Reduction of Bullwhip Effect in Supply Chain through Improved Forecasting Method: An Integrated DWT and SVM Approach

  • Sanjita Jaipuria
  • S. S. Mahapatra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


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.


Supply chain unscertainty Bullwhip effect ARIMA Discrete wavelets Least-square support vector machine 


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  1. 1.
    Davis, T.: Effective supply chain management. Sloan Management Review, 35–46 (Summer 1993)Google Scholar
  2. 2.
    van der Vorst, J.G.A.J., Beulens, A.J.M.: Identifying sources of uncertainty to generate supply chain redesign strategies. International Journal of Physical Distribution and Logistics Management 32, 409–430 (2002)CrossRefGoogle Scholar
  3. 3.
    Forrester, J.W.: Industrial dynamics–A major breakthrough for decision making. Harvard Business Review 36, 37–66 (1958)Google Scholar
  4. 4.
    Forrester, J.W.: Industrial Dynamics. MIT Press, Cambridge (1961)Google Scholar
  5. 5.
    Sterman, J.: Modelling managerial behaviour: Misperceptions of feedback in a dynamic decision making experiment. Management Science 35, 321–339 (1989)CrossRefGoogle Scholar
  6. 6.
    Lee, H.L., Padmanabhan, V., Whang, S.: The Bullwhip effect in supply chains. Sloan Management Review 38, 93–102 (1997a)Google Scholar
  7. 7.
    Lee, H.L., Padmanabhan, V., Whang, S.: Information Distortion in a Supply Chain: The Bullwhip Effect. Management Science 43, 546–558 (1997b)CrossRefzbMATHGoogle Scholar
  8. 8.
    Wright, D., Yuan, X.: Mitigating bullwhip effect by ordering policies and forecasting methods. International Journal of Production Economics 113, 587–597 (2008)CrossRefGoogle Scholar
  9. 9.
    Luong, H.T.: Measure of bullwhip effect in supply chains with autoregressive demand process. European Journal of Operational Research 180, 1086–1097 (2007)CrossRefzbMATHGoogle Scholar
  10. 10.
    Luong, H.T., Phien, N.H.: Measure of bullwhip effect in supply chains: The case of high order autoregressive demand process. European Journal of Operation Research 183, 197–209 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    Chen, F., Drezner, Z., Ryan, J.K., Simchi-Levi, D.: The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics 47, 269–286 (2000)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Hong, L., Ping, W.: Bullwhip effect analysis in supply chain for demand forecasting technology. System Engineering-Theory and Practice 27, 26–33 (2007)CrossRefGoogle Scholar
  13. 13.
    Zhang, X.: Evolution of ARMA Demand in supply chain. Manufacturing and Service Operations Management 6, 195–198 (2004)CrossRefGoogle Scholar
  14. 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
  15. 15.
    Duc, T.T.H., Luong, H.T., Kim, Y.-D.: A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process. European Journal of Operational Research 187, 243–256 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Gilbert, K.: An ARIMA supply chain model. Management Science 51, 305–310 (2005)CrossRefzbMATHGoogle Scholar
  17. 17.
    Boute, R.N., Lambrecht, M.R.: Exploring the bullwhip effect by mean of spreadsheet simulation. Informs Transaction on Education 10, 1–9 (2009)CrossRefGoogle Scholar
  18. 18.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  19. 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
  20. 20.
    Lu, C.-J., Lee, T.-S., Chiu, C.-C.: Financial time series forecasting using independent component analysis and support vector regression. Decision Support System 47, 115–125 (2009)CrossRefGoogle Scholar
  21. 21.
    Kim, K.-J.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–319 (2003)CrossRefGoogle Scholar
  22. 22.
    Zhiqiang, G., Huaiqing, W., Quan, L.: Financial time series forecasting using LPP and SVM optimization by PSO. Soft Computing 17, 805–818 (2013)CrossRefGoogle Scholar
  23. 23.
    Sudheer, C., Shrivastava, N.A., Panigrahi, B.K., Mathur, S.: Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101, 18–23 (2013)CrossRefGoogle Scholar
  24. 24.
    Hong, W.-C., Dong, Y., Zhang, W.Y., Chen, L.-Y., Panigrahi, B.K.: Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm. International Journal of Electrical Power & Energy Systems 44, 604–614 (2013)CrossRefzbMATHGoogle Scholar
  25. 25.
    Sudheer, C., Shrivastava, N.A., Panigrahi, B.K., Mathur, S.: Groundwater Level Forecasting Using SVM-QPSO. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 731–741. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  26. 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
  27. 27.
    Partal, T., Cigizoglu, H.K.: Prediction of daily precipitation using wavelet-neural networks. Hydrological Sciences Journal 54, 234–246 (2009)CrossRefGoogle Scholar
  28. 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. 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
  30. 30.
    Wei, S., Song, J., Khan, N.I.: Simulating and predicting river discharge time series using a wavelet-neural network hybrid modelling approach. Hydrological Process. 26, 281–296 (2012)CrossRefGoogle Scholar
  31. 31.
    Zhang, J., Tan, Z.: Day ahead electricity price forecasting using WT, CLSSVM and EGARCH model. Electrical Power and Energy Systems 45, 362–368 (2013)CrossRefGoogle Scholar
  32. 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
  33. 33.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Jenkins, G.: Time Series Analysis: Forecasting and Control, 3rd edn. Prentice-Hall, Englewood Cliffs (1994)zbMATHGoogle Scholar
  34. 34.
    Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting methods and applications, 3rd edn. John Wiley & Sons Inc., Singapore (1998)Google Scholar
  35. 35.
    Daubechies, I.: The wavelet transforms time–frequency localization and signal analysis. IEEE Transactions on Information Theory 36, 961–1005 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  36. 36.
    Kim, C.-K., Kwak, I.-S., Cha, E.-Y., Chon, T.-S.: Implementation of wavelets and artificial neural networks to detection of toxic response behaviour of chironomids (Chironomidae: Diptera) for water quality monitoring. Ecological Modelling 195, 61–71 (2006)CrossRefGoogle Scholar
  37. 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

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Sanjita Jaipuria
    • 1
  • S. S. Mahapatra
    • 1
  1. 1.Department of Mechanical EngineeringNational Institute of Technology RourkelaIndia

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