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A Classification Techniques For Quality Improvement

A Classification Techniques For Quality Improvement As we know, the quality of processes is technically depicted by variation, a product or process with the best quality must naturally require the variation as less as possible. The variation is usually reduced with many ways, say, by adjusting parameters settings under robust design with many turns expensive experiements. So ones are trying to reach the robusiness by detecting cheap and simple methods. In this paper, a both practical and simple technique for quality improvement, namely reducing the variation, by data classification is studied. First, all possible system factors are included, which may dominate the variation law. And then we make use of the past observations and their classification as well as boxplot charts to find out the internal rule between the variation and the system factor. Next, adjust the location of the system factor according to the rule so that the variation could, to some extent, be lessened. Finally, two typical quality improvement cases based on data classification are presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Asian Journal on Quality Emerald Publishing

A Classification Techniques For Quality Improvement

Asian Journal on Quality , Volume 2 (2): 10 – Aug 21, 2001

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Publisher
Emerald Publishing
Copyright
Copyright © 2001 MCB UP Ltd. All rights reserved.
ISSN
1598-2688
DOI
10.1108/15982688200100013
Publisher site
See Article on Publisher Site

Abstract

As we know, the quality of processes is technically depicted by variation, a product or process with the best quality must naturally require the variation as less as possible. The variation is usually reduced with many ways, say, by adjusting parameters settings under robust design with many turns expensive experiements. So ones are trying to reach the robusiness by detecting cheap and simple methods. In this paper, a both practical and simple technique for quality improvement, namely reducing the variation, by data classification is studied. First, all possible system factors are included, which may dominate the variation law. And then we make use of the past observations and their classification as well as boxplot charts to find out the internal rule between the variation and the system factor. Next, adjust the location of the system factor according to the rule so that the variation could, to some extent, be lessened. Finally, two typical quality improvement cases based on data classification are presented.

Journal

Asian Journal on QualityEmerald Publishing

Published: Aug 21, 2001

Keywords: Robust design; Data classification; Boxplot chart; Variation reduction

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