IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v118y2009i1p49-54.html
   My bibliography  Save this article

Demand forecasting methods in a supply chain: Smoothing and denoising

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925-5273(08)00246-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. John D. Sterman, 1989. "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment," Management Science, INFORMS, vol. 35(3), pages 321-339, March.
    2. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    3. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    4. Sterman, John D., 1989. "Misperceptions of feedback in dynamic decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 43(3), pages 301-335, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Yungao & Wang, Nengmin & He, Zhengwen & Lu, Jizhou & Liang, Huigang, 2015. "Analysis of the bullwhip effect in two parallel supply chains with interacting price-sensitive demands," European Journal of Operational Research, Elsevier, vol. 243(3), pages 815-825.
    2. Jaksic, Marko & Rusjan, Borut, 2008. "The effect of replenishment policies on the bullwhip effect: A transfer function approach," European Journal of Operational Research, Elsevier, vol. 184(3), pages 946-961, February.
    3. Manuel Brauch & Andreas Größler, 2022. "Holistic versus analytic thinking orientation and its relationship to the bullwhip effect," System Dynamics Review, System Dynamics Society, vol. 38(2), pages 121-134, April.
    4. Robert L. Bray & Haim Mendelson, 2012. "Information Transmission and the Bullwhip Effect: An Empirical Investigation," Management Science, INFORMS, vol. 58(5), pages 860-875, May.
    5. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 2004. "Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect"," Management Science, INFORMS, vol. 50(12_supple), pages 1887-1893, December.
    6. QU, Zhan & RAFF, Horst, 2023. "Two-part tariffs, inventory stockpiling, and the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 308(1), pages 201-214.
    7. Anantaram Balakrishnan & Joseph Geunes & Michael S. Pangburn, 2004. "Coordinating Supply Chains by Controlling Upstream Variability Propagation," Manufacturing & Service Operations Management, INFORMS, vol. 6(2), pages 163-183, July.
    8. de Lima, Daruichi Pereira & Fioriolli, José Carlos & Padula, Antonio Domingos & Pumi, Guilherme, 2018. "The impact of Chinese imports of soybean on port infrastructure in Brazil: A study based on the concept of the “Bullwhip Effect”," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 55-76.
    9. Disney, S.M. & Farasyn, I. & Lambrecht, M. & Towill, D.R. & de Velde, W. Van, 2006. "Taming the bullwhip effect whilst watching customer service in a single supply chain echelon," European Journal of Operational Research, Elsevier, vol. 173(1), pages 151-172, August.
    10. repec:ine:journl:v:28:y:2009:i:37:p:54-71 is not listed on IDEAS
    11. Cannella, Salvatore & Dominguez, Roberto & Framinan, Jose M., 2017. "Inventory record inaccuracy – The impact of structural complexity and lead time variability," Omega, Elsevier, vol. 68(C), pages 123-138.
    12. Gérard P. Cachon & Taylor Randall & Glen M. Schmidt, 2007. "In Search of the Bullwhip Effect," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 457-479, April.
    13. Carole Camisullis & Vincent Giard & Gisele Mendy-Bilek, 2010. "The information to share in upstream supply chains dedicated to mass production of customized products for allowing a decentralized management," Working Papers hal-00876993, HAL.
    14. Ouyang, Yanfeng, 2007. "The effect of information sharing on supply chain stability and the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1107-1121, November.
    15. T. Saikouk & I. Zouaghi & A. Spalanzani, 2011. "Mitigating Supply Chain System Entropy by the Implementation of RFID," Post-Print halshs-00665653, HAL.
    16. Yang Yu & Ray Qing Cao & Dara Schniederjans, 2017. "Cloud computing and its impact on service level: a multi-agent simulation model," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4341-4353, August.
    17. Sarac, Aysegul & Absi, Nabil & Dauzère-Pérès, Stéphane, 2010. "A literature review on the impact of RFID technologies on supply chain management," International Journal of Production Economics, Elsevier, vol. 128(1), pages 77-95, November.
    18. Ponte, Borja & Costas, José & Puche, Julio & Pino, Raúl & de la Fuente, David, 2018. "The value of lead time reduction and stabilization: A comparison between traditional and collaborative supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 165-185.
    19. Li, Xiaoming & Sridharan, V., 2008. "Characterizing order processes of using (R,nQ) inventory policies in supply chains," Omega, Elsevier, vol. 36(6), pages 1096-1104, December.
    20. Ciancimino, Elena & Cannella, Salvatore & Bruccoleri, Manfredi & Framinan, Jose M., 2012. "On the Bullwhip Avoidance Phase: The Synchronised Supply Chain," European Journal of Operational Research, Elsevier, vol. 221(1), pages 49-63.
    21. Chiang, Chung-Yean & Lin, Winston T. & Suresh, Nallan C., 2016. "An empirically-simulated investigation of the impact of demand forecasting on the bullwhip effect: Evidence from U.S. auto industry," International Journal of Production Economics, Elsevier, vol. 177(C), pages 53-65.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:118:y:2009:i:1:p:49-54. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.