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A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast

Author

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  • Che-Jung Chang

    (Ningbo University)

  • Jan-Yan Lin

    (Chung Yuan Christian University)

  • Peng Jin

    (Ningbo University)

Abstract

Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples.

Suggested Citation

  • Che-Jung Chang & Jan-Yan Lin & Peng Jin, 2017. "A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast," Computational and Mathematical Organization Theory, Springer, vol. 23(3), pages 409-422, September.
    • Handle: RePEc:spr:comaot:v:23:y:2017:i:3:d:10.1007_s10588-016-9234-0
    DOI: 10.1007/s10588-016-9234-0
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    References listed on IDEAS

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    1. Yokuma, J. Thomas & Armstrong, J. Scott, 1995. "Beyond accuracy: Comparison of criteria used to select forecasting methods," International Journal of Forecasting, Elsevier, vol. 11(4), pages 591-597, December.
    2. Huafeng Xu & Bin Liu & Zhigeng Fang, 2014. "New grey prediction model and its application in forecasting land subsidence in coal mine," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(2), pages 1181-1194, March.
    3. R. Rajesh & V. Ravi & R. Venkata Rao, 2015. "Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 53(1), pages 238-257, January.
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    Cited by:

    1. Che-Jung Chang & Guiping Li & Shao-Qing Zhang & Kun-Peng Yu, 2019. "Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China’s Sulfur Dioxide Emissions," IJERPH, MDPI, vol. 16(14), pages 1-10, July.
    2. Ding, Song & Hipel, Keith W. & Dang, Yao-guo, 2018. "Forecasting China's electricity consumption using a new grey prediction model," Energy, Elsevier, vol. 149(C), pages 314-328.
    3. Ding, Song & Zhang, Huahan, 2023. "Forecasting Chinese provincial CO2 emissions: A universal and robust new-information-based grey model," Energy Economics, Elsevier, vol. 121(C).
    4. Lifeng Wu & Xiaohui Gao & Yan Chen, 2019. "Memory Property of Grey Accumulation Generation Sequence," Complexity, Hindawi, vol. 2019, pages 1-10, July.

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