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Retrospective analysis for mining the causes in manufacturing processes

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conference contribution
posted on 2017-12-06, 00:00 authored by KP Pang, A B M Shawkat Ali
There has been a considerable growth in the use of Statistical Process Control (SPC) for improving the quality in business, industries, or software development since the last decade. However, the processes are growing much more complex, and there is a tremendous increase of data size owning to the use of automated record machine. The conventional SPC tools become less effective in analyzing and identifying the cause of the process failures. This paper extends the idea of the Modified Centered CUSUMS, and proposes a new data selection procedure so that the associative discovery technique can be used in retrospective SPC analysis. Through our approach, the common data mining method (i.e. associative discovery) can be used to find the hidden knowledge from the data, and identify the causes of the process failure or success for the quality improvement. Besides, the hidden information that we extracted from the data can be used as supplement for the cause and effect diagram in the on-line process control.


Start Date







IEEE Computer Society

Place of Publication

Washington, DC, USA

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Faculty of Business and Informatics; Monash University;

Era Eligible

  • Yes

Name of Conference

International Conference on Computational Intelligence for Modelling, Control and Automation