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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

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...

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Main Authors: Fradinata, E., Suthummanon, S., Suntiamorntut, W., M. M., Noor
Format: Article
Language:English
Published: Faculty Mechanical Engineering, UMP 2019
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/25245/1/Compare%20the%20forecasting%20method%20of%20artificial%20neural.pdf
http://umpir.ump.edu.my/id/eprint/25245/
http://journal.ump.edu.my/jmes/article/view/1218
https://doi.org/10.15282/jmes.13.2.2019.04.0401
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spelling my.ump.umpir.252452019-07-08T06:47:36Z http://umpir.ump.edu.my/id/eprint/25245/ Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain Fradinata, E. Suthummanon, S. Suntiamorntut, W. M. M., Noor TJ Mechanical engineering and machinery 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. Faculty Mechanical Engineering, UMP 2019 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/25245/1/Compare%20the%20forecasting%20method%20of%20artificial%20neural.pdf 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) http://journal.ump.edu.my/jmes/article/view/1218 https://doi.org/10.15282/jmes.13.2.2019.04.0401
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Fradinata, E.
Suthummanon, S.
Suntiamorntut, W.
M. M., Noor
Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
description 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.
format Article
author Fradinata, E.
Suthummanon, S.
Suntiamorntut, W.
M. M., Noor
author_facet Fradinata, E.
Suthummanon, S.
Suntiamorntut, W.
M. M., Noor
author_sort Fradinata, E.
title Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
title_short Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
title_full Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
title_fullStr Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
title_full_unstemmed Compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
title_sort compare the forecasting method of artificial neural network and support vector regression model to measure the bullwhip effect in supply chain
publisher Faculty Mechanical Engineering, UMP
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/25245/1/Compare%20the%20forecasting%20method%20of%20artificial%20neural.pdf
http://umpir.ump.edu.my/id/eprint/25245/
http://journal.ump.edu.my/jmes/article/view/1218
https://doi.org/10.15282/jmes.13.2.2019.04.0401
_version_ 1643670001156096000
score 13.211869

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