Computational Intelligence in Design and Manufacturing
Computational Intelligence in Design and Manufacturing
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
Multi-level fuzzy mining with multiple minimum supports
Expert Systems with Applications: An International Journal
A hybrid approach to supplier selection for the maintenance of a competitive supply chain
Expert Systems with Applications: An International Journal
Mining customer knowledge for product line and brand extension in retailing
Expert Systems with Applications: An International Journal
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
A new approach for evaluating agility in supply chains using Fuzzy Association Rules Mining
Engineering Applications of Artificial Intelligence
Development of an intelligent quality management system using fuzzy association rules
Expert Systems with Applications: An International Journal
An integrated method for finding key suppliers in SCM
Expert Systems with Applications: An International Journal
A search space reduction methodology for data mining in large databases
Engineering Applications of Artificial Intelligence
A rough set based approach to distributor selection in supply chain management
Expert Systems with Applications: An International Journal
Journal of Intelligent Manufacturing
Extracting performance rules of suppliers in the manufacturing industry: an empirical study
Journal of Intelligent Manufacturing
A fuzzy ANP model for supplier selection as applied to IC packaging
Journal of Intelligent Manufacturing
Decision-making for the best selection of suppliers by using minor ANP
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
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A company must purchase a lot of diverse components and raw material from different upstream suppliers to manufacture or assemble its products. Under this situation the supplier selection has become a critical issue for the purchasing department.The selection of suppliers depends on number of criteria and the challenge is to optimize selection process based on critical criteria and select the best supplier(s). During supplier selection process initial screening of potential suppliers from a large set is vital and the determination of prospective supplier is largely dependent on the criteria chosen of such pre-qualification. In the literature, many judgments based methods are proposed and derived criteria selection from the opinion of either the customers or the experts. All these techniques use the knowledge and experience of the decision makers. These methods inherit certain degree of uncertainty due to complex supply chain structure. The extraction of hidden knowledge is one of the most important tools to address such uncertainty and data mining is one such concept to account for such uncertainty and it has been found applicable in many scenarios. The proposed research aims to introduce a data mining approach, to discover the hidden relationships among the supplier's pre-qualification data with the overall supplier rating that have been derived after observation of previously executed work for a period of time. It provides an overview that how supplier's initial strength influence its final work performance.