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A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry

Version 2 2024-06-05, 03:03
Version 1 2019-01-22, 10:59
journal contribution
posted on 2024-06-05, 03:03 authored by CKH Lee, KL Choy, GTS Ho, KS Chin, Kris LawKris Law, YK Tse
In today's garment industry, garment defects have to be minimized so as to fulfill the expectations of demanding customers who seek products of high quality but low cost. However, without any data mining tools to manage massive data related to quality, it is difficult to investigate the hidden patterns among defects which are important information for improving the quality of garments. This paper presents a hybrid OLAP-association rule mining based quality management system (HQMS) to extract defect patterns in the garment industry. The mined results indicate the relationship between defects which serves as a reference for defect prediction, root cause identification and the formulation of proactive measures for quality improvement. Because real-time access to desirable information is crucial for survival under the severe competition, the system is equipped with Online Analytical Processing (OLAP) features so that manufacturers are able to explore the required data in a timely manner. The integration of OLAP and association rule mining allows data mining to be applied on a multidimensional basis. A pilot run of the HQMS is undertaken in a garment manufacturing company to demonstrate how OLAP and association rule mining are effective in discovering patterns among product defects. The results indicate that the HQMS contributes significantly to the formulation of quality improvement in the industry. © 2012 Elsevier B.V. All rights reserved.

History

Journal

Expert systems with applications

Volume

40

Pagination

2435-2446

Location

Amsterdam, The Netherlands

ISSN

0957-4174

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2012, Elsevier

Issue

7

Publisher

Elsevier

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