Bacalı Kombi Satış Miktarlarının Tahmininde Yapay Sinir Ağının Kullanılması ve Tedarik Zinciri Yönetimi İçerisindeki Önemi
Abstract
Çalışmanın amacı, PRODTR kodlu "Bacalı Kombi" ürün satışlarını Yapay Sinir Ağı kullanarak tahmin etmek ve bu tahminin tedarik zinciri yönetimi açısından önemini açıklamaktır. Veri seti olarak Türkiye İstatistik Kurumu'nun yayınladığı "Kombi (Bacalı)" isimli ürüne ait değişken değerleri kullanılmıştır. Yıllık veriler için yapay sinir ağı oluşturulmuş ve üretilen tahmin sonuçları gerçekleşen değerlerle karşılaştırılmıştır. Tahmin duyarlılığı hesaplanmış ve tedarik zinciri yönetimi açısından önemi vurgulanmıştır. MATLAB Neural Network Toolbox, Yapay Sinir Ağının eğitilmesi ve tahmin işleminin gerçekleştirilmesi için kullanılmıştır.
References
- Adewole, B. Z., Abidakun, O. A. ve Asere, A. A. (2013). Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner. Energy, Vol. 61, 606-611.
- Agatonovic-Kustrin, S. ve Beresford, R. (2000). Basic concepts of Artificial Neural Network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717-727.
- Al-Chalabi, H., Ahmadzadeh, F., Lundberg, J. ve Ghodrati, B. (2014). Economic lifetime prediction of a mining drilling machine using an Artificial Neural Network. International Journal of Mining, Reclamation and Environment, 28(5), 311-322.
Using Artificial Neural Network in Estimate of Sales Amounts of Vented Combi and Its Importance in Supply Chain Management
Abstract
Purpose of the study is to predict the sales of the "Combi (with chimney)" product coded PRODTR using Artificial Neural Network and to explain the importance of this prediction in terms of supply chain management. As the data set, variable values related to the product named "Combi (with chimney)" has published by the Turkish Statistical Institute were used. An artificial neural network has created for annual data, and the forecast results produced were compared with the actualized values. Forecast sensitivity has calculated and its importance for supply chain management is highlighted. MATLAB Neural Network Toolbox has used for to be trained the Artificial Neural Network and to be performed the prediction process.
References
- Adewole, B. Z., Abidakun, O. A. ve Asere, A. A. (2013). Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner. Energy, Vol. 61, 606-611.
- Agatonovic-Kustrin, S. ve Beresford, R. (2000). Basic concepts of Artificial Neural Network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717-727.
- Al-Chalabi, H., Ahmadzadeh, F., Lundberg, J. ve Ghodrati, B. (2014). Economic lifetime prediction of a mining drilling machine using an Artificial Neural Network. International Journal of Mining, Reclamation and Environment, 28(5), 311-322.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Artificial Intelligence (Other), Production and Operations Management, Supply Chain Management |
| Journal Section | Articles |
| Authors | |
| Publication Date | January 31, 2024 |
| Submission Date | October 14, 2023 |
| Acceptance Date | January 23, 2024 |
| Published in Issue | Year 2024 Volume: 15 Issue: 1 |
