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The studies related to the production industry are limited in the world and in our country.
Especially in iron and steel sector, quality levels of different types of products need to be
monitored. Iron and steel products obtained from the studies have prolonged their use and price
and sales superiority has been achieved. At the same time, the market value of the products
increases and there is a minimum loss of product. Therefore, studies in this field should be focused
on. On the basis of quality, instead of debugging errors is the approach of not making mistakes.
Instead of using your earnings as a philosophy, we should adopt an understanding of gaining from
our losses. Understanding the importance of quality work and improvements, the primary purpose
of enterprises is to support quality production by preventing or reducing errors in production. Data
mining has started to be used effectively in enterprises. Data mining involves the process of
selecting, organizing and modeling the most necessary data for business executives. At this point,
it is possible to define data mining as a set of techniques and concepts that produce new
information for decision-making processes. In this study, firstly the VM process is defined and
then the VM studies which are selected from the literature covering 2010-2018 and applied to
certain quality improvement problems in the manufacturing sector are evaluated. The definition of
process and product quality, estimation of quality, classification of quality and optimization of
quality parameters are discussed. In addition, the application of decision trees, one of the most
widely used and effective VM techniques, in order to determine the variables and levels that cause
production errors in an industrial organization is also included.
International Data Science & Engineering Symposium
IDSES
İsmail Burak AKINCI
Filiz ERSÖZ