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TSINGHUA SCIENCE AND TECHNOLOGY ISSN 1007-0214 14/18 pp589-595 Volume 9, Number 5, October 2004

Small Cell Layouts Based on Accounting Product Demand and Operating Sequences*

CHANG Jianfeng (常剑峰), ZHONG Yuexian (钟约先)**, HAN Zandong (韩赞东)

Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China

Abstract: Most current cell layout methods do not take into account the product demand and operating se-

quences, and may be too sophisticated for facilities with a relatively small number of products. A specific

method for designing small manufacturing cells was developed especially for the press production lines,

which is computationally simple, and yet considers product demand and the operating sequences. A simu-

lated application illustrates the robustness of the layouts to demand changes. The method uses simple rules

and database tools, so it is accessible to a wide range of facilities.

Key words: cells; press; demand; sequence; simulation

Introduction

Global competitiveness and market demand for rapid response have driven many firms to consider innova-tive approaches for better design and control of manu-facturing systems. Group technology (GT) can be used to enhance both flexibility and efficiency in today’s small-to-medium size lot production environment. In essence, GT attempts to decompose the manufacturing system into several manageable subsystems, which are also referred to as manufacturing cells.

However, the conversion from a process-based layout to a cell-based layout requires the generation and assessment of many possible cell configurations. Techniques are offered in the literature, but they are computationally demanding and not all methods take into account the product demand pattern or operating sequences[1,2]. Furthermore, they may require special analytical skills and facilities to be implemented. While the methods offered in the literature may be robust for very large facilities with many machines and products, these methods may be too sophisticated for

facilities with a much smaller number of products[3-8]. Furthermore, a cell-based layout that involves a large amount of intercell transfers is likely to be unstable with fluctuations in demand and operational performance. Other computational results suggest that cells involving long sequences of processes with little buffering between machines can reduce throughput considerably when dynamic manufacturing conditions occur[9,10].

A cell layout method is presented in this paper for manufacturing systems with a small number of prod-ucts and a small-to-medium size lot production envi-ronment. The method analyzes the product demand pattern and operating sequences to produce a cell-based layout.

1 Cell Layout Method

1.1 Data requirements

Making each product in the facility requires the following data:

• The sequence of machines through which the product passes during manufacture.

• Current or anticipated demand. The data may be arranged as shown in Table 1. In

this example, each product undergoes up to five

﹡

Received: 2003-12-31; revised: 2004-03-08 Supported by the “985” Fund of Tsinghua University.

﹡﹡ To whom correspondence should be addressed. E-mail: [email protected]; Tel: 86-10-62782127-1

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590 Tsinghua Science and Technology, October 2004, 9(5): 589–595

different operations, which may be performed on machines of type M1, M2, M3, M4, or M5. Each product is processed by no more than five machines. The sequence of operations undergone by each product is listed in the middle column, together with the demand for that product in the final column. Thus, product P0 has a total demand of 2000 pieces and is subject to five operations on machines M1, M2, and M3. Note that a product may require processing on the same type of machine more than once. The data in Table 1 is the original data set.

Table 1 Example product data

Part number Machine sequence Demand / piece

P0 M1 M2 M2 M2 M3 2000 P1 M1 M2 M2 4000 P2 M4 M5 M5 M5 5000 P3 M4 M5 M5 M5 M5 5000 P4 M4 M2 M2 M2 8000

1.2 Demand and sequence

To design the cells, two data sets should be generated from the original data set. One is the part order which describes the complexity of the machine sequence for each part, and the other is the sequence order which describes the part numbers associated with each exist-ing machine sequence. These two data sets can be eas-ily achieved by database operations.

The idea for determining the order depends on the desire of the plant designer, but generally, the parts and

sequences are ordered by demand, the length of ma-chine sequences, and other factors (such as the tonnage of the press), as shown in Tables 2 and 3.

Table 2 Part order data set

Part number Machine sequence Demand / piece

P4 M4 M2 M2 M2 8000 P3 M4 M5 M5 M5 M5 5000 P2 M4 M5 M5 M5 5000 P1 M1 M2 M2 4000 P0 M1 M2 M2 M2 M3 2000

Table 3 Sequence order data set

Machine sequence Part number Demand / piece

M4 M5 M5 M5 M5 P3, P2 10 000 M4 M2 M2 M2 P4 8000 M1 M2 M2 P1 4000 M1 M2 M2 M2 M3 P0 2000

Note that, the machine sequence for P2 is related to

that of P3, so P2 and P3 belong to the same sequence in Table 3.

1.3 Adaptability

The cell layout algorithm requires a matching table which compares two different types of machine se-quences quantificationally and gives suggestions on cell design changes.

Typical matching problems encountered in the cell layout procedure are classified and described in Table 4.

Table 4 Matching table

Example Description

Cell configuration Machine sequence New cell? Cell change Level

Equal M1 M2 M3 M1 M2 M3 No 1 Fully inclusive M1 M2 M2 M2 M2 M2 M2 No 2 Partly inclusive M1 M2 M2 M2 M2 M2 M2 M3 Maybe M1 M2 M2 M2 M3 3 Partly inclusive M1 M2 M2 M2 M2 M2 M3 Maybe M1 M2 M2 M2 M3 4 Partly inclusive M1 M2 M2 M2 M4 M2 M2 M2 Maybe Use other cell’s M4 5 Exclusive M1 M2 M2 M2 M4 M2 M2 M3 Yes 6

The first column in Table 4 describes the matching

result while the second and third columns give an ex-ample of a cell’s machine sequence and part sequence to be applied in the cell. The fourth column indicates whether a new cell is needed. If the fourth column is “maybe”, column five shows how the cell can be changed. The last column gives a quantitive measure-ment of the matching result. The level indicates how

well a cell matches a part, as determined by experience. These results are important in the design procedure since they greatly affect the layout. For example, Lev-els 3, 4, and 5 may indicate a change in the cell con-figuration. If the cell is not changed, Levels 3 and 4 will result in a machine being skipped and scheduling difficulties, while Level 5 will result in intercell trans-fers and scheduling difficulties.

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1.4 Cell design

The method is based on the construction of cell layouts based on a core of machines that satisfy the highest

demand and longest sequences. The method is suitable for the design of small cells of up to five or six ma-chines. A flowchart summarizing the algorithm logic is presented in Fig. 1.

Fig. 1 Cell design algorithm flow chart



The algorithm is based on a data set of unprocessed parts and sequences, which can be created in a data-base. The sequence data set is based on the data set of unprocessed parts. If a part is assigned to a cell, its state is “processed”; otherwise, it is “unprocessed”. In each circle in the algorithm, the state of some parts may change, so these two data sets should be re-created as the algorithm iterates.

The first sequence in the sequence list (such as Ta-ble 3) is selected and used to create a new cell. The first part in the part list (such as Table 2) is selected and compared with the current cell for compatibility. A matching table (such as Table 3) can be used to assess the adaptability. If the level is 1 or 2, they are matched and a check is made that the sequence satisfies con-straints on cell size, manufacturing capacity, and num-ber of movements. If the constraints are satisfied, this part is assigned to this cell and the state of this part is set to “processed” and the algorithm moves to the next part in the parts list. If they are not matched or the con-straints are not satisfied, a new cell is considered. The algorithm halts when all the parts are considered or the number of allowed cells is exceeded.

If the data set of unprocessed parts is empty, this ini-tial layout is the final layout; if not, the following steps are completed to design the final layout:

1) Start with the initial layout from the algorithm. 2) Sort the unprocessed part by complexity, select

the first one as the current part. 3) For each layout in the set of layouts, perform the

following steps: a) Calculate the level of adaptability of the part

and each cell (the level must be 3, 4, or 5); b) Select the cell with minimal level (may be

more than one selection); c) Refer to the matching table to change the cell.

One or several new layouts will be generated which are added to the set of layouts and remove the original layout (new cells can no longer be created).

4) Select the next part from the set of parts and re-peat from Step 3 until all parts are processed.

The result is a series of layouts with design assess-ment criteria used to choose the best or most suitable layout.

1.5 Design assessment criteria

The cell design criteria used in this paper are:

1) number of machines in each cell; 2) demand met; 3) demand which skips machines; 4) demand which makes one inter-cell transfer; 5) demand which makes more than one inter-cell transfer; 6) expense of purchasing and installing machines; 7) robustness to demand changes; and 8) scheduling feasibility.

These designs assume that a product cannot use a machine in the cell twice, i.e., it cannot use one ma-chine for two operations. So, the M1M1M2 sequence requires two M1 machines.

Criteria 1)-6) can be continued in a database while Criteria 7)-8) require a simulation as discussed in the next section.

2 Application to Press Production Lines

The method described in Section 1 was developed to assist a press shop that had to relocate its operations. In the original location, the facility had been arranged as a process-based layout, with all the machines of a par-ticular type located close together on the shop-floor. The new location had space for only 20 machines, bro-ken up into three or four small areas. About 200 differ-ent parts were made on these machines.

Under the process-based layout, the facility suffered from poor schedule adherence and unpredictable per-formance. The need to move to a new location pro-vided an opportunity to simplify and improve facility operations. The requirement of non-interrupted proc-essing for each product, the space constraints, and the need to simplify the facility operations created the pos-sibility of changing to a cell-based layout.

Seven different kinds of machines were included in the analysis. The products needed operations on be-tween one and six machines. These operations could be on more than one machine of the same type.

The cell design process consisted of three stages: 1) Static analysis to select the pool of 20 machines

which would make up the cells; 2) Design of manufacturing cells from the 20 ma-

chines; and 3) Dynamic simulation of the proposed new layout

to identify any outstanding problems and test the ro-bustness to system change.



2.1 Static analysis

The data required for the static analysis were: 1) The level of operating efficiency; 2) Production rates on each machine and setup times; and 3) Machine types required by each product.

The outcome of the static analysis is: 1) Selection of 20 machines to the basis for the cells design; 2) Rejec-tion of a small number of products which would re-quire under-utilized machines; and 3) Choice of cycle length and batch sizes.

2.2 Cell design

After choosing the set of machines, the next step was to decide how to group them into cells. The data con-sisted of the sequence of presses and the total demand for each of the 200 parts. A database tool (SQLserver 2000) was used to group the data into 58 different se-quences of presses. This is a considerable reduction on the number of possible ways of selecting between one and seven machines from a set of 20.

The 20 machines had to be arranged as short cells with no more than five machines in each cell. The initial stage of each design was to generate cells or partial cells to satisfy the very high demand sequences. Once these core presses had been arranged, the remaining presses were then accommodated around them. This was done to maximize the demand that

could be processed without intercell transfers or skipping presses in a line. Two cell designs were chosen for final comparison (shown in Fig. 2).

Fig. 2 Cell layouts

These designs were compared according to the ex-tent to which presses had to be skipped, or the products had to be moved from one cell to another. Table 5 shows part of the assessment of the two designs. The statistics of Table 5 suggest that Design B is better than Design A. More comparisons were performed to test the robustness.

Table 5 Comparison of two cell designs

Number of movements/piece Operation number Design A Design B

Sequence Demand /piece

0 1 2 3 0 1 2 3 1 M4 1000 1000 1000 M6 400 400 400

2 M1 M4 2000 2000 2000 M1 M6 400 400 400 M2 M6 400 400 400 M3 M6 100 100 100

3 M1 M4 M4 700 700 700 M2 M6 M6 500 500 500 M2 M6 M7 200 200 200 M2 M6 M5 100 100 100

M7 M5 M5 100 100 100 4 M6 M6 M6 M6 1500 1500 1500

M7 M7 M7 M7 2000 2000 2000 M3 M7 M7 M4 400 400 400 M1 M4 M4 M4 100 100 100 M3 M6 M6 M6 100 100 100 M7 M5 M5 M5 100 100 100

M4 M4 M4 M4 100 100 100



(Continued)

Number of movements/piece Design A Design B Operation number Sequence Demand /piece

0 1 2 3 0 1 2 3 5 M2 M7 M7 M7 M7 2000 2000 2000

M3 M7 M7 M7 M7 400 400 400

M6 M6 M6 M5 M5 100 100 100 Total 12 700 10 800 1400 100 400 11 200 1200 0 300 (%) 100 85.0 11.0 0.8 3.2 88.2 9.4 0 2.4

The column headings under Design A and Design B mean: 0, sequence is satisfied exactly by plan; 1, parts will have to skip presses in the line or swap cells once; 2, parts will have to skip presses or swap cells twice; and 3, not enough presses to meet the demand without passing through a machine more than once.

2.3 Robustness to demand changes

Two more investigations were performed to analyze the designs: robustness to plan changes in demand and in scheduling feasibility.

Since the demand for products would change over the next few years, the best design should not require too many alterations to the cell layouts in response to these changes. Since most changes would be declining products, they would not justify the expense of further re-configurations of the factory layout.

The cell designs were compared for robustness to planned changes in demand over the next three years by changing the demand patterns.

The much reduced number of sequences was then compared with the planned demand patterns to calcu-late how many intercell transfers would be needed for the two designs in the future. The comparison shows that future demand patterns do not require drastic overhauls of either cell design, with a reduced configu-ration based on the existing cells matching the needs.

The discrete event simulation tool of Quest was used for simulations of cell designs to test the demand levels for feasibility. The simulations included the possibility of products beginning processing part-way through a cell, rather than at the beginning. Intercell transfers were also accommodated, permitting products to leave a cell and possibly reenter into it. The complexity of programming these possibilities into the simulation re-flects the complexity of scheduling these possibilities in real press shops.

The simulations were extended to cope with the fol-lowing variabilities in processing:

1) Setup times: ±10% or ±20%; 2) Press stamping rates: ±5%, ±10%, or ±15%; 3) Batch size changes: 5-week or 6.5-week period;

4) Slight job order change. Increasing the press stamping rate by 5% is the same

as a demand decreasing by 5%. Figure 3 shows a simu-lation result, that indicates Design A load factor in-creases more than that of Design B as demand expands (or the press stamping decreases). Therefore, Design B is more reliable as demand (or press stamping rate) changes.

Fig. 3 Effect of demand fluctuation on load factor

The simulations clearly demonstrate the feasibility of the proposed designs and confirm the importance of maintaining the expected press stamping rates.

3 Conclusions

This paper proposes a practical cell-based design method, designed especially for small cells. The method was used with a case study to generate two cell designs that were compared based on their ability to meet the demand and scheduling. The comparison in-vestigated future planned demand and scheduling fea-sibility using simulation techniques. The results show that the method can be used to select the cell layout based on the expected future demand. The method uses simple rules and database tools, so it is accessible to a wide range of facilities.



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