Using data mining to improve supplier release stability

被引：1

作者：

Cavaretta, M ^{[1
]}

Chou, G ^{[1
]}

Madani, B ^{[1
]}

机构：

[1] Ford Motor Co, Res & Adv Engn, Dearborn, MI 48124 USA

来源：

NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society | 2005年

关键词：

D O I：

10.1109/NAFIPS.2005.1548543

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Communications of material requirements between a manufacturer and its supply base is fraught with inefficiencies. Suppliers complain that variation in material quantity requires them to keep extra capacity on hand, as well as preventing optimization of their labor and equipment. The manufacturer experiences issues with instability, also preventing optimization of labor and equipment. This paper proposes using data mining, a series of statistical and artificial intelligence techniques for extracting knowledge from large databases, to identify opportunities for reducing material requirement variation.

引用

页码：252 / 256

页数：5

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