Bir yem işletmesi için satış tahmin yöntemlerinin karşılaştırılması: Bir vaka çalışması

Öz

Son yıllarda küresel ısınmadan dolayı doğal kaynakların etkin ve verimli kullanımı dünyamız için daha da önemli bir hale gelmiştir. Azalan doğal kaynaklar tarım ve gıda zincirlerini daha etkin yönetim stratejileri benimsemeye zorlamaktadır. Başarılı bir yönetimin ilk şartı doğru ve güvenilir tahminlere dayalı planlar yapmaktır. Bu çalışmada tarım ve gıda zincirlerinin ilk halkası olan yem endüstrisinde yer alan bir işletme için gerçek verilere dayalı olarak oluşturulan tahmin modelleri karşılaştırılmıştır. Geleneksel istatistiksel zaman serisi yöntemleri popüler ve hesaplamalı olarak etkin iki yapay zekâ tekniği olan yapay sinir ağları ve destek vektör regresyonu yöntemleri ile karşılaştırılmıştır. Tahminlerin doğruluğu ortalama mutlak hata (MAE), ortalama mutlak yüzde hata (MAPE) ve ortalama hata kare (MSE) gibi üç farklı hata ölçütü kullanılarak hesaplanmıştır. Sonuçlar destek vektör regresyonu yönteminin zaman serisi ve yapay sinir ağları yöntemlerine göre daha iyi sonuçlar ürettiğini göstermiştir.

Anahtar Kelimeler

• Satış tahmini,Zaman serisi yöntemleri,Yapay sinir ağları,Destek vektör regresyonu,Tarım ve gıda zincirleri,Yem endüstrisi

A comparison of sales forecasting methods for a feed company: A case study

Abstract

Due to global warming in recent years, using natural resources in an effective way has become more and more important to our world. Decreasing natural resources are pushing agriculture and food chains to adopt more efficient management strategies. The first condition for a successful management is to make plans based on accurate and reliable forecasts. In this study, using real-world data, forecasting models are compared for the products of a feed company which is the first chain of agriculture and food chain systems. The traditional statistical time series methods are compared to two popular and effective computational intelligence techniques, i.e. artificial neural networks and support vector regression. The accuracy of the forecasts is calculated by three different error measures, i.e., the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the mean squared error (MSE). The results show that support vector machines produces significantly better results comparing to both time series methods and artificial neural networks.

Keywords

• Sales forecasting,Time series methods,Artificial neural networks,Support vector regression,Agriculture and food chains,Feed industry

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