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Jute Yarn Consumption Prediction by Artificial Neural Network and Multilinear Regression

Zeynep Didem Unutmaz Durmuşoğlu, Selma Gülyeşil

Abstract

In today’s increasing competitive market conditions, the companies operating in the production and service sectors should meet the demand of the customers in a timely and completely manner. Therefore, all resources (raw materials, semi-finished products, energy sources, etc.) should be planned and supplied at the right time, at the right place and at sufficient quantity based on an accurate forecast of the demand. In the literature, there have been few studies about forecasting of raw material consumption in a production sector. In this study, ANN method was employed to predict the raw material consumption of a carpet production company. The relevant variables of the actual data belonging to 2015-2016 and 2017 were used. In addition, a multiple linear regression (MLR) model was also established to compare the performance of ANN method. The results show that ANN method produces more accurate forecasts when compared to MLR method.

Keywords



Volume 3, No 3, Supplement, pp 43-53, 2018



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References
  • Azadeh, A., Ghaderi, S. F., Tarverdian, S., &Saberi, M. (2007). Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Applied Mathematics and Computation, 186(2), 1731-1741. https://doi.org/10.1016/j.amc.2006.08.093
  • Co, H. C., &Boosarawongse, R. (2007). Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers & Industrial Engineering, 53(4), 610-627. https://doi.org/10.1016/j.cie.2007.06.005
  • Fonseca, D. J., &Navaresse, D. (2002). Artificial neural networks for job shop simulation. Advanced Engineering Informatics, 16(4), 241-246. https://doi.org/10.1016/S1474-0346(03)00005-3
  • Gaafar, L. K., &Choueiki, M. H. (2000). A neural network model for solving the lot-sizing problem. Omega, 28(2), 175-184. https://doi.org/10.1016/S0305-0483(99)00035-3
  • Hamzacebi, C. (2008). Improving artificial neural networks’ performance in seasonal time series forecasting. Information Sciences, 178(23), 4550-4559. https://doi.org/10.1016/j.ins.2008.07.024
  • Hwarng, H. B. (2001). Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures. Omega, 29(3), 273-289. https://doi.org/10.1016/S0305-0483(01)00022-6
  • Lin, Y.-H., Shie, J.-R., & Tsai, C.-H. (2009). Using an artificial neural network prediction model to optimize work-in-process inventory level for wafer fabrication. Expert Systems with Applications, 36(2), 3421-3427. https://doi.org/10.1016/j.eswa.2008.02.009
  • Partovi, F. Y., &Anandarajan, M. (2002). Classifying inventory using an artificial neural network approach. Industrial Engineering, 16.
  • Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. https://doi.org/10.1016/S0925-2312(01)00702-0
  • Zhang, G. P., Patuwo, B. E., & Hu, M. Y. (2001). A simulation study of arti"cial neural networks for nonlinear time-series forecasting. Operations Research, 16.
  • Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501-514. https://doi.org/10.1016/j.ejor.2003.08.037
  • TCMB, http://www.tcmb.gov.tr/