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 :

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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 :

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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.

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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.

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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

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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

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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.

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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

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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

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