Throughput and Tester Utilization Improvement in the Hard Disk Drive ...

RESEARCH ARTICLE

Adv. Sci. Lett. 20, 455-459, 2014

Copyright © 2014 American Scientific Publishers All rights reserved Printed in the United States of America

Advanced Science Letters Vol. 20, 455–459, 2014

Throughput and Tester Utilization Improvement in the Hard Disk Drive Assembly Line using Hybrid Simulation Approach Hayati Mukti Asih*, Chong Kuan Eng Department of Manufacturing Management Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

Manufacturers of high mix and high volume products are forced to focus on strategic capacity planning in their production system to remain competitive. This work is based on a case study in a hard disk drive manufacturing company with the objective of improving the throughput and tester utilization in the testing assembly line. The problem is complex as there were multiple products, each product model undergoing different testing routes and testing duration. This is further complicated by 2 different fixed capacity testers where each tester is mix-loaded with different products. As the scenario is too complex to be solved by analytical methods, a hybrid simulation approach was proposed to find a feasible solution to the problem. The problem was first formulated with a mathematical model to optimize the number of testers with predefined configurations. Each configuration was then simulated to determine the throughput and tester utilization. Finally the TOPSIS and AHP methods were employed to determine the best configuration. The results of the proposed method improved throughput and tester utilization by 6.71% and 26.67%, respectively.

Keywords: Hard disk drive manufacturer; Hybrid approach; Simulation; AHP; TOPSIS; Capacity Planning

This work is based on a case study in a HDD company located in Malaysia. The general process flow in the assembly line is shown in figure 1.

1. INTRODUCTION Hard disk drive (HDD) manufacturing industry is characterized by the high – technology processes, high product mix, and high production volume1. The uncertainty of customers’ demand and short product life cycle are some of the challenges faced by HDD companies. Therefore these companies are compelled to continuously enhance the performance of production processes to reduce work-in-process (WIP), to enhance throughput and to increase the machine utilization. *

Email Address: hayati [email protected]

1

Adv. Sci. Lett. Vol. 20, No. 2, 2014

Fig.1. Process Flow Diagram of HDD Assembly Line The scope of this study is on the testing processes, which are identified as bottleneck for the assembly line. This is indicated by the dotted box in figure 1. 1936-6612/2014/20/455/008

doi: 10.1166/asl.2014.5336

RESEARCH ARTICLE

Adv. Sci. Lett. 4, 400–407, 2011 To support high-volume manufacturing and high product varieties in an economical scale, the company is continuously looking for advanced technology testing equipment. Currently, testing equipment is classified into two categories i.e. automatic and manual testers. These testers are capable of supporting testing process based on customers’ configuration. The automatic tester employed in the case company is newer technology and provides better quality outputs than the manual testers2 where mix load is considered. The mix - load tester is the ability of tester to load multi product types, simultaneously. There are two stages in the automatic testing process: Tester A and Tester B. Each tester has almost three thousand slots which are able to load for various product types simultaneously. Each slot consists of two cells that have to be loaded same product types. Each product type has their own process flow and has extremely different testing time. It makes the shop floor more complex and hard to handle. In addition, there is a robot for loading and unloading drive to each cell. Therefore, the planner should determine capacity planning by considering the ability of a robot as well. Observations have been conducted to study the process flow and relevant data were collected. Initial findings were found that the utilization of both testers were unfavorable due to uncertainty in both external and internal factors. The external factors include customer demand changes, new product development, and unexpected nature disaster which is consistent with those reported in other industries3-6. On the other hand, the internal factors include quality problems, tester breakdown, etc. In addition, the problem is further complicated by different product family going through different process route and different testing time. The combination of these factors contributes to high work-inprocess and low throughput resulting in difficulty to achieve production targets. Some approaches were studied to solve this mix-load tester problem. Assignment problem is one of the fundamental operation research problems with the objective to maximize profit on minimum investment by assigning task to each machine7. This approach is, however, unsuitable for this case study as one tester must contain maximum two family products. On the other hand, the basic idea of the bin packing problem is to minimize the number of bins to pack all the pieces8 is not applicable to optimize throughput and utilization. As the problem in this case study is too complex to be solved by the analytical method alone, a hybrid simulation approach is developed to find a feasible solution to the problem. The paper is organized as follows: Section 2 explains the literature review work on the issue discussed. Next, Section 3 describes the problem formulation. In Section 4, the detail of model development, verification and validation are discussed. Then, Section 5 presents the results and discussion followed by the conclusion. 2.

LITERATURE

REVIEW

AND

METHODOLOGY Capacity planning, which is closely related to demand forecast, is a process of adjusting the capacity of an organization in responding to the changing or predicted demand9. Strategic capacity planning in the semiconductor industry is an important process to improve business performance. It is because of the high capital investment cost, the complexity of the fabrication process, rapid changes in technology and product, the long lead time, and high uncertainty in demand and capacity10. One of the essential aspects of capacity planning is maximizing machine utilization in order to achieve production targets as the customer order11. Currently, the spreadsheet based capacity modeling approach is still being practiced in many companies, especially in the semiconductor industry12. In the literature 13, constraint-based Genetic Algorithm is proposed to solve machine loading problem to achieve optimal or near-optimal combination of operation-machine allocation of part types in a flexible manufacturing system in order to minimize the unbalance and maximize the throughput. Then, the literature 14 investigated machine loading problem of FMS. By using hybrid tabu search and simulated annealing-based heuristic approach, the unbalance system could be minimized, and throughput is maximized. However, there is not much work being reported on the type of problems described in our case company especially on the automatic testing process in a HDD manufacturing industry. A hybrid simulation is proposed to improve tester utilization. The problem was first formulated with a mathematical model-based mix-load tester. Then, each scenario of mix-load tester generated from mathematical model is evaluated using simulation modeling with throughput and average tester utilization as performance measures. Finally, TOPSIS and AHP methods were employed to determine the best set of combinations of mix-load tester. Figure 2 shows the general research methodology of this paper. START A Understanding the Process Flow

Experimental Design: 1. Developing mathematical modeling-based mix-load tester 2. Evaluating the combination of mix-load testers generated from mathematical model through simulation approach 3. Developing AHP and TOPSIS to choose the best combination/scenario

Problem Formulation

Literature Review Model Conceptualization

Data Collection and Analysis

Production runs and analysis

Model Translation YES

NO

Verified? NO

YES

NO

Validated?

A

More runs? NO

Documenting and reporting

END

Figure.2. Research Methodology of This Paper 3. PROBLEM FORMULATION

PROPOSED 2

RESEARCH ARTICLE

Adv. Sci. Lett. 20, 455-459, 2014

A. Notation The notations below are used to demonstrate the objective functions. Subscript m Tester types l Low-testing-time product; l = 1, 2, … , L h High-testing-time product; h = 1, 2, … , H r Robot type; r = 1, 2, … , R k Length of period in a day Parameters M Qml Qmh

tmh tr Dl Dh Nml Nmh Sml Smh Sm Cml Cmh

B. Formulation of Objective Function and Constraint The objective function of this research can be formulated as follows: L

minimize Z 



H



Dl

i 1

Cml

-

Dh

i 1

Cmh

The constraints are: N ml 

Dl / M K

Cml  N ml  K S ml 

C ml Qml

(1) (2) (3)

Smh  Sm  Sml

(4)

Cmh  Smh  Qmh

(5)

N mh 

3

Cmh K

Qml  Qmh 

Number of tester type Turn of low-testing-product l for tester type m Turn of high-testing-product h for tester type m Testing time for tester type m low-testingproduct l Testing time for tester type m high-testingproduct h Travel time of robot type r Demand of low-testing-product l Demand of high-testing-product h Hourly loading of tester type m for low-testingproduct l Hourly loading of tester type m for hightesting-product h Slot available for tester type m for low-testingproduct l Slot available for tester type m for high-testingproduct h Total slot for tester type m, 1 ≤ Sm ≤ 2880 Capacity of low-testing-product l for tester type m Capacity of high-testing-product h for tester type m

tml

Cml  Cmh  (

(6)

K

K  3600

tmh

 80%)

(7)

 95%

(8)

 95%

(9)

tml K

tr

Generally, this mathematical model-based mix-load tester is developed to maximize the tester utilization, with the ability to lower tester allocation and achieve the production target as well. First of all, the low-testing-time product is allocated to each tester type by determining the hourly loading in a day using Eq. (1). This refers to the amounts of product that must be loaded to each tester in an hour. Next, the capacity per tester is calculated for the number of products to be loaded in a day by multiplying hourly loading with length of periods in a day using Eq. (2). Next, the slot available is calculated to determine how many the number of slots needed for each tester for low-testing-time product using Eq. (3) which is by dividing the calculation result of Eq. (2) with turn. Turn is the probability of certain product family circulating in a day. It is calculated in Eq. (8) and Eq. (9) for low-testingtime product and high-testing-time product, respectively. After calculating the slots of low-testing-time product, Eq. (4) is used to compute the slots of high-testing-time product by subtracting total slot for each tester type with slot of low-testing-time product. Afterward, the capacity and the slots are calculated using Eq. (5) and Eq. (6), respectively. Eq. (7) is the constraint for robot capability inside each tester to load and unload product to each slot. Therefore, the capacity of both product types must not exceed than the travel time of a robot itself. 4. MODEL DEVELOPMENT, VERIFICATION AND VALIDATION A base model for the current system is first modeled and simulated. Next the combinations of mix-load testers generated from the mathematical model described above are experimented as described below. Each product family has their own process flow as seen in figure 3. The distributions of testing time and input quantity each product family are analyzed using Stat::Fit. This input data and some constraints related to automatic tester process for base model were collected from the shop floor of the company. For the base model, the process of four-biggest product families (i.e. namely product T, product S, product A and product B) at 45 Tester A and 11 Tester B are modeled and analyzed using discrete-event system simulation in the ProModel® 7.5 simulation software, as shown in figure 4. The simulation model is more credible and reduces the number of simplifying assumptions and give the more real characteristic of the system under

RESEARCH ARTICLE

Adv. Sci. Lett. 4, 400–407, 2011 study15,16. The warm-up is conducted to avoid misleading and initialization bias the estimated response measure when the model is situated in “unrealistic” state17. For this research, the warm-up is estimated to be ten days using Welch`s method. Product T Input quantity distribution 1 Testing time distribution 1

Automatic Testing Process

Product S Input quantity distribution 2 Testing time distribution 2

··

Sort into Queue (FIFO)

Tester A

Tester B

Label Printing

Product A Input quantity distribution 3 Testing time distribution 3

Product B Input quantity distribution 4 Testing time distribution 4

Fig.3. The Operation Process Flow Diagram

Then, these scenarios are compared with respect to two types of performance measures: throughput which measures the product produced and average tester utilization means the average percentage of the tester is utilized. After evaluating the combinations of mix-load tester in the simulation model, the multi criteria decision making (MCDM) analysis is proposed to choose the best scenarios. In this paper, the comparison between Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Planning (AHP) are developed. The two types of performance measure receive the same level of importance. Thus, the throughput has a weight of 0.5 and utilization tester also has a weight of 0.5. 5. RESULTS AND DISCUSSION

Fig.4. Partial screen shoot of base model in ProModel® The base model was verified by related personnel in company by using conceptual model, mathematical model-based mix-load tester and translation model that are already developed. Then, the validation is conducted to ensure the model’s output behavior has sufficient accuracy for the model’s intended purpose over the domain of the model`s intended applicability18. The simulation model is validated by comparing the simulation results with historical production data collected on 35 days by using student t test: paired two samples for means. The validation result shows no statistical significant difference with confidence level 99.8%, which implies that the model works as proposed and it can represent the real production system. In the base model, total throughput and average utilization tester are 2,904,287 drives and 66.98%, respectively. On the other hand, the actual system of throughput and average utilization tester are 2,706,569 drives and 71.13%, respectively. These real system data are set as the target for scenarios` model. In Table 1, some scenarios of mixload tester combination are shown with the total testers are 28 for Tester A and 8 for Tester B. Those scenarios were evaluated with the assumptions that the absenteeism of the operator and breakdown tester are infrequent Table 1 The combination of mix-load tester scenarios

For those scenarios, the models were executed for three replications runs. For each scenario, the average reading will be taken from each of the three replications runs. Different scenarios have each output performance. Table.2.Result of mix-load tester scenario Throughput Avg Tester Utilization 2,901,267 97% Scenario 1 2,786,287 99% Scenario 2 2,865,847 93% Scenario 3 2,780,760 90% Scenario 4 2,752,813 87% Scenario 5 2,781,500 89% Scenario 6 2,618,093 92% Scenario 7 2,578,853 89% Scenario 8 2,617,093 91% Scenario 9 2,564,407 86% Scenario 10 2,532,680 83% Scenario 11 2,565,327 86% Scenario 12 The result of the mix-load tester scenario is presented on table 2. From those scenarios, generally the utilization increases compared to the real system. But then, only scenario 1 until 6 that could achieve the production target which is 2,706,569 drives. Interestingly, scenario 2 has the highest average tester utilization among others. There was an increase in average tester utilization of around 27.87%. On the other hand, scenario 1 has the highest throughput among others at nearly 2,9 million drives. Therefore, AHP and TOPSIS are performed to choose the best combination of mix-load tester based on throughput and tester utilization. The result shows scenario 1 is the best set of combinations for mix-load tester among others. Finally, figure 4 shows the comparison result between real system and scenario 1 in terms of throughput and average utilization tester. By having the lower number tester which is 28 testers of Tester A and 11 testers of Tester B, the average utilization are much better than the real system while still satisfying the production target. The improvement is around 6.71% and 4

RESEARCH ARTICLE

Adv. Sci. Lett. 20, 455-459, 2014

26.67% for throughput and average utilization tester, respectively. [5]

[6]

[7]

(a) (b) Fig.4. Scenario 1 versus Real system: (a) Throughput result and (b) Utilization tester result

6. CONCLUSION In this study, a hybrid simulation approach is proposed to find a feasible solution to the problem of mix-load tester problem in a HDD manufacturer. Because of the complexity of the shop floor, and high uncertainty in both external and internal factors, a mathematical model was developed to generate various possible mixload tester configurations which were experimented using the ProModel® simulation software with throughput and tester utilization as performance measures. The simulation result is scenario 1 gives the highest throughput, on the other hand scenario 2 gives the best average utilization tester. Therefore, AHP and TOPSIS approach were proposed to choose the best set of combinations to mix-load tester. Interestingly, both MCDM methods choose scenario 1 as the first choice and scenario 5 as the last alternative. This proposed solution could be recommended to the industrial user.

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