Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 47, Issue 7, Pages 1733-1751Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207540701644235
Keywords
mass customisation; data mining; clustering; association rule algorithm
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Customers benefit from the ability to select their desired options to configure final products. Manufacturing companies, however, struggle with the dilemma of product diversity and manufacturing complexity. It is important, therefore, for them to capture correlations among the options provided to the customers. In this paper, a data mining approach is applied to manage product diversity and complexity. Rules are extracted from historical sales data and used to form sub-assemblies as well as product configurations. Methods for discovering frequently ordered product sub-assemblies and product configurations from 'if-then' rules are discussed separately. The development of the sub-assemblies and configurations allows for effective management of enterprise resources, contributes to the innovative design of new products, and streamlines manufacturing and supply chain processes. The ideas introduced in this paper are illustrated with examples and an industrial case study.
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