KaydedilenlerDerginizi ekleyin

Dergiler / Sakarya University Journal of Science / 2026 / Volume: 22 Issue: 5

Time Series Analysis of Sales Quantity In An Automotive Company and Estimation By Artificial Neural Networks

Seher Arslankaya(SAKARYA UNIVERSITY)0000-0001-6023-2901
Vildan Öz(SAKARYA UNIVERSITY)0000-0002-9015-1798
Sayfa
—
Yayın Tarihi
2018-10-01
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Estimation results with Regression Analysis (RA) and time series are compared with the estimation results made with artificial neural networks.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The automotive sector is an indicator sector that sheds light on the economies of the countries.
📊
Temel Bulgular & Veri:These two parameters affect the semi-finished and finished product inventory levels which lead to an increase in the total cost of the production system.
💡
Sonuç & Katkı:Estimation results with Regression Analysis (RA) and time series are compared with the estimation results made with artificial neural networks.
🔬Tür:Research Article
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:automotive industry · regression analysis · demand forecast

Abstract

The automotive sector is an indicator sector that sheds light on the economies of the countries. Demand forecasting in such an important sector, as it is in every sector, is an important work topic. Two important problems of a real production environment are uncertain demand and unbalanced production times. These two parameters affect the semi-finished and finished product inventory levels which lead to an increase in the total cost of the production system. Demand forecasting is the estimation of how much consumers and services they will demand in the future with the aid of variables. In this study, automotive sector, one of the most important sectors of today, has been estimated demand of sales quantities. Estimation results with Regression Analysis (RA) and time series are compared with the estimation results made with artificial neural networks.

🔖

Bu Makaleye Atıf Yap

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, Volume: 22 Issue: 5. https://doi.org/10.16984/saufenbilder.456518

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  1. Tozun, M.F., Premium Application in Dealership System: Example of Automotive Sector, Beykent University, Institute of Social Sciences, Department of Business Administration, M.Sc. Thesis,2017.
  2. Karaca, K., Comparing the Methods Used in Vehicle Demand Forecasting, Istanbul Technical University, Department of Industrial Engineering, M.Sc. Thesis,2015.
  3. Makridakis, S., Wheelwright, S.C. ve Hyndman, R.J. (1998). Forecasting:methods and applications. Third Edition. Wiley.
  4. Yazıcıoğlu, N. Demand Forecasting with Artificial Intelligence, Uludağ University, Institute of Science and Technology, Department of Industrial Engineering, M.Sc. Thesis, 2010.
  5. Bolt, G., 1994. Market and sales forecasting: a total approach, Kogan Page, Londra.
  6. Olgun, S. Demand Forecasting Methods in Supply Chain Management and Application of a Demand Forecasting Model Based on Artificial Intelligence, Istanbul University, Institute of Science and Technology, Department of Industrial Engineering, M.Sc. Thesis, 2009.
  7. Kılıç, G., Pamukkale University, Graduate School of Natural and Applied Sciences, Computer Engineering Department, Master Thesis, 2015.
  8. Es, H. Turkey Net Energy Demand Forecasting with Artificial Neural Networks, Gazi University, Natural Sciences Institute, Department of Industrial Engineering, Master's Thesis, 2013.
  9. Yan, X. ve Su, X.G. (2009). Linear Regression Analysis: The or yand Computing. First edition, World Scientific Publishing Company.
  10. Hanke, J.E. ve Wichern, D.W.(2009). Business forecasting.Ninth Edition, International Edition. Pearson.
  11. Yücesoy, M. Demand Estimation with Artificial Neural Networks in the Clean Sector, Istanbul Technical University, Institute of Science and Technology, Department of Industrial Engineering, M.Sc. Thesis, 2011.
  12. Haykin, S. O. (2008). Neural Networks and Learning Machines (3 edition.). New York: Pearson.
  13. Fyfe, C. (2000). Artificial neural networks and information theory. University of Paisley. http://bit.ly/2yTVxIY accessed from.
  14. Bolt, G., 1994. Market and sales forecasting: a total approach, Kogan Page, Londra.