Affiliation:
1. DOKUZ EYLÜL ÜNİVERSİTESİ
Abstract
Abstract Nowadays, businesses' forecasts to meet the demands have become more critical. This study aimed to predict the fifteen-day order demand for an order fulfillment center using a Multilayer Perceptron Neural Network (MLPNN). The dataset used in the study was created from a real database of a large Brazilian logistics company and thirteen variables. Linear Regression Coefficients (LRC) were used as a feature selection method to reduce estimation errors. The study showed that among the variables, order type_A (A5), order type_B (A6), and order type_C (A7) had the most significant impact on total order forecasting. The effect of A6 was found to be greater than the effect of A7 and A5. The performance of the proposed model was evaluated using the mean absolute percent error (MAPE). LRC-MLPNN provided a MAPE of 2.97%. The results showed that better forecasting performance was obtained by selecting the independent variables to be used as input to the forecasting model with LRC. The proposed model can also be applied to different estimation problems.
Publisher
Bitlis Eren Universitesi Fen Bilimleri Dergisi
Reference50 articles.
1. E. Eckhaus, “Consumer Demand Forecasting: Popular Techniques, Part 1: Weighted and Unweighted Moving Average,” 2010. [Online]. Available: http://www.purchasesmarter.com/articles/118. (Accessed: Nov. 22, 2021).
2. S. P. Sethi, H. Yan, and H. Zhang, Inventory and Supply Chain Management with Forecast Updates. Chapter 1. New York: USA/Springer, 2005.
3. R. Fildes, K. Nikolopoulos, S. F. Crone, and A. A. Syntetos, “Forecasting and operational research: a review,” Journal of the Operational Research Society, vol. 59 (9), pp. 1150–1172, September 2008.
4. J. P. Donate, P. Cortez, G. G. Sánchez, and A.S. Miguela, “Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble,” Neurocomputing, vol. 109, pp. 27–32, June 2013.
5. K. Tanaka, “A sales forecasting model for new-released and nonlinear sales trend products,” Expert Systems with Applications,” vol. 37, pp. 7387–7393, November 2010.