A review of data mining applications for quality improvement in manufacturing industry

Date

2011-09-15

Author

Köksal, Gülser
Batmaz, İnci

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Many quality improvement (QI) programs including six sigma, design for six sigma, and kaizen require collection and analysis of data to solve quality problems. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for QI in manufacturing. Although a few review papers have recently been published to discuss DM applications in manufacturing, these only cover a small portion of the applications for specific QI problems (quality tasks). In this study, an extensive review covering the literature from 1997 to 2007 and several analyses on selected quality tasks are provided on DM applications in the manufacturing industry. The quality tasks considered are; product/process quality description, predicting quality, classification of quality, and parameter optimisation. The review provides a comprehensive analysis of the literature from various points of view: data handling practices, DM applications for each quality task and for each manufacturing industry, patterns in the use of DM methods, application results, and software used in the applications are analysed. Several summary tables and figures are also provided along with the discussion of the analyses and results. Finally, conclusions and future research directions are presented.

Subject Keywords

Knowledge discovery in databases, Data mining, Quality improvement, Six sigma, Design for six sigma, Quality description, Prediction, Classification, Parameter optimisation, Data mining software, Manufacturing

URI

https://hdl.handle.net/11511/49103

Journal

EXPERT SYSTEMS WITH APPLICATIONS

DOI

https://doi.org/10.1016/j.eswa.2011.04.063

Collections

Department of Industrial Engineering, Article

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Citation Formats

G. Köksal and İ. Batmaz, “A review of data mining applications for quality improvement in manufacturing industry,” EXPERT SYSTEMS WITH APPLICATIONS, pp. 13448–13467, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/49103.