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Forecasting method for noisy demand

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  • Ferbar Tratar, Liljana

Abstract

Exponential smoothing methods are very commonly used for forecasting demand. Regarding the process of forecasting demand, the main approach towards the selection and optimisation of alternative methods relates to the minimisation of forecast error measures such as the mean square error (MSE). With regard to Pegels׳ classification of usage of proper forecasting methods, HW methods (additive and multiplicative) are appropriate for demand with trend and seasonality which corresponds to B-2 and B-3. But HW methods are not accurate enough for demand with large noise that is often a property of real data. In this paper we present improved an HW method for demand with noise and we demonstrate that a reduction in forecast error (MSE) can be reached. From the results, we prove that the proposed method is more accurate than the existing ones and that it is the proper choice for forecasting noisy demand. Furthermore, we show that essential reduction of supply chain costs can be achieved if we use improved the HW method for joined optimisation.

Suggested Citation

  • Ferbar Tratar, Liljana, 2015. "Forecasting method for noisy demand," International Journal of Production Economics, Elsevier, vol. 161(C), pages 64-73.
    • Handle: RePEc:eee:proeco:v:161:y:2015:i:c:p:64-73
    DOI: 10.1016/j.ijpe.2014.11.019
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    2. 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.
    3. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    4. Murray, Paul W. & Agard, Bruno & Barajas, Marco A., 2018. "ASACT - Data preparation for forecasting: A method to substitute transaction data for unavailable product consumption data," International Journal of Production Economics, Elsevier, vol. 203(C), pages 264-275.
    5. Puchalsky, Weslly & Ribeiro, Gabriel Trierweiler & da Veiga, Claudimar Pereira & Freire, Roberto Zanetti & Santos Coelho, Leandro dos, 2018. "Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand," International Journal of Production Economics, Elsevier, vol. 203(C), pages 174-189.
    6. Erkan Bayraktar & Kazim Sari & Ekrem Tatoglu & Selim Zaim & Dursun Delen, 2020. "Assessing the supply chain performance: a causal analysis," Annals of Operations Research, Springer, vol. 287(1), pages 37-60, April.
    7. Ferbar Tratar, Liljana & Strmčnik, Ervin, 2016. "The comparison of Holt–Winters method and Multiple regression method: A case study," Energy, Elsevier, vol. 109(C), pages 266-276.
    8. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
    9. 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.

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