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The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand

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  • Moon, Seongmin
  • Simpson, Andrew
  • Hicks, Christian

Abstract

The performance of alternative forecasting methods that use hierarchical and direct forecasting strategies for predicting spare parts demand depends on the demand features. This paper uses data obtained from the South Korean Navy to identify the demand features of the spare parts that influence on the relative performance of the alternative forecasting methods. A logistic regression classification model for predicting the relative performance of the alternative forecasting methods for the spare parts demand by multivariate demand features was developed. This classification model minimised forecasting errors and inventory costs.

Suggested Citation

  • Moon, Seongmin & Simpson, Andrew & Hicks, Christian, 2013. "The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand," International Journal of Production Economics, Elsevier, vol. 143(2), pages 449-454.
    • Handle: RePEc:eee:proeco:v:143:y:2013:i:2:p:449-454
    DOI: 10.1016/j.ijpe.2012.02.016
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    References listed on IDEAS

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    8. Moon, Seongmin & Hicks, Christian & Simpson, Andrew, 2012. "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study," International Journal of Production Economics, Elsevier, vol. 140(2), pages 794-802.
    9. J E Boylan & A A Syntetos & G C Karakostas, 2008. "Classification for forecasting and stock control: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 473-481, April.
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    Cited by:

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      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Tsionas, Mike G., 2022. "Random and Markov switching exponential smoothing models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
    4. Sbrana, Giacomo & Silvestrini, Andrea, 2014. "Random switching exponential smoothing and inventory forecasting," International Journal of Production Economics, Elsevier, vol. 156(C), pages 283-294.
    5. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
    6. Hakeem‐Ur Rehman & Guohua Wan & Raza Rafique, 2023. "A hybrid approach with step‐size aggregation to forecasting hierarchical time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 176-192, January.
    7. Poloni, Federico & Sbrana, Giacomo, 2015. "A note on forecasting demand using the multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 162(C), pages 143-150.

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