Establishment of semiconductor equipment purchasing evaluation model based on a knowledge management database

Ching-Chow Yang, Bai-Sheng Chen, Shu-Yun Peng, Tsu-Ming Yeh and Lung-Chia Wang

European Journal of Industrial Engineering, 2008, vol. 2, issue 1, 1-16

Abstract: The conventional method for selecting a suitable equipment supplier may have disadvantages such as failing to consider the supplier's technical experience and knowledge and inability of supplier management to make further forecast analysis to aid the decision maker in strategy optimisation. The investment in equipment is expensive but is the key success factor in semiconductor manufacturers' maintaining a competitive advantage. Moreover, in order to effectively evaluate the candidate equipment of suppliers, it is critical to include an evaluation of a candidate supplier's historical and test data. This research constructed an explicit knowledge Semiconductor Equipment Evaluation Model (SEEM) primarily to extract tacit supplier knowledge in fulfilling the semiconductor equipment purchase process. SEEM integrates the analytical hierarchy process, grey relational analysis and knowledge management database. The proposed model supplements the conventional method that considers only qualitative criteria or acquisition cost and provides transparency in the purchasing information and process. [Received 31 May 2007; Revised 18 July 2007; Accepted 26 July 2007]

Keywords: semiconductor manufacturing; equipment purchasing; knowledge management; grey relational analysis; GRA; analytical hierarchy process; AHP; equipment selection; equipment evaluation; purchasing information. (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=16326 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:eujine:v:2:y:2008:i:1:p:1-16

Access Statistics for this article

More articles in European Journal of Industrial Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().