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Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain

Fradinata, E. and Suthummanon, S. and Suntiamorntut, W. and M. M., Noor (2019) Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain. Journal of Mechanical Engineering and Sciences (JMES), 13 (2). pp. 4816-4834. ISSN 2289-4659 (print); 2231-8380 (online)

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Abstract

The objective of this study was to compare the Bullwhip Effect (BWE) in the supply chain through two methods and to determine the inventory policy for the uncertainty demand. It would be useful to determine the best forecasting method to predict the certain condition. The two methods are Artificial Neural Network (ANN) and Support Vector Regression (SVR), which would be applied in this study. The data was obtained from the instant noodle dataset where it was in random normal distribution. The forecasting demands signal have Mean Squared Error (MSE) where it is used to measure the bullwhip effect in the supply chain member. The magnification of order among the member of the supply chain would influence the inventory. It is quite important to understand forecasting techniques and the bullwhip effect for the warehouse manager to manage the inventory in the warehouse, especially in probabilistic demand of the customer. This process determines the appropriate inventory policy for the retailer. The result from this study shows that ANN and SVR have the variance of 0.00491 and 0.07703, the MSE was 1.55e-6 and 1.53e-2, and the total BWE was 95.61 and 1237.19 respectively. It concluded that the ANN has a smaller variance than SVR, therefore, the ANN has a better performance than SVR, and the ANN has smaller BWE than SVR. At last, the inventory policy was determined with the continuous review policy for the uncertainty demand in the supply chain member.

Item Type: Article
Additional Information: Indexed by SCOPUS
Uncontrolled Keywords: artificial neural network, support vector regression, supply chain
Subjects: T Technology > TJ Mechanical engineering and machinery
Faculty/Division: Faculty of Mechanical & Manufacturing Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 08 Jul 2019 06:47
Last Modified: 08 Jul 2019 06:47
URI: http://umpir.ump.edu.my/id/eprint/25245
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