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Effective layout designs for the Shojinka control problem for a TFT-LCD module assembly line
Journal of Manufacturing Systems 44 (2017) 255–269
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Journal of Manufacturing Systems journal homepage: www.elsevier.com/locate/jmansys
Full Length Article
Effective layout designs for the Shojinka control problem for a TFT-LCD module assembly line Peng-Sen Wang a , Taho Yang b,∗ , Min-Chih Chang b a b
Department of Industrial Management and Information, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan 701, Taiwan
a r t i c l e
i n f o
Article history: Received 3 February 2017 Received in revised form 24 June 2017 Accepted 10 July 2017 Keywords: Cellular layout Shojinka Mixed integer programming Toyota production system TFT-LCD
a b s t r a c t Attaining flexibility in the number of workers in a workshop to adapt to demand changes is termed Shojinka, and this is often combined with cellular layout design configuration. The machine layout for Shojinka must be appropriately designed to enable workers to walk easily between machines within a cell. This paper analyzes a problem of how to attain Shojinka under the limitations of a process where operators have restricted movement within a cell. The study is motivated by a practical flow shop problem with different machine rates among workstations. We first consider the decision of Shojinka to develop the cellular layout design under the limitations of a process. Next, we propose a mixed integer programming formulation to minimize the total number of operators. Taguchi’s signal-to-noise (SN) ratios are used to compare the robustness of different layouts when the demand changes. The present study uses a thin-film-transistor liquid-crystal display (TFT-LCD) module assembly line as a case study to illustrate the effectiveness of the proposed approaches. The results show that the proposed layout can decrease manpower and labor idle time ratio by 15.02% and 12.48% on average, respectively. © 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
1. Introduction Fluctuations in demand are inevitable in a volatile market and the strategy to accommodate this by developing flexibility in the number of workers in a workshop to adapt to changes in demand is referred to as Shojinka. That and Shoninka are two similar Japanese words, but they have different meanings. Shojinka, as used in the Toyota Production System (TPS), refers to changing the number of workers when production demand increases or decreases, which can be attained through multi-skilled workers performing multiple operations. Toyota trains workers to become multi-skilled through job rotation. The other term, Shoninka, indicates simply reducing the number of workers. Cellular layout refers to a type of layout where various machines are grouped in a cell. A cell is typically a replica version of a flow shop. The cells contain machines and tools needed to process families of parts with similar processing requirements. In TPS, each U-shaped process is a cell in which the products flow one at a time through each workstation. Shojinka requires that workers be able to respond to changes within a cycle time and an operation rou-
∗ Corresponding author. E-mail address: [email protected] (T. Yang).
tine. When an improvement has been made, manpower cost can be reduced by adjusting the number of operators. For a given cycle time, the demand for products often requires a fractional number of workers on a U-shaped line, such as 7.5 people. In this case, eight workers must be assigned to the line. As a result, the extra manpower will cause idle time or excess production. In order to eliminate waste, Toyota combines several U-shaped lines into one integrated line to overcome the problem of fractional manpower and achieve Shojinka. Under this layout, the range of jobs for which each worker is responsible can be easily be expanded or reduced [1]. The following example adapted from Monden [1] will illustrate how the concept is used to achieve Shojinka. Fig. 1 refers to a layout with five cells in which each digit represents a machine and each cell is manufacturing a different part. According to the customer demand for products, the cycle time of this layout is one minute per unit. Under this circumstance, seven people are working in the layout and each of their walking routes follows the arrow line. If customer demand decreases, resulting in the increase of cycle time to 1.2 min per unit, all operations in the layout will be redistributed and each worker will have more tasks than before. For example, in Fig. 2, worker 1’s walking route is enlarged since he has to do some additional jobs which originally belonged to worker 2. Similarly, worker 2 is reallocated to be responsible for parts of worker 3’s jobs. Consequently, expanding the walking route will
http://dx.doi.org/10.1016/j.jmsy.2017.07.004 0278-6125/© 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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Fig. 1. Allocation of operations among workers (cycle time = 1 min per unit).
Fig. 2. Allocation of operations among workers (cycle time = 1.2 min per unit).
eliminate workers 6 and 7 from the layout, and the fractional manpower that might have occurred in a U-shaped line is thus absorbed in various cells under this layout. However, the Shojinka decision is not always straightforward. A thin-film-transistor liquid-crystal display (TFT-LCD) module assembly line, for example, is limited by its manufacturing characteristics (e.g., cleanliness). As a result, the operators are restricted when walking between machines in a cell, so under such a constraint, it is more difficult to achieve Shojinka. Consequently, the objective of this study is to minimize the total number of workers through effective layout designs to achieve Shojinka, considering the specific process limitations arising from the TFT-LCD module assembly line. The remainder of this paper is organized as follows. Section 2 reviews the pertinent literature. Section 3 provides details of the proposed methodology. Empirical illustrations are discussed in Section 4. Conclusions are presented in the final section.
2. Literature review Shojinka is sometimes referred to as “flexible manpower line” or “labor linearity.” A flexible manpower line forms an individual cell within a linked-cell manufacturing system. Cells are designed so that throughput rates can be quickly and efficiently adjusted [2]. Typically, cells are manual since manual lines are far better suited to continual improvement than automatic lines [3]. Shojinka includes
several fundamental characteristics: U-shaped line, walk-routine design, multi-skilled workers, no barriers to operator movement, operator scheduled to takt time, one-piece production, and continuous improvements of the process. Walk-routine design for flexible manpower lines were studied by Stockton et al. [2,4]. When a company does not adopt the U-shaped line layout to attain Shojinka, but rather employs a multiple straight assembly line, then there are different parameters, as discussed by Gökc¸en et al. [5]. The cellular manufacturing system tries to combine the flexibility of a job shop with the productivity of a flow shop; and thereby be able to respond quickly to new market requirements. The cellular manufacturing system includes both cell formation and a specific layout design [6]. The process of determining component families and machine groups is referred to as the cell formation problem, and there have been several studies relating to this problem in the last decade [7,8]. These have suggested a number of solutions and approaches, such as mathematical programming, heuristic and metaheuristic methodologies, and artificial intelligence strategies. The layout design includes arranging cells on the shop floor (inter-cell layout) and arranging the facilities in the cells (intracell layout). A well-designed layout can reduce material handling cost, lead time and throughput time [9]. Although the importance of layout design is apparent in the cellular manufacturing system, it has not attracted much attention in comparison to cell formation [10,11]. In addition to the two technical issues, the benefit of peo-
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257
Fig. 3. (a) A typical linear layout. (b) Conceptual schema for the existing layout of the case problem.
ple issues is often neglected [12]. This paper focuses on achieving Shojinka through appropriate layout design. Song et al. [13] noted that although there has been much research on the issue of assembly line-balancing, few studies consider the role of operators of the assembly lines. Most of the line balancing research addresses how to assign tasks to workstations to obtain an optimal solution [14]. For example, in designing parallel assembly lines, Gökc¸en et al. [15] suggested that in a production facility, two or more straight assembly lines can be located in parallel so they can be balanced simultaneously. Akpinar and Baykasoglu [16] developed a method of mixed-integer linear mathematical programming (MILP) for mixed-model assembly line balancing problems with setups. Other relevant research on the multiple or parallel assemble line balancing problems is described in Lusa [17]. In the current paper, a TFT-LCD module assembly line is used to exemplify a typical flow shop problem with a linear layout. This specific application has process limitations due to the specific clean-room requirements, which are often found in electronics manufacturing, and for this example, an operator is not allowed to move between workstations that have different clean-room classes. This type of problem is important in practice, but we are not aware of any existing literature that addresses a similar problem. The present study develops a cellular design using parallel station layouts arranged to overcome the limitations of process constraints. 3. Proposed methodology In a typical linear layout, facilities are arranged in sequence based on manufacturing operation requirements, so products can flow smoothly through the system. Operator tasks consist of the loading and unloading of parts at machines and inspections at certain production stages. Workers must be able to walk easily between machines to finish tasks (Fig. 3a). One problem associated with this system is that when machines are arranged in a linear configuration, each line is independent from other lines. In this situation, the allocation of operations among workers to react to demand change for products often requires a fractional number of workers. A typical TPS approach combines U-shaped lines to solve this problem. Limited by the process considered, the case study has not adopted a U-shaped layout. The current layout is a linear layout and its conceptual schema is shown in Fig. 3b. Due to process limitations, it is not possible for operators to move between station 3 and station 4, nor between station 4 and station 5. In this situation, the company has more manpower problems (or slack manpower) than a typical linear layout in which operators can move freely between the different workstations. An operator balance chart (OBC) uses vertical bars to represent the work load for each operator. The vertical bar is developed
Fig. 4. OBC of the case problem.
by stacking small bars representing individual work elements. When customer demand changes, manpower can be reduced by redistributing workloads among operators based on the takt time requirement. Takt time is calculated as: Takt time = (Planned production time)/(Customer demand) If a longer takt time is desired when customer demand decreases, it is likely that fewer operators will be required, so excess operators can be assigned to other production lines. Conversely, with a shorter takt time, more operators will be required. Using OBC can redistribute work elements among operators by making the amount of work for each operator very close to the takt time. The OBC provides the management of an area so that expedient staffing decisions can be made [18]. The OBC of the case study is shown in Fig. 4, and is used as an illustration to describe Fig. 3b. Each station has one worker. When the takt time is equal to 250 s, this results in idle time. Ideally, the use of multi-skilled workers can achieve Shojinka benefits through the re-distribution of work elements among the workers. However, workers are not allowed to move between stations 3, 4 and 5 due to the differing clean-room requirements of those stations. If workers are unable to move between stations, we cannot obtain the benefit of Shojinka by adjusting work elements of operators to react to short-term changes in customer demand. In other words, the production line cannot run with more or fewer operators to approach a specific takt time. A three-phase methodology is here proposed to solve the proposed Shojinka problem: (a) redesigning new cellular layouts, (b) determining manpower level, and (c) evaluating and decisionmaking. The details are described as follows: 3.1. Redesigning new cellular layouts A workstation is a collection of one or more machines or manual stations that perform identical functions [19]. Two assembly lines can be combined by connecting them with one or more common
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Fig. 5. Cellular layout with P1 :P2 :P3 :P4 :P5 = 2:1:2:2:1. Table 1 Normalized workstation number ratio is rounded up to its nearest integer. Workstation number Process time (s/piece) Normalized stationnumber ratio
1 190 2
2 99 1
3 195 2
4 225 2
5 121 1
workstations. Workers can work in two or more different assembly lines at the same time. These connections may provide an opportunity to reduce the manpower requirement of the system [5]. This case study needs to revise its current layout to increase its flexibility and reduce its manpower. In this phase, the proposed layout is based on the least integer ratio of processing time for each workstation. We let P k be the processing time of process k,k = 1, 2, 3, 4, 5 and P 1 :P 2 :P 3 :P 4 :P 5 = a:b:c:d:e where the greatest (a, b, c, d, e) = 1, and a, b, c, d, e ∈ positive integer The above approach can rationalize the workstation requirements prior to the adjusting the required number of operators in order to properly balance the throughput capacity among the workstations. Table 1 is an illustrative example for tool requirements in a linear layout. The normalized workstation number ratio is rounded up to its nearest integer. If the normalized workstation-number ratio in a factory is 2:1:2:2:1, the cellular layout can be designed as Fig. 5. In Fig. 5, the cellular layout is further divided into two blocks (block 1 and block 2) due to cleanliness limitations according to the proposed TFT-LCD assembly line considerations. Block 1 contains five stations, where workers can walk between the stations, so the number of workers can be adjusted. Block 2 is a cleanroom and contains two workstations with three machines. Each machine requires a dedicated operator in attendance due to the consideration of work balance. For instance, if there is only one operator for the two machines of workstation 4, the throughput rate of workstation 4 will be much less than the throughput of workstation 5. In addition, the limitations on the shifting of manpower between workstations 4 and 5 are quite restrictive for the layout. Balancing the stations in blocks 1 and 2 and then adjusting the number of operators in block 1 help to achieve the Shojinka benefit which is the target of the present study. Fig. 5, which shows the layout configuration, is used as an example to develop a generic model in the next phase to illustrate and analyze the total manpower level.
ing decisions [20,21]. The mathematical formulation for integer programming is a linear programming model with the additional restriction that the variables must have integer values. If only some of the variables are required to have integer values, this model is called mixed integer programming (MIP). We can construct the mixed integer programming formulation to minimize the number of operators in the proposed cellular manufacturing layout. The assembly lines operate 21 h per day excluding breaks. Suppose there are U cells in the assembly factory. For a given cell: cell j, j = 1, 2, ..., U. In each cell, there is at least one block where operators can walk between stations, and one block where operators cannot. Given a set S = {1, 2, ..., s} of s blocks where the operators can move between stations in a cell, the notations are defined before the MIP formulation as follows: Parameters: Di = total demand for product i (pcs) N = number of different products U = total number of cells in the factory A = available operation time (in seconds) Mbj = number of stations in block b of cell j Wbj = number of processes in block b of cell j Lij = bottleneck operation time of cell j producing product i (in seconds) Cj = 1, if cell j is utilized; otherwise, Cj = 0 Pik = processing time for process k producing product i (in seconds) Decision variables: Tij = takt time of cell j producing product i (in seconds) tij = required production time of cell j producing product i (in seconds) Obj = number of operators in block b of cell j Then, the cellular manufacturing manpower problem can be formulated as the objective function (1), the constraints (2)–(7) and the sign restriction (8).
j=1
b=1
Obj × Cj
(1)
Subject to Tij ≥ Lij
for i = 1, 2, ..., N;
N
t i=1 ij
U j=1
3.2. Determining manpower level Mathematical programming can be used to develop cellular manufacturing models that integrate such aspects of manufactur-
U B
MinZ =
Cj =
≤A
for j = 1, 2, ...., U
(2) (3)
1 × tij × A Tij
1, if
j = 1, 2, ..., U
N
t i=1 ij
≥ Di >0
0, otherwise
for i = 1, 2, ..., N
for j = 1, 2, ..., U
(4)
(5)
P.-S. Wang et al. / Journal of Manufacturing Systems 44 (2017) 255–269
Obj × Tij −
Obj =
Wbj
P k=1 ik
≥0
for i = 1, 2, ..., N; j = 1, 2, ..., U; b ∈ S (6)
⎧ 0, if Cj = 0 ⎪ ⎨ ⎪ ⎩
for j = 1, 2, ..., U
(7)
and for i = 1, 2, ..., N; j = 1, 2, ..., U
(8)
The objective function (1) is used to minimize the total number of operators. The value of B is equal to 2 for the present study. Constraint (2) indicates the takt time is greater than or equal to the bottleneck operation time. Constraint (3) indicates that the sum of production time cannot exceed the available operation time. Constraint (4) ensures that total outputs are greater than or equal to demand. In the case study, A = (21 working hours per day × 3600 s per h)=75,600 s per day. Constraint (5) indicates whether the cell j is used or not. When the production time in a cell is greater than zero, it is used for production. In other words, when its production time is zero, it is idle. Constraint (6) indicates the idle time is greater than or equal to zero for each block when the blocks belong to S. It is calculated by multiplying the number of operators by the takt time and subtracting the total processing time because one operator may be in charge of several workstations. Constraint (7) indicates that when a cell is idle, the number of operators is zero in the cell. And the number of operators equals the number of stations when the blocks do not belong to S. In contrast, when the blocks belong to S, the number of operators is at least one, and at most equal to the number of stations (see Fig. 5). Restriction (8) is the nonnegativity constraint.
3.3. Evaluating and decision-making The previous phase has determined the manpower level for various cell configurations. In this phase, we use two performance indicators, the total number of operators and the labor idle time ratio, to evaluate the performance of cells. The former indicator can be formulated in the objective function (1), and the latter can be formulated in Eq. (9).
Labor idle time ratio =
SN = −10log10
1 2 yi n n
(10)
i=1
1, 2, ..., Mbj , if b ∈ S
Tij , tij ≥ 0
“smaller the better” case which is used the present study, suppose there are n quality characteristics, so the SN ratio is defined as:
/S Mbj , if b ∈
259
W
bj Bb=1 Obj × Tij − k=1 Pik /Tij
Bb=1 Obj
(9)
To find the most robust cellular layouts, it is necessary to make performance measurements to evaluate the functional stability of the system. Taguchi’s signal-to-noise (SN) ratios were proposed for evaluating this sort of robustness. The SN ratio is a measure used in science and engineering that compares the level of a desired signal to the level of background noise [22,23]. Taguchi suggests choosing an appropriate SN to analyze the variation. These SN ratios are derived from the quadratic loss function. Three of them, nominal the best, larger the better and smaller the better, are widely applicable. “Nominal the best” is chosen if the objective is to decrease variability around a specific target. If the system is optimized when the response is as large as possible, “larger the better” is used; and conversely, “smaller the better” is used when the response should be as small as possible. Factor levels that maximize the appropriate SN ratio are optimal. In this problem, we use “smaller the better” because the objective is to minimize the total number of operators and the labor idle time ratio. SN ratios are expressed on a decibel scale. For this
where yi represents the response value of the i-th quality characteristic. In yi refers to the i-th value of observation for the two performance indicators mentioned above. Stochastic programming is mathematical programming dealing with optimization problems that involve uncertain parameters [24]. Uncertainty is usually characterized by a probability distribution of the parameters. Stochastic programming can solve customer stochastic demand; for example, Lin et al. [25] used a stochastic dynamic programming for multi-site capacity planning in TFTLCD manufacturing under demand uncertainty. When some of the parameters are random, then solutions and the optimal objective value to the optimization problem are themselves random. A distribution of optimal decisions generally cannot be implemented. This study mainly stresses comparing robustness between four proposed layout designs, rather than finding the best solution. In addition, complex mathematical modeling is much more difficult to implement because it requires more time and effort for firms to absorb that complex knowledge. Empirically, the current study uses the SN ratio to evaluate the robustness of layouts.
4. Empirical illustrations TFT-LCD applications encompass a variety of consumer electronics, including personal digital assistants (PDA), cellular phones, digital cameras, computers, notebook computers, flat panel televisions, and others. There are three main production sequences for TFT-LCD: the TFT array process, the cell process, and module assembly [26]. The TFT fabrication process sequence is a series of deposition and etching sequences, as in integrated circuit fabrication. Both the thin-film-transistor plate and color-filter plate are joined together in what is called a cell assembly process. The final step is module assembly, integrating the drive integrated circuit (IC) onto the substrate to drive the display and attach the backlight (BL) to the module. The display then undergoes a final test to complete the operation. Some relevant research solutions have been widely used in TFT-LCD manufacturing to enhance the production efficiency and reduce production cost [27,28]. To illustrate the effectiveness of the proposed approaches, a case study was adopted from a TFT-LCD module manufacturing firm in Tainan, Taiwan. For this case study, detailed module processes and their characteristics are shown in Table 2. In Table 2, most of the module processes are manpower operations, and only the aging test is an automated operation. Automation is much less flexible than manual lines and it is difficult to change. Therefore, the aging test and all its following tasks cannot be integrated into the cellular design. The back bezel cleaning has a very short processing time. In addition, both the lamp test and the PCB (print circuit board) soldering have a long task time. These three are also not suitable for assignment to the cellular design. Based on the results above, the cellular design includes five processes: lamp assembly, cable assembly, inspection, BL assembly, and LCM (liquid crystal display module) assembly. Two of these, BL and LCM assemblies, cannot allow labor from other stations to enter due to the requirement for cleanliness. The existing company-case layout is illustrated as Fig. 6. The cellular design consists of the five processes mentioned above, and an operator is assigned to exactly one process. In this situation, the current layout needs to be improved to reduce manpower and wastage
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Table 2 Production data for the case problem.
Fig. 6. Existing layout.
due to idle time. The proposed 3-phase approach is adopted to solve the problems. 4.1. Redesigning cellular layouts for the case problem In Fig. 6, the inspection process has a short processing time. If we combine the cable assembly process and the inspection process into a single process, the resulting cellular design will be as described in Fig. 7, where the parallel station layouts are divided into two sides, producing the same or similar parts according to customer demand. The process contains 12 identical cells and each cell has four workstations. The ratio of each station within a cell is 1:1:1:1. Due to the requirement of high cleanliness both in the BL and LCM assembly stations, operators from other stations are not permitted to enter these stations. In this situation, work elements cannot be
Table 3 Normalized workstation number ratio for the manufacturing of product 1. Product 1
Lamp
Cable & inspection
BL
LCM
Process time (s) The least integer ratio∼ =
99 1
163 + 60 2
225 2
121 1
redistributed among operators to adjust workloads according to the takt time when customer demand changes. To overcome shortcomings of the current cellular layout, different types of cell configurations can be established according to the normalized workstation number ratios for the production of the different product types. The company produces three main products, and the resulting normalized workstation number ratios are shown in Tables 3–5.
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261
Fig. 7. Current cellular layout design.
Fig. 8. Proposed cellular layout design for the production of product 1.
Table 4 Normalized workstation number ratio for the manufacturing of product 2.
Table 6 Processing time of the different layout configurations for product 1 (seconds).
Product 2
Lamp
Cable & inspection
BL
LCM
Layouts
Lamp
Cable & inspection
BL
LCM
Process time (s) The least integer ratio∼ =
89 1
132 + 58 2
195 2
162 2
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
99 99 99 50a
223 112 112 75
225 113 113 75
121 121 61 61
a
Table 5 Normalized workstation number ratio for the manufacturing of product 3.
99/250.
4.2. Determining manpower levels for the case problem
Product 3
Lamp
Cable & inspection
BL
LCM
Process time (s) The least integer ratio∼ =
134 2
152 + 58 3
220 3
153 2
Let Qij be the quantity of product i inside cell j. In the proposed layouts, the processing time of parallel stations for each process, the maximum output and the utilization for each cell is: Processing time =
Considering the size of the company building, we organize the cellular design for the production of products 1, 2 and 3 according to the normalized workstation number ratios to achieve a balanced system. The resulting layout designs are illustrated in Figs. 8–10. In Fig. 8, the proposed layout for product 1 has 6 cells and each cell has 6 stations, each including one lamp station, two cable and inspection stations, two BL stations, and one LCM station, in that sequence. In other words, the ratio of parallel stations is 1:2:2:1 in a cell. In Fig. 9, the proposed layout for product 2 has 6 cells and each cell has 7 stations. The ratio of parallel stations is 1:2:2:2. Finally, in Fig. 10, the proposed layout for product 3 has 4 cells and each cell has 10 stations. The ratio of parallel stations is 2:3:3:2.
Max Qij =
Pik Number of parallel stations
A Lij
Utilization =
(11) (12)
Qij Max Qij
(13)
Using Eq. (11), the processing time of parallel stations in each cell for product 1, product 2, and product 3 is summarized in Tables 6, 7, and 8, respectively. The company works 21 h a day. Assuming the workers are multiskilled, using Eq. (12), the maximum output of each cell for each product is tabulated in Table 9. The maximum output of each layout for each product is tabulated in Table 10.
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Fig. 9. Proposed cellular layout design for the production of product 2.
Fig. 10. Proposed cellular layout design for the production of product 3.
Table 7 Processing time of the different layout configurations for product 2 (seconds).
Table 10 Maximum daily throughput for each layout configuration (pieces).
Layouts
Lamp
Cable & inspection
BL
LCM
Layouts
Product 1
Product 2
Product 3
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
89 89 89 45
190 95 95 64
195 98 98 65
162 162 81 81
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
4032 3744 4014 4032a
4644 2796 4626 3732
4116 2964 4122 3924
a
1008 × 4 = 4032.
Table 8 Processing time of the different layout configurations for product 3 (seconds). Layouts
Lamp
Cable & inspection
BL
LCM
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
134 134 134 67
210 105 105 70
220 110 110 74
153 153 77 77
These results can be used to determine specific values for the mixed integer programming parameters. The proposed approaches
are performed using Microsoft Excel since it is readily available on the shop-floor of the case company. In Figs. 11 and 12, we used a scenario based on a ratio of 1:1:1:1 layout and the demands of 1190, 1155, and 1320 pieces for products 1, 2, and 3, respectively to illustrate the performance in Excel. Fig. 11 indicates the objective function and constraints formulated by Excel. Fig. 12 is an outcome output by Excel, including six parts, as follows:
Table 9 Maximum output per day in each cell for each product (pieces). Layouts
Product 1
Product 2
Product 3
Number of cell
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
336 624 669 1008a
387 466 771 933
343 494 687 981
12 6 6 4
a
(21 × 60 × 60)/75 = 1008.
P.-S. Wang et al. / Journal of Manufacturing Systems 44 (2017) 255–269
Fig. 11. Objective function and constraints formulated by Excel.
263
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Fig. 12. Cell production information and operator balance chart.
P.-S. Wang et al. / Journal of Manufacturing Systems 44 (2017) 255–269 Table 11 Different demand levels for the different product types (pieces).
265
48
Demand level
Product 1
Product 2
Product 3
High Medium Low
3500 2750 2000
3500 2500 1500
4000 3000 2500
46 44 42 40 38 36
1) Cell production information calculating interface: an interface for calculating cell production information. 2) Operator balance chart drawing interface: an interface for calculating information about OBC and then completing the drawing. Using Excel Visual Basic for Applications (VBA) to connect interfaces and by “recording Macros” or writing VBA Programming. Parts of the programming codes are shown as follows:
34 32 30
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
Fig. 13. Trend chart for staffing level analysis at the high demand level.
30 25 20 15 10 5 0
3) Cell production information diagram: The results of cell production information calculating, including the number of cells used in a layout, the number of operators in each cell, the total number of operators, idle time, labor idle time ratio, takt time, and others, provide managers with detailed production information. 4) Cell allocations: A chart showing the types of cell allocation, with the current display of a ratio of 1:1:1:1 layout design. 5) Operator balance chart information calculation for product 2: Calculation results include takt time, process time, idle time, labor idle time ratio, and others to provide managers with the production situations for each operator. 6) Operator balance chart for product 2: The results of OBC information calculation form an operator balance chart. 4.3. Evaluating and decision-making for the case problem
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
Fig. 14. Trend chart for labor idle time ratio analysis at the high demand level. Table 12 Different demand rates for the different product types (%). Level
Product 1
Product 2
Product 3
1 2 3 4 5 6 7
30 10 34 60 10 60 30
10 30 33 10 60 30 60
60 60 33 30 30 10 10
Table 13 Structured experimental design (pieces). Customer demand level
Layout decisions are important since they require substantial investment, involve long-term commitments, and have a significant impact on the cost and efficiency of short-term operations. In order to determine a robust layout design, customer demand levels are classified as high, medium and low for products 1, 2 and 3 in the case company, based on the historical sales data as shown in Table 11. Consider each of the three product types with three levels (high, medium, and low). The different product-mix scenarios are found to construct a structured experimental design with seven replications: (30, 10, 60), (10, 30, 60), (34, 33, 33), (60, 10, 30), (10, 60, 30) (60, 30, 10), (30, 60, 10) as shown in Table 12 which can represent boundary conditions for the possible product-mix scenarios. The center point is (34, 33, 33). Table 13 is the multiplication of Tables 11 and 12. For example, the multiplication of high demands from Table 11, row 1 and Table 12, row 1 is: (3500 × 30%, 3500 × 10%, 4000 × 60%) = (1050, 350, 2400) which is shown as Table 13, row 1, column 1. Using Eqs. (1)–(13) and Table 13, the total number of operators and the labor idle time ratio at the high, medium and low levels for evaluating the manpower performance and the robustness of
Demand rate
High
Medium
Low
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
(1050:350:2400) (350:1050:2400) (1190:1155:1320) (2100:350:1200) (350:2100:1200) (2100:1050:400) (1050:2100:400)
(825:250:1800) (275:750:1800) (935:825:990) (1650:250:900) (275:1500:900) (1650:750:300) (825:1500:300)
(600:150:1500) (250:450:1500) (680:495:825) (1200:150:750) (200:900:750) (1200:450:250) (600:900:250)
Note: (product 1:product 2:product 3).
demand changes can be computed, as shown in Tables 14–20 and Figs. 13–19. We discuss the respective managerial insights for the three demand levels as follows: 4.3.1. At the high level In Tables 14 and 15, the cellular layout with a 2:3:3:2 ratio is the most robust because it has a maximum SN ratio, which means the least variance, for the total number of operators and the labor idle time ratio. In contrast, the ratio 1:2:2:1 cannot meet customer demand. Compared with the current layout using a 1:1:1:1 ratio, the proposed layout with a ratio of 2:3:3:2 reduces 6.72 workers on
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Fig. 15. Detailed workers’ assignments for the example problem.
Table 14 Staffing level analysis at the high demand level (persons).
30
The current and proposed layouts Demand rate
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (persons) SN ratio
48 44 44a 44 40 44 40 43.43 −32.77
– – – – – – – – –
41 41 40 41 35 39 35 38.86 −31.81
38 39 37b 36 36 35 36 36.71 −31.30
a b
Refer to Fig. 12. Refer to Table 16.
15 10 5 0 Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
28
The current and proposed layouts Demand rate
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (%) SN ratio
27 21 26a 25 20 29 23 24.43 −27.82
– – – – – – – – –
15 16 18 18 11 20 12 15.71 −24.09
8 11 12b 9 11 11 15 11.00 −20.98
b
20
Fig. 17. Trend chart for labor idle time ratio analysis at the medium demand level.
Table 15 Labor idle time ratio analysis at the high demand level (%).
a
25
26 24 22 20 18 16
Refer to Fig. 12. Refer to Table 16.
38
Fig. 18. Trend chart for staffing analysis at the low demand level.
30
36
25
34 32
20
30
15
28
10
26
5
24 22
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
0
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
Fig. 16. Trend chart for staffing level analysis at the medium demand level.
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Fig. 19. Trend chart for labor idle time ratio analysis at the low demand level.
4.28
The current and proposed layouts
94 27.44
12
0.69 0.91 1.59 1.09 100 95a 82 100 6.88 7.92 54.12 40.84
7 10b 20 11
Table 17 Staffing level analysis at the medium demand level (persons).
Demand rate
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (persons) SN ratio
36 36 32 36 32 32 32 33.71 −30.57
32 32 31 31 30 29 30 30.71 −29.75
33 33 28 33 28 28 27 30.00 −29.58
29 29 28 28 27 27 26 27.71 −28.86
1.00 0.38 0.00 0.00 1.38 18.91
81 81 99 81 26.89
10 9 8 10 37 cell 1 cell 2 cell 3 cell 4 Total Avg.
Note: Due to rounding, some totals may not correspond with the sum of the separate figures. a 182/1008 + 394/933 + 339/981 = 95. b 0.19 × 8.44 + 0.42 × 12.76 + 0.38 × 7.92 = 10.
0 394 761 0 1155 40.03 8.44 48.14 10.93
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (%) SN ratio
26 28 23 30 26 26 29 26.86 −28.61
17 19 20 19 13 14 17 17.00 −24.70
20 22 12 24 13 15 16 17.43 −25.08
9 11 12 10 13 12 13 11.43 −21.22
The current and proposed layouts
0.00 0.19 0.00 1.00 1.19 0 182 0 1008 1190
Demand rate
Table 19 Staffing level analysis at the low demand level (persons).
0.00 0.42 1.00 0.00 1.42
21.48 12.76 19.91 21.48
981 339 0 0 1320
77 86 195 121
The current and proposed layouts
5 4 3 5 17
5 5 5 5 20
Takt Time Output Block 2 Block 1 People /Cell
Product 1
267
Table 18 Labor idle time ratio analysis at the medium demand level (%).
111 81 161 75
Utilization Idle Time (%) ratio (%) Output Takt Time Output Production Idle Time Time ratio (%)
Product 2 Total idle manpower
Labor idle time ratio for the layout 37 Total number of operators
Table 16 Required staffing and production information for the example problem.
4.28 people
12 %
Production Idle Time Time ratio (%)
Product 3
Takt Time
demand
Production Idle Time Time ratio (%)
1155 Total 1190
1320
Product 2 Product 1
Product 3
People
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Demand level
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (persons) SN ratio
27 28 24 28 22 24 20 24.71 −27.92
26 26 22 22 21 21 20 22.57 −27.11
26 26 21 26 20 21 20 22.86 −27.24
25 25 24 25 19 19 18 22.14 −26.99
Table 20 Labor idle time ratio analysis at the low demand level (%). The current and proposed layouts Demand level
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (%) SN ratio
23 29 25 32 25 30 24 26.86 −28.64
19 13 15 11 20 18 18 16.29 −24.39
20 24 14 27 18 20 24 21.00 −26.60
17 21 25 24 13 12 16 18.29 −25.53
average, which means the required manpower is reduced by 15.47% and the labor idle time ratio is decreased by 13.43% on average. Similarly, comparing the proposed layout with a ratio of 1:2:2:2 to the current layout, the manpower is reduced by 4.57 workers or 10.52%, and the labor idle time ratio is reduced by 8.72%. Figs. 13 and 14 show that the proposed layout designs (except the one with the ratio of 1:2:2:1) consistently generate superior improvements in reducing the total number of operators and the labor idle time ratio to the current layout.
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Table 21 Summary of improvements by the proposed methodology. The proposed layouts Demand level
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
High Medium Low Average Improvement
43.43a , 24.43%b 33.71, 26.86% 24.71, 26.86% 33.95, 26.05% –
– 30.71, 17.00% 22.57, 16.29% – –
38.86, 15.71% 30.00, 17.43% 22.86, 21.00% 30.57, 18.05% 3.38, 8.00%
36.71, 11.00% 27.71, 11.43% 22.14, 18.29% 28.85, 13.57% 5.10, 12.48%
a b
Total number of operators. Labor idle time ratio.
To illustrate the detailed calculation of the required staffing level and production information, the example of level 3 demand rate, high demand level and 2:3:3:2 is adopted. For this example, the demands for products 1, 2 and 3 are 1190, 1155 and 1320 pieces, respectively. Then the required numbers of workers are determined by the proposed methodology, with the following results. Cell 1 has 10 workers (5 are in the adjustable zone; 5 are in the fixed zone), and at a takt time of 77 s the output of product 3 is 981 pieces per day. The labor idle time ratio in cell 1 is 7%. Cell 2 has 9 workers (4 are in the adjustable zone; 5 are in the fixed zone). At a takt time of 81 s for product 1, its output is 182 pieces in 0.19 day. Next, at a takt time of 81 s for product 2, its output is 394 pieces in 0.42 day. Finally, at a takt time of 86 s, the output of product 3 is 339 pieces in 0.38 day. In total, the labor idle time ratio in cell 2 is 10%. Cell 3 has 8 workers (3 are in the adjustable zone; 5 are in the fixed zone), and at a takt time of 99 s the output of product 2 is 761 pieces per day. The labor idle time ratio in cell 3 is 20%. Cell 4 has 10 workers (5 are in the adjustable zone; 5 are in the fixed zone), and at a takt time of 75 s the output of product 1 is 1008 pieces per day. The labor idle time ratio is 11% in cell 4. In sum, the total number of workers in this layout is 37. Its labor idle time ratio is 12% (about 4.28 people) and the utilization is about 94%. The results are shown in Excel form in Table 16, and Fig. 15 shows the detailed worker assignments. 4.3.2. At the medium level In Tables 17 and 18, a smaller average has a larger SN ratio in various layouts. The most robust cellular layout is 2:3:3:2 because it has a maximum SN ratio for the total number of operators and the labor idle time ratio. At the medium level, we still make respective comparisons of the total number of operators and the labor idle time ratio between the current and proposed layouts. The proposed layout with a ratio of 2:3:3:2 reduces the manpower by 6 workers or 17.79% and the labor idle time ratio is reduced by 15.43% on average. Likewise, the other two with the ratios of 1:2:2:1 and 1:2:2:2 both reduce manpower by 3 and 3.7 workers, respectively, indicating the improvements in manpower requirements of 8.89% and 11.01%. Their labor idle time ratios are decreased by 9.86% and 9.43%, respectively. Figs. 16 and 17 show that the proposed layout with the ratio of 2:3:3:2 consistently generates superior improvements in reducing the total number of operators and the labor idle time ratio in comparison to other layouts. 4.3.3. At the low level In Tables 19 and 20, 2:3:3:2 is still the most robust layout for the total number of operators as it is at the high and medium levels. Moreover, it is also the most robust for the labor idle time ratio at the two levels. However, at the low level, 1:2:2:1 becomes the most robust layout. Compared with the current layout, the proposed layout with the ratio of 2:3:3:2 reduces 2.57 workers on average, for a reduction in manpower by 10.4% and a decrease of 8.57% in the labor idle time ratio. Similarly, comparing the pro-
posed layouts with ratios of 1:2:2:1 and 1:2:2:2 to the current one, there are reductions in manpower of 2.14 and 1.85 workers, respectively, which means the required manpower is reduced by 8.66% and 7.49%. The labor idle time ratios are reduced by 10.57% and 5.86%, respectively. Figs. 18 and 19 show that the proposed layouts design consistently generates superior improvements in decreasing the total number of operators and the labor idle time ratio to the current layout. Table 21 is a summary comparing the current and proposed layouts according to the total number of operators and the labor idle time ratio at the high, medium and low levels. Since the layout with the ratio of 1:2:2:1 fails to meet customer demand at the high level, it is not a suitable layout design for the case company. Compared with the current layout, the proposed layout with the ratio of 2:3:3:2 reduces 5.1 workers on average, which means manpower need is reduced by 15.02% and the labor idle time ratio is reduced by 12.48%. Similarly, comparing the proposed layout with the ratio of 1:2:2:2 to the current one, the proposed layout reduces manpower by 3.38 workers or 9.96%. The labor idle time ratio is reduced by 8%. Since it is usually difficult to redesign a settled layout to accommodate the demand fluctuations, it is essential to select a robust design. If the case company allows only one layout, the proposed layout designs with the ratio of 2:3:3:2 will be the preferred choice.
5. Conclusions The benefit of Shojinka is well recognized and has been effectively implemented in the automobile industry. However, we are not aware of any literature that demonstrating the successful application of Shojinka in the TFT-LCD industry, which represents a cost-intensive industry. The present study proposes a MIP formulation to solve a Shojinka problem. A TFT-LCD module manufacturing is adopted for the empirical study to illustrate the effectiveness and efficiency of the proposed methodology. The results are promising. The empirical results show that, given a variety of demand scenarios, this method can reduce the average manpower and the average labor idle time ratio by 15.02% and 12.48%, respectively. The resulting layout design is easily implemented and is thus appropriate for practical applications. Since the present study assumes that only one layout design is selected as the final design, this constraint may be relaxed to include more than one design as the final layout design, such as a cell with 1:1:1:1 configuration and others with 1:2:2:1 or 1:2:2:2 together. In addition, block 2 includes the last two workstations (BL and LCM) that require a dedicated operator to attend each station for the present study. If this constraint can be relaxed due to future technology advancements, floating manpower between workstations 4 and 5 may be possible, which can further improve the benefit of the proposed Shojinka strategy. The present study further assumes that an operator is able to finish his/her own task independently; however, due to the growing work complexity; an
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operator may need to collaborate with the other operators to fulfill his/her jobs. These become future research opportunities for the present study. Acknowledgement This work was supported, in part, by the Ministry of Science and Technology, Taiwan, under grant MOST-104-2221-E-006-103MY3. References [1] Monden Y. Toyota production system: an integrated approach to just-in-time. 4th edition Boca Raton: CRC Press; 2011. [2] Stockton DJ, Ardon-Finch J, Khalil R. Design of flexible manpower line walk cycles for a fixed number of operators. Int J Prod Res 2005;43(3):583–96. [3] Balle F, Balle M. Feel the force of flexible manpower. Manuf Eng 2006;84:20–5. [4] Stockton DJ, Ardon-Finch J, Khalil R. Walk cycle design for flexible manpower lines using genetic algorithms. Int J Comput Integr Manuf 2005;18(1):15–26. [5] Gökc¸en H, Kara Y, Atasagun Y. Integrated line balancing to attain Shojinka in a multiple straight line facility. Int J Comput Integr Manuf 2010;23(5):402–11. [6] Houshyar AN, Leman Z, Moghadam HP, Sulaiman R. Review on cellular manufacturing system and its components. Int J Eng Adv Technol 2014;3(6):52–7. [7] Papaioannou G, Wilson JM. The evolution of cell formation problem methodologies based on recent studies (1997–2008): review and directions for future research. Eur J Oper Res 2010;206(3):509–21. [8] Merchichi S, Boulif M. Constraint-driven exact algorithm for the manufacturing cell formation problem. Eur J Ind Eng 2015;9(6):717–43. [9] Tompkins JA, White JA, Bozer YA, Tanchoco JMA. Facilities planning. 4th edition New York: Wiley and Sons; 2010. [10] Ariafar S, Ismail N. An improved algorithm for layout design in cellular manufacturing systems. J Manuf Syst 2009;28(4):132–9. [11] Javadi B, Jolai F, Slomp J, Rabbani M, Tavakkoli-Moghaddam R. An integrated approach for the cell formation and layout design in cellular manufacturing systems. Int J Prod Res 2013;51(20):6017–44. [12] Bidanda B, Ariyawongrat P, Needy KL, Norman BA, Tharmmaphornphilas W. Human related issues in manufacturing cell design, implementation, and operation: a review and survey. Comput Ind Eng 2005;48(3):507–23.
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Journal of Manufacturing Systems journal homepage: www.elsevier.com/locate/jmansys
Full Length Article
Effective layout designs for the Shojinka control problem for a TFT-LCD module assembly line Peng-Sen Wang a , Taho Yang b,∗ , Min-Chih Chang b a b
Department of Industrial Management and Information, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan 701, Taiwan
a r t i c l e
i n f o
Article history: Received 3 February 2017 Received in revised form 24 June 2017 Accepted 10 July 2017 Keywords: Cellular layout Shojinka Mixed integer programming Toyota production system TFT-LCD
a b s t r a c t Attaining flexibility in the number of workers in a workshop to adapt to demand changes is termed Shojinka, and this is often combined with cellular layout design configuration. The machine layout for Shojinka must be appropriately designed to enable workers to walk easily between machines within a cell. This paper analyzes a problem of how to attain Shojinka under the limitations of a process where operators have restricted movement within a cell. The study is motivated by a practical flow shop problem with different machine rates among workstations. We first consider the decision of Shojinka to develop the cellular layout design under the limitations of a process. Next, we propose a mixed integer programming formulation to minimize the total number of operators. Taguchi’s signal-to-noise (SN) ratios are used to compare the robustness of different layouts when the demand changes. The present study uses a thin-film-transistor liquid-crystal display (TFT-LCD) module assembly line as a case study to illustrate the effectiveness of the proposed approaches. The results show that the proposed layout can decrease manpower and labor idle time ratio by 15.02% and 12.48% on average, respectively. © 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
1. Introduction Fluctuations in demand are inevitable in a volatile market and the strategy to accommodate this by developing flexibility in the number of workers in a workshop to adapt to changes in demand is referred to as Shojinka. That and Shoninka are two similar Japanese words, but they have different meanings. Shojinka, as used in the Toyota Production System (TPS), refers to changing the number of workers when production demand increases or decreases, which can be attained through multi-skilled workers performing multiple operations. Toyota trains workers to become multi-skilled through job rotation. The other term, Shoninka, indicates simply reducing the number of workers. Cellular layout refers to a type of layout where various machines are grouped in a cell. A cell is typically a replica version of a flow shop. The cells contain machines and tools needed to process families of parts with similar processing requirements. In TPS, each U-shaped process is a cell in which the products flow one at a time through each workstation. Shojinka requires that workers be able to respond to changes within a cycle time and an operation rou-
∗ Corresponding author. E-mail address: [email protected] (T. Yang).
tine. When an improvement has been made, manpower cost can be reduced by adjusting the number of operators. For a given cycle time, the demand for products often requires a fractional number of workers on a U-shaped line, such as 7.5 people. In this case, eight workers must be assigned to the line. As a result, the extra manpower will cause idle time or excess production. In order to eliminate waste, Toyota combines several U-shaped lines into one integrated line to overcome the problem of fractional manpower and achieve Shojinka. Under this layout, the range of jobs for which each worker is responsible can be easily be expanded or reduced [1]. The following example adapted from Monden [1] will illustrate how the concept is used to achieve Shojinka. Fig. 1 refers to a layout with five cells in which each digit represents a machine and each cell is manufacturing a different part. According to the customer demand for products, the cycle time of this layout is one minute per unit. Under this circumstance, seven people are working in the layout and each of their walking routes follows the arrow line. If customer demand decreases, resulting in the increase of cycle time to 1.2 min per unit, all operations in the layout will be redistributed and each worker will have more tasks than before. For example, in Fig. 2, worker 1’s walking route is enlarged since he has to do some additional jobs which originally belonged to worker 2. Similarly, worker 2 is reallocated to be responsible for parts of worker 3’s jobs. Consequently, expanding the walking route will
http://dx.doi.org/10.1016/j.jmsy.2017.07.004 0278-6125/© 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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Fig. 1. Allocation of operations among workers (cycle time = 1 min per unit).
Fig. 2. Allocation of operations among workers (cycle time = 1.2 min per unit).
eliminate workers 6 and 7 from the layout, and the fractional manpower that might have occurred in a U-shaped line is thus absorbed in various cells under this layout. However, the Shojinka decision is not always straightforward. A thin-film-transistor liquid-crystal display (TFT-LCD) module assembly line, for example, is limited by its manufacturing characteristics (e.g., cleanliness). As a result, the operators are restricted when walking between machines in a cell, so under such a constraint, it is more difficult to achieve Shojinka. Consequently, the objective of this study is to minimize the total number of workers through effective layout designs to achieve Shojinka, considering the specific process limitations arising from the TFT-LCD module assembly line. The remainder of this paper is organized as follows. Section 2 reviews the pertinent literature. Section 3 provides details of the proposed methodology. Empirical illustrations are discussed in Section 4. Conclusions are presented in the final section.
2. Literature review Shojinka is sometimes referred to as “flexible manpower line” or “labor linearity.” A flexible manpower line forms an individual cell within a linked-cell manufacturing system. Cells are designed so that throughput rates can be quickly and efficiently adjusted [2]. Typically, cells are manual since manual lines are far better suited to continual improvement than automatic lines [3]. Shojinka includes
several fundamental characteristics: U-shaped line, walk-routine design, multi-skilled workers, no barriers to operator movement, operator scheduled to takt time, one-piece production, and continuous improvements of the process. Walk-routine design for flexible manpower lines were studied by Stockton et al. [2,4]. When a company does not adopt the U-shaped line layout to attain Shojinka, but rather employs a multiple straight assembly line, then there are different parameters, as discussed by Gökc¸en et al. [5]. The cellular manufacturing system tries to combine the flexibility of a job shop with the productivity of a flow shop; and thereby be able to respond quickly to new market requirements. The cellular manufacturing system includes both cell formation and a specific layout design [6]. The process of determining component families and machine groups is referred to as the cell formation problem, and there have been several studies relating to this problem in the last decade [7,8]. These have suggested a number of solutions and approaches, such as mathematical programming, heuristic and metaheuristic methodologies, and artificial intelligence strategies. The layout design includes arranging cells on the shop floor (inter-cell layout) and arranging the facilities in the cells (intracell layout). A well-designed layout can reduce material handling cost, lead time and throughput time [9]. Although the importance of layout design is apparent in the cellular manufacturing system, it has not attracted much attention in comparison to cell formation [10,11]. In addition to the two technical issues, the benefit of peo-
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257
Fig. 3. (a) A typical linear layout. (b) Conceptual schema for the existing layout of the case problem.
ple issues is often neglected [12]. This paper focuses on achieving Shojinka through appropriate layout design. Song et al. [13] noted that although there has been much research on the issue of assembly line-balancing, few studies consider the role of operators of the assembly lines. Most of the line balancing research addresses how to assign tasks to workstations to obtain an optimal solution [14]. For example, in designing parallel assembly lines, Gökc¸en et al. [15] suggested that in a production facility, two or more straight assembly lines can be located in parallel so they can be balanced simultaneously. Akpinar and Baykasoglu [16] developed a method of mixed-integer linear mathematical programming (MILP) for mixed-model assembly line balancing problems with setups. Other relevant research on the multiple or parallel assemble line balancing problems is described in Lusa [17]. In the current paper, a TFT-LCD module assembly line is used to exemplify a typical flow shop problem with a linear layout. This specific application has process limitations due to the specific clean-room requirements, which are often found in electronics manufacturing, and for this example, an operator is not allowed to move between workstations that have different clean-room classes. This type of problem is important in practice, but we are not aware of any existing literature that addresses a similar problem. The present study develops a cellular design using parallel station layouts arranged to overcome the limitations of process constraints. 3. Proposed methodology In a typical linear layout, facilities are arranged in sequence based on manufacturing operation requirements, so products can flow smoothly through the system. Operator tasks consist of the loading and unloading of parts at machines and inspections at certain production stages. Workers must be able to walk easily between machines to finish tasks (Fig. 3a). One problem associated with this system is that when machines are arranged in a linear configuration, each line is independent from other lines. In this situation, the allocation of operations among workers to react to demand change for products often requires a fractional number of workers. A typical TPS approach combines U-shaped lines to solve this problem. Limited by the process considered, the case study has not adopted a U-shaped layout. The current layout is a linear layout and its conceptual schema is shown in Fig. 3b. Due to process limitations, it is not possible for operators to move between station 3 and station 4, nor between station 4 and station 5. In this situation, the company has more manpower problems (or slack manpower) than a typical linear layout in which operators can move freely between the different workstations. An operator balance chart (OBC) uses vertical bars to represent the work load for each operator. The vertical bar is developed
Fig. 4. OBC of the case problem.
by stacking small bars representing individual work elements. When customer demand changes, manpower can be reduced by redistributing workloads among operators based on the takt time requirement. Takt time is calculated as: Takt time = (Planned production time)/(Customer demand) If a longer takt time is desired when customer demand decreases, it is likely that fewer operators will be required, so excess operators can be assigned to other production lines. Conversely, with a shorter takt time, more operators will be required. Using OBC can redistribute work elements among operators by making the amount of work for each operator very close to the takt time. The OBC provides the management of an area so that expedient staffing decisions can be made [18]. The OBC of the case study is shown in Fig. 4, and is used as an illustration to describe Fig. 3b. Each station has one worker. When the takt time is equal to 250 s, this results in idle time. Ideally, the use of multi-skilled workers can achieve Shojinka benefits through the re-distribution of work elements among the workers. However, workers are not allowed to move between stations 3, 4 and 5 due to the differing clean-room requirements of those stations. If workers are unable to move between stations, we cannot obtain the benefit of Shojinka by adjusting work elements of operators to react to short-term changes in customer demand. In other words, the production line cannot run with more or fewer operators to approach a specific takt time. A three-phase methodology is here proposed to solve the proposed Shojinka problem: (a) redesigning new cellular layouts, (b) determining manpower level, and (c) evaluating and decisionmaking. The details are described as follows: 3.1. Redesigning new cellular layouts A workstation is a collection of one or more machines or manual stations that perform identical functions [19]. Two assembly lines can be combined by connecting them with one or more common
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Fig. 5. Cellular layout with P1 :P2 :P3 :P4 :P5 = 2:1:2:2:1. Table 1 Normalized workstation number ratio is rounded up to its nearest integer. Workstation number Process time (s/piece) Normalized stationnumber ratio
1 190 2
2 99 1
3 195 2
4 225 2
5 121 1
workstations. Workers can work in two or more different assembly lines at the same time. These connections may provide an opportunity to reduce the manpower requirement of the system [5]. This case study needs to revise its current layout to increase its flexibility and reduce its manpower. In this phase, the proposed layout is based on the least integer ratio of processing time for each workstation. We let P k be the processing time of process k,k = 1, 2, 3, 4, 5 and P 1 :P 2 :P 3 :P 4 :P 5 = a:b:c:d:e where the greatest (a, b, c, d, e) = 1, and a, b, c, d, e ∈ positive integer The above approach can rationalize the workstation requirements prior to the adjusting the required number of operators in order to properly balance the throughput capacity among the workstations. Table 1 is an illustrative example for tool requirements in a linear layout. The normalized workstation number ratio is rounded up to its nearest integer. If the normalized workstation-number ratio in a factory is 2:1:2:2:1, the cellular layout can be designed as Fig. 5. In Fig. 5, the cellular layout is further divided into two blocks (block 1 and block 2) due to cleanliness limitations according to the proposed TFT-LCD assembly line considerations. Block 1 contains five stations, where workers can walk between the stations, so the number of workers can be adjusted. Block 2 is a cleanroom and contains two workstations with three machines. Each machine requires a dedicated operator in attendance due to the consideration of work balance. For instance, if there is only one operator for the two machines of workstation 4, the throughput rate of workstation 4 will be much less than the throughput of workstation 5. In addition, the limitations on the shifting of manpower between workstations 4 and 5 are quite restrictive for the layout. Balancing the stations in blocks 1 and 2 and then adjusting the number of operators in block 1 help to achieve the Shojinka benefit which is the target of the present study. Fig. 5, which shows the layout configuration, is used as an example to develop a generic model in the next phase to illustrate and analyze the total manpower level.
ing decisions [20,21]. The mathematical formulation for integer programming is a linear programming model with the additional restriction that the variables must have integer values. If only some of the variables are required to have integer values, this model is called mixed integer programming (MIP). We can construct the mixed integer programming formulation to minimize the number of operators in the proposed cellular manufacturing layout. The assembly lines operate 21 h per day excluding breaks. Suppose there are U cells in the assembly factory. For a given cell: cell j, j = 1, 2, ..., U. In each cell, there is at least one block where operators can walk between stations, and one block where operators cannot. Given a set S = {1, 2, ..., s} of s blocks where the operators can move between stations in a cell, the notations are defined before the MIP formulation as follows: Parameters: Di = total demand for product i (pcs) N = number of different products U = total number of cells in the factory A = available operation time (in seconds) Mbj = number of stations in block b of cell j Wbj = number of processes in block b of cell j Lij = bottleneck operation time of cell j producing product i (in seconds) Cj = 1, if cell j is utilized; otherwise, Cj = 0 Pik = processing time for process k producing product i (in seconds) Decision variables: Tij = takt time of cell j producing product i (in seconds) tij = required production time of cell j producing product i (in seconds) Obj = number of operators in block b of cell j Then, the cellular manufacturing manpower problem can be formulated as the objective function (1), the constraints (2)–(7) and the sign restriction (8).
j=1
b=1
Obj × Cj
(1)
Subject to Tij ≥ Lij
for i = 1, 2, ..., N;
N
t i=1 ij
U j=1
3.2. Determining manpower level Mathematical programming can be used to develop cellular manufacturing models that integrate such aspects of manufactur-
U B
MinZ =
Cj =
≤A
for j = 1, 2, ...., U
(2) (3)
1 × tij × A Tij
1, if
j = 1, 2, ..., U
N
t i=1 ij
≥ Di >0
0, otherwise
for i = 1, 2, ..., N
for j = 1, 2, ..., U
(4)
(5)
P.-S. Wang et al. / Journal of Manufacturing Systems 44 (2017) 255–269
Obj × Tij −
Obj =
Wbj
P k=1 ik
≥0
for i = 1, 2, ..., N; j = 1, 2, ..., U; b ∈ S (6)
⎧ 0, if Cj = 0 ⎪ ⎨ ⎪ ⎩
for j = 1, 2, ..., U
(7)
and for i = 1, 2, ..., N; j = 1, 2, ..., U
(8)
The objective function (1) is used to minimize the total number of operators. The value of B is equal to 2 for the present study. Constraint (2) indicates the takt time is greater than or equal to the bottleneck operation time. Constraint (3) indicates that the sum of production time cannot exceed the available operation time. Constraint (4) ensures that total outputs are greater than or equal to demand. In the case study, A = (21 working hours per day × 3600 s per h)=75,600 s per day. Constraint (5) indicates whether the cell j is used or not. When the production time in a cell is greater than zero, it is used for production. In other words, when its production time is zero, it is idle. Constraint (6) indicates the idle time is greater than or equal to zero for each block when the blocks belong to S. It is calculated by multiplying the number of operators by the takt time and subtracting the total processing time because one operator may be in charge of several workstations. Constraint (7) indicates that when a cell is idle, the number of operators is zero in the cell. And the number of operators equals the number of stations when the blocks do not belong to S. In contrast, when the blocks belong to S, the number of operators is at least one, and at most equal to the number of stations (see Fig. 5). Restriction (8) is the nonnegativity constraint.
3.3. Evaluating and decision-making The previous phase has determined the manpower level for various cell configurations. In this phase, we use two performance indicators, the total number of operators and the labor idle time ratio, to evaluate the performance of cells. The former indicator can be formulated in the objective function (1), and the latter can be formulated in Eq. (9).
Labor idle time ratio =
SN = −10log10
1 2 yi n n
(10)
i=1
1, 2, ..., Mbj , if b ∈ S
Tij , tij ≥ 0
“smaller the better” case which is used the present study, suppose there are n quality characteristics, so the SN ratio is defined as:
/S Mbj , if b ∈
259
W
bj Bb=1 Obj × Tij − k=1 Pik /Tij
Bb=1 Obj
(9)
To find the most robust cellular layouts, it is necessary to make performance measurements to evaluate the functional stability of the system. Taguchi’s signal-to-noise (SN) ratios were proposed for evaluating this sort of robustness. The SN ratio is a measure used in science and engineering that compares the level of a desired signal to the level of background noise [22,23]. Taguchi suggests choosing an appropriate SN to analyze the variation. These SN ratios are derived from the quadratic loss function. Three of them, nominal the best, larger the better and smaller the better, are widely applicable. “Nominal the best” is chosen if the objective is to decrease variability around a specific target. If the system is optimized when the response is as large as possible, “larger the better” is used; and conversely, “smaller the better” is used when the response should be as small as possible. Factor levels that maximize the appropriate SN ratio are optimal. In this problem, we use “smaller the better” because the objective is to minimize the total number of operators and the labor idle time ratio. SN ratios are expressed on a decibel scale. For this
where yi represents the response value of the i-th quality characteristic. In yi refers to the i-th value of observation for the two performance indicators mentioned above. Stochastic programming is mathematical programming dealing with optimization problems that involve uncertain parameters [24]. Uncertainty is usually characterized by a probability distribution of the parameters. Stochastic programming can solve customer stochastic demand; for example, Lin et al. [25] used a stochastic dynamic programming for multi-site capacity planning in TFTLCD manufacturing under demand uncertainty. When some of the parameters are random, then solutions and the optimal objective value to the optimization problem are themselves random. A distribution of optimal decisions generally cannot be implemented. This study mainly stresses comparing robustness between four proposed layout designs, rather than finding the best solution. In addition, complex mathematical modeling is much more difficult to implement because it requires more time and effort for firms to absorb that complex knowledge. Empirically, the current study uses the SN ratio to evaluate the robustness of layouts.
4. Empirical illustrations TFT-LCD applications encompass a variety of consumer electronics, including personal digital assistants (PDA), cellular phones, digital cameras, computers, notebook computers, flat panel televisions, and others. There are three main production sequences for TFT-LCD: the TFT array process, the cell process, and module assembly [26]. The TFT fabrication process sequence is a series of deposition and etching sequences, as in integrated circuit fabrication. Both the thin-film-transistor plate and color-filter plate are joined together in what is called a cell assembly process. The final step is module assembly, integrating the drive integrated circuit (IC) onto the substrate to drive the display and attach the backlight (BL) to the module. The display then undergoes a final test to complete the operation. Some relevant research solutions have been widely used in TFT-LCD manufacturing to enhance the production efficiency and reduce production cost [27,28]. To illustrate the effectiveness of the proposed approaches, a case study was adopted from a TFT-LCD module manufacturing firm in Tainan, Taiwan. For this case study, detailed module processes and their characteristics are shown in Table 2. In Table 2, most of the module processes are manpower operations, and only the aging test is an automated operation. Automation is much less flexible than manual lines and it is difficult to change. Therefore, the aging test and all its following tasks cannot be integrated into the cellular design. The back bezel cleaning has a very short processing time. In addition, both the lamp test and the PCB (print circuit board) soldering have a long task time. These three are also not suitable for assignment to the cellular design. Based on the results above, the cellular design includes five processes: lamp assembly, cable assembly, inspection, BL assembly, and LCM (liquid crystal display module) assembly. Two of these, BL and LCM assemblies, cannot allow labor from other stations to enter due to the requirement for cleanliness. The existing company-case layout is illustrated as Fig. 6. The cellular design consists of the five processes mentioned above, and an operator is assigned to exactly one process. In this situation, the current layout needs to be improved to reduce manpower and wastage
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Table 2 Production data for the case problem.
Fig. 6. Existing layout.
due to idle time. The proposed 3-phase approach is adopted to solve the problems. 4.1. Redesigning cellular layouts for the case problem In Fig. 6, the inspection process has a short processing time. If we combine the cable assembly process and the inspection process into a single process, the resulting cellular design will be as described in Fig. 7, where the parallel station layouts are divided into two sides, producing the same or similar parts according to customer demand. The process contains 12 identical cells and each cell has four workstations. The ratio of each station within a cell is 1:1:1:1. Due to the requirement of high cleanliness both in the BL and LCM assembly stations, operators from other stations are not permitted to enter these stations. In this situation, work elements cannot be
Table 3 Normalized workstation number ratio for the manufacturing of product 1. Product 1
Lamp
Cable & inspection
BL
LCM
Process time (s) The least integer ratio∼ =
99 1
163 + 60 2
225 2
121 1
redistributed among operators to adjust workloads according to the takt time when customer demand changes. To overcome shortcomings of the current cellular layout, different types of cell configurations can be established according to the normalized workstation number ratios for the production of the different product types. The company produces three main products, and the resulting normalized workstation number ratios are shown in Tables 3–5.
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261
Fig. 7. Current cellular layout design.
Fig. 8. Proposed cellular layout design for the production of product 1.
Table 4 Normalized workstation number ratio for the manufacturing of product 2.
Table 6 Processing time of the different layout configurations for product 1 (seconds).
Product 2
Lamp
Cable & inspection
BL
LCM
Layouts
Lamp
Cable & inspection
BL
LCM
Process time (s) The least integer ratio∼ =
89 1
132 + 58 2
195 2
162 2
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
99 99 99 50a
223 112 112 75
225 113 113 75
121 121 61 61
a
Table 5 Normalized workstation number ratio for the manufacturing of product 3.
99/250.
4.2. Determining manpower levels for the case problem
Product 3
Lamp
Cable & inspection
BL
LCM
Process time (s) The least integer ratio∼ =
134 2
152 + 58 3
220 3
153 2
Let Qij be the quantity of product i inside cell j. In the proposed layouts, the processing time of parallel stations for each process, the maximum output and the utilization for each cell is: Processing time =
Considering the size of the company building, we organize the cellular design for the production of products 1, 2 and 3 according to the normalized workstation number ratios to achieve a balanced system. The resulting layout designs are illustrated in Figs. 8–10. In Fig. 8, the proposed layout for product 1 has 6 cells and each cell has 6 stations, each including one lamp station, two cable and inspection stations, two BL stations, and one LCM station, in that sequence. In other words, the ratio of parallel stations is 1:2:2:1 in a cell. In Fig. 9, the proposed layout for product 2 has 6 cells and each cell has 7 stations. The ratio of parallel stations is 1:2:2:2. Finally, in Fig. 10, the proposed layout for product 3 has 4 cells and each cell has 10 stations. The ratio of parallel stations is 2:3:3:2.
Max Qij =
Pik Number of parallel stations
A Lij
Utilization =
(11) (12)
Qij Max Qij
(13)
Using Eq. (11), the processing time of parallel stations in each cell for product 1, product 2, and product 3 is summarized in Tables 6, 7, and 8, respectively. The company works 21 h a day. Assuming the workers are multiskilled, using Eq. (12), the maximum output of each cell for each product is tabulated in Table 9. The maximum output of each layout for each product is tabulated in Table 10.
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Fig. 9. Proposed cellular layout design for the production of product 2.
Fig. 10. Proposed cellular layout design for the production of product 3.
Table 7 Processing time of the different layout configurations for product 2 (seconds).
Table 10 Maximum daily throughput for each layout configuration (pieces).
Layouts
Lamp
Cable & inspection
BL
LCM
Layouts
Product 1
Product 2
Product 3
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
89 89 89 45
190 95 95 64
195 98 98 65
162 162 81 81
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
4032 3744 4014 4032a
4644 2796 4626 3732
4116 2964 4122 3924
a
1008 × 4 = 4032.
Table 8 Processing time of the different layout configurations for product 3 (seconds). Layouts
Lamp
Cable & inspection
BL
LCM
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
134 134 134 67
210 105 105 70
220 110 110 74
153 153 77 77
These results can be used to determine specific values for the mixed integer programming parameters. The proposed approaches
are performed using Microsoft Excel since it is readily available on the shop-floor of the case company. In Figs. 11 and 12, we used a scenario based on a ratio of 1:1:1:1 layout and the demands of 1190, 1155, and 1320 pieces for products 1, 2, and 3, respectively to illustrate the performance in Excel. Fig. 11 indicates the objective function and constraints formulated by Excel. Fig. 12 is an outcome output by Excel, including six parts, as follows:
Table 9 Maximum output per day in each cell for each product (pieces). Layouts
Product 1
Product 2
Product 3
Number of cell
1:1:1:1 1:2:2:1 1:2:2:2 2:3:3:2
336 624 669 1008a
387 466 771 933
343 494 687 981
12 6 6 4
a
(21 × 60 × 60)/75 = 1008.
P.-S. Wang et al. / Journal of Manufacturing Systems 44 (2017) 255–269
Fig. 11. Objective function and constraints formulated by Excel.
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Fig. 12. Cell production information and operator balance chart.
P.-S. Wang et al. / Journal of Manufacturing Systems 44 (2017) 255–269 Table 11 Different demand levels for the different product types (pieces).
265
48
Demand level
Product 1
Product 2
Product 3
High Medium Low
3500 2750 2000
3500 2500 1500
4000 3000 2500
46 44 42 40 38 36
1) Cell production information calculating interface: an interface for calculating cell production information. 2) Operator balance chart drawing interface: an interface for calculating information about OBC and then completing the drawing. Using Excel Visual Basic for Applications (VBA) to connect interfaces and by “recording Macros” or writing VBA Programming. Parts of the programming codes are shown as follows:
34 32 30
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
Fig. 13. Trend chart for staffing level analysis at the high demand level.
30 25 20 15 10 5 0
3) Cell production information diagram: The results of cell production information calculating, including the number of cells used in a layout, the number of operators in each cell, the total number of operators, idle time, labor idle time ratio, takt time, and others, provide managers with detailed production information. 4) Cell allocations: A chart showing the types of cell allocation, with the current display of a ratio of 1:1:1:1 layout design. 5) Operator balance chart information calculation for product 2: Calculation results include takt time, process time, idle time, labor idle time ratio, and others to provide managers with the production situations for each operator. 6) Operator balance chart for product 2: The results of OBC information calculation form an operator balance chart. 4.3. Evaluating and decision-making for the case problem
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
Fig. 14. Trend chart for labor idle time ratio analysis at the high demand level. Table 12 Different demand rates for the different product types (%). Level
Product 1
Product 2
Product 3
1 2 3 4 5 6 7
30 10 34 60 10 60 30
10 30 33 10 60 30 60
60 60 33 30 30 10 10
Table 13 Structured experimental design (pieces). Customer demand level
Layout decisions are important since they require substantial investment, involve long-term commitments, and have a significant impact on the cost and efficiency of short-term operations. In order to determine a robust layout design, customer demand levels are classified as high, medium and low for products 1, 2 and 3 in the case company, based on the historical sales data as shown in Table 11. Consider each of the three product types with three levels (high, medium, and low). The different product-mix scenarios are found to construct a structured experimental design with seven replications: (30, 10, 60), (10, 30, 60), (34, 33, 33), (60, 10, 30), (10, 60, 30) (60, 30, 10), (30, 60, 10) as shown in Table 12 which can represent boundary conditions for the possible product-mix scenarios. The center point is (34, 33, 33). Table 13 is the multiplication of Tables 11 and 12. For example, the multiplication of high demands from Table 11, row 1 and Table 12, row 1 is: (3500 × 30%, 3500 × 10%, 4000 × 60%) = (1050, 350, 2400) which is shown as Table 13, row 1, column 1. Using Eqs. (1)–(13) and Table 13, the total number of operators and the labor idle time ratio at the high, medium and low levels for evaluating the manpower performance and the robustness of
Demand rate
High
Medium
Low
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
(1050:350:2400) (350:1050:2400) (1190:1155:1320) (2100:350:1200) (350:2100:1200) (2100:1050:400) (1050:2100:400)
(825:250:1800) (275:750:1800) (935:825:990) (1650:250:900) (275:1500:900) (1650:750:300) (825:1500:300)
(600:150:1500) (250:450:1500) (680:495:825) (1200:150:750) (200:900:750) (1200:450:250) (600:900:250)
Note: (product 1:product 2:product 3).
demand changes can be computed, as shown in Tables 14–20 and Figs. 13–19. We discuss the respective managerial insights for the three demand levels as follows: 4.3.1. At the high level In Tables 14 and 15, the cellular layout with a 2:3:3:2 ratio is the most robust because it has a maximum SN ratio, which means the least variance, for the total number of operators and the labor idle time ratio. In contrast, the ratio 1:2:2:1 cannot meet customer demand. Compared with the current layout using a 1:1:1:1 ratio, the proposed layout with a ratio of 2:3:3:2 reduces 6.72 workers on
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Fig. 15. Detailed workers’ assignments for the example problem.
Table 14 Staffing level analysis at the high demand level (persons).
30
The current and proposed layouts Demand rate
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (persons) SN ratio
48 44 44a 44 40 44 40 43.43 −32.77
– – – – – – – – –
41 41 40 41 35 39 35 38.86 −31.81
38 39 37b 36 36 35 36 36.71 −31.30
a b
Refer to Fig. 12. Refer to Table 16.
15 10 5 0 Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
28
The current and proposed layouts Demand rate
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (%) SN ratio
27 21 26a 25 20 29 23 24.43 −27.82
– – – – – – – – –
15 16 18 18 11 20 12 15.71 −24.09
8 11 12b 9 11 11 15 11.00 −20.98
b
20
Fig. 17. Trend chart for labor idle time ratio analysis at the medium demand level.
Table 15 Labor idle time ratio analysis at the high demand level (%).
a
25
26 24 22 20 18 16
Refer to Fig. 12. Refer to Table 16.
38
Fig. 18. Trend chart for staffing analysis at the low demand level.
30
36
25
34 32
20
30
15
28
10
26
5
24 22
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
0
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7
Fig. 16. Trend chart for staffing level analysis at the medium demand level.
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Fig. 19. Trend chart for labor idle time ratio analysis at the low demand level.
4.28
The current and proposed layouts
94 27.44
12
0.69 0.91 1.59 1.09 100 95a 82 100 6.88 7.92 54.12 40.84
7 10b 20 11
Table 17 Staffing level analysis at the medium demand level (persons).
Demand rate
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (persons) SN ratio
36 36 32 36 32 32 32 33.71 −30.57
32 32 31 31 30 29 30 30.71 −29.75
33 33 28 33 28 28 27 30.00 −29.58
29 29 28 28 27 27 26 27.71 −28.86
1.00 0.38 0.00 0.00 1.38 18.91
81 81 99 81 26.89
10 9 8 10 37 cell 1 cell 2 cell 3 cell 4 Total Avg.
Note: Due to rounding, some totals may not correspond with the sum of the separate figures. a 182/1008 + 394/933 + 339/981 = 95. b 0.19 × 8.44 + 0.42 × 12.76 + 0.38 × 7.92 = 10.
0 394 761 0 1155 40.03 8.44 48.14 10.93
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (%) SN ratio
26 28 23 30 26 26 29 26.86 −28.61
17 19 20 19 13 14 17 17.00 −24.70
20 22 12 24 13 15 16 17.43 −25.08
9 11 12 10 13 12 13 11.43 −21.22
The current and proposed layouts
0.00 0.19 0.00 1.00 1.19 0 182 0 1008 1190
Demand rate
Table 19 Staffing level analysis at the low demand level (persons).
0.00 0.42 1.00 0.00 1.42
21.48 12.76 19.91 21.48
981 339 0 0 1320
77 86 195 121
The current and proposed layouts
5 4 3 5 17
5 5 5 5 20
Takt Time Output Block 2 Block 1 People /Cell
Product 1
267
Table 18 Labor idle time ratio analysis at the medium demand level (%).
111 81 161 75
Utilization Idle Time (%) ratio (%) Output Takt Time Output Production Idle Time Time ratio (%)
Product 2 Total idle manpower
Labor idle time ratio for the layout 37 Total number of operators
Table 16 Required staffing and production information for the example problem.
4.28 people
12 %
Production Idle Time Time ratio (%)
Product 3
Takt Time
demand
Production Idle Time Time ratio (%)
1155 Total 1190
1320
Product 2 Product 1
Product 3
People
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Demand level
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (persons) SN ratio
27 28 24 28 22 24 20 24.71 −27.92
26 26 22 22 21 21 20 22.57 −27.11
26 26 21 26 20 21 20 22.86 −27.24
25 25 24 25 19 19 18 22.14 −26.99
Table 20 Labor idle time ratio analysis at the low demand level (%). The current and proposed layouts Demand level
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 Level 7 Average (%) SN ratio
23 29 25 32 25 30 24 26.86 −28.64
19 13 15 11 20 18 18 16.29 −24.39
20 24 14 27 18 20 24 21.00 −26.60
17 21 25 24 13 12 16 18.29 −25.53
average, which means the required manpower is reduced by 15.47% and the labor idle time ratio is decreased by 13.43% on average. Similarly, comparing the proposed layout with a ratio of 1:2:2:2 to the current layout, the manpower is reduced by 4.57 workers or 10.52%, and the labor idle time ratio is reduced by 8.72%. Figs. 13 and 14 show that the proposed layout designs (except the one with the ratio of 1:2:2:1) consistently generate superior improvements in reducing the total number of operators and the labor idle time ratio to the current layout.
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Table 21 Summary of improvements by the proposed methodology. The proposed layouts Demand level
1:1:1:1
1:2:2:1
1:2:2:2
2:3:3:2
High Medium Low Average Improvement
43.43a , 24.43%b 33.71, 26.86% 24.71, 26.86% 33.95, 26.05% –
– 30.71, 17.00% 22.57, 16.29% – –
38.86, 15.71% 30.00, 17.43% 22.86, 21.00% 30.57, 18.05% 3.38, 8.00%
36.71, 11.00% 27.71, 11.43% 22.14, 18.29% 28.85, 13.57% 5.10, 12.48%
a b
Total number of operators. Labor idle time ratio.
To illustrate the detailed calculation of the required staffing level and production information, the example of level 3 demand rate, high demand level and 2:3:3:2 is adopted. For this example, the demands for products 1, 2 and 3 are 1190, 1155 and 1320 pieces, respectively. Then the required numbers of workers are determined by the proposed methodology, with the following results. Cell 1 has 10 workers (5 are in the adjustable zone; 5 are in the fixed zone), and at a takt time of 77 s the output of product 3 is 981 pieces per day. The labor idle time ratio in cell 1 is 7%. Cell 2 has 9 workers (4 are in the adjustable zone; 5 are in the fixed zone). At a takt time of 81 s for product 1, its output is 182 pieces in 0.19 day. Next, at a takt time of 81 s for product 2, its output is 394 pieces in 0.42 day. Finally, at a takt time of 86 s, the output of product 3 is 339 pieces in 0.38 day. In total, the labor idle time ratio in cell 2 is 10%. Cell 3 has 8 workers (3 are in the adjustable zone; 5 are in the fixed zone), and at a takt time of 99 s the output of product 2 is 761 pieces per day. The labor idle time ratio in cell 3 is 20%. Cell 4 has 10 workers (5 are in the adjustable zone; 5 are in the fixed zone), and at a takt time of 75 s the output of product 1 is 1008 pieces per day. The labor idle time ratio is 11% in cell 4. In sum, the total number of workers in this layout is 37. Its labor idle time ratio is 12% (about 4.28 people) and the utilization is about 94%. The results are shown in Excel form in Table 16, and Fig. 15 shows the detailed worker assignments. 4.3.2. At the medium level In Tables 17 and 18, a smaller average has a larger SN ratio in various layouts. The most robust cellular layout is 2:3:3:2 because it has a maximum SN ratio for the total number of operators and the labor idle time ratio. At the medium level, we still make respective comparisons of the total number of operators and the labor idle time ratio between the current and proposed layouts. The proposed layout with a ratio of 2:3:3:2 reduces the manpower by 6 workers or 17.79% and the labor idle time ratio is reduced by 15.43% on average. Likewise, the other two with the ratios of 1:2:2:1 and 1:2:2:2 both reduce manpower by 3 and 3.7 workers, respectively, indicating the improvements in manpower requirements of 8.89% and 11.01%. Their labor idle time ratios are decreased by 9.86% and 9.43%, respectively. Figs. 16 and 17 show that the proposed layout with the ratio of 2:3:3:2 consistently generates superior improvements in reducing the total number of operators and the labor idle time ratio in comparison to other layouts. 4.3.3. At the low level In Tables 19 and 20, 2:3:3:2 is still the most robust layout for the total number of operators as it is at the high and medium levels. Moreover, it is also the most robust for the labor idle time ratio at the two levels. However, at the low level, 1:2:2:1 becomes the most robust layout. Compared with the current layout, the proposed layout with the ratio of 2:3:3:2 reduces 2.57 workers on average, for a reduction in manpower by 10.4% and a decrease of 8.57% in the labor idle time ratio. Similarly, comparing the pro-
posed layouts with ratios of 1:2:2:1 and 1:2:2:2 to the current one, there are reductions in manpower of 2.14 and 1.85 workers, respectively, which means the required manpower is reduced by 8.66% and 7.49%. The labor idle time ratios are reduced by 10.57% and 5.86%, respectively. Figs. 18 and 19 show that the proposed layouts design consistently generates superior improvements in decreasing the total number of operators and the labor idle time ratio to the current layout. Table 21 is a summary comparing the current and proposed layouts according to the total number of operators and the labor idle time ratio at the high, medium and low levels. Since the layout with the ratio of 1:2:2:1 fails to meet customer demand at the high level, it is not a suitable layout design for the case company. Compared with the current layout, the proposed layout with the ratio of 2:3:3:2 reduces 5.1 workers on average, which means manpower need is reduced by 15.02% and the labor idle time ratio is reduced by 12.48%. Similarly, comparing the proposed layout with the ratio of 1:2:2:2 to the current one, the proposed layout reduces manpower by 3.38 workers or 9.96%. The labor idle time ratio is reduced by 8%. Since it is usually difficult to redesign a settled layout to accommodate the demand fluctuations, it is essential to select a robust design. If the case company allows only one layout, the proposed layout designs with the ratio of 2:3:3:2 will be the preferred choice.
5. Conclusions The benefit of Shojinka is well recognized and has been effectively implemented in the automobile industry. However, we are not aware of any literature that demonstrating the successful application of Shojinka in the TFT-LCD industry, which represents a cost-intensive industry. The present study proposes a MIP formulation to solve a Shojinka problem. A TFT-LCD module manufacturing is adopted for the empirical study to illustrate the effectiveness and efficiency of the proposed methodology. The results are promising. The empirical results show that, given a variety of demand scenarios, this method can reduce the average manpower and the average labor idle time ratio by 15.02% and 12.48%, respectively. The resulting layout design is easily implemented and is thus appropriate for practical applications. Since the present study assumes that only one layout design is selected as the final design, this constraint may be relaxed to include more than one design as the final layout design, such as a cell with 1:1:1:1 configuration and others with 1:2:2:1 or 1:2:2:2 together. In addition, block 2 includes the last two workstations (BL and LCM) that require a dedicated operator to attend each station for the present study. If this constraint can be relaxed due to future technology advancements, floating manpower between workstations 4 and 5 may be possible, which can further improve the benefit of the proposed Shojinka strategy. The present study further assumes that an operator is able to finish his/her own task independently; however, due to the growing work complexity; an
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