International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

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

Affiliation(s):

1Industrial Engineering, Universitas Serambi Mekkah, Banda Aceh, Indonesia
2Industrial Engineering, Prince of Songkla University, Hatyai, Thailand
3Computer Engineering, Prince of Songkla University, Hatyai, Thailand

https://doi.org/10.21833/ijaas.2017.010.011

Full Text - PDF          XML

Abstract:

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: 

https://doi.org/10.21833/ijaas.2017.010.011

Citation:

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

Permanent Link:

http://www.science-gate.com/IJAAS/V4I10/Fradinata.html


References (20)

  1. Cachon GP, Randall T, and Schmidt GM (2007). In search of the bullwhip effect. Manufacturing and Service Operations Management, 9(4): 457-479. https://doi.org/10.1287/msom.1060.0149 
  2. Chandra C and Kumar S (2000). Supply chain management in theory and practice: A passing fad or a fundamental change?. Industrial Management and Data Systems, 100(3): 100-114. https://doi.org/10.1108/02635570010286168 
  3. Ciptomulyono U (2008). Fuzzy goal programming approach for deriving priority weights in the analytical hierarchy process (AHP) method. Journal of Applied Sciences Research, 4(2): 171-177.     
  4. Dejonckheere J, Disney SM, Lambrecht MR, and Towill DR (2003). Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research, 147(3): 567-590. https://doi.org/10.1016/S0377-2217(02)00369-7 
  5. Disney SM and Towill DR (2003). Vendor-managed inventory and bullwhip reduction in a two-level supply chain. International Journal of Operations and Production Management, 23(6): 625-651. https://doi.org/10.1108/01443570310476654 
  6. Fradinata E, Suthummanon S, and Suntiamorntut W (2015). Forecasting determinant of cement demand in Indonesia with artificial neural network. Journal of Asian Scientific Research, 5(7): 373-384. https://doi.org/10.18488/journal.2/2015.5.7/2.7.373.384 
  7. Hattab N, Hambli R, Motelica-Heino M, and Mench M (2013). Neural network and Monte Carlo simulation approach to investigate variability of copper concentration in phytoremediated contaminated soils. Journal of Environmental Management, 129: 134-142. https://doi.org/10.1016/j.jenvman.2013.07.003          PMid:23916835 
  8. He Z, Wen X, Liu H, and Du J (2014). A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509: 379-386. https://doi.org/10.1016/j.jhydrol.2013.11.054 
  9. Jek Siang J (2005). Jaringan syaraf tiruan pemrograman menggunakan matlab. Penerbit Andi, Yogyakarta, Indonesia.     
  10. Jüttner U, Christopher M, and Baker S (2007). Demand chain management-integrating marketing and supply chain management. Industrial Marketing Management, 36(3): 377-392. https://doi.org/10.1016/j.indmarman.2005.10.003 
  11. Kihoro J, Otieno R, and Wafula C (2004). Seasonal time series forecasting: A comparative study of ARIMA and ANN models. Asian Journal of Science and Technology, 5(2): 41-49.     
  12. Kristianto Y, Helo P, Jiao JR, and Sandhu M (2012). Adaptive fuzzy vendor managed inventory control for mitigating the Bullwhip effect in supply chains. European Journal of Operational Research, 216(2): 346-355. https://doi.org/10.1016/j.ejor.2011.07.051 
  13. Kwok TY and Yeung DY (1997). Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks, 8(3): 630-645. https://doi.org/10.1109/72.572102           PMid:18255666 
  14. Law R (2000). Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21(4): 331-340. https://doi.org/10.1016/S0261-5177(99)00067-9 
  15. Lee HL, Padmanabhan V, and Whang S (2004). Information distortion in a supply chain: The bullwhip effect. Management Science, 50(12_supplement): 1875-1886.     
  16. Lenny Koh SC, Demirbag M, Bayraktar E, Tatoglu E, and Zaim S (2007). The impact of supply chain management practices on performance of SMEs. Industrial Management and Data Systems, 107(1): 103-124. https://doi.org/10.1108/02635570710719089 
  17. Nikdel N, Nikdel P, Badamchizadeh MA, and Hassanzadeh I (2014). Using neural network model predictive control for controlling shape memory alloy-based manipulator. IEEE Transactions on Industrial Electronics, 61(3): 1394-1401. https://doi.org/10.1109/TIE.2013.2258292 
  18. Nyoman Pujawan I, Er M, Kritchanchai D, and Somboonwiwat T (2014). Uncertainty and schedule instability in supply chain: Insights from case studies. International Journal of Services and Operations Management, 19(4): 468-490. https://doi.org/10.1504/IJSOM.2014.065670 
  19. Saaty TL (2004). Decision making—the analytic hierarchy and network processes (AHP/ANP). Journal of Systems Science and Systems Engineering, 13(1): 1-35. https://doi.org/10.1007/s11518-006-0151-5 
  20. Zequeira RI, Valdes JE, and Berenguer C (2008). Optimal buffer inventory and opportunistic preventive maintenance under random production capacity availability. International Journal of Production Economics, 111(2): 686-696. https://doi.org/10.1016/j.ijpe.2007.02.037