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Quality improvement calls data mining: the case of the seven new quality tools

Quality improvement calls data mining: the case of the seven new quality tools PurposeThe purpose of this paper is to propose a way of implementing data mining (DM) techniques and algorithms to apply quality improvement (QI) approaches in order to resolve quality issues (Rokach and Maimon, 2006; Köksal et al., 2011; Kahraman and Yanik, 2016). The effectiveness of the proposed methodologies is demonstrated through their application results. The goal of this paper is to develop a DM system based on the seven new QI tools in order to discover useful knowledge, in the form of rules, that are hidden in a vast amount of data and to propose solutions and actions that will lead an organization to improve its quality through the evaluation of the results.Design/methodology/approachFour popular data-mining approaches (rough sets, association rules, classification rules and Bayesian networks) are applied on a set of 12,477 case records concerning vehicle damages. The set of rules and patterns that is produced by each algorithm is used as an input in order to dynamically form each of the seven new quality tools (QTs).FindingsThe proposed approach enables the creation of the QTs starting from the raw data and passing through the DM process.Originality/valueThe present paper proposes an innovative work concerning the formation of the seven new QTs of quality management using DM popular algorithms. The resulted seven DM QTs were used to identify patterns and understand, so they can lead even non-experts to draw useful conclusions and make decisions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Benchmarking: An International Journal Emerald Publishing

Quality improvement calls data mining: the case of the seven new quality tools

Benchmarking: An International Journal , Volume 25 (1): 29 – Feb 5, 2018

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1463-5771
DOI
10.1108/BIJ-06-2016-0093
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to propose a way of implementing data mining (DM) techniques and algorithms to apply quality improvement (QI) approaches in order to resolve quality issues (Rokach and Maimon, 2006; Köksal et al., 2011; Kahraman and Yanik, 2016). The effectiveness of the proposed methodologies is demonstrated through their application results. The goal of this paper is to develop a DM system based on the seven new QI tools in order to discover useful knowledge, in the form of rules, that are hidden in a vast amount of data and to propose solutions and actions that will lead an organization to improve its quality through the evaluation of the results.Design/methodology/approachFour popular data-mining approaches (rough sets, association rules, classification rules and Bayesian networks) are applied on a set of 12,477 case records concerning vehicle damages. The set of rules and patterns that is produced by each algorithm is used as an input in order to dynamically form each of the seven new quality tools (QTs).FindingsThe proposed approach enables the creation of the QTs starting from the raw data and passing through the DM process.Originality/valueThe present paper proposes an innovative work concerning the formation of the seven new QTs of quality management using DM popular algorithms. The resulted seven DM QTs were used to identify patterns and understand, so they can lead even non-experts to draw useful conclusions and make decisions.

Journal

Benchmarking: An International JournalEmerald Publishing

Published: Feb 5, 2018

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