The usage of data mining techniques for quality improvement in manufacturing industry

Abstract

Quality is a major requirement of competition in today's world markets. Organizations give much more importance to quality design, control and improvement of products and processes, and accomplish these with the participation of all employees. As a result, it is aimed to achieve customer satisfaction along with reduction in cost and increase in productivity and profitability. In quality improvement (QI) studies, a variety of analyses are performed by collecting data from the field, customer and manufacturing. In these analyses, an increasing number of data mining (DM) approaches are being used, especially for large datasets with too many and mixed type of input and output variables. However, DM is still not widely known and utilized by people practicing QI, and there is no sufficient research into the possible contributions of it to QI. In this study, first of all, the DM process is defined, and then selected DM applications on certain QI problems in manufacturing industry, published in 1997-2007, are examined. Among the QI problems, the followings are studied: description of product and process quality, prediction of quality, classification of quality, and optimization of quality parameters. Moreover, a case study is presented, which utilizes a commonly used and effective DM technique called decision trees for identifying influential process variables and their levels that cause casting defects in a casting company.

Keywords

• manufacturing industry,quality,improvement,data mining

İmalat sektöründe kalite iyileştirmede veri madenciliği tekniklerinin kullanımı

Abstract

Günümüzde kalite dünya pazarında rekabetin ana unsurlarından biri haline gelmiştir. İşletmeler artık ürün ve süreçlerin kalite tasarım, kontrol ve iyileştirme çalışmalarına daha fazla önem vermekte, bu çalışmaları da tüm çalışanların katılımıyla gerçekleştirmektedir. Sonuç olarak müşteri memnuniyetinin kazanılmasıyla birlikte maliyetlerin düşürülmesi, verimlilik ve kârlılığın artırılması istenmektedir. Kalite iyileştirme çalışmalarında sıklıkla sahadan, müşteriden ve üretimden veriler toplamak yoluyla çeşitli analizler yapılmaktadır. Bu analizlerde, özellikle karışık tipte ve çok sayıda girdi ve çıktı değişkenine sahip büyük miktardaki veri kümeleri için giderek daha fazla veri madenciliği (VM) yaklaşımları kullanılmaktadır. Ancak VM, kalite iyileştirme çalışmalarında bulunanlar tarafından hâlâ yeterince tanınmayan ve kalite iyileştirmeye olası katkıları yeterince araştırılmamış bir alandır. Bu çalışmada, öncelikle VM süreci tanımlanmış ve ardından 1997-2007 yılları arasını kapsayan literatürden seçilen, imalat sektöründe belirli kalite iyileştirme problemlerine uygulanmış VM çalışmaları değerlendirilmiştir. Kalite iyileştirme problemlerinden süreç ve ürün kalitesinin tanımlanması, kalitenin tahmini, kalitenin sınıflandırılması ve kalite parametrelerinin optimizasyonu üzerinde durulmuştur. Çalışmada ayrıca, en yaygın kullanılan ve etkili VM tekniklerinden karar ağaçlarının bir döküm fabrikasında döküm hatalarına neden olan değişkenleri ve seviyelerini belirlemek amacıyla yapılan uygulamaya yer verilmiştir.

Keywords

• imalat sanayii,kalite,iyileştirme,veri madenciliği

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