YSA, ARIMA ve ARIMAX Yöntemleriyle Satış Tahmini: Beyaz Eşya Sektöründe bir Uygulama

Beyaz eşya sektörü Türkiye’nin istihdama ve ihracata verdiği katkılardan dolayı lokomotif sektörlerinden biridir. Son yıllarda yaşanan teknolojik gelişmeler ve küreselleşme ile birlikte yaşanan zorlu rekabet koşullarından etkilenen sektörler içerisinde yer almaktadır. Etkili bir üretim planlaması; mevcut talebi zamanında ve minimum maliyetle karşılayabilmelidir. Mevcut talebi tespit edebilmek ise iyi bir satış tahmini ile mümkün olmaktadır. Bu yönüyle satış tahmini, karar vericilerin başarılı olmalarında önemli bir rol oynamaktadır. Bu çalışmada, beyaz eşya sektörü için bir satış tahmini modeli önerilmiştir. Bulaşık makinesi, çamaşır makinesi, buzdolabı, küçük ev aletleri ve televizyon ürünleri için 46 aylık satış verileri kullanılmıştır. Satışları etkileyen faktörler olan döviz kuru, tatil günleri, tüketici güven endeksi (TGE), üretici fiyat endeksi (ÜFE) ve bölgedeki konut satışları, açıklayıcı değişken olarak kullanılmıştır. Yapay sinir ağları (YSA), ARIMA ve ARIMAX yöntemleri ile elde edilen sonuçlar, ortalama kareli hata (OKH) performans kriterine göre kıyaslandığında en isabetli tahminlerin YSA yöntemi kullanılarak elde edildiği söylenebilir.

Sales Forecast with YSA, ARIMA and ARIMAX Methods: An Application in the White Goods Sector

The white goods sector is one of the locomotive sectors of the country due to the contributions of employment and exports. The technological developments and globalization experienced in recent years are among the sectors that are affected by the changing and competitive conditions. Efficient production planning; must meet the current demand in time and with minimal cost. Determining the current demand is possible with a good sales forecast. In this sense, sales forecasting plays an important role in the success of decision makers. In this study, the sales forecast model for the white goods sector was proposed. The 46 months’ sales data have been used for dishwashers, refrigerators, small house appliances and televisions. The exchange rate, holiday days, consumer confidence index, producer price index, housing sales in the region are used as explanatory variable. It can be said that the most accurate estimates are obtained by using the ANN method when mean squared error (MSE) compared which is the performance criterion.

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