Enhancing product quality of a process
| Date | 24 August 2012 |
| Published date | 24 August 2012 |
| DOI | https://doi.org/10.1108/02635571211264618 |
| Pages | 1181-1200 |
| Author | Cebrail Çiflikli,Esra Kahya‐Özyirmidokuz |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Enhancing product quality
of a process
Cebrail C¸iflikli and Esra Kahya-O
¨zyirmidokuz
Kayseri Vocational College, Erciyes University, Kayseri, Turkey
Abstract
Purpose – Data mining (DM) is used to improve the performance of manufacturing quality control
activity, and reduces productivity loss. The purpose of this paper is to discover useful hidden patterns
from fabric data to reduce the amount of defective goods and increase overall quality.
Design/methodology/approach – This research examines the improvement of manufacturing
process via DM techniques. The paper explores the use of different preprocessing and DM techniques
(rough sets theory, attribute relevance analysis, anomaly detection analysis, decision trees and rule
induction) in carpet manufacturing as the real world application problem. SPSS Clementine
Programme, Rosetta Toolkit, ASP (Active Server Pages) and VBScript programming language
are used.
Findings – The most important variables of attributes that are effective in product quality are
determined. A decision tree (DT) and decision rules are generated. Therefore, the faults in the process
are detected. An on-line programme is generated and the model’s results are used to ensure the
prevention of faulty products.
Research limitations/implications – In time, this model will lose its validity. Therefore, it must be
redeveloped periodically.
Practical implications – This study’s productivity can be increased especially with the help of
artificial intelligence technology. This research can also be applied to different industries.
Originality/value – The size and complexity of data make extraction difficult. Attribute relevance
analysis is proposed for the selection of the attribute variables. The knowledge discovery in databases
process is used. In addition, the system can be followed on-line with this interactive ability.
Keywords Data mining, Decisiontrees, Rough sets, Quality control,Productivity rate
Paper type Research paper
Introduction
Quality has increasingly played a key role in manufacturing to enhance a firm’s
competitive standing in today’s highly competitive business environment. Efficient
analysis of quality control data is critical for improving the availability and productivity
of the manufacturing system. Firms therefore should use new techniques that will
enhance product quality. To build quality into the process, the amount of collected data
must be converted to useful knowledge. Technological development has brought new
dimensions to quality improvement such as data mining (DM) which is used to improve
the performance of manufacturing quality control activity.
The large amounts of data, which are generated and collected during daily
operations and which contain hundreds of attributes in manufacturing, need to be
analysed for valuable decision making information about the process. Traditional
engineering techniques can help to some extent but there is a need to apply DM
technology to the manufacturing sector. DM is increasingly used in manufacturing for
understanding and then predicting valuable data. The overall goal of the DM process is
to extract knowledge from data in a human-understandable structure.
The current issue and full text archive of this journal is available at
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Enhancing
product quality
1181
Received 22 February 2012
Revised 25 April 2012
Accepted 26 April 2012
Industrial Management & Data
Systems
Vol. 112 No. 8, 2012
pp. 1181-1200
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635571211264618
A common and intuitive approach to problem solving is to examine what has
happened in the past to better understand the process, and then to predict and improve
future system performance. Hence, the error rates in manufacturing are commonly
used for knowledge acquisition to assist quality control engineers. DM can he lp in
identifying the patterns that lead toward potential failure of manufacturing equipment.
This methodology helps in identifying not only defective products but can also
simultaneously the determine significant factors that influence the success or failure of
the process (Harding et al., 2006).
Competitive improvement can be achieved in many ways, for example by improving
the quality of products or by reducing material waste, production or overhead costs, or
by decreasing the time to launch a new or improved product. DM can support these
improvements, through the extraction of knowledge from either existing data
warehouses, or from current production data. Applying this knowledge can help to
improve the quality of products by better controlling the manufacturing processes and
methodologies, and by ke eping product and producti on parameters in range
(Shahbaz et al., 2010).
This research is also important in fault diagnosis, such as predicting assembly
errors and defects, which is used to improve the performance of manufacturing quality
control activity.
Manufacturers are able to identify the characteristics surrounding defective
products. By understanding these characteristics, changes can be made to the
manufacturing process to improve the quality of the products being produced.
High-quality products lead to the improved reputation of an organisation within its
sector. Managers can use their own knowledge and experience along with the DM
model to determine unknown and complex relationships. DM methods can provide
significant value to all the data collected, stored and managed by manufacturers
during the last several years.
This paper presents an analysis of product quality improvement in the carpet
manufacturing industry via DM. It examines a large amount of data with too many
different categories of qualitative attributes in carpet manufacturing. Not only do we
detect and isolate faults, we also propose a C5.0 decision tree (DT) model which provides
an effective method of decision making. In addition, an on-line data collection and
preprocessing application is generated. VBScript programming language and activ e
server pages (ASP) using internet allowed the access of data from a wide variety of
sources.
The rest of the paper is organised as follows. The next section presents the
literature. In the third section, the data are described. In the fourth section, the
preprocessing techniques including data cleaning, missing value analysis, data
integration and transformation, data reduction, and link analysis are applied. In the
fifth section, a DT model is developed. Discussions are covered in the fifth section and
in the sixth section we draw our conclusions.
Literature review
The use of DM techniques in manufacturing began in the 1990s. In recent years, the
literature has presented several studies that examine the implementation of DM
techniques in manufacturing (Kumar et al., 2007; Li and Yeh, 2008; Ciflikli and
Kahya-O
¨zyirmidokuz, 2008; Gebus and Leiviska, 2009; Kusiak and Smith, 2007;
IMDS
112,8
1182
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