Bir Çimento Firmasında İstatistiksel Zaman Serileri Yöntemleri ve Derin Öğrenme ile Talep Tahminleme

Öz

Talep tahminleri, üretim planlama, finansal planlama, bütçeleme, satın alma, satış gibi birçok iş sürecinin yönetilebilmesi için kritik öneme sahiptir. Talep tahminlerinin yüksek doğrulukla elde edilmesi, tüm tedarik zinciri yönetimi süreçlerinin başarısı için kilit bir faktördür. Bu çalışmada talep tahminleme problemi, bir çimento firmasının en yüksek satış payına sahip bir ürün grubunun geçmiş satış verileri kullanılarak ele alınmıştır. Ele alınan veri seti tek değişkenli bir zaman serisidir. Veri setinin ilk üç yılı eğitim, son yılı ise test seti olarak kullanılmıştır. Tahminleme için öncelikle geleneksel istatistiksel zaman serileri analiz yöntemleri uygulanmıştır. Eğitim setinde, uygulanan yöntemler içinde en başarılı olan istatistiksel zaman serileri yöntemi Basit Mevsimsel Yöntem (Simple Seasonal Method - SSM) olmuştur. SSM modelinin performansı, derin öğrenme yöntemlerinden Uzun-Kısa Süreli Bellek (Long-Short Term Memory-LSTM) temelli olarak geliştirilen modelin performansıyla karşılaştırılmıştır. LSTM modeli geliştirilirken ızgara (grid) arama yapılmış ve hiper-parametrelerin değerleri için en başarılı kombinasyon belirlenmiştir. Bu konfigürasyonla eğitilen LSTM modeli test setinde uygulanmıştır. Modellerin test setindeki tahmin performansları karşılaştırıldığında, LSTM modelinin SSM modeline göre MAPE ölçütünde %34,57, RMSE ölçütünde ise %33,74 iyileştirme sağladığı görülmüştür.

Anahtar Kelimeler

• Talep tahminleme
• Tek değişkenli zaman serileri
• İstatistiksel zaman serileri yöntemleri
• Derin öğrenme
• , Derin öğrenme

Demand Forecasting with Statistical Time Series Analysis Methods and Deep Learning in a Cement Firm

Abstract

Demand forecasts are critical for managing various business processes, including production planning, financial planning, budgeting, purchasing, and sales. Therefore, obtaining demand forecasts with high accuracy is a critical factor in successfully managing all supply chain management processes. This study addresses the demand forecasting problem by using actual past sales data of a cement firm’s one product group, which has the largest sales share among all product groups. The handled data set is a univariate time series. The first three years of the data set are used as the training set; the remaining one-year data is used as the test set. Firstly, classical statistical time series analysis methods are applied to the training data. Among the methods, the Simple Seasonal Method (SSM) has the best performance. The performance of the SSM model is compared to the model developed based on Long-Short Term Memory (LSTM), a deep learning method well-known for its success for time series data. A grid search approach is performed to determine the best combination of the values of hyper-parameters for LSTM models. Finally, the selected LSTM model configuration is applied to the test set. The prediction performances of the models in the test set indicate that the LSTM model provides 34.57% improvement in the MAPE criterion and 33.74% in the RMSE criterion compared to the SSM model.

Keywords

• Demand forecasting
• Univariate time series
• Statistical time series analysis methods
• Deep learning
• LSTM

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