International Journal of Lean Thinking Volume 7, Issue 2 (December 2016)
A SURVEY OF THE LITERATURE ON ASSEMBLY LINE
BALANCING AS IT RELATES TO THE APPAREL INDUSTRY
Tom McNamaraa*
a Supply
*
Chain Management Department, ESC Rennes School of Business, 2 Rue Robert d’Arbrissel 35065 Rennes, France
ABSTRACT
KEYWORDS
The fashion industry, including the design, production, shipping,
sales and marketing of clothing, is one of the largest on the planet.
It is also extremely labour intensive. With regard to the fabrication
of garments, ideally, if each item is processed on an assembly line
in a predetermined order, with no two operators working on the
same piece at the same time, no problems due to imbalance should
occur. But this is rarely the case. It is believed that there is a great
deal of lost productivity and decreased efficiency as a result of
assembly lines being unbalanced (i.e. a misallocation or
suboptimal use of resources exists). This article provides a review
of the literature on assembly line balancing, more specifically, as
it relates to the apparel industry. Relevant findings are provided in
an attempt to aid production managers who are responsible for the
efficient operation of apparel assembly lines.
Assembly line; assembly
Corresponding author: Tom McNamara
E-mail: [email protected], Tel.:
line
balancing;
simulation;
analysis.
algorithm, production
ARTICLE INFO
Received
Accepted
Available online
8
Aug
15 Dec
5 Jan
2016
2016
2017
Tom McNamara / International Journal of Lean Thinking Volume 7, Issue 2 (December 2016)
1. Introduction
The apparel market, worldwide, is estimated to be valued at US$ 3 trillion, and is responsible for 2
percent of the world's Gross Domestic Product (GDP). The number of people employed in textiles and
clothing is believed to have been almost 60 million in 2014 alone (Fashion United, 2016). The creation
of an individual garment involves a four part process; 1) The actual design of the fashion item and
patterns, 2) The arrangement and cutting of the fabric into the associated patterns, 3) Stitching and
sewing the component pieces into a final product and 4) Packaging and shipping the finished goods. The
most import phase of the four is the actual stitching of the garments. (Chen et al., 2014). A key
component in the fabrication of apparel would be assembly lines, which are special flow orientated
production line systems. While often considered anachronistic, assembly lines play an important role in
modern industry. Every year, worldwide, billions of dollars are spent on the design, installation,
operation, and maintenance of production lines (Hillier and So 1996). They are typical in the
manufacturing of high quantity standardized commodities and are by far the most commonly used
method of mass production (Kalir and Sarin 2009). Assembly lines are normally comprised of a series
of interconnected work stations in which work pieces (jobs) move from one station to the next (usually
respecting some precedent constraint) with certain operations being repeatedly performed within a
certain period of time (Becker and Scholl 2006). The time required to complete the work allocated to
each station is known as the service time. The time available at each station for the performance of the
work (or processing) is known as the cycle time, with the cycle time normally being larger than the
service time.
A production line is considered to be in balance if the average service time for all of the work stations
is the same. An unbalanced production line is one in which station service times’ means vary. This
unbalanced condition is usually referred to as “degree of imbalance” and can be represented as a
percentage, the formula for which is:
(Mean Cycle Time for an Unbalanced Line / Mean Cycle Time for a Balanced Line) * 100
The ideal notionally balanced production line is rarely found in practice. Almost all lines have some
degree of imbalance. Even in lines comprised wholly of automated machines making a single product
type, it is possible, due to technical constraints, to have non identical mean processing times
(Tempelmeier, 2003). In the majority of cases, this unbalance is due to the nature of the task at hand,
which may involve certain technological or precedence restrictions, making it difficult to divide the
work into even portions that can be distributed along a line. Another complicating factor is the fact that
manual operators, even performing rudimentary tasks, have different mean processing times. This
fluctuation in output levels is a well studied and well observed phenomenon (Rothe, 1946; Rothe, 1947;
Rothe, 1951; Rothe and Nye, 1958; Rothe, 1978; Rothe and Nye 1958; Rothe and Nye, 1961; Schmidt
and Hunter, 1983). These differences in mean processing time can be due to a whole host of reasons,
such as differences in skills, training, competencies, capabilities and motivation. Manufacturing systems
in which the processing times are considered to be random variables are known as “stochastic”
production lines. It has been observed that the degree in the variability of the output of a production line
increases as the complexity of the task at hand increases (Hunter et al., 1990) and that the work time
distributions of manual workers is positively skewed (Buxey et al., 1973).
While the first modern working assembly line is credited to Henry Ford in 1913, it really wasn’t until
the 1950s that engineers recognized that there was a problem with regard to a line’s balance, thus giving
rise to the situation that would eventually be known as the Assembly Line Balancing Problem (ALBP)
(McMullen and Tarasewich 2003). Solving this problem normally involves partitioning work optimally
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among the various stations in an assembly line while at the same time respecting existing precedent
constraints. This can be a hugely time consuming endeavour due to the inherent difficulty of dividing
labour and tasks into equal time units that can be evenly distributed along a line. The main thrust of this
process is often focused on trying to minimize the number of stations for a fixed cycle time (the type I
problem) or to minimize the cycle time for a fixed number of stations (the type II problem).
There are two basic methods that can be employed when addressing the ALBP; algorithmic or heuristic
solutions. Tempelmeier (2003) states that only a limited number of companies actually utilize analytical
or algorithmic methods when trying to balance their lines. Due to the enormous complexities involved
in analysing multi station production lines, algorithms tend to become impractical quite quickly. Erel
and Sarin (1998) argue that heuristic methods have more relevance in arriving at realistic solutions.
There is an extensive amount of research available on efforts to solve the line balancing problem in
different types of production environments and contexts (for recent reviews please refer to Battaia and
Dolgui, 2013; Boysen and Fliedner, 2007; Boysen et al., 2008). An area of research having great
relevance to the study of assembly lines (especially the manual type) would be studies of production
systems as they relate to the apparel industry. This would be due to the fact that most of the work
performed in the manufacture of clothing is labour intensive and done manually, thus giving rise to
stochastic (variable) behavior (Eryuruk, 2012; Gungor and Agac, 2014). Theoretically, if each garment
is being worked on in a predetermined order, with no two operators processing the same work piece at
the same time, no balancing problems should arise (Chan et al., 1998). But in actual practice, this is
rarely the case.
To the best of the present author’s knowledge, there is a lack of a survey into assembly line balancing
efforts as they relate to the apparel industry. Much of the literature dealing with apparel assembly lines
concerns itself with both simulation and analytical investigations into improving line performance, as
well as the practical application of any derived solutions and findings. What follows is a survey of this
literature.
2. Line Balancing Investigations Based on Simulation
One of the earliest studies using simulation was one carried out by Cocks and Harlock (1989), who
specifically studied the sewing function at a garment manufacturing facility. A computer programme
was developed to improve system performance that could take into account semiautomated and fully
automated processes, as well as machine unreliability and material starving. Fozzard et al. (1996)
developed a simulation model which could make allowances for variations in worker performance, line
unreliability and defects in a clothing production line. Rosser et al. (1991) investigated the feasibility of
modelling a facility producing men’s denim trousers by way of simulation. Results showed that
computer-generated results closely mirrored those of the actual system. Wang et al. (1991) performed a
study into the practicality of using simulation to evaluate the performance of a modular production
system making women’s slacks. Line configurations were arrived at which improved performance and
brought the line into an approximate state of balance. Oliver et al. (1994) did a comparative analysis
study by simulating the performance of three different types of production systems commonly employed
in the clothing industry; push, kanban and modular (i.e. team based). Generally, it was found that a
modular line arrangement provided the best performance, resulting in lower levels of work in process
(WIP). Rotab Khan (1999) showed the feasibility and practicality of using Microsoft’s spreadsheet
package Excel in the modelling and simulation of a garment assembly line. “What-if” scenarios were
used to arrive at different line configurations which resulted in higher productivity and lower cost.
Zielinski and Czacherska (2004) made an effort to optimize the sewing operations of a clothing
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manufacturer through simulation. Different line configurations were modelled, with recommendations
being presented to improve facility performance. Rajakumar et al. (2005) developed a simulation
programme for balancing a manual clothing production line that could apply different scheduling
strategies with regard to the allocation of work to line operators. Gurkan and Taskin (2005) studied the
line balancing problem as it applied to a mill producing weaved fabric. A simulation model was created
based on empirical observations, with alternative line configurations being arrived at that improved
performance and reduced work in process. Kalaoğlu and Saricam (2007) simulated a modular
production system manufacturing sweat shirts in which various line configurations were analysed, with
the relative advantages and disadvantages of each being reported. Kurşun et al. (2007), for their part,
modelled an assembly process producing T-shirts using the simulation programme Enterprise
Dynamics. Empirical data from time studies were employed in order to identify and highlight production
constraints, with strategies for improved resource allocation being arrived at for use by managers. Güner
and Ünal (2008) employed simulation in an effort to balance an assembly line manufacturing T-shirts.
Empirical data was used to model a system, with various production scenarios being run and analysed,
resulting in possible line configurations and allocation of resources that could improve performance.
Zieliński (2008) used simulation to perform a comparative analysis of two different assembly lines
engaged in sewing activities. Results were arrived at highlighting the relative merits of having workers
allocated in a classical linear assembly line vs. having workers allocated to teams, with the latter
generally found to be more efficient. Kursun and Kalaoglu (2009) simulated an existing sweatshirt
assembly line, determining the location of bottlenecks and arriving at line configuration alternatives to
improve system performance through “what-if” analysis. Kitaw et al. (2010) investigated a facility
fabricating polo-shirts, using the standard simulation software package Simul-8 to model an assembly
line, with the express purpose of identifying production constraints. Various alternative systems
configurations were studied and analysed in order to arrive at suggested resource reallocations that
would improve line performance. Bahadir (2011) modelled an existing garment assembly line (the
sewing operations of trousers) using simulation. Production bottlenecks were identified and several
scenarios with regard to the allocation of resources were offered for improving performance. Eryuruk
(2012) used heuristic methods combined with simulation in order to balance a production line that
fabricated dresses. The study resulted in an improved production line design, one capable of
manufacturing multiple products with an almost 15% reduction in the number of work stations. Ramlan
and Tan (2012) employed simulation to model an existing clothing production line making short sleeve
T-shirts. Bottlenecks were identified and recommendations were offered with regard to the allocation of
capacity which were expected to result in increased throughput and reduced cycle times. Dai et al. (2013)
developed a computer assisted method for balancing an apparel assembly line. A case study application
showed the practicality of the arrived at method. Experimental results indicated that optimal solutions
were possible that increased efficiency and productivity, and which also reduced cycle time. Islam et al.
(2014), in a practical case study, used work-time observations combined with simulation in order to
improve the line balance and facility layout of the sewing operations of a garment producer
manufacturing T-shirts. Various line configurations and allocations of resources were analysed, with
suggestions being given as to how to improve system performance. Rahman et al. (2015) developed a
line balancing software that incorporated the Ranked Positional Weight (RPW) method first developed
by Helgeson and Birnie (1961) and applied it to an assembly line producing trousers. The derived
technique was shown to be capable of reducing the number of work stations and improve the throughput
of the line. Kayar and Akalin (2015) did a comparative simulation study of the performance of a manual
production line versus that of a production line comprised of workers using machines capable of
performing more than one task (automats). In general, it was found that the performance of purely
manual lines was inferior to that of the production line employing automats. In a more recent
investigation, Kayar and Akalin (2016) carried out a line balancing investigation that simulated a facility
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that produced knitted blouses. An assembly line was balanced using both the Hoffman heuristic method
(Hoffman, 1963) and simulation, with a reported line efficiency of well over 90% being achieved.
3. Analytical Investigations into Line Balancing
Several analytical investigations have also been carried out in an effort to improve assembly lines as
they relate to the apparel industry. Betts and Mahmoud (1992) studied the line balancing problem in the
context of a system manufacturing women’s blouses by using a modified version of a branch and bound
algorithm that they first proposed in Betts and Mahmoud (1989). Hui and Ng (1999) investigated the
line balancing problem by analysing production data from a facility that fabricated men’s shirts. Results
showed that for a balancing initiative to be effective, the variation associated with task times should be
taken into account, not just the averages of task completion times. Eryuruk et al. (2008) did a critical
analysis of the Probabilistic Line Balancing method (PLB) developed by El-Sayed and Boucher (1985)
and the RPW method (Helgeson and Birnie, 1961) by applying both to a clothing company’s mixed
model trouser assembly line. In general, it was found that the RPW method was easier to manipulate
and resulted in higher line efficiencies. Ünal et al. (2009) derived a line balancing algorithm for a
clothing assembly line in which the relative performance of different line configurations was critically
evaluated through simulation. Eryuruk et al. (2011) applied the heuristic line balancing method
developed by El-Sayed and Boucher (1985) to a mixed model clothing assembly line, with results
showing that improvements in efficiency were possible through the optimization of work allocation.
Yao (2011) also showed that the application of line balancing heuristics to a clothing production line
could lead to improvements in efficiency. Gürsoy (2012) used an integer mathematical programming
method to improve the performance of the sewing operations of a clothing manufacturer. An heuristic
method was arrived at which was transferred to a usable software programme which provided solutions
for reducing the number of operators in an assembly line. Dundar et al (2012) employed a mathematical
approach in an attempt to balance a production line manufacturing basic T-shirts. Suggestions were
offered with regard to the beneficial allocation of workers and tasks in order to reduce line idle time.
Shumon et al. (2012) calculated Standard Allowable Minute (SAM) values in order to balance an apparel
manufacturing line. The arrived at solution involved the novel combination of a modular production line
with elements of a traditional linear system. The reconfigured line involved in the study was able to
achieve an over 20% increase in both line efficiency and worker productivity. Guner et al (2013)
analysed several line balancing techniques in order to determine their efficiency as they related to the
apparel industry. In the context of the specific line studied, no one method was found to be substantially
better (in terms of efficiency) than another. Gungor and Agac (2014) studied the assembly line balancing
problem as it relates to a production line manufacturing multi-model men’s shirts. Line configurations
were arrived at using computer analysis employing the RPW method, which reduced inefficiencies due
to balancing loss. Jaganathan (2014) studied the sewing operations of a clothing manufacturer in an
effort to improve its performance. The Largest Candidate Rule (LCR) algorithm (Moodie and Young,
1965) was employed in an effort to bring the facility into balance. Suggestions for reconfiguring an
assembly line were presented in which the above mentioned author argued that there would be an almost
50% increase in line efficiency as well as a 25% increase in hourly productivity. Karabay (2014) did a
comparative study of various assembly line balancing techniques in the context of a facility producing
women’s blouses. A critical assessment as to the relative merits and deficiencies of the various methods
was provided as a guide to facility managers. Kayar and Akalin (2014) investigated an assembly line
that made knitted blouses using the RPW method based on empirical data that was collected and
analyzed. Suggestions as to improved work allocation and line configurations were presented which
would arguably improve line efficiency by almost 8%. Kayar and Akyalçin (2014) used several different
assembly line balancing techniques to analyse a production line that manufactured T-shirts. Results were
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provided showing the relative performance and effectiveness of the different methods surveyed.
Morshed and Palash (2014) obtained data from empirical observations of the sewing operations of a
clothing manufacturer. Data was analysed in an attempt to reduce the number of workstations for a
predetermined cycle time. Line arrangements were arrived at that increased both labour productivity and
line efficiency. Nabi et al. (2015) investigated the line balancing problem by utilizing Standard Minute
Value (SMV) calculations of an apparel assembly line. Bottlenecks were identified, and through worker
training, improved worker motivation and a reallocation of workload, line productivity and efficiency
were both improved. Another SMV study was carried the one carried out by Islam et al. (2015), where
an assembly system producing cotton jackets was analysed in order to improve resource utilization and
work allocation. SMV calculations derived from empirical observation were used to identify production
constraints and to develop plans for the reconfiguration of individual workers as well as for the provision
of addition resources. A “Skills Matrix” was developed to aid managers in the assignment of workers
which, the above mentioned authors argued, would result in either optimal or near optimal line
performance.
A popular method used in industrial line balancing studies would be genetic algorithms (GA). These are
probabilistic search procedures based on a search technique derived from principles found in natural
genetics and evolutionary science, with their use first being put forth by Holland (1975). One of the
earliest applications of GA to the clothing industry was carried out by Chan et al. (1998), who applied
their method to an assembly system manufacturing men’s shirts. Wong (2003) presented a generic
optimised table-planning (GOTP) method that incorporated a genetic algorithm approach for improving
the cutting operations in apparel manufacturing. The model was tested using a range of production batch
sizes taken from actual production facilities, with the results showing that improvements in performance
were possible. Wong et al. (2005a) used a real-time segmentation rescheduling (RSR) method that
incorporated genetic algorithms to address the line balancing problem of a clothing manufacturer. The
arrived at solution was capable of balancing lines having dynamic factors such as machine stoppages.
Experimentation using data from a functioning production facility showed the method’s efficiency.
Wong et al. (2005b) derived an optimization method for balancing the cutting operations of an apparel
production line based on GA. The method’s efficiency was determined through experimentation based
on actual production data. Guo et al. (2006) presented a genetic optimization method capable of dealing
with a garment assembly line producing multiple products. Its performance was verified through
experimentation using empirical data. Song et al. (2006) were able to determine a method for the optimal
allocation of operators in order to balance a line. Their solution employed recursive algorithms to
generate and explore all practical solutions, with the method’s efficiency being verified through a
practical application at a clothing manufacturing facility. Wong et al. (2006) derived a genetic
optimisation method for balancing a clothing assembly line. In a practical case study application, the
algorithm was shown to be efficient. Results also indicated that there was a margin of diminishing
returns in terms of worker training, in that workers who could perform more than three sewing
operations brought little benefit in terms of line balance.
Another interesting avenue of research involves the use of grouping genetic algorithms (GGA), which
were first proposed by Falkenauer (1992). Chen et al. (2009) presented a grouping genetic algorithm for
determining the correct workload allocation for the balancing of production lines in the apparel industry.
Application to an actual facility verified the algorithm’s veracity. Chen et al. (2012) derived a GGA
capable of balancing sewing lines. A practical application of the algorithm in a garment manufacturing
facility resulted in facility managers achieving higher labour utilization rates and higher throughput
levels. Chen et al. (2014) employed GGA in order to solve the line balancing problem in terms of
minimizing the number of work stations for a given cycle time (the type I ALBP). The efficiency of the
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developed heuristic was verified using empirical data from a sportswear factory combined with
computational experiments. The aforementioned authors argued that their arrived at algorithm could be
of great aid to production managers interested in reducing cycle times and increasing labour utilization.
4. Conclusions and Practical Implications for Managers
When one looks at the literature on balancing efforts as they relate to assembly lines in the apparel
industry, a stark choice appears to present itself. On the one hand, while simulation studies involving
standard or “off the shelf” software packages are considered to be readily usable and can provide quick
solutions, the findings are quite often line or facility specific. These investigations can present valuable
insights to production managers who are operating similar lines or facilities, but the danger is that actual
results will vary or differ greatly.
The benefit of analytical or algorithmic studies is that they often provide heuristics which will more than
likely be generalizable across different facilities and the multitude of assembly lines that produce the
various garments that consumers demand. However, their implementation might be beyond the skills
and abilities of many of the line managers who will most likely be responsible for implementing them
(to be fair, this could very well be true for simulation solutions as well).
Islam et al. (2014) argue that the study of how to improve the performance of apparel assembly lines
continues to be relevant research issues. And as global consumers become ever more demanding, and
the challenges presented by “fast fashion” only become more severe, garment production facilities will
need to become even more efficient. But as Abraham and Allio (2006) point out, quite often the findings
of academic research do not make their way fully to the practitioners in industry where they would have
the most immediate and lasting impact. To highlight the importance of this subject, in a survey of
garment manufacturers in Bangladesh, Ferdous (2015) found evidence that the more effort a garment
manufacturer puts into regular line balancing initiatives, the higher the productivity they experienced as
compared to companies that assigned a lower priority to line balancing.
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