Reducing the bullwhip effect from signal demand of hybrid artificial neural network models of supply chain in Indonesia

et al. Fradinata
2017 International Journal of Advanced and Applied Sciences  
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 a 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 a random normal distribution. The
more » ... recasting 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. Keywords: Artificial neural network; support vector regression; bullwhip effect; supply chain. Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
doi:10.21833/ijaas.2017.010.011 fatcat:34bcaiklobdz7mhwxk77bjpcpm