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Mixed-model assembly line sequencing at Hyundai Motor CompanyDuplaga, Edward A;Hahn, Chan K;Hur, DaesikProduction and Inventory Management Journal; Third Quarter 1996; 37, 3; ABI/INFORM Completepg. 20

MIXED-MODEL ASSEMBLY LINE SEQUENCING AT HYUNDAI MOTOR COMPANY

EDWARD A. DUPLAGA, PHD

CHAN K. HAHN, PHD

DAESIK HUR Department of Management, Bowling Green State University, Bowling Green, OH 43403

In many industries, competitive pressures and changing consumer preferences now require an in­creasing variety of products provided in a very cost­effective manner. This means that many repetitive manufacturers that used to offer mass-produced stan­dard items now have to produce more varieties using their current production facilities. The automobile in­dustry, where a large volume production requirement must be combined with widely varying small lot "make-to-order" requirements, is a good example of this type of situation. Many automobile companies have adopted mixed-model assembly lines to provide an increased flexibility while still maintaining most of the cost benefits inherent in high-volume, mass-pro­duction systems.

Although several mixed-model sequencing proce­dures have been developed and presented in the lit­erature, there are relatively few studies describing the actual use of these procedures by organizations. Therefore, the purpose of this article is to describe and illustrate the mixed-model sequencing approach used by Hyundai Motor Company.

MIXED-MODEL SEQUENCING PROBLEM

Each product assembled on the line tends to require a variety of parts, and these parts often vary from product to product. Therefore, the amount of work re­quired to assemble the individual products also varies, creating an uneven flow of work between the stations along the line. This situation may create two possible problems-a balance delay and a high in-process in­ventory level-due to uneven usage of parts.

In this article, we use the term "model" to refer to a generic product line such as the Accent. On the other hand, we use the term "model type" to indicate a spe­cific combination of components or options available within a model (e.g., an Accent with automatic trans­mission, anti-lock brakes, etc.).

Since the line is balanced on the basis of an average cycle time for the model, the ability of individual sta-

tions to keep up with the work is influenced by the order in which model types are sequenced on the line. For example, if several model types with relatively longer task times (i.e., more parts or options) are suc­cessively scheduled, a bottleneck will occur and, even­tually, line stoppages will result. Thus the sequencing of model types on the line has implications for the uti­lization of the line capacity. Okamura and Yamashina [ 7] discussed this issue and presented a heuristic pro­cedure for sequencing products on a mixed-modelline with the objective of minimizing line stoppages.

For mixed-model assembly lines operating in a JIT system, another commonly pursued objective for se­quencing products is to achieve a steady demand for component parts [1]. If the assembly line withdraws parts in a variable manner in regards to timing and quantity, then the feeding operations will have to plan for as much inventory, equipment and man­power as necessary to meet the peak in the variance of quantities demanded. This is a situation to be avoided under JIT. Monden [6] noted that a steady demand for component parts is essential to the pull system of JIT production. Thus, since the final assem­bly schedule determines the demand for component parts, the order in which model types are assembled has important inventory and capacity implications for the JIT system.

Currently at Hyundai, only one model is produced on a given assembly line. Therefore, Hyundai's mixed­model sequencing problem is defined as follows: Given a daily production requirement by model type, and assuming that line balancing has already oc­curred, determine the sequence of introducing model types to the assembly line so as to smooth the demand for component parts and minimize the balance delays.

Several procedures have been designed to develop an assembly sequence for a mixed-model assembly line [1, 3, 4, 7, 8]. Most of these procedures have the objective of either minimizing balance delays or smoothing the demand for component parts. In con­trast, the sequencing methods developed by Toyota

20 PRODUCTION AND INVENTORY MANAGEMENT JOURNAL-Third Quarter, 1996, ©APICS

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Sequence Position

FIGURE 1: 1300 cc engine usage

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Motor Corporation, as described by Monden [5], try to address both balance delay problems and part us­age issues in the assembly line sequencing situation. The mixed-model sequencing approach used by Hyundai Motor Company of Korea, presented in this article, is a modified version of Toyota's Goal Chasing Method I.

MIXED-MODEL SEQUENCING AT HYUNDAI

At Hyundai, the master production schedule is de­veloped in monthly time buckets on an eight-month rolling horizon basis. Hahn et al. [2] provide a detailed description of the master production scheduling pro­cess at Hyundai. The confirmed master production schedule for the next month is further broken down into weekly and daily production schedules. The fi­nalized daily and weekly schedules are distributed to each plant's production planning department, which then requests the data processing department to de­velop the daily sequencing lists for the assembly lines. The daily sequencing list specifies the order of vehicles to be assembled each day by the assembly line.

In the early days when production volumes and va­riety were relatively low, Hyundai used informal methods for developing the daily sequence list. These approaches were adequate when the situation was rel­atively simple with only a few options to consider. However, as the variety of options and possible com­binations increased, Hyundai realized the need for a

MIXED-MODEL ASSEMBLY LINE SEQUENCING

more formal and rigorous approach to sequencing ve­hicles on the line. Also, while earlier sequencing ap­proaches were more concerned with leveling the work load at each station on the line, as the company became more involved in JIT production methods, the focus shifted towards the impact of the final assembly se­quence on the rate of demand for component parts. These issues led to the development and adoption of a heuristic procedure-a simplified version of Goal Chasing I (GCl) used at Toyota (see [5]).

The basic logic behind Hyundai's heuristic proce­dure is that if the deviation between the expected use of components (based on a constant usage rate) and the actual use can be minimized, then the goals of min­imizing the balance delays and smoothing the part us­age rate can be realized. Smoothing or leveling the de­mand for component parts is essential to the pull system of JIT production. If the final assembly se­quence groups vehicles together which require com­mon components, then the corresponding demand for the components will be lumpy over the course of the day. For example, Figures 1 and 2 provide illustrations of relatively smooth consumption patterns and very non-smooth consumption patterns for two component parts.

A flowchart summarizing Hyundai's heuristic se­quencing approach is presented in Figure 3. Starting with the first position in the sequence list, an overall measure of deviation is computed for each model type,

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FIGURE 2: 1500 cc engine usage

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10 11 12 13

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Set ~k = ~k- 1 +S;•i for all j

No

No

Determine S;i and Compute Ri Set k = I and Xi<k-1) = 0 for all j

Compute Dil: for alii and Choose i • which minimizes remaining Dil:

A vehicle of model type i • is assigned to the kth position in the sequence

Model type i* is excluded from next sequence determination

FIGURE 3: Computational procedure for mixed-model sequencing at Hyundai

and the model type that has the smallest deviation measure is selected first. This procedure is repeated one position at a time until all scheduled vehicles are sequenced in the list.

More formally, Hyundai defines the overall mea­sure of deviation between the expected component or option usage rates and the actual usage rates for model type i, given k-1 vehicles have already been assigned to the sequence, as:

TABLE 1: Conditions Considered For Daily Sequencing List

Condition

1. Door Type:

2. Engine: 3. Trim Level: 4. Transmission (T/M): 5. Drive Type

6. Region: 7. Air Bag System: 8. Power Steering: 9. Anti-Lock Brakes

1 0. Rear Spoiler: 11 . Delivery Wrapping: 12. Driving Instruction

Vehicle:

where

Choices

3-Door (3DR), 4-Door (4DR), 5-Door (5DR)

1300 cc (13), 1500 cc (15) L, GL, GLS (most luxurious) Manual (5), Automatic (A4) Right-Hand Drive (RH), Left-Hand

Drive (LH) Domestic, Export Yes (0), No Yes (0), No Yes (0), No Yes (0), No California, Canada, Puerto Rico, Other Yes (0), No

Ri = average quantity of option j required per ve­hicle when averaged over the daily produc­tion schedule

= NJQ (Ni = total quantity of option j to pro­duce all Q vehicles in the daily production schedule)

Xi<k-l> =actual usage of option j for assembling the first k-1 vehicles in the sequence

S;i = required quantity of option j for assembling one unit of model type i.

The term (Xi<k-Jl + S;i) is equal to the actual usage of option j for assembling the first k vehicles in the se­quence if model type i is selected for the kth position in the sequence list. On the other hand, the determi­nation of the expected usage is based on achieving a constant speed in consuming each component. The ex­pected usage rate is an average units of that particular option per vehicle. For example, if 600 of a total 860 units of the Accent model to be produced for the day

TABLE 2: Sample Problem Data

Model Door Trim Drive Air Units Type Type Engine Level TIM Type Bag Required

A 4DR 13 GLS A4 LH 0 4 B 3DR 13 GLS 5 RH 0 2 c 3DR 13 GL A4 LH 3 D 4DR 15 L 5 LH 0 3 E 5DR 15 GL A4 RH

22 PRODUCTION AND INVENTORY MANAGEMENT JOURNAL- Third Quarter, 1996, ©APICS

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require an air conditioner, the average or expected us­age rate would be 600/860 or .70 unit per vehicle (i.e., R). Then, up to the kth vehicle in the sequence, k (0.70) represents the total expected usage of air conditioners. The expected usages for the other options are deter­mined in a similar manner.

While Toyota's Goal Chasing Method I computes the deviation Dik over all components feeding the line, Hyundai's modified version does not calculate the overall measure of deviation for the usage rates for all parts. For example, if a given option, such as transmission, has two possible choices (e.g. , manual or automatic), the calculation of Dik only includes one of the two option choices. Hyundai reasons that if each vehicle assembled requires a transmission, and attention is focused on leveling the usage of one type of transmission, then the usage rate of the other transmission type will automatically be leveled. Hyundai's approach was developed to provide a reasonable sequence that approximates the result produced by GC1 while reducing the computations required.

AN EXAMPLE OF THE APPROACH

The development of the daily sequencing list will be demonstrated with data drawn from one of Hyundai's Accent model lines. The Number-1 line in Plant 1 is used to produce a variety of the Accent model for ex­port markets. Currently, the line produces 860 units per day. At present, 12 different factors are considered when the daily sequencing list is developed for the line. These factors are summarized in Table 1. Taking into account that not all possible combinations of these factors are compatible, there are approximately 200

different types of Accent model cars that are available to the consumer.

We will illustrate the sequencing procedure with a simplified version of the actual problem. The sam­ple problem is summarized in Table 2. Let's assume that the assembly line is scheduled to produce 13 total units of five different types of the Accent model. Each model type has different option re­quirements. For example, type A is a 4-door model with a 1300 cc engine, automatic transmission, GLS trim, air bag, and steering wheel on the left side. The line is balanced based on a weighted average cycle time for the model types to be assembled, and each required option is installed at the specified work sta­tion on the line. Hyundai rebalances the line each time a new model type is introduced.

As previously stated, at each step of building the sequence, the model type that minimizes the overall measure of deviation is selected. To select the first ve­hicle in the sequence (k = 1), o il is calculated for each Accent model type. For example, if model type A is chosen as the first vehicle to assemble, then the devi­ations between the expected and actual usages of each option are calculated as:

1300 cc 3 Door 4 Door AutoT/M GLS Trim GL Trim Air Bag LH Drive

(kRj - xj<k- 1) - s ij) 1 X 9/ 13 - 0 - 1 = -4/13 1 X 5/ 13 - 0 - 0 = 5/ 13 1 X 7/ 13 - 0 - 1 = -6/ 13 1 X 8/ 13 - 0 - 1 = -5/13 1 X 6/ 13 - 0- 1 = -7/13 1 X 4/ 13 - 0 - 0 = 4/ 13 1 X 9/ 13 - 0- 1 = -4/13 1 X 10/13 - 0 - 1 = -3/ 13

The overall deviation for choosing model type A is

DAl = J(-4/13? + (5 / 13? + (-6/13? + (-5/13)2 + (-7 /13)2 + (4/13)2 + (-4/ 13)2 + (-3/13?

= 1.066

To measure the impact of model type B being selected as the first vehicle in the sequence, 0 81 is calculated using the same set of option choices from the previous calculation.

0 81 = J(-4/13)2 + (-8/13)2 + (7/13f + (8/13f + (-7/13)2 + (4 / 13)2 + (-4/13) 2 + (10/13)2

= 1.488

In a similar manner, we get the following results.

Dc1 = 1.462, 0 01 = 1.294, DE1 = 1.682

Since model type A generates the smallest measure

MIXED-MODEL ASSEMBLY LINE SEQUENCING

of overall deviation, model type A is selected as the vehicle in the first position of the daily sequence list. To select the second vehicle in the sequence (k = 2), Di2 is calculated for each Accent model type that re-

23

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TABLE 3: Result of Sequence on Component Usage

Model Type

3-Door 4-Door 5-Door 1300 cc Engine 1500 cc Engine L Trim GL Trim GLS Trim Automatic T /M Manual T/M RH Drive LH Drive Air Bag

A c D B A

quires more production. For example, since model type A requires three more units in the daily schedule (see Table 2), DA2 is calculated as follows.

1300 cc 3 Door 4 Door

(kRi- Xi<k- 1>- S;i) 2 X 9/13 - 1 - 1 = - 8/13 2 X 5/13 - 0 - 0 = 10/13 2 X 7/13 - 1 - 1 = -12/13

E D

Auto TIM GLS Trim GL Trim Air Bag LH Drive

c A B D c A

2 X 8/ 13 - 1 - 1 = -10/ 13 2 X 6/13 - 1 - 1 = -14/13 2 X 4/13 - 0 - 0 = 8/13 2 X 9/13 - 1 - 1 = - 8/ 13 2 X 10/13 - 1 - 1 = - 6/13

The overall deviation for choosing model type A is

DA2 = ) (-8/13)2 + (10/13)2 + (-12/13)2 + (-10/13)2 + (-14/13)2 + (8/13)2 + (-8 / 13)2 + (-6/13)2

= 2.132

Calculating D;2 for the other model types we get the following values.

D82 = 1.643, DC2 = 1.243,

D02 = 1.619, DE2 = 1.389

Therefore, since Dc2 is the smallest value, model type C is chosen for the second position in the sequence list.

The procedure is repeated until all vehicles to be scheduled are assigned to the sequence list. As a result, the complete sequence for this example will be:

A-C-D-B-A-E-D-C-A-B-D-C-A.

Table 3 illustrates the impact of the generated se­quence list on the usage rates of the different compo­nents or options for the sample problem. It shows that the usage rates for the different component or option items are fairly well balanced. The same information is presented in graphical form in Figures 1 and 2 for the engine options. As shown, the assembly sequence list generates a relatively steady demand for the two types of engines, but the sequence list does not allow a perfectly balanced mixed-model sequence.

Finally, Table 4 shows a portion of an actual se­quence list containing 860 Accent models to be pro­duced by the line for a given day after the sequencing procedure is completed considering all 12 factors cur­rently used by Hyundai (see Table 1). This list is used to specify the assembly sequence of vehicles.

CONCLUSION

By using the heuristic procedure, Hyundai gener­ates six daily sequence lists for all of its passenger car assembly lines. However, it must be pointed out that the sequence lists do not get executed exactly as orig­inally developed for a variety of reasons. In some cases, the painted body storage area may not have enough capacity to support the developed sequence list and may force changes to the sequence. Also, dur­ing the actual assembly operations, the sequence often must be changed at the line due to defective parts or the discovery of unacceptable workmanship. In fact, due to shop floor conditions, Hyundai may experience up to a 15% change in the sequence list.

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From Hyundai's point of view, its version of the mixed-model assembly sequencing procedure gener­ates a reasonable solution in a timely manner. By gen­erating reasonable mixed-model sequences in a timely manner, Hyundai's heuristic procedure provides a good compromise between the costs of developing a sequence and the benefits of a reasonable solution. As shown in Figures 1 and 2, the procedure would not generate a perfect solution where the usage rate is con­stant and the balance delay is zero. Because of the ever­changing shop floor conditions, it is impossible to find the optimum solution to the problem. However, the results have been adequate in the given situation where the conditions have been very complex and un­predictable. While the detail of the situation described in this article may be specific to Hyundai, its experi­ence can provide some general insights into the mixed­model sequencing problem for other organizations.

ACKNOWLEDGMENT

The authors wish to acknowledge the valuable com­ments and assistance of H. M. Koo, Executive Man­aging Director of Hyundai Motor Company.

REFERENCES

1. Ding, F., and L. Cheng. "An Effective Mixed-Model Assembly Line Sequencing Heuristic for Just-in-Time Production Sys­tems." Journal of Operations Management 11, no. 1 (1993): 45-50 .

2. Hahn, C. K., E. A. Duplaga, and K. Y. Kim. "Production/Sales Interface: MPS at Hyundai Motor." International Journal of Pro­duction Economics 37, no. 1 (1994): 5-17.

3. Kilbridge, M., and L. Wester. "The Assembly Line Model-Mix Sequencing Problem." Proceedings of the Third International Con­ference on Operations Research, Oslo. Paris: English University Press, 1963: 247.

4. Miltenburg, J. "Level Schedules for Mixed-Model Assembly Lines in Just-in-Time Production Systems." Management Science 35, no. 2 (1989): 192-207.

5. Monden, Y. Toyota Production System. Norcross, GA: Industrial Engineering and Management Press, 1983.

6. --. "What Makes the Toyota Production System Really Tick?" Industrial Engineering 13, no. 1 (1981 ): 36-46.

7. Okamura, K., and H. Yamashina. "A Heuristic Algorithm for the Assembly Line Model-Mix Sequencing Problem to Minimize the Risk of Stopping the Conveyor." International Journal of Produc­tion Research 17, no. 3 (1979): 233-247.

8. Thomopoulos, N. T. "Line Balancing-Sequencing for Mixed­Model Assembly." Management Science 14, no. 2 (1967): 59-75.

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About the Authors-

EDWARD A. DUPLAGA, PhD, is an assistant professor of operations management in the Department of Management at Bowling Green State University. He received his PhD from the University of Iowa. His research has focused on several aspects of manufacturing planning and control systems and has appeared in the Journal of Operations Management, International Journal of Production Research, and In­ternational Journal of Production Economics. Dr. Duplaga is a member of ASQC and the Toledo Chapter of APICS.

CHAN K. HAHN, PhD, is NAPM Professor, Owens-Illinois Professor, and Distinguished University Professor of Management at Bowling Green State University. He teaches in the areas of purchasing/ materials management, production planning/control, and manufacturing/materials strategy. His past and present leadership positions include associate editor of the Journal of Purchasing and Materials Man­agement, vice president of the Decision Sciences Institute, and Production/Operations Management Di­vision chair of the Academy of Management. He is also a past president of the Midwest Decision Sciences Institute. He received his MBA and PhD from The Ohio State University.

DAESIK HUR is currently pursuing his MBA at Bowling Green State University. He received his BA and MA in economics from Yonsei University, Korea. Mr. Hur worked as a research staff member of the corporate planning department of Hyundai Motor Company.

PRODUCTION AND INVENTORY MANAGEMENT JOURNAL-Third Quarter, 1996, ©APICS