DERİN SİNİR AĞLARI KULLANARAK TÜRKİYE’DEKİ OTOMOBİL SATIŞLARININ TAHMİNİ İÇİN BİR MODEL

Yıl 2020, Cilt: 31 Sayı: 1, 57 - 74, 30.04.2020

Sema Kayapınar Kaya , Özal Yıldırım

Öz

Özet: Tüm kararlarda itici güç olan tedarik zinciri sürecinde talep tahmini, tedarik zinciri sürecinin en önemli bileşenlerinden birisidir. Gelecekteki ürün ve hizmetlerin tahmin edilmesi, diğer tüm tahminlerin başlangıç noktası olup diğer tüm süreçlerin temel girdisini oluşturmaktadır. Bu çalışmada, otomobil satış tahmini için 8 katmanlı Derin Sinir Ağ modeli önerilmiştir. Modelin girdileri, Döviz Kuru, Gayrisafi Yurt içi Hasıla, Tüketici Güven Endeksi ve Tüketici Fiyat Endeksi gibi faktörlerden oluşmaktadır. Modelin çıkışına göre araç satış tahmini yapılmıştır. 2011 ve 2018 yılları arasında aylık bazda toplam 90 veri toplanılarak analizler yapılmıştır.

Anahtar Kelimeler

Derin Sinir Ağı, Tedarik Zinciri Yönetimi, Satış tahmini

Destekleyen Kurum

yok

Proje Numarası

yok

A PREDICTION MODEL FOR AUTOMOBILE SALES IN TURKEY USING DEEP NEURAL NETWORKS

Öz

Demand prediction in the supply chain process, which is the driving force in all decisions, is one of the most important components of the supply chain process. Prediction of future goods and services is the starting point of all other predictions and provides the basic entry to all other functions. In this study, an 8-layer Deep Neural Network model was recommended for vehicle sales prediction. The inputs of the model consist of features, such as exchange rate, the gross domestic product, consumer confidence index, and the consumer price index. The vehicle sales forecast was made according to the output of the model. We analyzed a total of 90 data on a monthly basis between the years of 2011 and 2018 was collected.

Anahtar Kelimeler

Deep Neural Network, Supply Chain Management

Proje Numarası

yok

Kaynakça

• Aburto, L., & Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, 7(1), 136–144. doi: 10.1016/j.asoc.2005.06.001.
• Akyurt, İ. Z. (2015). Talep Tahmininin Yapay Sinir Ağlarıyla Modellenmesi: Yerli Otomobil Örneği. 23, 147–157.
• Anggraeni, W., Vinarti, R. A., & Kurniawati, Y. D. (2015). Performance Comparisons between Arima and Arimax Method in Moslem Kids Clothes Demand Forecasting: Case Study. Procedia Computer Science, 72, 630–637. doi: 10.1016/j.procs.2015.12.172.
• Arslankaya, S., & Öz, V. (2018). Time Series Analysis of Sales Quantity in an Automotive Company and Estimation by Artificial Neural Networks. Sakarya University Journal of Science, 1–1. doi: 10.16984/saufenbilder.456518.
• Chang, P.-C., & Wang, Y.-W. (2006). Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert Systems with Applications, 30(4), 715–726. doi: 10.1016/j.eswa.2005.07.031
• Chawla, A., Singh, A., Lamba, A., Gangwani, N., & Soni, U. (2019). Demand Forecasting Using Artificial Neural Networks—A Case Study of American Retail Corporation. In H. Malik, S. Srivastava, Y. R. Sood, & A. Ahmad (Eds.), Applications of Artificial Intelligence Techniques in Engineering (pp. 79–89). Springer Singapore.
• Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information. Management Science, 46(3), 436–443. doi: 10.1287/mnsc.46.3.436.12069.
• Chen, Y., Yao, M., & Zhang, J. (2018). Research on Auto Sales Forecast Based on Online Reviews-Take R Brand Automobile as an Example. 2018 15th International Conference on Service Systems and Service Management (ICSSSM), 1–5. doi: 10.1109/ICSSSM.2018.8465048.
• Disney, S. M., Farasyn, I., Lambrecht, M., Towill, D. R., & de Velde, W. V. (2006). Taming the bullwhip effect whilst watching customer service in a single supply chain echelon. European Journal of Operational Research, 173(1), 151–172. doi: 10.1016/j.ejor.2005.01.026.
• Fantazzini, D., & Toktamysova, Z. (2015). Forecasting German car sales using Google data and multivariate models. International Journal of Production Economics, 170, 97–135. doi: 10.1016/j.ijpe.2015.09.010.
• Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, 1–13.
• Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A., & Sassi, R. J. (2016). Study on Daily Demand Forecasting Orders using Artificial Neural Network. IEEE Latin America Transactions, 14(3), 1519–1525. doi: 10.1109/TLA.2016.7459644.
• Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1), 3–23. doi: 10.1016/j.ijforecast.2008.11.010.
• Fleurke, S. J. O. E. R. T. (2017). Forecasting Automobile Sales using an Ensemble of Methods. WSEAS Transactions on Systems, WSEAS, 16, 337-345. Gahirwal, M. (2013). Inter Time Series Sales Forecasting. ArXiv:1303.0117.
• Galina Merkuryeva, Aija Valberga, & Alexander Smirnov. (2018). Demand forecasting in pharmaceutical supply chains: A case study. 149, 3–10. doi: 10.1016/j.procs.2019.01.100
• Gavcar, E., Şen, S., & Aytekin, A. (1999). Prediction Forecasting of the Papers used in Turkey. Turkish Journal of Agriculture and Forestry, 23(2), 203-212.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning (Vol 1). MIT Press.
• Hsiao, J. M., & Shieh, C. J. (2006). Evaluating the value of information sharing in a supply chain using an ARIMA model. The International Journal of Advanced Manufacturing Technology, 27(5), 604–609. doi: 10.1007/s00170-004-2214-4.
• Hülsmann, M., Borscheid, D., Friedrich, C. M., & Reith, D. (2011). General Sales Forecast Models for Automobile Markets Based on Time Series Analysis and Data Mining Techniques. Advances in Data Mining. Applications and Theoretical Aspects, 255–269.
• Issam Nouiri, Samar ben Ammar, Khaoula Belhsen, Amal Jridi, Malek Derbala, & Khaoula Neffati. (2019). Drinking water demand forecasting using Artificial Neural Network in Tunisia. 11–14. Retrieved from https://www.researchgate.net/publication/331812737_Drinking_water_demand_forecasting_using_Artificial_Neural_Network_in_Tunisia.
• Jaipuria, S., & Mahapatra, S. S. (2014). An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, 41(5), 2395–2408. doi: 10.1016/j.eswa.2013.09.038.
• Jebaraj, S., & Iniyan, S. (2015). Oil demand forecasting for India using artificial neural network. International Journal of Global Energy Issues, 38(4–6), 322–341. doi: 10.1504/IJGEI.2015.070280
• Karaatlı, M., Helvacıoğlu, Ö. C., Ömürbek, N., & Tokgöz, G. (2012). Yapay sinir ağları yöntemi ile otomobil satış tahmini. Uluslararası Yönetim İktisat ve İşletme Dergisi, 8(17), 87–100.
• Kingma, D. P., & Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980
• Kochak, A., & Sharma, S. (2015). Demand forecasting using neural network for supply chain management. International journal of mechanical engineering and robotics research, 4(1), 96-104.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 1097–1105.
• LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.
• LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551.
• Matsumoto, M., & Ikeda, A. (2015). Examination of demand forecasting by time series analysis for auto parts remanufacturing. Journal of Remanufacturing, 5(1), 1. doi: 10.1186/s13243-015-0010-y
• Pai, P., & Liu, C. (2018). Predicting Vehicle Sales by Sentiment Analysis of Twitter Data and Stock Market Values. IEEE Access, 6, 57655–57662. doi: 10.1109/ACCESS.2018.2873730
• Sarikaya, R., Hinton, G. E., & Deoras, A. (2014). Application of deep belief networks for natural language understanding. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 22(4), 778–784.
• Shed Shahabuddin. (2009). Forecasting automobile sales | Management Research News. 32(7), 670–682.
• Slimani, I., Farissi, I. E., & Achchab, S. (2015). Artificial neural networks for demand forecasting: Application using Moroccan supermarket data. 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), 266–271. doi: 10.1109/ISDA.2015.7489236
• Šubelj, L., Furlan, Š., & Bajec, M. (2011). An expert system for detecting automobile insurance fraud using social network analysis. Expert Systems with Applications, 38(1), 1039–1052. doi: 10.1016/j.eswa.2010.07.143
• Sultan, J. A., & Jasim, R. M. (2016). Demand forecasting using artificial neural networks optimized by artificial bee colony. Int J. Manage, Inf. Technol. Eng, 4(7), 77-88. TCMB. (2019). Retrieved from https://www.tcmb.gov.tr/wps/wcm/connect/en/tcmb+en.
• Vahabi, A., Seyyedi, S., & Alborzi, M. (2016). A Sales Forecasting Model in Automotive Industry using Adaptive Neuro-Fuzzy Inference System(Anfis) and Genetic Algorithm(GA). International Journal of Advanced Computer Science and Applications, 7(11). doi: 10.14569/IJACSA.2016.071104
• Wang, F.-K., Chang, K.-K., & Tzeng, C.W. (2011). Using adaptive network-based fuzzy inference system to forecast automobile sales. Expert Systems with Applications, 38(8), 10587–10593. doi: 10.1016/j.eswa.2011.02.100
• Wang, S.-J., Huang, C.-T., Wang, W.-L., & Chen, Y.-H. (2010). Incorporating ARIMA forecasting and service-level based replenishment in RFID-enabled supply chain. International Journal of Production Research, 48(9), 2655–2677. doi: 10.1080/00207540903564983
• Yildirim, Ö. (2018). A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Computers in Biology and Medicine, 96, 189–202.