Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30140
Validation and Selection between Machine Learning Technique and Traditional Methods to Reduce Bullwhip Effects: a Data Mining Approach

Authors: Hamid R. S. Mojaveri, Seyed S. Mousavi, Mojtaba Heydar, Ahmad Aminian

Abstract:

The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. Finally, forecasting methods' stability is analyzed by using raw data and even the effectiveness of clustering analysis is measured.

Keywords: Artificial Neural Networks (ANN), bullwhip effect, demand forecasting, Support Vector Machine (SVM).

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060157

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1651

References:


[1] C. W. J. Granger, "Can we improve the perceived quality of economic forecasts?", J. Appl. Econom.,. Vol. 11, 1996, pp. 455-473.
[2] P. H. Franses, G. Draisma, "Recognizing changing seasonal patterns using artificial neural networks", J. Econometrics, Vol. 81, 1997, pp. 273-280.
[3] R. T. Peterson, "Forecasting practices in the retail industry", J. Bus. Forec., vol. 12, 1993, pp. 11-14.
[4] M. Qi, G. S. Maddala, "Economic factors and the stock market: a new perspective", J. Forec., vol. 18 no. 3, 1999, pp. 151-166.
[5] M. Qi, "Nonlinear predictability of stock returns using financial and economic variables", J. Bu. Econom. Statist., vol. 17, no. 4, 1999, pp. 419-429.
[6] M. Qi, "Financial applications of artificial neural networks", in Handbook of Statistics, Statistical Methods in Finance, vol. 4, G. S. Maddala, C. R. Rao, Ed. North-Holland, Elsevier Science Publishers, Amsterdam, 1996, pp. 529-552.
[7] K. A. Krycha, U. Wagner, "Applications of artificial neural networks in management science: a survey", J. Retail. Cons. Ser., vol. 6, 1999, pp. 185-203.
[8] G. P. Zhang, B. E. Patuwo, M. Y. Hu, "Forecasting with artificial neural networks: the state of the art", Intl. J. Forec., vol. 14, no. 1, 1998, pp. 35-62.
[9] B. Jeong, H. Jung, N. Park, "A computerized causal forecasting system using genetic algorithms in supply chain management", J. Sys. Software, vol. 60, 2002, pp. 223-237.
[10] T. R. Willemain, C. N. Smart, H. F. Schwarz, "A new approach to forecasting intermittent demand for service parts inventories", Intl. J. Forec., vol. 20, 2004, pp. 375- 387.
[11] C. S. Hilas, S. K. Goudos, J. N. Sahalos, "Seasonal decomposition and forecasting of telecommunication data: A comparative case study", Technol. Forec. Soc. Change, vol. 73, 2006, pp. 495-509.
[12] S. Kang, "An investigation of the use of feedforward neural networks for forecasting", Ph.D. Dissertation, Kent State University, Kent, Ohio, 1991.
[13] T. Hill, M. O'Connor, W. Remus, "Neural network models for time series forecast", Manag. Sci., vol. 42, no. 7, 1996, pp. 1082-1092.
[14] M. Nelson, T. Hill, B. Remus, M. O'Connor, "Can neural networks be applied to time series forecasting learn seasonal patterns: an empirical investigation", in Proc. 27th Ann. Int. Conf. System Sciences, Hawaii, 1994, pp. 649-655.
[15] B. Foster, F. Collopy, L. Ungar, "Neural network forecasting of short, noisy time series", Comput. Chem. Eng., vol. 16, no. 12, 1992, pp. 293- 297.
[16] M. Dugan, K. A. Shriver, A. Peter, "How to forecast income statement items for auditing purposes", J. Bus. Forec., vol. 13, 1994, pp. 22-26.
[17] I. Alon, "Forecasting aggregate retail sales: the Winters' model revisited", in The 1997 Annual Proceedings, J. C. Goodale, Ed., Midwest Decision Science Institute, 1997, pp. 234-236.
[18] I. Alon, Q. Min, R. Sadowski, "Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional method", J. Retail. Cons. Ser., vol. 8, 2001, pp. 147-156.
[19] T. M. O'Donovan, Short Term Forecasting: An Introduction to the Box- Jenkins Approach. NY: New York, Wiley, 1983.
[20] R. Sharda, R. Patil, "Connectionist approach to time series prediction: an empirical test", J. Intell. Manufac., vol. 3, 1992, pp.317-323.
[21] Z. Tang, C. de Almeida, P. Fishwick, "Time series forecasting using neural networks vs. Box-Jenkins methodology", Simulation, vol. 57, no. 5, 1990, pp.303-310.
[22] V. Vapnik, The Nature of Statistical Learning Theory. New York, Springer, 1995.
[23] V. Vapnik, S. E. Golowich, A. Smola, "Support vector method for function approximation, regression estimation, and signal processing", Adv. Neural inf. Process. Syst., vol. 9, 1997, pp. 287-291.
[24] S. Mukherjee, E. Osuna, F. Girosi, "Nonlinear prediction of chaotic time series using support vector machines", in Proc. IEEE NNSP, 1997.
[25] S. Ruping, K. Morik, "Support Vector Machines and Learning about Time", in Proc. of ICASSP, 2003.
[26] J. Han, M. Kamber, Data Preprocessing, Data Mining, concepts and techniques, 2nd ed., Morgan Kaufman Publisher, 2006, ch2.
[27] T. Calinski, J. Harabasz, "A Dendrite Method for Cluster Analysis", Commun. Statist., Vol. 3, 1974, pp. 1-27.
[28] K. Hornik, M. Stinchcombe, H. White, "Multilayer feedforward networks are universal approximators", Neural Net., vol. 2, 1989, pp. 359-366.
[29] K. Hornik, M. Stinchcombe, H. White, "Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks", Neural Net., vol. 3, 1990, pp. 551-560.
[30] H. White, "Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings", Neural Net., vol. 3, 1990, pp. 535-549.
[31] H. Demuth, M. Beale, Neural Network Toolbox User's Guide, Version 3.0. The Math Works, Inc., 1997, pp. 5-35.
[32] D. J. C. MacKay, "Bayesian interpolation", Neural Comput., vol. 4, 1992, pp. 415-447.
[33] V. N. Vapnik, Statistical Learning Theory. NY: New York, John Wiley & Sons, 1998.
[34] G. L. Lilien, P. Kotler, Marketing Decision Making: A Model Building Approach. NY: New York, Harper and Row Publishers, 1983.