Order Demand Forecast Using a Combined Approach of Stepwise Linear Regression Coefficients and Artificial Neural Network

Year 2022, Volume: 11 Issue: 2, 564 - 573, 30.06.2022

Serdar Gündoğdu

https://doi.org/10.17798/bitlisfen.1059772

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.

Keywords

Feature selection, forecast, linear regression, neural network

References

• 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).
• S. P. Sethi, H. Yan, and H. Zhang, Inventory and Supply Chain Management with Forecast Updates. Chapter 1. New York: USA/Springer, 2005.
• 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.
• 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.
• 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.
• P. C. Chang, and Y.W. Wang, “Fuzzy Delphi and backpropagation model for sales forecasting in PCB industry,” Expert Systems with Applications, vol. 30, pp. 715–726, May 2006.
• Y. Ni, and F. Fan, “A two-stage dynamic sales forecasting model for the fashion retail,” Expert Systems with Applications, vol.38, pp. 1529–1536, March 2011.
• Z. L. Sun, T. M. Choi, K. F. Au, and Y. Yu, “Sales forecasting using extreme learning machine with applications in fashion retailing,” Decision Support Systems, vol. 46, pp. 411–419, December 2008.
• P. Kumar, M. Herbert, and S. Rao, “Demand forecasting using Artificial Neural Network Based on Different Learning Methods: Comparative Analysis", IJRASET, vol. 2, pp. 364-374, April 2014.
• G. S. Groppo, M. A. Costa, and M. Libânio, “Predicting water demand: a review of the methods employed and future possibilities”, Water supply, vol. 19, pp. 2179-2198, August 2019.
• G. P. Zhang, “An investigation of neural networks for linear time-series forecasting,” Computers&Operations Research, vol. 28, pp. 1183–1202, October 2001.
• J. Adamowski, and C. Karapataki, “Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms," Journal of Hydrologic Engineering, vol. 15, pp. 729–743, October 2010.
• J. Caiado, “Performance of combined double seasonal univariate time series models for forecasting water demand,” Journal of Hydrologic Engineering, vol. 15, pp. 215–222, March 2010.
• J. Adamowski, H. F. Chan, S. O. Prasher, B. Ozga-Zielinski, and A. Sliusarieva, “Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada,” Water Resources Research, vol. 48, pp. 1-14, January 2012.
• M. Ghiassi, D. K. Zimbra, and H. Saidane, “Urban water demand forecasting with a dynamic artificial neural network model", Journal of Water Resources Planning and Management, vol. 134, pp. 138–146, March 2008.
• M. Fırat, M. A. Yurdusev, and M. E. Turan, “Evaluation of artificial neural network techniques for municipal water consumption modeling,” Water Resources Management, vol. 23, pp. 617–632, March 2009.
• M. Fırat, M .E. Turan, and M. A. Yurdusev, “Comparative analysis of neural network techniques for predicting water consumption time series,” Journal of Hydrology, vol. 384, pp. 46–51, April 2010.
• A. Altunkaynak, M. Özger, and M. Çakmakcı, “Water consumption prediction of Istanbul city by using fuzzy logic approach,” Water Resources Management, vol. 19, pp. 641–654, October 2005.
• M. Fırat, M. E. Turan, and M. A. Yurdusev, “Comparative analysis of fuzzy inference systems for water consumption time series prediction,” Journal of Hydrology, vol. 374, pp. 235–241, August 2009.
• C. Peña-Guzmán, J. Melgarejo, and D. Prats, “Forecasting water demand in residential, commercial, and industrial zones in Bogotá, Colombia, using Least-Squares Support Vector Machines,” Mathematical Problems in Engineering, vol. 2016, pp. 1-10, December 2016.
• M. Nasseri, A. Moeini, and M. Tabesh, “Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming,” Expert Systems with Applications, vol. 38, pp. 7387–7395, June 2011.
• A. Chawla, A. Singh, A. Lamba, N. Gangwani, and U. Soni, “Demand Forecasting using Artificial Neural Networks – A case study of American Retail Corporation,” in Advances in Intelligent Systems and Computing: Applications of Artificial Intelligence Techniques in Engineering, H. Malik, S. Srivastava, Y. Sood., A. Ahmad, Eds. Singapore: Springer, 2019. pp. 79-89.
• A. Öztekin, R. Kizilaslan, S. Freund, and A. Iseri “A data analytic approach to forecasting daily stock returns in an emerging market,” European Journal of Operational Research, vol. 253, pp. 697-710, September 2016.
• R. J. Kuo, and K. C. Xue, “A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights,” Decision Support Systems, vol. 24, pp. 105–126, December 1998.
• R. Law, “Backpropagation learning in improving the accuracy of neural network-based tourism demand forecasting,” Tourism Management, vol. 21, pp. 331–340, August 2000.
• Z. Ying, and X. Hanbin, “Study on the model of demand forecasting based on artificial neural network,” in 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Hong Kong, China, August 10-12, 2010, pp. 382-386.
• S. Bhadouria, and A. Jayant, “Development of ANN Models for Demand Forecasting,” American Journal of Engineering Research (AJER), vol. 6, pp. 142-147, December 2017.
• P. du Jardin, and E. Séverin, “Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time,” European Journal of Operational Research, vol. 221, pp. 378-396, September 2012.
• Q. Cao, B. T. Ewing, and M. A. Thompson, “Forecasting wind speed with recurrent neural networks,” European Journal of Operational Research, vol. 221, pp. 148-154, August 2012.
• G. Sermpinis, K. Theofilatos, A. Karathanasopoulos, E. F. Georgopoulos, and C. Dunis, “Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization", European Journal of Operational Research, vol. 225, pp. 528-540, March 2013.
• M. S. Kim, “Modeling special-day effects for forecasting intraday electricity demand,” European Journal of Operational Research, vol. 230, pp. 170-180, October 2013.
• K. Venkatesh, V. Ravi, A. Prinzie, and D. V. Poel, “Cash demand forecasting in ATMs by clustering and neural networks,” European Journal of Operational Research, vol. 232, pp. 383-392, January 2014.
• R. P. Ferreira, A. Martiniano, A. Ferreira, A. Ferreira, and R. J. Sassi, “Study on Daily Demand Forecasting Orders using Artificial Neural Network,” IEEE Latin America Transactions, vol. 14, pp. 1519-1525, March 2016.
• W. Fu, C.- F. Chien, and Z.- H. Lin, “A hybrid forecasting framework with neural network and time-series method for intermittent demand in semiconductor supply chain,” in Advances in Production Management Systems: IFIP WG 5.7 International Conference, APMS 2018, Seoul, Korea, August 26-30, 2018, I. Moon, G. M. Lee, J. Park, D. Kiritsis G. Cieminski, Eds. Springer, Cham., 2018. pp. 65-72.
• P. Hietaharju, M. Ruusunen, and K. Leiviska, “Enabling Demand Side Management: Heat Demand Forecasting at City Level,” Materials, vol. 12, pp. 1-17, January 2019.
• H. Peng, C. Ding, and F. Long, “Minimum redundancy maximum relevance feature selection,” IEEE Intelligent Systems, vol. 20, pp. 70–71, December 2015.
• J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu, “Feature Selection: A Data Perspective,” ACM Computing Surveys, vol. 50, pp. 1-45, November 2018.
• Y. Zhang, D. Gong, X. Gao, T. Tian, and X. Sun, “Binary differential evolution with self-learning for multi-objective feature selection,” Information Sciences, vol. 507, pp. 67-85, January 2020.
• R.A. Chinnathambi, M. Campion, A. S. Nair, and P. Ranganathan, “Investigation of Price-Feature Selection Algorithms for the Day-Ahead Electricity Markets,” in 2018 IEEE Electrical Power and Energy Conference (EPEC), Toronto, ON, Canada, October 10-11, 2018, pp. 1-6.
• UCI Machine Learning Repository, “Daily Demand Forecasting Orders Data Set,” 2017. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Daily+Demand+Forecasting+Orders (Accessed: Nov. 22, 2021).
• K. Abrougui, K. Gabsi, B. Mercatoris, C. Khemis, R. Amami, and S. Chehaibi, “Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR),” Soil and Tillage Research, vol. 190, pp. 202-208, July 2019.
• N. Susijawati, A. Setiawan, G. M. Putri, S. Maryam, A. Firasati, and M. Alwi, “The Effect of Organizational Commitment and Organizational Support as Intervening Variables to Turnover Intention of Employees,” in International Symposium on Social Sciences, Education, and Humanities (ISSEH 2018), in Advances in Social Science, Education and Humanities Research, vol. 306, March 2019, pp. 283-285.
• J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis A Global Perspective. England/Pearson Education Limited, 2010.
• Statistics Solutions website, “Regression”. [Online]. Available: https://www.statisticssolutions.com/directory-of-statistical-analyses-regression-analysis/regression/. [Accessed: Dec. 05, 2021).
• M. Elbisy, and F. Osra, “Application of Group Method of Data Handling Type Neural Network for Significant Wave Height Prediction,” American Journal of Neural Networks and Applications, vol. 5, pp. 51-57, November 2019.
• R. J. Kuo, M. C. Shieh, J.W. Zhang, and K.Y. Chen, “The application of an artificial immune system-based backpropagation neural network with feature selection to an RFID positioning system,” Robotics and Computer-Integrated Manufacturing, vol. 29, pp. 431-438, December 2013.
• M. Lashkarbolooki, B. Vaferi, A. Shariati, and A. Z. Hezave, “Investigating vapor–liquid equilibria of binary mixtures containing supercritical or near-critical carbon dioxide and a cyclic compound using cascade neural network,” Fluid Phase Equilibria, vol. 343, pp. 24-29, April 2013.
• P. L. Narayana, J. H. Kim, A. K. Maurya, C. H. Park, J.- K. Hong, J.- T. Yeom, and N. S. Reddy, “Modeling Mechanical Properties of 25Cr-20Ni-0.4C Steels over a Wide Range of Temperatures by Neural Networks,” Metals, 10, pp. 1-12, February 2020.
• J. FrosT, “Guide to Stepwise Regression and Best Subsets Regression,” 2020. [Online]. Available: https://statisticsbyjim.com/regression/curve-fitting-linear-nonlinear-regression/. (Accessed: Dec. 05, 2021).
• S. B. Zhou, L. Shengjie, and X. Yiming, “Effects of Filler Characteristics on the Performance of Asphalt Mastic: A Statistical Analysis of the Laboratory Testing Results,” International Journal of Civil Engineering, vol. 16, pp. 1175–1183, September 2018.