Title :
Planning Product Configurations Based on Sales Data
Author :
Kusiak, Andrew ; Smith, Mathew R. ; Zhe Song
Author_Institution :
Iowa Univ., Iowa City
fDate :
7/1/2007 12:00:00 AM
Abstract :
Manufacturing companies are 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.
Keywords :
integer programming; manufactured products; manufacturing industries; mass production; pattern clustering; product customisation; sorting; integer-programming model; k-means clustering algorithm; manufacturing companies; mass customization; product configuration planing; sales data; sorting-based algorithm; Algorithm design and analysis; Clustering algorithms; Costs; Data analysis; Economies of scale; Manufacturing industries; Marketing and sales; Mass customization; Mass production; Testing; Clustering, mass customization; product complexity reduction; product configuration management; sales data;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/TSMCC.2007.897503