International Journal of Advanced and Applied Sciences
Int. j. adv. appl. sci.
Print ISSN: 2313-626X
Volume 4, Issue 10 (October 2017), Pages: 64-75
Original Research Paper
Title: Reducing the bullwhip effect from signal demand of hybrid artificial neural network models of supply chain in Indonesia
Author(s): Edy Fradinata 1, 2, *, Sakesun Suthummanon 2, Wannarat Suntiamorntut 3
1Industrial Engineering, Universitas Serambi Mekkah, Banda Aceh, Indonesia
2Industrial Engineering, Prince of Songkla University, Hatyai, Thailand
3Computer Engineering, Prince of Songkla University, Hatyai, Thailand
Full Text - PDF XML
The bullwhip effect becomes a problem for the factory to manage the inventory policy in the warehouse. This study proposes to reduce the bullwhip effect through signal demand forecast from hybrid artificial neural network (ANN) models. The original ANN is combined with analytical hierarchy process (AHP), Monte Carlo simulation (MC), and geometric random distribution at the parts of the input weight and input bias from the ANN. The variation of forecast signal demands from the hybrid models are used to reduce the variance from the signal customer demands. The results from this study, AHPiwANNb has the smallest mean square error (MSE) from signal demands, it implys that the variance signal demands should reduce the bullwhip effect (BWE) in the supply chain. It can be concluded that the small variance signal demand should reduce the bullwhip effect in the supply chain.
© 2017 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Bullwhip effect, Signal demand, Hybrid ANN, Supply chain
Article History: Received 2 May 2017, Received in revised form 12 August 2017, Accepted 21 August 2017
Digital Object Identifier:
Fradinata E, Suthummanon S, and Suntiamorntut W (2017). Reducing the bullwhip effect from signal demand of hybrid artificial neural network models of supply chain in Indonesia. International Journal of Advanced and Applied Sciences, 4(10): 64-75
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