Data Mining for Improving Manufacturing Processes

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Author(s): Lior Rokach (Ben-Gurion University, Israel)
Copyright: 2009
Pages: 7
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch066

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Abstract

In many modern manufacturing plants, data that characterize the manufacturing process are electronically collected and stored in the organization’s databases. Thus, data mining tools can be used for automatically discovering interesting and useful patterns in the manufacturing processes. These patterns can be subsequently exploited to enhance the whole manufacturing process in such areas as defect prevention and detection, reducing flow-time, increasing safety, etc. When data mining is directed towards improving manufacturing process, there are certain distinctions that should be noted compared to the classical methods employed in quality engineering, such as the experimental design. In data mining the primary purpose of the targeted database is not data analysis; the volume of the collected data makes it impractical to explore it using standard statistical procedures (Braha and Shmilovici, 2003).

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