TY  - JOUR
T1  - KALİTE ÖLÇÜMÜNDE İŞGÜCÜ PLANLAMASI VE YAPAY GÖRME SİSTEMLERİ
TT  - LABOR PLANNING AND ARTIFICIAL VISION SYSTEMS IN QUALITY MEASUREMENT
AU  - Özcan, Ali
AU  - Sümen, Halil Halefşan
AU  - Erkasap, Ahmet
PY  - 2023
DA  - August
Y2  - 2023
DO  - 10.51947/yonbil.1341749
JF  - Uluslararası Akademik Yönetim Bilimleri Dergisi
JO  - YONBIL
PB  - Yüksekbilgili Eğitim ve Danışmanlık Ltd. Şti.
WT  - DergiPark
SN  - 2149-1984
SP  - 29
EP  - 42
VL  - 9
IS  - 14
LA  - tr
AB  - Günümüzde Moore yasasına uygun şekilde gücü hızla artıp ucuzlayan bilgisayarlar yapay görme sistemlerinin yaygınlaşmasının önünü açmıştır. Bununla beraber yapay görme sistemlerinin yazılımlar, kameralar, ışık sistemleri gibi başka bileşenler gerektirmesi ve bu bileşenlerin entegrasyonu ile eğitim çalışmaları konuyu ciddi bir yatırım konumuna getirmektedir. Kuşkusuz yapay görme sistemlerinin endüstride ve iç lojistik uygulamalarda kalitesizlik maliyetlerini azaltan çok önemli bir araç olması yatırımları teşvik etmektedir. Ancak ekonomiklik ilkesine uyması da vazgeçilemez koşuldur. Dolayısı ile bu sistemlerin kullanıldıkları süre boyunca götürülerinin üstünde getiri sağlamalarına dikkat edilmelidir. Bu çalışmada kalite iyileştirmeleri sağlamak amaçlı kullanılması düşünülen yapay görme sistemlerinin yatırım karlılığının anlaşılmasını kestirecek bir model tanıtılmaktadır. Ülkemiz genelinde yürüttüğümüz araştırmalar ile EMVA (European Machine Vision Association) ile yaptığımız görüşmeler çerçevesinde temin edilen veriler benzer bir modelin bulunmaması nedeniyle kimi karsız yatırımların yapılmakta olduğu, kimi karlı olabilecek yatırım fırsatlarının kaçırıldığı gerçeklerini ortaya çıkartmıştır. Model, kalitesizlik maliyetlerini, yapay görme sisteminin tahmini bedelini, kalitesizlik maliyetlerinde yapay görme sistemi sayesinde sağlanabilecek tasarruf tutarını veri olarak almakta ve işgücü maliyetleri açısından da bir öneri geliştirmektedir.
KW  - Yapay görme
KW  - kalite kontrol
KW  - kalitesizlik maliyetleri
KW  - işgücü maliyetleri
N2  - Today, computers, whose power is rapidly increasing and becoming cheaper in accordance with Moore's law, have paved the way for the widespread use of machine vision systems. However, the fact that machine vision systems require other components such as software, cameras, lighting systems, and the integration of these components and training studies make the subject a serious investment. Undoubtedly, the fact that machine vision systems are a very important tool that reduces the costs of poor quality in industry and internal logistics applications encourages investments. However, it is also indispensable that they comply with the principle of economy. Therefore, care should be taken to ensure that these systems provide returns in excess of their costs for the duration of their use. In this study, we introduce a model to predict the return on investment of machine vision systems that are intended to be used for quality improvements. Research conducted in Turkey and discussions with EMVA (European Machine Vision Association) have revealed that due to the lack of a similar model, some unprofitable investments are being made and some potentially profitable investment opportunities are being missed. The model takes as data the costs of poor quality, the estimated cost of the machine vision system, the amount of savings that can be achieved through the machine vision system in poor quality costs and develops a proposal in terms of labor costs.
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UR  - https://doi.org/10.51947/yonbil.1341749
L1  - https://dergipark.org.tr/tr/download/article-file/3326281
ER  -