A novel hybrid MCDM approach for outsourcing vendor selection: A case study for a semiconductor company in Taiwan

Ya Ti Lin, Chia Li Lin, Hsiao Cheng Yu, Gwo Hshiung Tzeng

Research output: Contribution to journalArticle

75 Citations (Scopus)

Abstract

Outsourcing manufacturing is a trend in today's global business operations. Vendor selection is an essential factor affecting outsourcing operations performance and has been determined as a multiple criteria decision making (MCDM) problem. The purpose of this paper is to propose a novel hybrid MCDM technique to cope with the complex and interactive vendor evaluation and selection problem which can determine the structural relationships and the interrelationships amongst all the evaluation's dimensions and support the Analytic Network Process (ANP) method to arrange appropriate weightings to each dimension and criterion in the evaluation model by summarizing the opinions of the experts. Finally, the overall performance ranking of all alternatives can be obtained to assist the decision making. Taiwan semiconductor industry is the largest provider in worldwide market. It is vertically disintegrated and outsourcing is a main stream practice. Wafer-testing is a critical manufacturing step in the semiconductor supply chain that distinguishes whether the IC can meet the specifications. Consequently, it's crucial to establish a thorough model for selecting the wafer-testing vendors in order to achieve the success of the outsourcing operation. A case study of a Taiwan semiconductor company in selecting its wafer-testing outsourcing vendor by using the proposed MCDM technique is demonstrated to enhance the decision making quality. The results and proposed solution can be referred to by not only the semiconductor companies, but also other industries so that they can improve the vendor selection process in order to achieve a higher performance and satisfaction level.

Original languageEnglish
Pages (from-to)4796-4804
Number of pages9
JournalExpert Systems With Applications
Volume37
Issue number7
DOIs
Publication statusPublished - 2010 Jul 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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