discrete event simulation modeling to improve productivity

CIE42 Proceedings, 16-18 July 2012, Cape Town, South Africa © 2012 CIE & SAIIE

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DISCRETE EVENT SIMULATION MODELING TO IMPROVE PRODUCTIVITY ON AN AUTOMOTIVE PRODUCTION LINE

JM Baraka1, A.K. Naicker2* and R.Singh3 1Department of Industrial Engineering

Durban University of Technology, South Africa

[email protected] 2Department of Industrial Engineering


[email protected] 3Department of Industrial Engineering


[email protected]

ABSTRACT

Discrete event simulation modelling is a useful tool in helping decision makers in making operative, tactical and strategic decisions. The quick evaluation of possible scenarios in order to identify potential areas of improvement in a system is a pre-requisite in a globally competitive manufacturing environment. In instances were simulation technology is not used the critical evaluation of complex production lines becomes a time-consuming and tedious exercise.

The work presented in this case study is the development of a simulation model of a complex manufacturing automotive production line. “SIMUL8” was the discrete event simulation modelling software used in the case study presented. Systematic analysis was conducted on the simulation model in order to identify areas of in-efficiency related to labour utilization and production capacity. From the analysis it was established that improvements could be made to labour utilization through a re-distribution of work content as well as production capacity through the elimination of work stoppages and delays.


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1 INTRODUCTION

In recent years there has been a strong drive by parent automotive manufacturers to transform the focus of their local operations in South Africa to concentrate more on manufacturing for the global market [1]. The automotive sector has been continuously striving to increase efficiencies and improve productivity. Lean manufacturing, six-sigma, TQM and benchmarking are just some of the techniques and methodologies being employed in this regard. Originally, assembly lines were developed for a cost efficient mass production of standardized products, designed to exploit a high specialization of labour and the associated learning effects [9].

There’s much to understand, about factory simulation software and its capabilities, which include how to determine plant capacity, balance manufacturing lines, manage bottlenecks, solve inventory and work-in-process problems, test new scheduling practices, justify capital expenditures, optimize production rates, [4]. According to [3], businesses today can make use of simulation modelling to improve business performance. Simulation modelling software has reached a technological level that enables easy user interaction and provides a high degree of flexibility and integration.

Therefore, the aim of this study was to use a discrete event simulation package to reduce the wastes that occur in a manufacturing line, within the local automotive sector, and implement an Assembly Line Balancing (ALB) model appropriate in terms of areas of in-efficiency related to labour utilization and production capacity.

Under the term assembly line balancing (ALB) various optimization models have been introduced and discussed in the literature, which are aimed at supporting the decision maker in configuring efficient assembly systems [10].

This paper highlights a systematic approach to the evaluation and modification of a complex assembly system with many processes and resources. Iteratively, processes and resources that affect system performance were identified one at a time, and the system was modified accordingly until satisfactory performance was obtained [6].

2 BACKGROUND

Businesses use Simulation to refine the underlying business processes and to exploit the latest technologies for better performance in order to remain competitive [3]. Simulation is the construction and use of a computer-based representation, or model, of some part of the real world as a substitute vehicle for experimentation and behaviour prediction [2]. A standard motor manufacturing plant is composed of various departments such as welding, painting, assembling, logistics etc. In order to identify areas of in-efficiency in such a system a careful analysis needs to be undertaken of all subsystems from a holistic perspective. The manufacturing line under discussion in this paper is composed of Carbon Dioxide (CO2) Welding, Quality checking, De-burring and Fitment (Flap and Bumper), Door Fitment (rear and front), Deck, Hood and Fender Fitment processes and also the Final setting process. These processes are conducted on a moving assembly conveyor line with a line speed of 29.6 sec per meter. The line produces on average 3 300 units a month and 825 units a week using 14 operators with each process time ranging from 98 seconds to 159.68 seconds (Negative exponential distribution) per station. Using finite sources in a single channel and multiple phase line [5], one car shell is introduced in the line every 163.2 seconds.

This assembly line process is illustrated as a flow diagram of the existing line layout given as Figure 1.


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CO2RH & LH

QUALITYDE-BURR FUEL

FLAP &BUMPER LH & RH

DOORS RH & LH

HOOD FENDERS RH & LH FINAL SETTING

RH & LH

DECK10

REAR DOORS (10)FRONT DOORS (10)

ROOP SIDE RAIL (50)

DECK HOODRH & LH

Figure 1: Flow Diagram Of The Existing Layout Of The Assembly Line

After successfully building and running the Simulation model, areas of deficiency and waste on the line was revealed and highlighted.

3 PROBLEM DEFINITION

As mentioned in our background of the paper, this assembly line is consisted of 8 work stations (denoted as k) arranged along a conveyor belt of 44 meters (5.5 meters per station). The work pieces (jobs) are consecutively launched down the line and are moved from station to station. At each station, certain operations are performed in both side of the body shell within the given cycle time of 163.2 seconds.

Manufacturing a product on an assembly line requires partitioning the total amount of work into a set 𝑽𝑽 = 𝟏𝟏,… ,𝒏𝒏 of elementary operations named tasks. Performing a task (j) takes a task time 𝒕𝒕𝒋𝒋 and requires certain equipment, additional parts and/or skills of workers. The total work load necessary for assembling a work piece at a particular work station (k) is measured by the sum of task times( 𝒕𝒕𝒔𝒔𝒔𝒔𝒔𝒔). Due to technological and organizational conditions, precedence constraints between the tasks have to be observed [10]. After collection of all the data required, the challenge rising is the redistribution of elements at each workstation (k) in order to maximize the line efficiency and the operators accessibility to the equipment.

The Assembly Line Balancing Problem (ALBP) in the paper is aimed at finding a feasible line balance, a proper assignment of tasks in each station and an optimization of operator’s services leading to maximization of the company revenue. The station time, 𝑡𝑡 𝑠𝑠 is computed as:

𝑡𝑡 𝑠𝑠 = 𝑡𝑡∈

(1)

where 𝑆𝑆 is a set of tasks assigned to a station (k) and constitutes the station work load. Equation 1 was used to determine the cycle times for each station as shown below in Table 1.


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Table 1: Summary Of Process/Work-Centre Data

Station number

(k) Process Cycle time 𝑡𝑡 𝑠𝑠 (sec) № of operators

1 LHS Co2 Welding 98.31 1 RHS Co2 Welding 126.07 1

2 Quality Checking 163 3

3 LHS De-burr and Fitment 134.16 1 RHS De-burr and Fitment 153.43 1

4 LHS Door Fitment 159.68 1 RHS Door Fitment 159.94 1

5 Deck Fitment 159.64 1

6 Hood Fitment and Quality inspection*

85.89 30.84

1

7

LHS Fender Fitment Quality inspection*

90 22

1

RHS Fender Fitment Quality inspection*

87 22

1

8 Final setting 163 1

* These quality inspections are carried out by the operators from workstation 2

Figure 2: Slat Line Operation Time & The Inspection Time

The cycle time c = 163.2 seconds is fixed due to its dependence on the conveyor movement. Knowing that the line balance is feasible only if the station time of neither station exceeds c. In case of 𝑡𝑡 𝑆𝑆 < 𝑐𝑐, the station k has an idle time of 𝑐𝑐 − 𝑡𝑡 𝑆𝑆 time units in each cycle. Therefore, With c = 163.2 sec and k = 8 stations, no station will be able to combine with

98.31 126.07 134.16 153.43 159.68 159.94 159.64

85.89 90 87

0

0 0 0 0 0 0

30.84 22 22

LHS CO2 RHS CO2 LHS DE-‐BURR & FUEL FLAP

RHS DE-‐BURR & BUMPER

LHS HINGES & DOORS

RHS HINGES & DOORS

DECK HOOD LHS FENDER PROCESS

RHS FENDER PROCESS

CYCLE TIME(SEC) INSPECTION/POLYNET


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another station in order to save on labour and facility costs except Station 6 and Station 7 which stand respectively for the DECK and the HOOD:

S1 = {1}, S2 = {2}, S3 = {3}, S4 = {4}, S5 = {5}, S6 = {6, 7} and S7 = {8}.

Because of the long-term effect of balancing decisions, the used objectives have to be carefully chosen considering the strategic goals of the enterprise. A usual surrogate objective consists in maximizing the line utilization which is measured by the line efficiency E as the productive fraction of the line’s total operating time and directly depends on the cycle time c and the number of stations k. In the simplest case, the line efficiency is defined as follows [10]:

𝑬𝑬 = 𝒕𝒕𝒔𝒔𝒔𝒔𝒔𝒔𝒌𝒌 ×𝑪𝑪

(2)

The basic problem described so far is called a simple assembly line balancing problem (SALBP) in the literature [12]. Four versions are defined by using different objectives [13]. : SALBP-E maximizes the line efficiency E, SALBP-1 minimizes the number k of stations given the cycle time c, SALBP-2 minimizes c given k, while SALBP-F seeks for a feasible solution given k and c.

4 PRINCIPLE MODEL FEATURES

In developing the model a series of assumptions and model parameters were developed. These included:

Work-centre distributions were based on historical observed data.

Resource breakdown times included repair times.

The supply of stock to the manufacturing line such as bolts, nuts, hinges etc. was taken as being unlimited and available for use through the study.

The manufacturing line works for 2 shifts, 16 hours per day. Day shift start 07:00 are to 15:30pm and night shift start 20:00pm to 4:00am.

The assembly conveyor under consideration is 44 meters (5.5 meters per station) long and runs at a speed of 29.6 seconds per meter matching the cycle time of the line.

The Overall performance ratio (OPR) of the production line was taken as 90%

The variance is proportional to the processing time [6].

The line is composed of 11 operators and 3 inspection operators. The Co2 Welding, De-burr and Fitment, Door Fitment, Fender Fitment process have two operators in each side of the shell (Left Hand side and Right Hand Side). Deck and Hood in the other hand has only one operator each allowing the operation to run for the entire shift.

5 MODEL ANALYSIS

5.1 Current Scenario

In developing the model an inter-arrival rate of 2.72 minutes (163.2 seconds) was established for a motor vehicle shell to enter the system. Parts replenishments took place on trolleys with a batch size of 10 units. The model used a routing - in function for part collection. At the end of a shift the line stops and a change over occurs before the next shift begins. The simulation model shown in Figure 3 was run for 19200 time units to ensure for reliable outcome.


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Figure 3: Current Simulation Model The results yielded from the simulation ran are tabulated in Table 2 given below

Table 2: Results From The Current Scenario From Current Simulation Model

Operation Time stopped (%) Utilization (%) Time Waiting (%)

Welding (CO2) 11.26 25.81 4.48

Quality Check 9.94 37.3 5.06

De-burring 1 9.71 30.82 12.35

Door Fitment 9.78 25.67 13.84

Deck Fitment 11.43 28.46 18.83

Hood Fitment 9.24 26.88 35.20

Fender Fitment 11.24 31.45 38.37

Final Setting 9.63 27.37 34.82

Total 82.23 221.63 162.94

The most utilised operation in the line is Quality checking (37.30%). By adding the stoppage time of all operation and adding in the entire waiting time one would note that the biggest problem in the line is the waiting time (162.94) followed by the stoppage time (82.23).


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Figure 4: Current Performance Measure From Table 2

The graph reveals that the Fender Fitment is the operation with the highest “Time Waiting” and lowest “Time Working” while Quality Check is the Operation with the lowest “Time Waiting” and the highest “Working Time”

Imbalances in the system resulted in the time waiting growing proportionately larger as progress was made through the system.

5.2 Improvement Scenario

Knowing that the simulation approach enabled inexpensive analyses of the proposed system, from which necessary modifications could be made to improve its performance before actually implementing it [6]. First, the imbalance in the line between operations needed to be critically analysed and investigated. Upon further investigation it was found that the Hood and Fender processes had inspection processes built into the cycle times.

To be able to meet the customer requirement and to improve the line performance measure, the assembly line needs to apply a new model.

Knowing that Mixed model lines are typically used to accomplish the final assembly of automobiles, small and large trucks, we used this model to determine the number of workers and other operating parameters, also to balance the line accurately [7].

5.2.1 Determining The Number Of Workers On The Line

Using the data from Table 1, to determine number of worker required for a mixed model assembly line, we will use the following equation [7]:

𝑤𝑤 = (3)

Where w = number of workers;

𝑊𝑊𝑊𝑊 = workload to be accomplished by the workers in the scheduled time period (min);

𝐴𝐴𝐴𝐴 = available time per worker (min/worker), where

𝑊𝑊𝑊𝑊 = 𝑅𝑅 𝑇𝑇 (4)

25.81 37.3 32.31 36.63 35.95 26.88 25.19 37.37

11.26 9.94 9.71 9.78 11.43

9.24 11.24 9.63

4.48 5.06 12.35 13.84 18.83 35.2 38.37 34.82

CURRENT PERFORMANCE MEASURE Time Working Time stopped Time Waiting


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Where Rpj = production rate of model j (vehicles/day) and Twcj = work content time of model j (min/vehicle).

In mixed model assembly line balancing, the total work element times per shift were used. The objective function can be expressed as:

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑤𝑤 𝐴𝐴𝐴𝐴 −𝑊𝑊𝑊𝑊 𝑜𝑜𝑜𝑜 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐴𝐴𝐴𝐴 − 𝑇𝑇𝑇𝑇 (5)

Total time per work element (k) is given by

𝑇𝑇𝑇𝑇 = 𝑅𝑅 𝑇𝑇 (6)

Where 𝑇𝑇𝑇𝑇 = Total time every work station is expected to complete the task.

Tejk = time to perform work element (k) on vehicle j

Total service times at each station are computed as:

𝑇𝑇𝑇𝑇 = 𝑇𝑇𝑇𝑇∈ (7)

Where TTsi =total service time at station i.

Work flow dynamic behaviour in manual assembly systems are affected by many factors, the two common ones are the average utilization and the number of tasks per job and work-in-process inventory levels [8]. Basic terminology regarding queuing systems for both infinite and finite source has been adequately covered [5]. This includes characteristics of waiting lines, arrival and service patterns, queue discipline and measures of waiting line performance. System utilization is calculated as:

𝜌𝜌 = (8)

Where 𝜆𝜆= vehicle assembly rate; M=number of servers; 𝜇𝜇 = service rate per server.

The average time a vehicle is in the assembly line, 𝑊𝑊 is given as:

𝑊𝑊 = 𝑊𝑊 + (9)

Where 𝑊𝑊 = average time a vehicle waits in the queue.

5.2.2 Mixed Model Line Balancing

The objective in mixed model line balancing is to spread the workload amongst stations as evenly as possible [7]. In order to reduce the difficulties in sequence planning, the line balancing can, for instance, seek to minimize variances in station times over all models, known as horizontal balancing [14]. After using equation (3) to find the total number of operators, the time per element, Total service times at each station, the average time a vehicle waits in the queue and the system utilization, a simulation model as shown in Figure 5 was run taking to account the improvements discovered.


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Figure 5: Model showing combination of two processes

The combined operation of the hood and fender assembly resulted in a reduction of the labour work content on the assembly line (i.e. a reduction in labour of 2 people). They were in turn relocated to another department where a need was identified. Figure 6 illustrates the comparison of the operator utilisation between the original and the revised scenarios as discussed above.

Figure 6: Comparative graph of operator utilisation (current & improved models)

Table 3 illustrates the results from the model after the changes were made. Most significant was the decrease in the time that work-centres spent waiting in the system. This also translated into an increase in the production capacity of the line.


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Table 3: Results From The Improved Scenario

Operation Time stoppage (%) Utilization (%) Time Waiting (%)

Welding (CO2) 11.26 26.83 4.93

Quality Check 9.94 38.76 5.05

De-burring 1 9.71 33.58 13.45

Door Fitment 9.78 38.09 15.07

Deck Fitment 11.43 37.39 20.07

Hood and Fender Fitment 11.24 31.91 31.23

Final Setting 9.63 38.89 31.83

total 72.99 245.45 121.63

Figure 6: Improved Performance Measure

Looking at the above figure(table 3), one would note that by combining the Hood to the LHS Fender and to the Hood to the RHS Fender the time waiting decreases from 162.94 in total to 121.63, same as the stoppage from 82.23 to 72.99.

As shown in Figure 6, there is clear improved in Utilization (From 221.63 to 245.45).

26.83 38.76 33.58 38.09 37.39 31.91 38.89 11.26

9.94 9.71 9.78 11.43 11.24 9.63 4.93 5.05 13.45 15.07 20.07 31.23 31.83

IMPROVED PERFORMANCE MEASURE Time Working Time stopped Time Wai�ng


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Figure 7: Current &Improved Process

6 CONCLUSION

The use of simulation software to improve competitiveness in the motor industry has far reaching consequences [1]. The final assembly of cars are mainly carried out on paced mixed-model lines using manual labor [15]. The study provided in this paper illustrates the potential of the software in analysing complex manufacturing systems. After simulation, the Balance of automobile assembly lines needs to deal with a number of peculiarities, most of which are related to the fact that work pieces are comparatively large. As a consequence, a station can often be subdivided into parallel workplaces, where operators work simultaneously on the same work piece [16].The proposed framework will allow the company to improve the current weak points and bottlenecks in the manufacturing line under observation. The net effect of the improvement as suggested in this case study resulted in an overall increase of 4% in the production capacity without considering the lack of motivation and the low level of satisfaction, which is typically caused by the high Repetitiveness of elementary operations [9]. The decreased time waiting and the improvement in the labour utilisation yielded further improvements in terms of labour utilisation.

7 REFERENCES

[1] Ndamase, NM. & Steyn, JL., 2011. Technology transfer competitiveness in the automotive industry: Case Study of parts suppliers for Toyota SA Motors.

[2] HoHoeks, BW.1994. The impact of simulation in manufacturing decision making.

[3] Murphy, CA. and Perera, 2002. Simulation Practices and Theory 9, 273–291.

[4] Gould, Lawrence S, Jan 2002. Simulation: The real story. AutomotiveDesign&Production, Jan 2002, 114 (1); pp. 44.

[5] William J.Stevenson, 2005. Operation management,𝟖𝟖𝒓𝒓𝒓𝒓 Edition, pp 779-813.

[6] J Y H Fuh*, Y S Wong *, C Y Yee*, L Zhuang’ and K S Neo*, 1996. Computer integrated Manufacturing systems, 9(1), pp 19-31.

[7] Mikell P.Groover,2008. Automation, Production systems and computer- integrated Manufacturing, 3rd Edition, pp 438-457.

109.38 89.92 85.89

21.81 17.13 41.29

163 163 163

YAMAZUMI - CURRENT

Inspection Production

Takt Time

55.39 36.16

87 90

163 163

YAMAZUMI -‐ IMPROVEMENT

Hood Process Fender Process

Takt Time


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[8] Enns, S. T. 1998. Work flow analysis using queuing decomposition models. Computers & Industrial Engineering 34(2): pp 371-383.

[9] Shtub, A., Dar-El, E.M.1989. A methodology for the selection of assembly systems. International Journal of Production Research 27(1), pp 175–186.

[10] Nils Boysen, Malte Fliedner, Armin Scholl, 2006. Assembly line balancing: Which model to use when?

[11] Salveson, M.E. 1955. The assembly line balancing problem. The Journal of Industrial Engineering, 6 (3), pp 18–25.

[12] Baybars, I., 1986. A survey of exact algorithms for the simple assembly line balancing problem. Management Science 32(1), pp 909–932.

[13] Scholl, A., 1999. Balancing and sequencing assembly lines, 2 Edition, Physica, Heidelberg.

[14] Merengo, C., Nava, F., Pozetti, A. 1999. Balancing and sequencing manual mixed- model assembly lines. International Journal of Production Research 37(1), pp 2835–2860.

[15] Meyr, H. 2004. Supply chain planning in the German automotive industry. OR Spectrum 26(1), pp 447–470.

[16] Akagi, F., Osaki, H., Kikuchi, S. 1983. A method for assembly line balancing with more than one worker in each station, International Journal of Production Research 21(1), pp 755–770.

discrete event simulation modeling to improve productivity

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