Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15045
Title: Design of an intelligent supplier knowledge management system - an integrative approach
Authors: Choy, KL 
Tan, KH
Chan, FTS 
Keywords: Artificial neural networks
Case-based reasoning
On-line analytical processing
Supplier and knowledge management
Issue Date: 2007
Publisher: Professional Engineering Publishing Ltd
Source: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2007, v. 221, no. 2, p. 195-211 How to cite?
Abstract: The drive to cut costs continually and focus on core competencies has driven many companies to outsource some or all of their production. Unlike the past, companies can no longer concentrate only on their own internal business operations, but have to work with customers and suppliers effectively and efficiently. The integration of customer demand and supplier capability to facilitate supplier management using data mining and artificial intelligence technologies has become a promising solution for outsourced-type companies in outsourcing manufacturing operations to suitable suppliers. The result is to form a supply network on which they depend on the provision of products and services. In this paper, a supplier knowledge management system (SKMS) is introduced for such a purpose. By using its hybrid on-line analytical processing (OLAP)/artificial neural networks (ANNs)/case-based reasoning (CBR) approach in predicting future customer demands and allocating suitable suppliers during the order fulfilment process, it is found that the overall efficiency in the whole supply chain is greatly enhanced. A case study using the SKMS to integrate the order subcontracting system of Farnell Newark-InOne (Shanghai) Limited is presented. Through the use of the SKMS, the demand of customers is related to the supplier's capabilities both efficiently and effectively while, at the same time, valuable supplier knowledge is also accumulated by the company.
URI: http://hdl.handle.net/10397/15045
DOI: 10.1243/09544054JEM627
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