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

Abstract

Purpose

The 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/approach

Four 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).

Findings

The proposed approach enables the creation of the QTs starting from the raw data and passing through the DM process.

Originality/value

The 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.

Citation

Tsironis, L.K. (2018), "Quality improvement calls data mining: the case of the seven new quality tools", Benchmarking: An International Journal, Vol. 25 No. 1, pp. 47-75. https://doi.org/10.1108/BIJ-06-2016-0093

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