Consumer Product Demand Forecasting based on Artificial Neural Network and Support Vector Machine
The nature of consumer products causes the difficulty
in forecasting the future demands and the accuracy of the forecasts
significantly affects the overall performance of the supply chain
system. In this study, two data mining methods, artificial neural
network (ANN) and support vector machine (SVM), were utilized to
predict the demand of consumer products. The training data used was
the actual demand of six different products from a consumer product
company in Thailand. The results indicated that SVM had a better
forecast quality (in term of MAPE) than ANN in every category of
products. Moreover, another important finding was the margin
difference of MAPE from these two methods was significantly high
when the data was highly correlated.
[1] K. Bansal, S. Vadhavkar, and A. Gupta, "Neural Networks Based
Forecasting Techniques for Inventory Control Applications," Data Min
Knowl Disc, vol. 2, no. 1, pp. 97-102, 1998.
[2] F.E.H. Tay and L. Cao, "Application of Support Vector Machines in
Financial Time Series Forecasting," Omega, vol. 21, pp. 309-317, 2001.
[3] K. Kim, "Financial Time Series Forecasting using Support Vector
Machines," Neurocomputing, vol. 55, no. 1-2, pp. 307-319, 2003.
[4] W. Huang, Y. Nakamoria and S.-Y. Wang, "Forecasting Stock Market
Movement Direction with Support Vector Machine," Comput Oper Res ,
vol. 32, no.10, pp. 319-326, 2005.
[5] Z. Hua and B. Zhang, "A Hybrid Support Vector Machines and Logistic
Regression Approach for Forecasting Intermittent Demand of Spare
Parts," Appl Math Comput, vol. 181, pp. 1035-1048, 2006.
[6] R.S. Gutierrez, A.O. Solis and S. Mukhopadhyay, "Lumpy Demand
Forecasting using Neural Networks," Int J Product Econ, vol. 111, pp.
409-425, 2008.
[7] K. Kandananond, ""Forecasting Electricity Demand in Thailand with an
Artificial Neural Network Approach," Energies, vol. 4, pp. 1246-1257,
2011.
[8] StatSoft, Inc. (2011). Electronic Statistics Textbook. Tulsa, OK:
StatSoft. WEB: http://www.statsoft.com/textbook/.
[1] K. Bansal, S. Vadhavkar, and A. Gupta, "Neural Networks Based
Forecasting Techniques for Inventory Control Applications," Data Min
Knowl Disc, vol. 2, no. 1, pp. 97-102, 1998.
[2] F.E.H. Tay and L. Cao, "Application of Support Vector Machines in
Financial Time Series Forecasting," Omega, vol. 21, pp. 309-317, 2001.
[3] K. Kim, "Financial Time Series Forecasting using Support Vector
Machines," Neurocomputing, vol. 55, no. 1-2, pp. 307-319, 2003.
[4] W. Huang, Y. Nakamoria and S.-Y. Wang, "Forecasting Stock Market
Movement Direction with Support Vector Machine," Comput Oper Res ,
vol. 32, no.10, pp. 319-326, 2005.
[5] Z. Hua and B. Zhang, "A Hybrid Support Vector Machines and Logistic
Regression Approach for Forecasting Intermittent Demand of Spare
Parts," Appl Math Comput, vol. 181, pp. 1035-1048, 2006.
[6] R.S. Gutierrez, A.O. Solis and S. Mukhopadhyay, "Lumpy Demand
Forecasting using Neural Networks," Int J Product Econ, vol. 111, pp.
409-425, 2008.
[7] K. Kandananond, ""Forecasting Electricity Demand in Thailand with an
Artificial Neural Network Approach," Energies, vol. 4, pp. 1246-1257,
2011.
[8] StatSoft, Inc. (2011). Electronic Statistics Textbook. Tulsa, OK:
StatSoft. WEB: http://www.statsoft.com/textbook/.
@article{"International Journal of Business, Human and Social Sciences:57019", author = "Karin Kandananond", title = "Consumer Product Demand Forecasting based on Artificial Neural Network and Support Vector Machine", abstract = "The nature of consumer products causes the difficulty
in forecasting the future demands and the accuracy of the forecasts
significantly affects the overall performance of the supply chain
system. In this study, two data mining methods, artificial neural
network (ANN) and support vector machine (SVM), were utilized to
predict the demand of consumer products. The training data used was
the actual demand of six different products from a consumer product
company in Thailand. The results indicated that SVM had a better
forecast quality (in term of MAPE) than ANN in every category of
products. Moreover, another important finding was the margin
difference of MAPE from these two methods was significantly high
when the data was highly correlated.", keywords = "Artificial neural network (ANN), Bullwhip effect,
Consumer products, Demand forecasting, Supply chain, Support
vector machine (SVM).", volume = "6", number = "3", pages = "324-4", }