Skip to main content
SpringerLink
Log in

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

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8298))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Davis, T.: Effective supply chain management. Sloan Management Review, 35–46 (Summer 1993)

    Google Scholar 

  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)

    Article  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 

  5. Sterman, J.: Modelling managerial behaviour: Misperceptions of feedback in a dynamic decision making experiment. Management Science 35, 321–339 (1989)

    Article  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 

  7. Lee, H.L., Padmanabhan, V., Whang, S.: Information Distortion in a Supply Chain: The Bullwhip Effect. Management Science 43, 546–558 (1997b)

    Article  MATH  Google Scholar 

  8. Wright, D., Yuan, X.: Mitigating bullwhip effect by ordering policies and forecasting methods. International Journal of Production Economics 113, 587–597 (2008)

    Article  Google Scholar 

  9. Luong, H.T.: Measure of bullwhip effect in supply chains with autoregressive demand process. European Journal of Operational Research 180, 1086–1097 (2007)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  MATH  MathSciNet  Google Scholar 

  12. Hong, L., Ping, W.: Bullwhip effect analysis in supply chain for demand forecasting technology. System Engineering-Theory and Practice 27, 26–33 (2007)

    Article  Google Scholar 

  13. Zhang, X.: Evolution of ARMA Demand in supply chain. Manufacturing and Service Operations Management 6, 195–198 (2004)

    Article  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-y

    Google Scholar 

  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)

    Article  MATH  MathSciNet  Google Scholar 

  16. Gilbert, K.: An ARIMA supply chain model. Management Science 51, 305–310 (2005)

    Article  MATH  Google Scholar 

  17. Boute, R.N., Lambrecht, M.R.: Exploring the bullwhip effect by mean of spreadsheet simulation. Informs Transaction on Education 10, 1–9 (2009)

    Article  Google Scholar 

  18. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Book  MATH  Google 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  21. Kim, K.-J.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–319 (2003)

    Article  Google Scholar 

  22. Zhiqiang, G., Huaiqing, W., Quan, L.: Financial time series forecasting using LPP and SVM optimization by PSO. Soft Computing 17, 805–818 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Chapter  Google 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 

  27. Partal, T., Cigizoglu, H.K.: Prediction of daily precipitation using wavelet-neural networks. Hydrological Sciences Journal 54, 234–246 (2009)

    Article  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 

  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)

    Article  Google Scholar 

  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)

    Article  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 

  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)

    MATH  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 

  35. Daubechies, I.: The wavelet transforms time–frequency localization and signal analysis. IEEE Transactions on Information Theory 36, 961–1005 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  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)

    Article  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 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Rourkela, 769008, Odisha, India

    Sanjita Jaipuria & S. S. Mahapatra

Authors
  1. Sanjita Jaipuria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. S. Mahapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electrical Engineering Dept, IIT Delhi, Hauz Khas, 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  4. SRM Engineering College, Department of Electrical and Electronics Engineering, SRM University, Potheri, Kattankulathur, 603203, Kancheepuram, Tamil Nadu, India

    Shubhransu Sekhar Dash

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Jaipuria, S., Mahapatra, S.S. (2013). Reduction of Bullwhip Effect in Supply Chain through Improved Forecasting Method: An Integrated DWT and SVM Approach. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-03756-1_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03755-4

  • Online ISBN: 978-3-319-03756-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation