scholarly journals Manufacturing Quality Improvement with Data Mining Outlier Approach against Conventional Quality Measurements

2015 ◽  
Vol 8 (15) ◽  
Author(s):  
R. Shankar ◽  
M. Sundararajan
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Manufacturing Quality ◽  
Quality Measurements

Introduction to Data Mining and its Applications to Manufacturing

2008 ◽  
pp. 146-168 ◽  
Author(s):  
Jose D. Montero
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Statistical Methods ◽  
Business Intelligence ◽  
Computational Intelligence ◽  
Data Warehousing ◽  
Great Promise ◽  
Main Stream ◽  
Manufacturing Companies ◽  
Using Data

This chapter provides a brief introduction to data mining, the data mining process, and its applications to manufacturing. Several examples are provided to illustrate how data mining, a key area of computational intelligence, offers a great promise to manufacturing companies. It also covers a brief overview of data warehousing as a strategic resource for quality improvement and as a major enabler for data mining applications. Although data mining has been used extensively in several industries, in manufacturing its use is more limited and new. The examples published in the literature of using data mining in manufacturing promise a bright future for a broader expansion of data mining and business intelligence in general into manufacturing. The author believes that data mining will become a main stream application in manufacturing and it will enhance the analytical capabilities in the organization beyond what is offered and used today from statistical methods.

A Journey from Big Data to Data Mining in Quality Improvement

2021 ◽  
pp. 31-43
Author(s):  
Sharad Goel ◽  
Prerna Bhatnagar
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Quality Improvement

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

2018 ◽  
Vol 25 (1) ◽  
pp. 47-75 ◽  
Author(s):  
Loukas K. Tsironis
Keyword(s):  
Data Mining ◽  
Quality Improvement ◽  
Quality Management ◽  
Rough Sets ◽  
Design Methodology ◽  
Classification Rules ◽  
Content Type ◽  
Useful Knowledge ◽  
Innovative Work ◽  
Quality Tools

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.

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