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Integrated Forecasting Using the Discrete Wavelet Theory and Artificial Intelligence Techniques to Reduce the Bullwhip Effect in a Supply Chain

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  • Lakhwinder Pal Singh

    (Dr BR Ambedkar National Institute of Technology)

  • Ravi Teja Challa

    (Dr BR Ambedkar National Institute of Technology)

Abstract

To enhance the commercial competitive advantage of a firm in a constantly changing business environment, it is very important to enhance the supply chain performance by making it more flexible to adopt any type of changes in dynamic business environment. To improve the supply chain performance and make it more flexible it is essential to control the order amplification or bullwhip effect (BWE) through various stages of supply chain and control the inventory costs by controlling net stock amplification (NSA). These tasks should be done by using accurate demand forecasting. The current study demonstrates a forecasting methodology about nonlinear customer demand in a multilevel supply chain (SC) structure through; integrated techniques of discrete wavelet theory and artificial intelligence techniques including artificial neural networks and adaptive network-based fuzzy inference system. The effectiveness of forecasting models to deal with nonlinear data and how they improved the flexibility of SC is demonstrated by calculating BWE and NSA for real world data.

Suggested Citation

  • Lakhwinder Pal Singh & Ravi Teja Challa, 2016. "Integrated Forecasting Using the Discrete Wavelet Theory and Artificial Intelligence Techniques to Reduce the Bullwhip Effect in a Supply Chain," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 17(2), pages 157-169, June.
    • Handle: RePEc:spr:gjofsm:v:17:y:2016:i:2:d:10.1007_s40171-015-0115-z
    DOI: 10.1007/s40171-015-0115-z
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    Cited by:

    1. Marina Johnson & Rashmi Jain & Peggy Brennan-Tonetta & Ethne Swartz & Deborah Silver & Jessica Paolini & Stanislav Mamonov & Chelsey Hill, 2021. "Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(3), pages 197-217, September.
    2. Fan He & Xuansen He, 2019. "A Continuous Differentiable Wavelet Shrinkage Function for Economic Data Denoising," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 729-761, August.
    3. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    4. Lechtenberg, Sandra & Hellingrath, Bernd, 2021. "Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests," ERCIS Working Papers 37, University of Münster, European Research Center for Information Systems (ERCIS).

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