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THE EFFECT OF LIGHTING ON THE WORKER PRODUCTIVITY: A STUDY AT MALAYSIA ELECTRONICS INDUSTRY

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  • University Malaysia Kelantan Bachok

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The objective of this study is to determine the effects of lighting on the operators' productivity at Malaysian electronic industry. One electronic components assembly factory had been chosen as a subject for the study. The subjects were workers at the assembly section of the factory. The environment examined was the Illuminance (lux) of the surrounding workstation area. Two sets of representative data, the illuminance level and production rate were collected during the study. The production rate data were collected through observations and survey questionnaires while the illuminance level was measured using photometer model RS 180-7133.The correlation and linear regression analysis were conducted in order to obtain the relationship between the effects of level Illuminance (lux) and the worker productivity. The results from the correlation analysis reveal that there is a linear relationship between the Illuminance level and the productivity of the workers. The linear regression analysis further reveals that there is a linear equation model with positive slope to describe the relationship of Illuminance level and workers productivity for the assembly section involved. The linear regression line obtained is Y = 0.5634X – 158.16.
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THE EFFECT OF LIGHTING ON THE WORKER PRODUCTIVITY:
A STUDY AT MALAYSIA ELECTRONICS INDUSTRY
1Ahmad Rasdan Ismail, 2Mat Rebi Abdul Rani, 3Zafir Khan Mohamed Makhbul and
1Baba Md Deros
1Department of Mechanical and Materials Engineering, Faculty of Engineering,
National University of Malaysia, 43600 UKM Bangi, Selangor, MALAYSIA
2Department of Manufacturing and Industrial Engineering, Faculty of Mechanical
Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, MALAYSIA
3School of Business Management, Faculty of Economics and Business,
National University of Malaysia, 43600 UKM Bangi, Selangor, MALAYSIA
arasdan@eng.ukm.my
Abstract
The objective of this study is to determine the effects of lighting on the operators’ productivity at
Malaysian electronic industry. One electronic components assembly factory had been chosen as a
subject for the study. The subjects were workers at the assembly section of the factory. The
environment examined was the Illuminance (lux) of the surrounding workstation area. Two sets of
representative data, the illuminance level and production rate were collected during the study. The
production rate data were collected through observations and survey questionnaires while the
illuminance level was measured using photometer model RS 180-7133.The correlation and linear
regression analysis were conducted in order to obtain the relationship between the effects of level
Illuminance (lux) and the worker productivity. The results from the correlation analysis reveal that
there is a linear relationship between the Illuminance level and the productivity of the workers. The
linear regression analysis further reveals that there is a linear equation model with positive slope to
describe the relationship of Illuminance level and workers productivity for the assembly section
involved. The linear regression line obtained is Y = 0.5634X 158.16.
Keywords: productivity, illuminance, relationship
1. INTRODUCTION
Improving workers productivity, occupational
health and safety are major concerns of industry,
especially in developing countries. However,
these industries are featured with improper
workplace design, ill-structured jobs, mismatch
between workers’ abilities and job demands,
adverse environment, poor human-machine
system design and inappropriate management
programs (Shikdar and Sawaqed, 2003).
Such conditions could lead to workplace
hazards, poor worker health, disabilities, and
affect the productivity of workers and quality of
products. Work injuries create significant
economic and humanitarian consequences to our
society. Furthermore, work injuries have been
associated with psychological distress, decreased
participation in daily living activities and negative
effects on family well-being (Kirsh and McKee,
2003).
Light, noise, air quality and the thermal
environment were considered factors that would
influence the acceptability and performance on
the occupants of premises. (Olesen, 1995). Dua
(1994) stated that lower emotional health is
manifested as psychological distress,
depression and anxiety, whereas lower physical
health is manifested as heart disease, insomnia,
headaches, and infections. These health
problems could lead to organizational symptoms
such as job dissatisfaction, absenteeism, and
poor work quality. Irritated, sore eyes and throat,
hoarseness, stuffy congested nose, excessive
mental fatigue, headache and unusual tiredness
were all signs of the negative workplace
environmental conditions (Tarcan et al., 2004).
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 rated 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 and
Sawaqed (2003) pointed out that there was high
correlation between performance indicators and
health, facilities, and environmental attributes. In
other words, companies with higher health,
facilities, and 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 (Leaman, 1995).
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 entire nations. Increased attention
had focused on the relationship between the work
environment and productivity since the 1990s.
Laboratory and field studies showed that the
physical and chemical factors in the work
environment could have a notable impact on the
health and performance of the occupants, and
consequently on the productivity.
Workplace environmental conditions, such
as lighting, indoor air quality, and acoustics have
significant relationships with workers’ satisfaction
and performance (Tarcan, et al., 2004; Marshall
et al., 2002; Fisk, 2000). Indoor air quality could
have a direct impact on health problems and
leads to uncomfortable workplace environments
(Czubaj, 2002; Shiaw-Fen Ferng, 2002; Wilson,
2001).
In a metal industry, Van Bommel et al.
(2002) conducted a study on the effect of
increasing the illuminance in the metal industry
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 lux (minimum) to 500 lux could lead
to an increase of productivity from 3% to 11%
based on the realistic assumptions that the
increase of illuminance from 300 lux to 2000 lux
would increase the productivity from 15% to 20%.
.
2. METHODOLOGY
2.1 Selection of Location and Subjects
A Japanese based electronics company had
been selected as a place of study. A line
producing a product over a period of time and
under the effects of certain illumination level was
chosen. This criterion is essential in order to
obtain the relationship of the illuminance on the
worker productivity based on output of
assemblies among operators. The production line
was consist of 11 woman operators. Their task
is to assemble an electronics parts on the circuit
board for the television system tuner. Figure 1
shows the production line layout while in Figure
2 shows the flow chart of work sequences on
the production line. The standard production
rate determined by the previous feasibility study
to assemble a complete television tuner was
250 units for every hour of production.
Figure 1 The production line layout
Figure 2 The works sequence to assemble
complete TV tuner
2.2 Data Gathering and Analysis
Inferential statistics (i.e. the number of
production rate and illumination level) were
computed to gain a generalization of
relationship between production rate and
illumination levels. Further correlation and
simple regression analysis were performed to
obtain the relationship and hence the testing of
the hypotheses. The alpha for all hypotheses
testing was 0.05. The variables in this study
were production rate and illuminance.
Correlation analysis procedure was used to
examine if there was any relationship between
illuminance and production rate (i.e. whether the
relationship was linear (either positive or
negative). The simple regression analysis was
conducted to obtain the mathematical equation
in order to present the effect of illuminance on
the production rate at that particular production
line. Lastly, for hypotheses testing, ANOVA and
t-test have been administered. The hypotheses
Production Line
Store
of this study were:
Hypothesis
H1: There is a relationship between production
rate and illuminance in the population
studied.
H2: The relationship between illuminance and
production rate is significant.
The sample was inclusive of 10 female operators
whose age were in the range between 20 30
years old comprised mostly of local citizen of non-
degree holders and had been working with the
organizations for less than 5 years. Majority of the
respondents reported that they work for more
than 49 hours per week. The measurements of
illuminance level was performed using
photometer model RS 180-7133. The workers’
performance level was represented by the
production rate. The amount of the products
assembled were recorded for every 30 minutes
and data was compared to the levels of
illuminance.
3. RESULTS
The result of this study was based on the case
study conducted on the production line in the
electronic factory. The hypotheses for this study
was the production rate that have a direct
relationship with the illuminance level. The levels
of illuminance were taken to identify the effect of
lighting on the worker performances.
Table 1 shows the data of production rate, ,
illuminance level and the time taken for every 30
minutes. A graph was plotted to show the
relationship between the production rate and the
illuminance level. Figure 3 shows the graph to
describe the relationship between production rate
versus illuminance level. Based on the graph in
Figure 3, we can note that the production rate
were increases as we increase the illuminance.
The model of equation used is Ŷ = ß0 +
ß1X. The result from the analysis revealed the
regression linear equation obtained is Y =
0.5634X 158.16 where the Y representing
production rate and X representing illuminance
level. A hypothesis testing was conducted in
order to determine if there is a relationship or not
between productions rate (Y) and illuminance
level (X). The hypotheses were:
Ho : ρ = 0(There is no relationship between
production rate and illuminance level in the
population studied).
Ha : ρ
0 (There is a relationship between
production rate and illuminance level in the
population studied).
Table 1 Data on the Illuminance level,
Production Rate and Time
Time
(Hrs)
Production
Target
(Units)
Production
Rate
(Units)
8.25 -
8.55
125
124
8.55 -
9.25
125
128
9.25 -
9.55
125
126
10.55-
11.25
125
120
11.25-
11.55
125
126
11.55-
12.25
125
129
13.40-
14.10
125
113
14.10-
14.40
125
117
14.40-
15.10
125
111
Kadar Pengeluaran melawan Kadar
Kecerahan
y = 0.5634x - 158.16
R2 = 0.7743
110
115
120
125
130
470 480 490 500 510
Kadar Ke cerahan (lux)
Kadar Pengeluaran(unit)
Figure 3 Graph of Production Rate versus
Illuminance level
From Figure 3, the correlation coefficient r, is
0.879964 which indicates a strong linear
relationship between the production rate as a
dependant variables and illuminance level level
as an independent variables to a significant
level of 0.05 (p < 0.05). The coefficient of
determination, r2 at 0.774337 indicate that
77.43% of the production rate variance had
relationship with illuminance level variance. The
value of correlation coefficient, r is then
compared to the value from the Table of Critical
Correlation Coefficient Value. The significance
level was selected at 0.05 ( = 0.05). The value
of degree of freedom (n-2) is 7. The correlation
coefficient, r from the analysis exceeds the
critical value of r at 0.666 (according to Table of
Critical Correlation Coefficient Value) thus that
Ho is rejected. We can conlude the production
rate and illuminance level has positive
significant (r(8) = 0.88142, p < 0.05relationship
Illuminance Level (lux)
Graph Production Rate (Units) versus
Illuminance Level (lux)
Production Rate (Units)
and). The results based on the correlation
analysis is presented in Table 2.
Table 2 Correlation Analysis
Column 1
Column 2
Column 1
1
Column 2
0.879964
1
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 was conducted were conducted. The
results for regression, ANOVA and t-test analysis
were presented in Table 3. The hypothesis were:
Ho: = 0(The relationship between illuminance
level and production rate is not
significant)
Ha:
0(The relationship between illuminance
level and production rate is significant)
The relationship between WBGT and production rate is significant.
Table 3 Regression, ANOVA and t-test Analysis
Regression Statistics
Multiple R
0.879964419
R Square
0.774337378
Adjusted R
Square
0.74209986
Standard Error
5.248362898
Observations
9
ANOVA
df
SS
MS
F
Significance F
Regression
1
661.6317638
661.6317638
24.01975832
0.001751136
Residual
7
192.8171918
27.54531311
Total
8
854.4489556
Coefficients
Standard Error
t Stat
P-value
Intercept
329.4329061
34.1348461
9.650926949
2.70277E-05
Illuminance
1.37447335
0.28044778
4.900995646
0.001751136
* p < 0.05
The F value from the ANOVA is 24.02. The value
of the significance level was selected at 0.05 ( =
0.05). Based on the Table Critical Value F
Distribution, the F[0.05] is equal to 5.59. Since the
F(model) = 24.02 > F[0.05] = 5.59, we can reject
Ho: = 0 in favor of Ha:
0 at the 0.05
significance level. It suggests strong evidence of
significant relationship between the level of
illuminance and the production rate. Hence there
is a strong evidence that the simple linear model
relating production rate and illuminance level is
significant. A t-test was conducted to determine
the significance of regression coefficient, ß1. The
value of t from the analysis is 4.90. This value is
significant under testing level of 0.05. It shows
that the power of prediction for the equation
model is significant (t = 4.90, p < 0.05). The
relationship between WBGT and production rate
is significant.
4. DISCUSSION
From the literature, only a few studies have
been conducted in the area to establish an
equation model related of environmental effect
to productivity. The authors believe the study
had achieved the objective to obtain an equation
model to relate the illuminance level to
production rate in a quantitative way by
inferential statistical analysis. The finding from
the current investigation corresponds to the
result of study by Bommel et al. (2002). The
equation model is useful to manufacturing
engineers as a guideline to determine the
illuminance level during the feasibilities study to
allow production line achieves the optimum
output.
The equation model is useful to engineers
in design lighting systems in order to minimize
the use of power and control the productivity of
workers. The equation model obtained is only
applicable to present the current condition for
the selected area of assembly workstation at
Malaysian electronics industries. From the
results of hypotheses testing, it can be
concluding that there is a significant relationship
between illuminance level and production rate.
Further test proved that the equation model
could be strongly used to predict the production
rate based on a certain illuminance level
provided by lighting systems in particular
organization.
5. CONCLUSIONS
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. 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 electronic manufacturing
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.
6. ACKNOWLEDGEMENT
The authors would like to acknowledge the
cooperation given by Hitachi Electronic
Products(M) Sdn. Bhd.
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