Publication: Robotik süreç otomasyonu kullanarak çalışan performans kpı’larının yapay sinir ağları ile tahmini : çağrı merkezi üzerine bir uygulama
Abstract
Küreselleşme ve artan rekabet ortamında işletmelerin asıl amacı karlılık sağlayarak faaliyetlerine devam etmesidir. Günümüzde işletmelerin faaliyetlerini devam ettirmesi ve mevcut hedeflerine ulaşmasında en önemli faktör çalışanlarıdır. Çalışanları organizasyonel hedeflere yönlendirmede, performans değerlemenin ve performans tahmininin önemli bir yeri vardır. Çalışmamızda en uygun zaman aralığında, çalışan performansına ait verilerin robotik süreç otomasyonu kullanılarak sistemden alınması ve yapay sinir ağları ile tahmin edilmesi amaçlanmıştır. Yapay sinir ağları kullanılarak, çalışanın geçmiş dönem verileri üzerinden performans tahminine yönelik modeller oluşturulmuştur. Sonraki aşamada oluşturulan modeller karşılaştırılmıştır.
In globalization and increasing competition, the main purpose of businesses is to continue their activities by providing profitability. Today, the most important factor for businesses to continue their activities and achieve their current goals is their employees. Performance evaluation and performance estimation play an important role in guiding company employees towards organizational goals. In our study, it was aimed to predict employee performance in the most appropriate time interval. And the data is taken from the system by robotic process automation. Therefore, models for performance estimation based on the employee's previous period data were created using artificial neural networks. Then, the result of these two models are evaluated and compared.
In globalization and increasing competition, the main purpose of businesses is to continue their activities by providing profitability. Today, the most important factor for businesses to continue their activities and achieve their current goals is their employees. Performance evaluation and performance estimation play an important role in guiding company employees towards organizational goals. In our study, it was aimed to predict employee performance in the most appropriate time interval. And the data is taken from the system by robotic process automation. Therefore, models for performance estimation based on the employee's previous period data were created using artificial neural networks. Then, the result of these two models are evaluated and compared.
