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Demand forecasting methods in a supply chain: Smoothing and denoising

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  • Ferbar, Liljana
  • Creslovnik, David
  • Mojskerc, Blaz
  • Rajgelj, Martin

Abstract

A widespread forecasting method within supply chain models is the exponential smoothing method. The use of a particular forecasting method affects the costs of a supply chain. To improve the efficiency of the supply chain costs, this paper introduces the theory of wavelets. An application of this theory to the field of forecasting is wavelet denoising. Results obtained by the exponential smoothing method are compared to the results obtained by wavelet denoising. This comparison is supported by simulation experiments which include incorporation of forecasting algorithms within supply chain models. Different series of simulated data are used for testing these two methods and it is shown that wavelet denoising has an edge over the exponential smoothing method cost-wise.

Suggested Citation

  • Ferbar, Liljana & Creslovnik, David & Mojskerc, Blaz & Rajgelj, Martin, 2009. "Demand forecasting methods in a supply chain: Smoothing and denoising," International Journal of Production Economics, Elsevier, vol. 118(1), pages 49-54, March.
    • Handle: RePEc:eee:proeco:v:118:y:2009:i:1:p:49-54
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    References listed on IDEAS

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    Cited by:

    1. Joanna Bruzda, 2020. "Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 309-336, March.
    2. Warren Liao, T. & Chang, P.C., 2010. "Impacts of forecast, inventory policy, and lead time on supply chain inventory--A numerical study," International Journal of Production Economics, Elsevier, vol. 128(2), pages 527-537, December.
    3. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    4. Milan Bašta, 2018. "Time series forecasting with a prior wavelet-based denoising step," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2018(1), pages 5-24.
    5. Joanna Bruzda, 2020. "The wavelet scaling approach to forecasting: Verification on a large set of Noisy data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 353-367, April.
    6. Zhu, Xiaowei & Mukhopadhyay, Samar K. & Yue, Xiaohang, 2011. "Role of forecast effort on supply chain profitability under various information sharing scenarios," International Journal of Production Economics, Elsevier, vol. 129(2), pages 284-291, February.
    7. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
    8. Bruzda, Joanna, 2020. "Demand forecasting under fill rate constraints—The case of re-order points," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1342-1361.
    9. Sun, Edward W. & Chen, Yi-Ting & Yu, Min-Teh, 2015. "Generalized optimal wavelet decomposing algorithm for big financial data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 194-214.

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