期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
卷 37, 期 4, 页码 602-609出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCC.2007.897503
关键词
clustering; mass customization; product complexity reduction; product configuration management; sales data
Manufacturing companies are currently focusing on mass customization. Delivering products that meet the requirements of individual customers complicates the production process, and diminishes the benefits of the economy of scale. By exploring commonality among products, this complexity can be significantly reduced. To determine product configurations sought by the customers and to produce them in large quantities, a new approach is proposed. The proposed approach uses a modified k-means clustering algorithm to analyze past sales data for capturing prime product configurations. The most suitable configurations are selected by solving an integer-programming model or using a sorting-based algorithm. The proposed approach was tested with an industrial case study involving sales data of large trucks collected over a period of one year.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据