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Tom McNamara

a*

a Supply Chain Management Department, ESC Rennes School of Business, 2 Rue Robert d’Arbrissel 35065 Rennes, France

* Corresponding author: Tom McNamara

E-mail: tom.mcnamara@esc-rennes.com, Tel.:

LEAN MANUFACTURING IMPLEMENTATION FOR

MULTINATIONAL COMPANIES WITH PRODUCTION

SUBSIDIARY IN BRAZIL: DEVELOPMENT OF A ROADMAP

A B S T R A C T

K E Y W O R D S

A R T I C L E I N F O

Received 8 Aug 2016

Accepted 15 Dec 2016

Available online 5 Jan 2017

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.

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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

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69 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

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70 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

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71 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

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72 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

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73 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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