Optimising product configurations with a data-mining approach

Zhe Song · Andrew Kusiak

92 citations0/0 files storedInternational Journal of Production Research

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Product Development and Customization Manufacturing Process and Optimization Data Mining Algorithms and Applications

Abstract

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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References

2004Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management 2003Product modularity: Definitions and benefits 1998Managing Broader Product Lines through Delayed Differentiation Using Vanilla Boxes 2003Product modularity: measures and design methods 2000Mass customisation — an automotive perspective 2004A purchase-based market segmentation methodology 2004Product portfolio identification based on association rule mining 2004Data-mining-based methodology for the design of product families

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