Reduction of Bullwhip Effect in Supply Chain through Improved Forecasting Method: An Integrated DWT and SVM Approach
In a supply chain, forecasting method directly influences the bullwhip effect (BWE) and net-stock amplification (NSAmp) which adversely impact on performance of supply chain. However, such adverse effects can be moderated through use of realistic and accurate demand forecasting models. In the present study, an integrated approach of discrete wavelet transforms (DWT) analysis and least-square support vector machine (LSSVM) is proposed for demand forecasting. Initially, the proposed DWT-LSSVM model is tested and validated using a data set from open literature. A comparative study between Autoregressive Integrated Moving Average (ARIMA) and proposed model has been made. Further, the model is tested with demand data collected from two different manufacturing firms. It is observed that proposed model outperforms ARIMA model in respect to accurate estimation of demand and reduce BWE.
KeywordsSupply chain unscertainty Bullwhip effect ARIMA Discrete wavelets Least-square support vector machine
Unable to display preview. Download preview PDF.
- 1.Davis, T.: Effective supply chain management. Sloan Management Review, 35–46 (Summer 1993)Google Scholar
- 3.Forrester, J.W.: Industrial dynamics–A major breakthrough for decision making. Harvard Business Review 36, 37–66 (1958)Google Scholar
- 4.Forrester, J.W.: Industrial Dynamics. MIT Press, Cambridge (1961)Google Scholar
- 6.Lee, H.L., Padmanabhan, V., Whang, S.: The Bullwhip effect in supply chains. Sloan Management Review 38, 93–102 (1997a)Google Scholar
- 14.Bandyopadhyay, S., Bhattacharya, R.: A generalized measure of bullwhip effect in supply chain with ARMA demand process under various replenishment policies. International Journal of Advance Manufacturing Technology (2013), doi:10.1007/s00170-013-4888-yGoogle Scholar
- 19.Chauchard, F., Cogdill, R., Roussel, S., Roger, J.M., Bellon-Maurel, V.: Application of LS-SVM to non-linear phenomena in NIR spectroscopy: Development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems 71(2), 141–150 (2004)CrossRefGoogle Scholar
- 26.Sudheer, C., Maheswaran, R., Panigrahi, B.K., Mathur, S.: A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Computing and Application, 1–9 (2013)Google Scholar
- 28.Peixian, L., Zhixiang, T., Lili, Y., Kazhong, D.: Time series prediction of mining subsidence based on a SVM. Mining Science and Technology 21, 557–562 (2011)Google Scholar
- 29.Khan, A.A., Shahidehpour, M.: One day ahead wind speed forecasting using wavelets. In: IEEE/PES Power Systems Conference and Exposition, PSCE 2009, Seattle, WA, pp. 1–5 (2009)Google Scholar
- 32.Aggarwal, S.K., Saini, L.M., Kumar, A.: Electricity price forecasting using wavelet domain and time domain features in a regression based technique. International Journal of Recent Trends in Engineering 2, 33–37 (2009)Google Scholar
- 34.Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting methods and applications, 3rd edn. John Wiley & Sons Inc., Singapore (1998)Google Scholar
- 37.Shengxian, C., Yanhui, Z., Jing, Z., Dayu, Y.: Experimental Study on Dynamic Simulation for Biofouling Resistance Prediction by Least Squares Support Vector Machine. In: 2012 International Conference on Future Electrical Power and Energy Systems, Energy Procedia, vol. 17, pp. 74–78 (2012)Google Scholar