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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)

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.

Keywords

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

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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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