Economic models for vendor evaluation with quality cost analysis
Management Science
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Fuzzy DEMATEL method for developing supplier selection criteria
Expert Systems with Applications: An International Journal
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In mass customization, different kinds of customer requirements should be satisfied by the manufacturer. Supplier selection is one important task in supply-chain management. Effective supplier selection calls for robust analytical methods and decision-support tools. This research aims to develop a supplier selection methodology based on extended quality function deployment (QFD) and data-mining (DM) techniques. Through considering customer requirement and performance of components in a product's full life-cycle, the manufacturer can use data-mining techniques to find out quality requirements correlated to customer categories, product usage patterns, and frequent fault patterns in order to select the proper combination of suppliers. In this way, the manufacturer can decrease costs, raise product quality, and improve customer satisfaction. Related data-mining algorithms for supplier selection are presented. Customer requirement analysis is also studied in the paper, and transcendental and empirical customer requirement analysis methods are put forward. A case study is provided in detail. Finally, as a part of the supply-chain management system, a supplier selection prototype system is designed and implemented. Evaluation of experiments in an automobile manufacturing enterprise verifies the feasibility and efficiency of our method.