A Review on the Application of Neural Networks for Decreasing Bullwhip Effect in Supply Chain
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DOI: https://doi.org/10.15866/ireme.v9i5.6271
Abstract
The bullwhip effect can be described as increased demand variability in supply chains. Bullwhip effect is one of the most popular research problems in the field of supply chain management because in the case of bullwhip effect the small changes in demands of customers may lead to a large variations in the orders consequently, the organization faces the overestimated in their orders. There are different methods of forecasting which are used in supply chain in order to decrease the bullwhip effect. In recent years many researches are using neural networks as one of the useful tools for forecasting. The aim of this literature review is to describe the concept of bullwhip effect and its disadvantages and causes of it. Moreover, to provide a list of applications of neural networks in decreasing bullwhip effect in supply chain and suggest future studies based on the literature.
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Forrester, J. W. (1958). Industrial dynamics: a major breakthrough for decision makers. Harvard business review, 36(4), 37-66.
Forrester, J. (1961). Industrial Dynamics, Cambridge. MIT Press, MA.
http://dx.doi.org/10.1126/science.135.3502.426-a
Lee, H., Padmanabhan, V. and Whang, S. (1997a), "Information distortion in a supply chain: the bullwhip effect", Management Science, Vol. 43 No. 4, pp. 546-58.
http://dx.doi.org/10.1287/mnsc.43.4.546
Lee, H., Padmanabhan, V. and Whang, S. (1997b), “The bullwhip effect in supply chains”, Sloan Management Review, Vol. 38 No. 3, pp. 93-102.
http://dx.doi.org/10.1109/emr.2015.7123235
Fransoo, J. C., and Wouters, M. J. (2000). Measuring the bullwhip effect in the supply chain. Supply Chain Management: An International Journal, 5(2), 78-89
http://dx.doi.org/10.1108/13598540010319993
Ko, M., Tiwari, A., and Mehnen, J. (2010). A review of soft computing applications in supply chain management. Applied Soft Computing, 10(3), 661-674.
http://dx.doi.org/10.1016/j.asoc.2009.09.004
Paik, S. K. (2003). Analysis of the Causes of" Bullwhip" Effect in a Supply Chain: A Simulation Approach (Doctoral dissertation, George Washington University).
Yao, D. Q. (2001). Study of bullwhip effect and channel design in supply chains. Milwaukee: University of Wisconsin–Milwaukee.
Wu, D. Y., and Katok, E. (2006). Learning, communication, and the bullwhip effect. Journal of Operations Management, 24(6), 839-850.
http://dx.doi.org/10.1016/j.jom.2005.08.006
Chen, L., and Lee, H. L. (2012). Bullwhip effect measurement and its implications. Operations Research, 60(4), 771-784
http://dx.doi.org/10.1287/opre.1120.1074
Ma, Y., Wang, N., Che, A., Huang, Y., and Xu, J. (2013). The bullwhip effect on product orders and inventory: a perspective of demand forecasting techniques. International Journal of Production Research, 51, 281–302.
http://dx.doi.org/10.1080/00207543.2012.676682
Luong, H. T., and Phien, N. H. (2007). Measure of bullwhip effect in supply chains: The case of high order autoregressive demand process. European Journal of Operation Research, 183, 197–209
http://dx.doi.org/10.1016/j.ejor.2006.09.061
Chen, F., Drezner, Z., Ryan, J. K., and Simchi-Levi, D. (2000). The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics, 47, 269–286.
http://dx.doi.org/10.1002/(sici)1520-6750(200006)47:4%3C269::aid-nav1%3E3.3.co;2-h
Hong, L., and Ping, W. (2007). Bullwhip effect analysis in supply chain for demand forecasting technology. System Engineering-Theory and Practice, 27, 26–33
http://dx.doi.org/10.1016/s1874-8651(08)60044-7
Bandyopadhyay, S., and Bhattacharya, R. (2013). A generalized measure of bullwhip effect in supply chain with ARMA demand process under various replenishment policies. International Journal of Advance Manufacturing Technology, 68, 963–979.
http://dx.doi.org/10.1007/s00170-013-4888-y
Gilbert, K. C., and Chatpattananan, V. (2006). An ARIMA supply chain model with a generalized ordering policy. Journal of Modelling in Management, 1, 33–51
http://dx.doi.org/10.1108/17465660610667793
Kochak, A., and Sharma, S. (2015). Demand forecasting using neural network for supply chain management, International journal of mechanical engineering and robotics research, Vol. 4, No. 1.
Jaipuria, S., and Mahapatra, S. S. (2014). An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, 41(5), 2395-2408.
http://dx.doi.org/10.1016/j.eswa.2013.09.038
Wei, S., Song, J., and Khan, N. I. (2012). Simulating and predicting river discharge time series using a wavelet-neural network hybrid modelling approach. Hydrological Process, 26, 281–296
http://dx.doi.org/10.1002/hyp.8227
Jain, A. K., Mao, J., and Mohiuddin, K. M. (1996). Artificial neural networks: A tutorial. Computer, 29(3), 31-44.
http://dx.doi.org/10.1109/2.485891
Liwicki, M., Graves, A., and Bunke, H. (2012). Neural networks for handwriting recognition. In Computational intelligence paradigms in advanced pattern classification (pp. 5-24). Springer Berlin Heidelberg.
http://dx.doi.org/10.1007/978-3-642-24049-2_2
Dahl, G. E., Yu, D., Deng, L., and Acero, A. (2012). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, 20(1), 30-42.
http://dx.doi.org/10.1109/tasl.2011.2134090
Hu, J., Wang, J., and Ma, K. (2015). A hybrid technique for short-term wind speed prediction. Energy.
http://dx.doi.org/10.1016/j.energy.2014.12.074
Ren, C., An, N., Wang, J., Li, L., Hu, B., and Shang, D. (2014). Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting. Knowledge-Based Systems, 56, 226-239.
http://dx.doi.org/10.1016/j.knosys.2013.11.015
Marvuglia, A., and Messineo, A. (2012). Using recurrent artificial neural networks to forecast household electricity consumption. Energy Procedia, 14, 45-55.
http://dx.doi.org/10.1016/j.egypro.2011.12.895
Dejonckheere, J., Disney, S. M., Lambrecht, M. R., and Towill, D. R. (2003). Measuring and avoiding the bullwhip effect: A control theoretic approach. European Journal of Operational Research, 147(3), 567-590.
http://dx.doi.org/10.1016/s0377-2217(02)00369-7
Buffa, E. S., and Miller, J. G. (1979). Production-inventory systems: planning and control. Richard D Irwin.
Sterman, J. D. (1989). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management science, 35 (3), 321-339.
http://dx.doi.org/10.1287/mnsc.35.3.321
Machuca, J. A., and Barajas, R. P. (2004). The impact of electronic data interchange on reducing bullwhip effect and supply chain inventory costs. Transportation Research Part E: Logistics and Transportation Review, 40(3), 209-228.
http://dx.doi.org/10.1016/j.tre.2003.08.001
Zarandi, M. F., Pourakbar, M., and Turksen, I. B. (2008). A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems. Expert systems with applications, 34(3), 1680-1691.
http://dx.doi.org/10.1016/j.eswa.2007.01.031
Agrawal, S., Sengupta, R. N., and Shanker, K. (2009). Impact of information sharing and lead time on bullwhip effect and on-hand inventory. European Journal of Operational Research, 192(2), 576-593.
http://dx.doi.org/10.1016/j.ejor.2007.09.015
Bhattacharya, R., and Bandyopadhyay, S. (2011). A review of the causes of bullwhip effect in a supply chain. The International Journal of Advanced Manufacturing Technology, 54(9-12), 1245-1261.
http://dx.doi.org/10.1007/s00170-010-2987-6
Luong, H. T., and Phien, N. H. (2007). Measure of bullwhip effect in supply chains: The case of high order autoregressive demand process. European Journal of Operational Research, 183(1), 197-209.
http://dx.doi.org/10.1016/j.ejor.2006.09.061
Duc, T. T. H., Luong, H. T., and Kim, Y. D. (2008). A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process. European Journal of Operational Research, 187(1), 243-256.
http://dx.doi.org/10.1016/j.ejor.2007.03.008
Fiala, P. (2005). Information sharing in supply chains. Omega, 33(5), 419-423.
http://dx.doi.org/10.1016/j.omega.2004.07.006
Efendigil, T., Önüt, S., and Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3), 6697-6707.
http://dx.doi.org/10.1016/j.eswa.2008.08.058
Balan, S., Vrat, P., and Kumar, P. (2007). Reducing the Bullwhip effect in a supply chain with fuzzy logic approach. International Journal of Integrated Supply Management, 3(3), 261-282.
http://dx.doi.org/10.1504/ijism.2007.012630
Carbonneau, R., Laframboise, K., and Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154.
http://dx.doi.org/10.1016/j.ejor.2006.12.004
NoorulHaq, A., and Kannan, G. (2006). Effect of forecasting on the multi-echelon distribution inventory supply chain cost using neural network, genetic algorithm and particle swarm optimisation. International Journal of Services Operations and Informatics, 1(1), 1-22.
http://dx.doi.org/10.1504/ijsoi.2006.010186
Ponte, B., Ruano, L., Pino, R., and de la Fuente, D. (2015). The Bullwhip effect in water demand management: taming it through an artificial neural networks-based system, Journal of Water Supply: Research and Technology—AQUA Vol 64 No 3 pp 290–301.
http://dx.doi.org/10.2166/aqua.2015.087
Tozan, H., and Vayvay, O. (2009). Hybrid grey and ANFIS approach to bullwhip effect in supply chain networks. WSEAS Trans Syst, 8, 461-470.
http://dx.doi.org/10.5772/14675
Aburto, L., and Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, 7(1), 136-144.
http://dx.doi.org/10.1016/j.asoc.2005.06.001
Lei, Z., Yi-jun, L., and Yao-qun, X. (2006, October). Chaos synchronization of bullwhip effect in a supply chain. In Management Science and Engineering, 2006. ICMSE'06. 2006 International Conference on (pp. 557-560). IEEE.
http://dx.doi.org/10.1109/icmse.2006.313955
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