Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
| Autor: | M. M. Noor, Sakesun Suthummanon, Edy Fradinata, Wannarat Suntiamorntut |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Measure (data warehouse)
Mathematical optimization Artificial neural network Mean squared error Computer science Mechanical Engineering Supply chain Computational Mechanics Probabilistic logic Energy Engineering and Power Technology 02 engineering and technology Variance (accounting) Industrial and Manufacturing Engineering Support vector machine 020303 mechanical engineering & transports Fuel Technology 0203 mechanical engineering Mechanics of Materials Bullwhip effect 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | Journal of Mechanical Engineering and Sciences; Vol 13 No 2 (2019): (June 2019); 4816-4834 |
| ISSN: | 2381-3652 2231-8380 2289-4659 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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