American Journal of Applied Sciences

Evaluating the Maintenance Performance of the Semiconductor Factories Based on the Analytical Hierarchy Process and Grey Relational Analysis

Fei-Long Chen and Yun-Chin Chen

DOI : 10.3844/ajassp.2010.568.574

American Journal of Applied Sciences

Volume 7, Issue 4

Pages 568-574


Problem statement: Maintenance is an important factor in semiconductor factories, not only because of costs and the need for the uninterrupted operation of semiconductor equipment, but also the time and expense required for maintenance. If maintenance procedures are not performed properly, the equipment will have low efficiency or break down, production capacity will decrease and the company will incur extra costs. Therefore, the evaluation of maintenance performance has become a critical issue in semiconductor industries. Approach: This study evaluated maintenance performance by using the Analytical Hierarchy Process (AHP), Grey Relational Analysis (GRA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The weight of maintenance indicators was derived by AHP method, which were input to the GRA and TOPSIS method for evaluate the performance of Condition-Based Maintenance (CBM) and Time-Based Maintenance (TBM) strategies. Results: Actual data was provided by a well-known semiconductor factory in Taiwan. This study evaluated and compared the performance of different maintenance strategies implemented in semiconductor companies. Empirical results indicated that the CBM strategy had better maintenance performance than the TBM strategy in semiconductor companies and the maintenance indicators which should be improved were also identified. Conclusion/Recommendations: The feasibility of the maintenance evaluation method was demonstrated through an actual scenario, which can help managers make decisions objectively and distinguish the advantages and disadvantages of the maintenance strategy.


© 2010 Fei-Long Chen and Yun-Chin Chen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.