
effect towards workers. However, Tsutsumi et al. (2007) reported their subjects were
more tired at 70% RH after relative humidity (%) step change. The obtained
relationship model between relative humidity and production rate was y = 0.022x +
2.833. The findings on the effects of relative humidity on productivity are not in line
y = -0.835x + 61.38
0
10
20
30
40
50
60
70
Re
lati
ve h
um
idit
y\%
Time
52
with finding by Tsutsumi et al. (2007), who found that the subjects‟ performance was
equal under different level of relative humidity. However, in his journal, Tsutsumi
(2007) also reported that their subjects were more tired after a step change in relative
humidity.
Regression and ANOVA analysis
The results for regression and ANOVA analysis were presented in tables below. The
coefficient of determination, R2, of 0.011 indicates that 1.1% of the production rate
variation was due to relative humidity variation. The hypothesis was as follows:
Ho: β = 0 (The relationship between relative humidity (%) and production rate is not
significant)
Ho: β ≠ 0 (The relationship between relative humidity (%) and production rate is
significant)
Table 4.3: Regression and ANOVA analysis of relative humidity
Model R R
Square
Adjusted
R Square
Std. Error
of the
Estimate
1 .106(a) .011 -.079 .89560
a. Predictors: (Constant), Relative Humidity
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression .100 1 .100 .125 .731(a)
Residual 8.823 11 .802
Total 8.923 12
a. Predictors: (Constant), Relative Humidity
b. Dependent Variable: Production Rate
In a multiple linear regression model, it is customary to refer to R2 as the coefficient
of the multiple determinations. For the productivity regression model, R2 = 0.011 and
the output report R2 x 100% = 1.1%. This can be interpreted using the equation
53
model obtained, which has approximately 1.1% of the observed variability in
productivity. This means that environmental factors (dependent variable) in this
model are able to predict productivity 1.1% accuracy. In order to understand the
significance of the regression relationship between relative humidity level and the
production rate for the area of population, an F-test were conducted. The F value
from the ANOVA is 0.125. The value of the significance level was selected to be
0.05 (α = 0.05). Because the P value is 0.731, we can reject Ho: β ≠ 0 in favor of Ho:
β = 0 at the 0.05 significance level. This strongly suggests that there is no
relationship between the relative humidity and the production rate. Thus, there is
strong evidence that the simple linear model relating production rate and relative
humidity (%) is not significant.
4.2.3 Result for Air Velocity
The air velocity levels were taken to identify the effect of air velocity on the
worker performances. The data of production rate and air velocity (ms-1
) are taken
for every 30 minutes. A graph was plotted to show the relationship between the
production rate and the air velocity levels. The graph in Figure 4.9 describes the
relationship between production rates versus air velocity levels. Based on the graph,
we can note that the production rates were decreased the air velocity increased.
Table 4.4: Air velocity, production rate and time data
Time Production Rate(units) Air Velocity(ms-1
)
10.02-10.32 3 0.2
10.32-10.52 3 0.17
11.02-11.32 5 0.2
11.32-11.52 5 0.13
12.02-12.32 5 0.1
12.32-12.52 5 0.1
14.02-14.32 3 0.17
14.32-14.52 4 0.17
15.02-15.32 5 0.17
15.32-15.52 3 0.23
16.02-16.32 4 0.27
16.32-16.52 4 0.17
17.02-17.32 4 0.2
54
Figure 4.9: Graph of production rate versus air velocity
Figure 4.10: Time series of air velocity data measured at the workstation
From Figure 4.10, it can be seen that the air velocity trend is not really
uniform. The values of the air velocity measured in the workstation are increasing
y = -8.980x + 5.652R² = 0.247
2
3
4
5
6
0.05 0.1 0.15 0.2 0.25 0.3
Pro
du
ctio
n R
ate
(un
its)
Air Velocity(ms-1)
y = 0.004x + 0.145
0
0.05
0.1
0.15
0.2
0.25
0.3
Air
Ve
loci
ty\m
/s
Time
55
and decreasing un-uniformly in matters of time. There are times when the value of
the air velocity will peak and drop. This condition happens because of the tools that
were used by the workers, painting air-sprays are producing high speed of air which
affecting the measured data. The average air velocity in the workstation is 0.175 m/s
which almost follow the standard value which the value should be less than 40 fpm
or 0.2m/s (ASHRAE Standard 55). This may because of the workstation is located
indoor and having good ventilation system. The obtained relationship model between
air velocity and production rate was y = -8.980x + 5.652. Federspiel et al. (2004) had
investigated the relationship of ventilation rates with the performance of nurses in
health industry working at a call centre. The findings from the study indicated that
the effect of ventilation rate on workers‟ performance at this call centre was very
small (probably less than 1%) or nil. However, there is some evidence of workers‟
performance improvements at 2% or more when the ventilation rate per person was
very high.
Regression and ANOVA analysis
The results for regression and ANOVA analysis were presented in tables below. The
coefficient of determination, R2, of 0.247 indicates that 24.7% of the production rate
variation was due to air velocity. The hypothesis was as follows:
Ho: β = 0 (The relationship between air velocity and production rate is not
significant)
Ho: β ≠ 0 (The relationship between air velocity and production rate is significant)
Table 4.5: Regression and ANOVA analysis of air velocity
Model R R
Square
Adjusted
R Square
Std. Error
of the
Estimate
1 .497(a) .247 .179 .78157
a. Predictors: (Constant), Air Velocity
56
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression 2.204 1 2.204 3.608 .084(a)
Residual 6.719 11 .611
Total 8.923 12
a. Predictors: (Constant), Air Velocity
b. Dependent Variable: Production Rate
In a multiple linear regression model, it is customary to refer to R2 as the coefficient
of the multiple determinations. For the productivity regression model, R2 = 0.247 and
the output report R2 x 100% = 24.7%. This can be interpreted using the equation
model obtained, which has approximately 24.7% of the observed variability in
productivity. This means that environmental factors (dependent variable) in this
model are able to predict productivity 24.7% accuracy. In order to understand the
significance of the regression relationship between air velocity level and the
production rate for the area of population, an F-test were conducted. The F value
from the ANOVA is 3.608. The value of the significance level was selected to be
0.05 (α = 0.05). Because the P value is 0.084, we can reject Ho: β = 0 in favor of Ho:
β ≠ 0 at the 0.05 significance level. This strongly suggests that there is a significant
relationship between the air velocity and the production rate. Thus, there is strong
evidence that the simple linear model relating production rate and air velocity is
significant.
4.2.4 Result for Air Temperature
The air velocity levels were taken to identify the effect of air velocity on the
worker performances. The data of production rate and air temperature (°C) are taken
for every 30 minutes. A graph was plotted to show the relationship between the
production rate and the air temperature (°C) levels. The graph in Figure 4.11
describes the relationship between production rates versus air temperature (°C)
levels. Based on the graph, we can note that the production rates were decreased the
air temperature (°C) increased.
57
Table 4.6: Air temperature, production rate and time data
Time Production Rate(units) Air temperature(°C)
10.02-10.32 3 32.47
10.32-10.52 3 31.53
11.02-11.32 5 32.77
11.32-11.52 5 32.13
12.02-12.32 5 32.07
12.32-12.52 5 32.1
14.02-14.32 3 34.3
14.32-14.52 4 34.63
15.02-15.32 5 35.13
15.32-15.52 3 34.73
16.02-16.32 4 35.1
16.32-16.52 4 34.77
17.02-17.32 4 34.03
Figure 4.11: Graph of production rate versus air temperature
y = -0.107x + 7.675R² = 0.028
2
3
4
5
6
31 31.5 32 32.5 33 33.5 34 34.5 35 35.5
Pro
du
ctio
n R
ate
(un
its)
Air temperature(°C)
58
Figure 4.12: Time series of air temperature data measured at the workstation
Figure 4.12 presented the air temperature against time that measured in a
Painting Room workstation. The measurement conducted from 10.02am to 5.32pm.
The lowest air temperature was obtained about at the starting of measurement
31.5°C. At first the air temperature start at 32.5°C, then it dropped slightly, before
the WBGT was increased until evening. Meanwhile, the air temperatures were
fluctuating few times. One of main reason could be because the measurement device
is placed nearer to door where it opens and thus it has much influences of outside
environment. The integrated thermal comfort equipment used to measure the data at
this location where it required connecting to laptop. Since, the temperature at the
room was quite high and in consideration of laptop condition, the equipment was
place nearer to door. The measurement day was sunny. The actual air temperature
could be higher than measured if the equipment was placed far away from the door
or centre of room. However, the air temperature is also affected by an industrial fan
and 2 air flow fan that were placed in the room. Without them, the air temperature
might get higher value. The maximum air temperature obtained is 35.13°C at 3.02pm
and 3.32pm. The average air temperature at the room is 33.52°C and it is considered
slightly warm temperature environment. the standard thermal comfort for winter is
68° to 74°F (20° to 23.5°C) and for summer is 73° to 79°F (22.5° to 26°C)(ASHRAE
Standard 55). The obtained relationship model between air temperature and
y = 0.282x + 31.54
29
30
31
32
33
34
35
36
Air
te
mp
era
ture
\◦C
Time
59
production rate was y = -0.107x + 7.675. The findings for air temperature were
similar to finding of Fisk (2000) where by increasing the air ventilation will
significantly increase the performance of the operators. Previous research done by
Ettner and Grzywacz (2001) showed that the work environments were associated
with perceived effects of work on health. This research used a national sample of
2,048 workers who were asked to rate the impact of their respective jobs on their
physical and mental health. Regression analyses proved that workers‟ responses were
significantly correlated with health outcomes. In addition, Shikdar and Sawaqed
(2003) pointed out that there are high correlations between performance indicators
and health, facilities, and environmental attributes. In other words, companies with
higher risks of environmental problems could face more problems in performance
such as low productivity, and high absenteeism. Employees experiencing discomfort
and dissatisfaction at work could have their productivity affected because their
inability to perform their work properly. The productivity increase cause by the air
temperature could be related to the attention and cognitive aspect of the operators
which has been studied by Staffan and Knez (2001). They found that the
combination of air temperature and illuminance level had a significant effect on
cognitive performance.
Regression and ANOVA analysis
The results for regression and ANOVA analysis were presented in tables below. The
coefficient of determination, R2, of 0.28 indicates that 28% of the production rate
variation was due to air temperature. The hypothesis was as follows:
Ho: β = 0 (The relationship between air temperature and production rate is not
significant)
Ho: β ≠ 0 (The relationship between air temperature and production rate is
significant)
60
Table 4.7: Regression and ANOVA analysis of air temperature
Model R R Square Adjusted
R Square
Std. Error
of the
Estimate
1 .168(a) .028 -.060 .88784
a. Predictors: (Constant), air temperature
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression .252 1 .252 .320 .583(a)
Residual 8.671 11 .788
Total 8.923 12
a. Predictors: (Constant), air temperature
b. Dependent Variable: Production Rate
In a multiple linear regression model, it is customary to refer to R2 as the coefficient
of the multiple determinations. For the productivity regression model, R2 = 0.28 and
the output report R2 x 100% = 28%. This can be interpreted using the equation model
obtained, which has approximately 28% of the observed variability in productivity.
This means that environmental factors (dependent variable) in this model are able to
predict productivity at 28% accuracy. In order to understand the significance of the
regression relationship between air temperature level and the production rate for the
area of population, an F-test were conducted. The F value from the ANOVA is
0.320. The value of the significance level was selected to be 0.05 (α = 0.05). Because
the P value is 0.583, we can reject Ho: β ≠ 0 in favor of Ho: β = 0 at the 0.05
significance level. This strongly suggests that there is no significant relationship
between the air temperature and the production rate. Thus, there is strong evidence
that the simple linear model relating production rate and air temperature is not
significant. From study done by A. R. Ismail (2010) to indicate relationship between
all parameters and workers productivity, a significantly that air temperature has a
strong effect to employee productivity in the studied workstation.
61
4.2.5 Result for Noise
The air velocity levels were taken to identify the effect of noise (dB) on the
worker performances. The data of production rate and noise (dB) are taken for every
30 minutes. A graph was plotted to show the relationship between the production rate
and the noise (dB) levels. The graph in Figure 4.13 describes the relationship
between production rates versus noise (dB) levels. Based on the graph, we can note
that the production rates were decreased the noise (dB) increased.
Table 4.8: Noise, production rate and time data
Time Production Rate(units) Noise(dB)
10.02-10.32 3 69
10.32-10.52 3 70.9
11.02-11.32 5 61.7
11.32-11.52 5 63
12.02-12.32 5 61.9
12.32-12.52 5 60.2
14.02-14.32 3 59.1
14.32-14.52 4 59.3
15.02-15.32 5 64.2
15.32-15.52 3 60.2
16.02-16.32 4 59.9
16.32-16.52 4 60.5
17.02-17.32 4 60.5
62
Figure 4.13: Graph of production rate versus noise
Figure 4.14: Time series of noise data measured at the workstation
Figure 4.14 described the noise against time graph that measured at Painting
Room workstation. The measurement started at 10.02am and the noise was increased
a little before decrease drastically from maximum value of 70.9dB to 61.7dB at
11.02am. Then the noise were dropping constantly for certain moments between
11.32am until 2.32pm and this can be clearly observed from figure above .After that,
y = -0.064x + 8.088R² = 0.076
2
3
4
5
6
58 60 62 64 66 68 70 72
Pro
du
ctio
n R
ate
(un
its)
Noise(dB)
y = -0.631x + 66.75
0
10
20
30
40
50
60
70
80
No
ise
\dB
Time
63
the relative humidity started to increase slightly to 64.2dB before decreased back to
60dB and stay increasing constantly for two hour. To conclude, noise values are
shown some fluctuation in measured value until the measurement stopped at 5.32pm.
The noise obtained is 59.1dB at 2.02pm until 2.32pm. The average noise in the
location is 62.34dB. The limitation of noise at industrial, commercial and traffic
areas generally is 70 dB in 24 hours (World Health Organization (WHO) Guidelines
for Community Noise, 1999). The obtained relationship model between noise and
production rate was y = -0.064x + 8.088. Khan et al. (2005) had studied the effect of
noise to productivity on data entry task on computers for short duration. The study
was conducted at four levels of noise intensity at 70 dB, 80, 90 dB and 100 dB. In the
study, all the subjects involving a group of male had to look at small chucks of data,
memorize it and then type it to the computer. At the same time, the recorded noise
was subsequently played in a randomized manner during experimental sessions. The
study showed that the effect of noise is statistically significant (F3, 27=2.96; p>0.05)
because it was found that human performance affected and improved as the noise
level was increased. The finding was contradict with the general perception that
people working under noisy environment pay more attention and concentrates more
on their assigned task.
Regression and ANOVA analysis
The results for regression and ANOVA analysis were presented in tables below. The
coefficient of determination, R2, of 0.076 indicates that 7.6% of the production rate
variation was due to noise. The hypothesis was as follows:
Ho: β = 0 (The relationship between noise and production rate is not significant)
Ho: β ≠ 0 (The relationship between noise and production rate is significant)
Table 4.9: Regression and ANOVA analysis of noise
Model R R
Square
Adjusted
R Square
Std. Error
of the
Estimate
1 .276(a) .076 -.008 .86576
64
a. Predictors: (Constant), Noise
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression .678 1 .678 .905 .362(a)
Residual 8.245 11 .750
Total 8.923 12
a. Predictors: (Constant), Noise
b. Dependent Variable: Production Rate
In a multiple linear regression model, it is customary to refer to R2 as the
coefficient of the multiple determinations. For the productivity regression model, R2
= 0.076 and the output report R2 x 100% = 7.6%. This can be interpreted using the
equation model obtained, which has approximately 7.6% of the observed variability
in productivity. This means that environmental factors (dependent variable) in this
model are able to predict productivity at 7.6% accuracy. In order to understand the
significance of the regression relationship between air temperature level and the
production rate for the area of population, an F-test were conducted. The F value
from the ANOVA is 0.905. The value of the significance level was selected to be
0.05 (α = 0.05). Because the P value is 0.362, we can reject Ho: β = 0 in favor of Ho:
β ≠ 0 at the 0.05 significance level. This strongly suggests that there is a significant
relationship between the noise and the production rate. Thus, there is strong evidence
that the simple linear model relating production rate and noise is significant. Previous
study that has been done by Ismail et al. (2010), reveals that there is a linear equation
model with negative slope to describe the relationship of sound pressure level (dB)
and workers productivity for the assembly section involved. A study on exposure to
noise, the attitudes and knowledge towards noise-induced hearing loss at steel rolling
mills industry in Africa by Olege et al. (2005), indicated that 93% of workers
demonstrated awareness of the hazard of noise to hearing and 10% of workers
complained of hearing loss. Noise measurement showed that 53% of factory workers
were exposed to noise levels more than 85 dB. There is a statistically significant (P<
0.001) relationship between the measured sound levels and awareness of noise
exposure.
65
4.3 PMV AND PPD ANALYSIS
Predicted mean vote (PMV) is a parameter for assessing thermal comfort in
an occupied zone based on the conditions of metabolic rate, clothing, air velocity
besides temperature and humidity. All the air velocity, temperature and humidity
data were measured using Thermal comfort instrument, while the activity level and
occupants clothing were observed during the measurement. PMV values refer the
ASHRAE thermal sensation scale (Son et al, 2008) that ranges from –3 to 3 as
follows: 3=hot, 2=warm, 1=slightly warm, 0=neutral, –1=slightly cool, –2=cool, –
3=cold. Referring to ISO7730, the value of both parameters is estimated as in Table
4.10:
Table 4.10: Metabolic rate value of the workers in the workstation (ISO7730)
Location Metabolic rate
Description Met W/m
2
Painting
Room 2.0 116
Standing and medium activity (painting
using spray, and lift object)
Table 4.11: Clothing insulation value of the workers in the workstation (ISO7730)
Location
Clothing
Insulation Description
Clo m2.K/W
Painting Room 0.75 0.115 For wearing underpants, shirt,
trousers, socks and shoes.
Table 4.10 above is about the metabolic rate of the workers in the selected
workstation. The workers in the workstation are doing medium activities which are
preparing the paint, painting using spray, and lifting object. So, from these activities,
according to ISO7730, it can be used to estimate their metabolic rate. Whilst, Table
66
4.11 is about the thermal insulation for typical combinations of clothing that were
wear by the workers. By observation, the workers were wearing similar type of
clothing which includes underpants, shirts, trousers, socks and shoes. Once again,
referring to ISO7730, the clothing insulation values can be determined. Using both
above values and plus with the measured values, PMV and PPD values for all
measured locations were calculated using online thermal comfort calculator which
based on ISO7730 (1993). The PMV is an index that predicts the mean value of the
votes of a large group of persons on the 7-point thermal sensation scale, based on the
heat balance of the human body. Thermal balance is obtained when the internal heat
production in the body is equal to the loss of heat to the environment. In a moderate
environment, the human thermoregulatory system will automatically attempt to
modify skin temperature and sweat secretion to maintain heat balance. The
calculated PMV and PPD values are shown in Table 4.12 below:
Table 4.12: PMV and PPD values at measured locations
Location PMV PPD
(%) Thermal comfort condition
Painting Room 2.7 96.7 Warm and slightly hot environment.
The PMV thermal sensation scale value is uncovered that, the environment in
the Painting Room should be nearly uncomfortable for the workers to do their works
because the condition in the room is warm and slightly hot. This is shown by the
values of PMV and PPD, which are 2.7 and 96.7% respectively. The PPD value
show that 96.7% which means that most of the people are dissatisfied with the
condition whilst the remaining of 3.3% people are still preferred to work at such of
that situation. A study done by Ismail et al. (2009) obtained that the thermal comfort
assessments of this station which is the scale PMV is 2.1 and PPD is 19% are likely
to be satisfied by the worker. It shows that the condition of both location of the study
were almost same.
67
Upon completing this study, it can be seen that the parameters that have
significant impact or effects towards the production rates are noise and air velocity,
while the other factors such as illuminance, air temperature and relative humidity did
not give significant effects to the workers‟ productivity in the selected workstation.
According to the Fisk and Rosenfeld (1997), productivity was one of the most
important factors affecting the overall performance to any organization, from small
enterprises to the entire nations. Increased attention had focused on the relationship
between the work environment and productivity since the 1990s. This study also
highlight that the value of PMV and PPD are calculated to be 2.7 and 96.7%
respectively and show that the workstation has warm and slightly hot condition. To
compare this study‟s findings with the past study is quite different. Meanwhile, the
thermal comfort assessment at body assembly station shows that the PMV index was
between the range of 1.76 and 2.1. The average metabolic rate of worker at this
station is 116 W/m2 with the clothing rate of 1.1 clo for long sleeves. As a result, the
PPD value higher than tire receiving station with 65% to 81% (Ismail et al., 2009).
This shows that the thermal sensation at body assembly was warm. Furthermore, the
paint shop area considered as most discomfort environment with PMV value was 2.1
and 2.8 with PPD value was 81.1% to 97.8% (Ismail et al., 2010). The average
metabolic rate of worker at this station is 93 W/m2 with the clothing rate of 0.9 clo
for long sleeves. This showed that at the paint shop area the thermal sensation was
warm and almost hot. Compare to this study which the PPD value obtained was
96.7%, it shown that the environmental condition in the location of this study is
better compared to the other studies. The difference values between all the studies
may be because of the various types of activities or task performed which influence
metabolic rate and different attire requirements at workstations at automotive
industry and manufacturing industry also influences the resulted predicted mean vote
(PMV) values. However, the productivity in the selected location was not influenced
by other factor such as the level of occupational stress of the workers. This was
supported by the findings of Yao et al. (2009), which the study demonstrates that
level of physiological stress has increased job satisfaction, and level of psychological
stress had not decreased job satisfaction. Further, the study confirms that
occupational stress does act as a partial determinant of job satisfaction in the stress
models of the organizational sector sample.
68
4.4 COMPARISON OF RESULTS TO STANDARD VALUES
Table 4.13 below shows the comparison between the experimental values that
obtained from this study to the standard values. This to show whether the selected
workstation follows the standard environmental values that has been set.
Table 4.13: Comparison of results to standard values
Parameters Experimental
values
Standard values
I. Illuminance (lx) 100.5 lux > 200 lux
ISO 8995-1:2002 (CIE
2001/ISO 2002)
II. Air Velocity
(ms-1
)
0.18 ms-1
< 0.2 ms-1
(ASHRAE Standard 55)
III. Relative
Humidity (%)
55.5 % 20% to 60%
(ASHRAE Standard 55)
IV. Air Temperature
(°C)
33.5 °C 22.5°C to 26°C
(ASHRAE Standard 55)
V. Noise (dB) 62.3 dB < 70dB
(World Health Organization
(WHO) Guidelines for
Community Noise, 1999)
69
CHAPTER 5
CONCLUSION AND RECOMMENDATION
5.1 INTRODUCTION
Chapter 5 will explain about the conclusion that has been made after finishing
this study. All the findings will be concluded in this chapter as well as some
suggestion and recommendation which can improve the condition of the workstation.
5.2 RECOMMENDATION
As for recommendation, most of the industry in Malaysia should become
more responsible especially in the scope of environmental factors, because we
already know their effect on workers‟ productivity. Industry must alert on the
minimum standards of environmental parameters that already been set to avoid any
problem to the workers. For this workstation which has been selected as the study
location, it still needed to improve the environmental condition in the area so that it
will achieve the minimum standard. It is to make sure that all the workers in the
workstation can work comfortably and avoiding any harm and danger towards them.
5.3 CONCLUSION
This study had achieved its objective to obtain a prediction equation model,
which relates the environmental factors to production rate in a quantitative way by
using inferential statistical analysis. The linear equation model is useful to
production engineers as a guideline to determine the right illuminance level (lx),
relative humidity (%), air velocity (m/s), noise (dB) and air temperature (°C) during
the feasibilities study to allow production line achieves the optimum output. The
productivity prediction equation model obtained is:
70
Productivity = 29.242 – 0.009 illuminance + 6.022 relative humidity – 8.98 air
velocity – 0.064 noise – 0.107 air temperature
Nevertheless after conducting the p-test, the results show that only two factors
contributed to the productivity, which is air velocity and noise. Therefore, the correct
productivity prediction equation model is:
Productivity = 13.74 – 8.98 air velocity – 0.064 noise
Research on the relationship of workplace environmental factors to the
productivity or performance is very limited and characterized by a short time
perspective or perception with emphasis on survey methods, statistical analysis,
satisfaction and the preferences measurement. The study like this is important to help
the industry no matter it is manufacturing or automotive. This is due to by
conducting more similar research, indirectly, it will find the weakness of production
line in term or environmental ergonomic and human comfort. This study is done to
prove empirically the previous perception studies based on the role of environmental
factors to productivity. It is expected that this study would be beneficial to the
automotive industries in Malaysia. The research findings are restricted to the
Malaysian workplace environment, where the awareness among workers on
productivity is still low. The results might vary for tests carried out for different
sample sizes, types of industries and countries.
71
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79
APPENDIX A
Time Illuminance(Lux) Relative Humidity(%) Air Velocity(ms-1
) Air Temperature(°C) Noise(dB) Production Rate
10.02-10.12 335 65 0.2 32.8 62.3
10.12-10.22 271.9 63 0.2 32.5 73.2
10.22-10.32 97.7 61 0.2 32.1 71.6
10.32-10.42 93.6 62 0.1 31.5 70.5
10.42-10.52 136 61 0.2 31.3 70.8
10.52-11.02 231.1 59 0.2 31.8 71.4
11.02-11.12 147 57 0.2 32.7 61.1
11.12-11.22 96.2 56 0.2 33 61.1
11.22-11.32 75.4 57 0.2 32.6 62.9
11.32-11.42 78 59 0.2 32.1 61.6
11.42-11.52 79.5 58 0.1 32.2 63.7
11.52-12.02 75.4 58 0.1 32.1 63.7
12.02-12.12 88.8 58 0.1 32.1 62.1
12.12-12.22 73.5 59 0.1 32.2 61
12.22-12.32 68.2 59 0.1 31.9 62.5
12.32-12.42 63.1 59 0.1 31.8 60.9
12.42-12.52 70.8 59 0.1 32.1 62.3
12.52-13.02 106.3 55 0.1 32.4 60.5
13.02-13.12 123.3 53 0.3 33.6 60.4
13.12-13.22 108 50 0.1 34.5 60.7
13.22-13.32 96 49 0.1 34.8 59.6
13.32-13.42 77.6 48 0.2 34.7 58.9
13.42-13.52 65.1 51 0.1 34.8 59.3
13.52-14.02 77.3 51 0.2 34.4 59.1
14.02-14.12 112.7 50 0.2 34.3 58.7
14.12-14.22 100 47 0.2 35.6 60
14.22-14.32 74.2 48 0.1 35.5 58.8
14.32-14.42 80.5 50 0.2 35 58.7
14.42-14.52 108.7 53 0.1 34.2 59.3
14.52-15.02 65.5 54 0.2 34.3 60
15.02-15.12 69.5 54 0.3 34 62.5
15.12-15.22 82 54 0.2 33.7 68.8
15.22-15.32 94.1 53 0.2 33.9 61.3
15.32-15.42 83.5 52 0.3 34.4 61
15.42-15.52 89.8 52 0.2 34.8 60.7
15.52-16.02 85.6 51 B 35 59
16.02-16.12 91.1 51 0.3 35.1 60.1
16.12-16.22 77.8 50 0.2 35 60.4
16.22-16.32 91.9 51 0.3 35.2 59.2
16.32-16.42 89.4 53 0.1 34.8 60.4
16.42-16.52 84.8 53 0.2 34.8 58.3
16.52-17.02 72 53 0.2 34.7 62.8
17.02-17.12 69.1 54 0.2 34.5 59.9
17.12-17.22 42.6 54 0.2 34.1 60.6
17.22-17.32 25.2 55 0.2 33.5 60.9
5
3
4
4
4
0
0
3
4
3
3
5
5
5
5
80
APPENDIX B
Nam
e :
Mu
ham
mad
Nai
f H
elm
i B
in A
bd
ul
Man
ap
Mat
rix
No.:
ME
08
02
2
Pro
ject
Tit
le :
Mod
elli
ng
of
En
vir
on
men
tal
Fac
tors
tow
ard
Hu
man
Pro
du
ctiv
ity
at M
anu
fact
uri
ng
In
du
stry
Su
per
vis
or
: Ir
. A
hm
ad R
asd
an I
smai
l
Ses
sion
: 2
01
1/2
01
2 (
PS
M 1
)
W
eek
A
ctiv
ity
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pro
ject
Pro
gress
Rep
ort
Pro
gress
Intr
odu
ctio
n (C
hap
ter
1)
Lit
erat
ure
Rev
iew
(C
hap
ter
2)
Met
hodolo
gy (
Chap
ter
3)
Rep
ort
for
PS
M1
Fin
al y
ear
pro
ject
1 p
rese
nta
tion
Get
the
pro
ject
tit
le a
nd a
rran
ge
wee
kly
appoin
tmen
t w
ith s
uper
vis
or.
Sta
te t
he
obje
ctiv
e, s
cope
and bac
kgro
und o
f th
e st
udy. (
Chap
ter
1)
Lis
t of
com
pan
y f
or
pro
ject
Rev
iew
rel
ated
jou
rnal
s an
d r
efer
ence
s
Sel
ect
a co
mpan
y
Dat
a co
llec
tion
15
53
19
71
46
42
13
11
10
81
2
81
APPENDIX C
Nam
e :
Muh
amm
ad N
aif
Hel
mi
Bin
Abd
ul M
anap
Mat
rix
No.
: M
E08
022
Pro
ject
Tit
le :
M
odel
lin
g of
En
viro
nm
enta
l F
acto
rs t
owar
d H
uman
Pro
duct
ivit
y at
Man
ufac
turi
ng
Indu
stry
Sup
ervi
sor
: Ir
. A
hm
ad R
asda
n I
smai
l
Ses
sion
: 2
011/
2012
(P
SM
2)
Wee
k
Act
ivit
y
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Pla
nnin
g
Act
ual
Con
clu
sion
(C
hapt
er 5
)
Com
pile
rep
ort
for
PS
M2
Fin
al y
ear
proj
ect
2 p
rese
ntat
ion
34
5
Rep
ort
Pro
gre
ss
Doc
um
enti
ng r
esu
lt
Mod
elin
g co
nstr
uct
ion
Mod
el v
erif
icat
ion
Mod
el m
odif
icat
ions
Sta
tist
ical
Ana
lysi
s
Res
ult
and
Dis
cuss
ion
(Cha
pter
4)
12
13
14
15
Dat
a an
alys
is
Pro
ject
Pro
gre
ss
67
89
10
11
12
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