An intelligent support system for supplier evaluation

Fong-Ching Yuan, Chi-I Hsu, Chaochang Chiu and Pei-Lun Hsu

International Journal of Management and Enterprise Development, 2005, vol. 2, issue 3/4, 441-457

Abstract: Supplier evaluation is critical for Supply Chain Management (SCM). Based on the information gained from formalised supplier evaluation processes, a company can determine the relationship with the suppliers. If manufacturers can cooperate or interact with suppliers very well, it helps to maximise the productivity at the minimum cost while satisfying customer requirements. Case-Based Reasoning (CBR) provides a suitable mean for improving the effectiveness of complex and unstructured decision making, such as supplier evaluation. It is a machine reasoning method that adapts previous similar cases to infer further similarity. For effective case retrieval, a Genetic Algorithm (GA)-based approach used to determine the most appropriate feature weights is employed to enhance the case-matching process. Our proposed GA-CBR methodology used to predict vendor evaluation has been proven very effective and has better prediction accuracy over the results from other approaches as it minimises the effect of qualitative factors.

Keywords: supplier evaluation; supply chain management; SCM; case based reasoning; genetic algorithms; vendor evaluation; supplier selection; intelligent selection. (search for similar items in EconPapers)
Date: 2005
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=6572 (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:ijmede:v:2:y:2005:i:3/4:p:441-457

Access Statistics for this article

More articles in International Journal of Management and Enterprise Development from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().