Ergonomics has traditionally been used to improve the workers performance
by discovering the factors that contribute to their performance. Many organizations
are forced to consider effects of environmental factors towards their workers
performance in terms of safety and efficiency (Ismail et
al., 2009). In recent years, there has been increasing interest in the
automotive industry. Automotive industry is a booming industry which encompasses
similar areas of activities (Yazdani et al., 2009)
in Malaysia. Over the years production of car has also showed an increasing
trend where in 1999, almost 254,000 cars produced but doubled (442,000 cars)
in 2007 with employing of 47,000 workers in Malaysia (OICA,
2007). 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 Sawadeq, 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.
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 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 humidity, indoor air quality and acoustics have significant relationships
with workers satisfaction and performance (Marshall
et al., 2002; Fisk, 2000). Indoors air quality
could have a direct impact on health problems and leads to uncomfortable workplace
environments (Juslen and Tenner, 2005; Fisk
and Rosenfeld, 1997; Marshall et al., 2002).
Shikdar and Sawaqed (2003) pointed out that there was
high correlation between performance indicators and health, facilities and environmental
attributes. In the 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,
Staffan and Knez (2001) had investigated on how noise,
air temperature and illuminance combine or interact in their effects on cognitive
performance. The results from Staffan and Knez study showed that they worked
faster in noise but at the cost of lesser accuracy. Interactions were found
between noise and heat on the long-term recall of a text and between noise and
light on the free recall of emotionally toned words. In metal industry, 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 of 300 lux (minimum) to 500 lux could
lead to an increase of productivity from 3 to 11% based on realistic assumptions
that the increase of illuminance from 300 lux to 2000 lux would increase the
productivity from 15 to 20% (Bommel et al., 2002).
Juslen and Tenner (2005) described the mechanisms involved
in enhancing human performance by changing the lighting in the industrial workplace
through visual performance, visual comfort, visual ambience, interpersonal relationships,
biological clock, stimulation, job satisfaction, problem solving, the halo effect
and the change process.
Robust design is an engineering methodology for obtaining product and process
conditions, which are minimally sensitive to the various causes of variation
to produce high-quality products with low development and manufacturing costs
(Park, 1996). Taguchis parameter design is an important
tool for robust design. It offers a simple and systematic approach to optimize
design for performance, quality and cost. Two major tools used in robust design
are (Park, 1996; Unal and Dean, 1991;
||Signal to noise ratio, which measures quality with emphasis
||Orthogonal arrays, which accommodate many design factors simultaneously
Taguchis approach is totally based on statistical design of experiments
(Park, 1996) and this can economically satisfy the needs
of problem solving and product or process design optimization (http://www.vkroy.com/up-doe.html).
By applying this technique one can significantly reduce the time required for
experimental investigation, as it is effective in investigating the effects
of multiple factors on performance as well as to study the influence of individual
factors to determine which factor has more influence, which less (Park,
Some of the previous works that used the Taguchi method as tool for design of
experiment in various areas including metal cutting are listed in the references
(Lin, 2002; Tsui, 1999; Zhang
and Wang, 1998; Si and Tong, 1997; Kopac,
The most important stage in the design of an experiment lies in the selection
of control factors. As many factors as possible should be included, so that
it would be possible to identify non-significant variables at the earliest opportunity.
Taguchi creates a standard orthogonal array to accommodate this requirement.
Depending on the number of factors, interactions and levels needed, the choice
is left to the user to select either the standard or column-merging method or
idle-column method, or etc. Two of the applications in which the concept of
S/N ratio is useful are the improvement of quality through variability reduction
and the improvement of measurement. The S/N ratio characteristics can be divided
into three categories when the characteristic is continuous (Park,
Nominal is the best characteristic:
Smaller the better characteristics:
Larger the better characteristics:
is the average of observed data, sy2 is variance of y,
n is number of observations and y is the observed data. For each type of the
characteristics, with the above
S/N ratio transformation, the higher the S/N ratio the better is the result.
MATERIALS AND METHODS
Selection of location and subjects: The study is conducted on a selected work station in the automotive industry where it refers mainly towards the assembly section or the manual production line where human energy are involve for in the manufacturing activity. This study was conducted on September 2009.
Figure 1a-d shows the work sequences for the complete assembly of body switch backdoor. Process (a) and (b) is the process to insert the contact spring into the body switch. For the process (c), the wire is inserted into the part and taping. Lastly the body switch is done with a soldering process.
Figure 2 show the flow process at body switch backdoor assembly. The production is desired to be the repeated production of the same component throughout the entire shift and this is to ensure on the consistency of the data collected towards the data analysis later. Priority of study will be given to the work station where the environmental factors will sponsor the most towards effect of the productivity. One automotive vendor has been selected as a place of study. A line producing a product over a period of time and under the effects of certain relative humidity, illuminance and WBGT was chosen. There are three process involve in order to complete the body switch. Worker 1 starts with the process 1 and pass the complete part to the operator 2 to do the process 2. Worker 1 completed the process 1 until the certain quantity and move to process 3. After achieve the target for the process 3, worker 1 back to process 1. The process rotate until 3.15 pm, where all the process stops and start to bundling and packaging.
This criterion is essential in order to obtain the which factors contribute utmost to the worker productivity based on output of assemblies among operators. The production line was consist of 5 woman operators. The task is to assemble an automotive parts which is known as body switch backdoor. The standard production rate determined by the previous feasibility study to assemble a complete door check was 392 units for every hour of production.
Experimental method: The Taguchi design of experiment was employed in
this experiment with two factors at two levels each. The fractional factorial
designs used was a standard L8 orthogonal array (Park,
||(a-d) Production sequence for the complete assembly of body
||The flow process at body switch backdoor assembly
This orthogonal array was chosen because of its minimum number of experimental
trials. Each row of the matrix represented one trial. However, the sequence
in which those trials were carried out was random, the factors and levels identified
in this study were shows in Table 1.
Table 1 shows the factors and levels used in the experiment,
for the illuminance factor, the range of illuminance level has be chosen whether
below 500 lux for discomfort level and more than 500 lux for comfort level (Bommel
et al., 2002; Juslen and Tenner, 2005). A
relative humidity factor consists of two level which is below 75% for the comfort
level and more than 75% for discomfort level (Tsutsumi
et al., 2007). Lastly for WBGT factor, it consists of two levels which
are below than 27°C for comfort level and more than 27°C for discomfort
level (Niemela et al., 2002).
||Factors and levels used in the experiment
||Experimental Layout Using an L8 Orthogonal Array
Orthogonal array experiments: To select an appropriate orthogonal array
for experiments, the total degree of freedom needs to be determined. The degree
of freedom are defined as the number of comparisons between factors that need
to be determine which level is better and specifically how much better. In the
present study, since each factor has two levels therefore, there are three degrees
of freedom. In this study an L8 orthogonal array is used and shown
in Table 2.
Table 2 shows the standardize Taguchi method under L8 orthogonal array. Each factor consists of two levels which are level 1 for the minimum value and level 2 for the maximum value.
RESULTS AND DISCUSSION
The result of this study was based on the case study conducted on the production line in the automotive vendor factory. The hypotheses for this study was the production rate have a direct relationship with the illuminance, humidity and WBGT level. The levels of illuminance, humidity and WBGT were taken to identify their effect on the worker performances. The objective of the experiment is to obtain the optimize the environmental parameters (WBGT, illuminance and humidity) in order to obtain the better results for production rate (high value) and therefore the optimum characteristic of environment should be quantify.
Linear relation analysis: Figure 3 represents the
linear regression model between production rate and illuminance level. The linear
regression model obtain is Production Rate = 1.75 Illuminance-694. Based on
Fig. 3, the production rate were improved with the increase
of the illuminance (lux).
||Results of production rate versus illuminance level
||Results of production rate versus relative humidity
The results obtained for the illuminance is in-line with the finding from
Bommel et al. (2002) and Juslen
and Tenner (2005) where the increasing of illuminance levels lead to an
increase in productivity. The same trend is observed for the relative humidity
(%) and the productivity as shown in Fig. 4.
Figure 4 represents the linear regression model between production
rate and relative humidity. The linear regression model obtain is Production
Rate = 5.27 Relative Humidity-229. The positive trend on the effect of relative
humidity towards productivity is in-line 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. However,
Tsutsumi et al. (2007) reported their subjects
were more tired at 70% RH after relative humidity (%) step change.
Figure 5 represents the linear regression model between production
rate and WBGT. The linear regression model obtain is Production Rate = -24.8
WBGT + 876. The trend obtained for illuminance and relative humidity towards
productivity is different compared to the relation of Wet Bulb Globe Temperature
(WBGT) and productivity. Figure 5 shows by increasing the
Wet Bulb Globe Temperature will reduce the performance and productivity of the
||Results of production rate versus WBGT
It is encouraging to compare this figure with that found by Niemela
et al. (2002), who reported that productivity decrease of 2.2% per
°C when the temperature increased above 25°C. The findings for Wet Bulb
Globe Temperature is contradicted with finding of Fisk and
Rosenfeld (1997) where by increasing the air ventilation will significantly
increase the performance of the operators. The productivity increase cause by
the WBGT 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.
Taguchi approach: Taguchi recommends analyzing the means and S/N ratio
using conceptual approach that involves graphing the effects and visually identifying
the factors that appear to be significant, without using ANOVA, thus making
the analysis simple (Park, 1996). It shows that the relative
humidity is the most significant factor in controlling the production rate,
followed by the illuminance and WBGT. The L8 orthogonal array for
environment parameters and production rate were show in Table
Table 4 shows the mean S/N ratio for each factor at two levels. From this table, the highest value for each mean S/N ratio for each factor can be identified clearly. The optimum condition for the production rate are at level 1 (less than 500 lux) of illuminance, level 1 (less than 75%) of humidity and level 2 of WBGT (more than 27°C).
Analysis of Variance (ANOVA): The purpose of ANOVA is to investigate
which of the factors significantly affect the workers productivity. Table
5 shows the results of analysis of variance. Statistically F-test can be
used to determined which factors have significant effect on the workers
||Experimental result for productivity and S/N ratio
||Response Table for signal to noise ratios (larger is Better)
||Results of the analysis of variance for S/N ratios
The sequential sums of squares (Seq SS) measure the reduction in the residual
sums of squares provided by each additional term in the model. The adjusted
sums of squares (Adj SS) measure the reduction in the residual sums of squares
provided by each term relative to a model containing all the other terms. The
F value for each factor is then a ratio of the MS to the mean square of error.
The larger the F value, the greater the effect on the performance characteristics
(productivity of workers) due to change of operating factors. Usually, when
F>4 it means that the change of operating factors has a significant effect
on the quality characteristics.
From the results of the ANOVA presented in Table 5, it can be seen that only illuminance is statistically significant. However, each factor contributes to the quality characteristics and the rank order is illuminance (rank 1), relative humidity (rank 2) and WBGT (rank 3) respectively. Therefore, based on the S/N ratio and ANOVA analysis, the optimal parameters for achieving optimum productivity in terms of environmental factors are illuminance level at level 1 (<500lux), relative humidity at level 1 (<75%) and WBGT at level 2 (>27°C).
From the literature, only a few studies have been conducted in the area to establish a dominant environmental parameter contributed to the worker productivity. The authors believe the study had achieved the objective in order to establish the dominant environmental parameters contributed to the productivity.
The finding from the current investigation corresponds to the result found
by Bommel et al. (2002) and Juslen
and Tenner (2005) where the increasing of illuminance levels lead to an
increase in productivity. This also accords with our earlier observations, which
showed that the production rate and illuminance level has positive significant
relationship (Ismail et al., 2007). The two dominant
factors obtain in this study will provide a guideline to assist engineers to
determine the illuminance and relative humidity level during the feasibilities
study to allow assembly production line achieves the optimum output.
The finding also will be useful to engineers in design the lighting systems
in order to improve the comfort in the workstation area and control the productivity
of workers. The dominant environmental parameter obtain in this study is only
applicable to present the current condition for the selected area of assembly
workstation at Malaysian automotive industries. From the results of the study
also concluded that there is relationship between illuminance, humidity and
WBGT level with production rate. Therefore the findings from this study are
in line with the previous study that indicated illuminance and WBGT play an
important role in controlling the production rate because the both factors will
contributed to the comfort level of worker (Shikdar and
Sawadeq, 2003; Olesen, 1995; Dua,
Past research on the modeling relationship of workplace environmental factors to the productivity or performance is very limited. In addition they are characterized by a short time perspective, not enough engineering data regarding the lead time, expected output capacity or perception with emphasis on survey methods, statistical analysis, satisfaction and the preferences measurement. This study was done to prove empirically the previous perception studies, which based on the role of environmental factors to productivity. It is hoped that this study would be beneficial to the automotive manufacturing industries in Malaysia.
The research findings are restricted to the Malaysian workplace environment, where the awareness among workers on improving productivity is still low. The results might vary for tests carried out for different sample sizes, types of industries and countries. The study could be more extensive if the fraction of defect rate for the product is included in the analysis. Nevertheless the authors believed the modeling of production rate, as a time series data is more than adequate to understand the affect of environmental factors towards productivity.
The authors would like to thanks National University of Malaysia and Ministry of Higher Education Malaysia for their support in providing a research grant for a project Modeling Relationship of Thermal Comfort and Productivity in Malaysia Energy Intensive Industries (UKM-GUP-TK-08-16-059).