E-ticaret sektörü için makine öğrenimi tabanlı talep tahmin modellerinin geliştirilmesi

Yıl 2025, Cilt: 7 Sayı: 1, 13 - 20

Alim Toprak Fırat , Onur Aygün , Mustafa Göğebakan , Mehmet Fatih Akay , Ceren Ulus

https://doi.org/10.70669/ijedt.1567739

Öz

E-ticaret sektörü son yıllarda hızlı ve dinamik bir büyüme göstermiştir. Bu rekabetçi sektörde lider olmayı hedefleyen şirketler için değişen tüketici taleplerine verimli ve maliyet etkin bir şekilde yanıt vermek büyük önem taşımaktadır. Bu bağlamda, gelecekteki ürün talebini doğru bir şekilde tahmin etme yeteneği hayati hale gelmektedir. Bu çalışma, gelecekteki ürün talebini tahmin etmek amacıyla, Çok Katmanlı Algılayıcı (MLP), Çok Ufuklu Çeyrek Tekrarlayan Sinir Ağı (MQRNN) ve Rastgele Orman (RF) gibi makine öğrenimi tabanlı teknikler kullanılarak tahmin modelleri geliştirmeyi amaçlamaktadır. Hızlı Tüketim Ürünleri (FMCG) için günlük satış verilerine dayalı olarak 1 Ocak 2023 ile 25 Ağustos 2024 tarihleri arasındaki dönemi kapsayan bu modeller, Temmuz ve Ağustos aylarına yönelik talep tahmini yapmak için oluşturulmuştur. Modellerin performansları Ortalama Mutlak Yüzde Hatası (MAPE) metriği kullanılarak değerlendirilmiştir. MLP, MQRNN ve RF kullanılarak geliştirilen tahmin modelleri incelendiğinde, en iyi performansı MQRNN modelinin gösterdiği gözlemlenmiştir.

Anahtar Kelimeler

E-Ticaret, Talep Tahmini, Makine Öğrenimi

Development of machine learning based demand forecasting models for the e-commerce sector

Öz

The e-commerce sector has undergone rapid and dynamic growth in recent years. For companies aspiring to lead in this competitive industry, it is crucial to efficiently and cost-effectively respond to evolving consumer demands. In this context, the ability to accurately forecast future product demand becomes imperative. This study aims to develop forecasting models utilizing machine learning-based techniques, specifically Multi-Layer Perceptron (MLP), Multi-Horizon Quantile Recurrent Neural Network (MQRNN), and Random Forest (RF), to predict future product demand. The demand forecasting models were developed for the months of July and August, based on daily sales data for Fast-Moving Consumer Goods (FMCG) products spanning from January 1, 2023, to August 25, 2024. The models’ performances were evaluated using Mean Absolute Percentage Error (MAPE). Upon examining the forecasting models developed using MLP, MQRNN, and RF, it has been observed that MQRNN exhibited the superior performance.

Anahtar Kelimeler

E-Commerce, Demand Forecasting, Machine Learning

Kaynakça

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